pax_global_header00006660000000000000000000000064151505011610014505gustar00rootroot0000000000000052 comment=8a1b10faf994c5566e6f08242af360e017ba2f52 apjanke-octave-tablicious-8a1b10f/000077500000000000000000000000001515050116100171355ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/.editorconfig000066400000000000000000000006621515050116100216160ustar00rootroot00000000000000# EditorConfig for Tablicious # # This is set up to conform to standard GNU Octave code style, to the extent that # EditorConfig supports that. # # See: https://wiki.octave.org/Octave_style_guide root = true [*] insert_final_newline = true charset = utf-8 indent_style = space trim_trailing_whitespace = true [*.m] indent_size = 2 [*.cc] indent_size = 2 [*.{pl,pm}] indent_size = 4 [Makefile] indent_style = tab indent_size = 4 apjanke-octave-tablicious-8a1b10f/.gitignore000066400000000000000000000007371515050116100211340ustar00rootroot00000000000000# .gitignore for Tablicious # I don't remember why these iris data files are excluded? datasets/iris/iris.csv datasets/iris/iris.mat # Binary artifacts and intermediate build stuff *.o *.oct /target cmake-build-debug # Intermediate doc-generation files /doc/*.dvi /doc/*.qhp /doc/topics.tmp /doc/TIMESTAMP # Data and log droppings *.log # Editor and IDE config and droppings !.idea .idea/workspace.xml .vscode # GitHub Pages deployment stuff /docs/Gemfile.lock /docs/_site apjanke-octave-tablicious-8a1b10f/.mailmap000066400000000000000000000012131515050116100205530ustar00rootroot00000000000000# Prevent git from showing duplicate names with commands like "git shortlog" # See the manpage of git-shortlog for details. # The syntax is: # Name that should be used Bad name # # You can skip Bad name if it is the same as the one that should be used, and is unique. # # This file is up-to-date if the command git log --format="%aN <%aE>" | sort -u # gives no duplicates. # # apjanke's notes: # The "bad" name isn't really bad per se, just not my canonical one. Andrew Janke Andrew Janke Ray Zimmerman Ray Zimmerman apjanke-octave-tablicious-8a1b10f/.vscode/000077500000000000000000000000001515050116100204765ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/.vscode/launch.json000066400000000000000000000010611515050116100226410ustar00rootroot00000000000000{ // Use IntelliSense to learn about possible attributes. // Hover to view descriptions of existing attributes. // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 "version": "0.2.0", "configurations": [ { "type": "OctaveDebugger", "request": "launch", "name": "Suppliers Parts", "program": "ex_01_suppliers_parts", "octave": "octave-cli", "sourceFolder": "${workspaceFolder}/inst", "workingDirectory": "${workspaceFolder}/inst/examples", "autoTerminate": true } ] }apjanke-octave-tablicious-8a1b10f/CHANGES.txt000066400000000000000000000177551515050116100207650ustar00rootroot00000000000000Tablicious Changelog ==================== 0.4.7 (2026-02-28) ------------------ * Date/time fixes * Add datevec() methods for duration and calendarDuration; add multi-argout form for datetime.datevec(). ([#153](https://github.com/apjanke/octave-tablicious/issues/153)) * Add duration.hms() method. * Fix table.rowfun() bugs that broke it. ([#155](https://github.com/apjanke/octave-tablicious/issues/155)) * Fix the "blank" ctor calling form (`table ('Size',sz, 'VariableTypes',types)`). ([#145](https://github.com/apjanke/octave-tablicious/issues/145)) 0.4.6 (2026-02-16) ------------------ * Work around `unicode2native` buffer length bug in `string.strlength`. ([#143](https://github.com/apjanke/octave-tablicious/issues/143)) * Fix `datetime` TimeZone support on big-endian systems. ([#151](https://github.com/apjanke/octave-tablicious/issues/151)) * Add missing `datetime.ismissing` method. * Fix bad `categorical.ne` true result for missing values. * Unit tests * Centralize test running code in tblish.internal.runtests, and add MP-Test support to it. * Add some more unit tests. * Clarify and expand licensing documentation. * Update source file headers to uniformly say "GPLv3+" instead of "GPLv2+", matching the `DESCRIPTION` file and my original intent. ([#149](https://github.com/apjanke/octave-tablicious/issues/149)) * Doco updates: Add category directory entry to the texi doco. * Internal changes * Change some more code to GNU Octave style. 0.4.5 (2025-07-03) ------------------ * Add `startsWith` and `endsWith` methods to `string`. ([#141](https://github.com/apjanke/octave-tablicious/pull/141)) * Fix `string.regexprep` "demote_strings" error. ([#139](https://github.com/apjanke/octave-tablicious/issues/139)) * Fix `make test` failure due to missing `runtests` in Octave 8.x. ([#140](https://github.com/apjanke/octave-tablicious/issues/140)) 0.4.4 (2024-10-23) ------------------ * Improvements (hopefully) to duration-related arithmetic and datatype interaction. * Fix hi-bound and striding bugs in `datetime.colon` (`:` operator). * Add missing `calendarDuration.mtimes` method. 0.4.3 (2024-07-06) ------------------- * Fix "[foo (...)]" syntax-ish errors in some functions. * Affected datetime, calendarDuration, localdate, splitapply, mustBeCellstr, and some display functions using tblish.internal.mycombvec. * `table` fixes * Correctly handle nested tables in some cases ()- and {}-indexed assignments, when indexing in to those nested tables. * Fix assignment to some `table.Properties.` pseudo-properties. * Apply object validation after more mutation operations. * Fix variable number display in table.summary. * Fix duration ctor bug that returned NaNs in cellstr row vectors. ([#134](https://github.com/apjanke/octave-tablicious/issues/134)) 0.4.2 (2024-02-07) ------------------ * Add `end` override methods to string, datetime, etc. so `x(end)` indexing works correctly. * `table` fixes * table: summary: omit NaNs from mean(), include NaN counts. * table: Fix rownames, broken by bad internal `.Properties` reference. * categorical.summary: include frequencies in output ### Documentation * Alphabetize methods under classes in alphabetical index. 0.4.1 (2024-02-07) ------------------ ### Bug Fixes * string: * string: Fix missing trailing newline in string display in `string.disp`. * string: Add `+` operator overload that does string concatenation. Version 0.4.0 (2024-02-07) -------------------------- ### Breaking Changes * Remove `(fill,rm,standardize)missing` functions, deferring to the Statistics package. * `table` arrays: * `table`: Have `fieldnames (obj)` expose variable and dimension names and Properties, instead of the internal implementation properties. * `table`: Remove `grpstats` method and replace with `tblish.table.grpstats()` function, to avoid overriding or interacting with Statistics' `grpstats()` function. * `table`: Deprecate `rows`, `columns` methods. * `datetime: * Deprecate `.dnums` property access. * This was only public for debugging purposes during initial development, and not intended to be part of the public API. The `datenum` method is the supported way to get datenums here. * Fix `string.missing()` to actually return a missing value, instead of "". * Make internal-use properties on the main classes Access = private, now that the code is a little more mature. ### Changed * Require Octave 7.x or later. * Fix Octave 7.x and 8.x compatibility in installation and package loading. * `table` arrays: * `table`: Fix "preallocation" constructor to use 0s and ""s for fill values, unless missings are requested using an alternate pseudotype. (For compatibility with Matlab.) * `table`: Fix some advanced ()- and {}-indexing cases, including some related to multi-variable subscripting, multi-column table variables, multi-column RHS inputs, and the combination of the three. * `table`: Remove `reshape`, `resize`, `vec`, `hasrownames`, and `size_equal` methods. * `table`: Make `congruentize` a private function. * Add `istable`, `istimetable`, and `istabular` functions. * Fix `x(ix) = []` element deletion for string, datetime, table, and other classes. * Add idempotent one-arg constructor forms for string, datetime, categorical. (But not table.) * Add `NaS`, `NaC`, and `todatetime` functions. * Make `head` and `tail` functions instead of methods on `table`. * Add `string.ne` method. * Fix concatenation of `@missing` arrays. * Rename `+tablicious` package to `+tblish`; merge `+octave` package into `+tblish`. * Move `+table_examples` package to `+tblish/+examples`. * Move the `sizeof()` override methods to a `tblish.sizeof()` function to avoid shadowing core Octave's `sizeof`, until I understand if it's intended to support extension by classdefs. * Fix the doco generation, including publishing to GitHub Pages. ### Internal * Convert code style to GNU Octave code style, mostly. * Move `mustBe*` functions to a `validators/` subdirectory, just for tidiness. * Revamp doc-generation tools. * Add experimental MP-Test based unit tests to supplement BISTs, for detailed testing of `table` behavior. Version 0.3.7 (2023-01-06) -------------------------- * Fix installation under Octave 7.x. (#91) * Fix orphaned sections in documentation. * Add table.size_equal method. (#81) * Add string.empty static method. Internal refactoring: * Rearrange doco files so the main source file is named `*.texi` and its extension is recognized by editors. Version 0.3.6 (2021-07-05) -------------------------- * Fix multi-argout calling form of table.size. (Thanks, @rdzman!) * Fix table dot-indexing assignment by having table.numel always return 1. * Support arbitrary strings as variable names (#82). Version 0.3.5 (2020-01-30) -------------------------- * Fix compiler warnings on systems with 32-bit `octave_idx_type` Version 0.3.4 (2020-01-28) -------------------------- * Octave 6.x compatibility: move some mustBe* validators into compatibility shims Version 0.3.3 (2020-01-28) -------------------------- * Fix version metadata in the distribution Version 0.3.2 (2020-01-28) -------------------------- * sprintf and fprintf support for datetime, categorical, and other classes * Added this ChangeLog * Octave 6.x compatibility fix: use different function for registering QHelp doco Version 0.3.1 (2020-01-27) -------------------------- * Fix a dumb bug that broke string * Fix "shadowed function" warning in Octave 5.x Version 0.3.0 (2020-01-18) -------------------------- * Various bugfixes * Added some examples This is the release that I think is ready for general consumption by the user community. Version 0.2.0 (2019-05-05) -------------------------- * More basic table stuff * Now with datetime and duration, since I’ve merged the Chrono package into this project * Documentation * @string and @categorical * The start of some example data sets * Bug fixes, of course Version 0.1.0 (2019-04-30) -------------------------- Initial release! This library is about 2/3 feature complete. Most basic table, string, categorical, and Missing Data stuff is working, and there's documentation. apjanke-octave-tablicious-8a1b10f/CODE_OF_CONDUCT.md000066400000000000000000000065271515050116100217460ustar00rootroot00000000000000# Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at [floss@apjanke.net]. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq [floss@apjanke.net]: mailto:floss@apjanke.net apjanke-octave-tablicious-8a1b10f/CONTRIBUTING.md000066400000000000000000000037161515050116100213750ustar00rootroot00000000000000# Contributing to Tablicious for GNU Octave ## Introduction Thank you for considering contributing to this project. Any contribution is extremely welcome and appreciated. These guidelines are designed to help all contributors understand how to work and interact within this project. ## Basics ### No MATLAB! Do not use MATLAB to develop this project! That could be a violation of your MATLAB license terms, because the Matlab license includes a Non-Compete clause. * Only use Octave and reference to the [publicly-available MATLAB documentation](https://www.mathworks.com/help/matlab/) to develop this project. * This includes using MATLAB for testing or benchmarking purposes. Please do not submit any contributions that include MATLAB test or benchmark results. They will be politely but summarily deleted. ### Other basics * Bug reports and merge requests via GitHub are very welcome. * Contributions include testing, writing more tests, documentation, submitting bug reports, and proposing new features and tests. * Issues and changes should be small and focused on a particular topic. * Contributors are expected to abide by the [code of conduct](CODE_OF_CONDUCT.md). * Problems with documentation (including stuff that’s just missing) are considered bugs, and bug reports against it are welcome. ## Community All project interaction takes place on GitHub. The project community is essentially the original author (Andrew Janke) at the moment. However, this project exists within the greater GNU Octave community. It is the author's hope that at some point in the future, this project's components will be merged into GNU Octave and cease to be developed in an independent project. The GitHub home for this project is . ## Code conventions Use GNU Octave code style. ### Naming conventions Anything in a namespace with `internal` in its name is for the internal use of this package, and is not intended for use by user code. apjanke-octave-tablicious-8a1b10f/CONTRIBUTORS.md000066400000000000000000000002351515050116100214140ustar00rootroot00000000000000Contributors ============ * Andrew Janke (primary author) () * Ray Zimmerman (contributed original MP-Test based unit tests for table) apjanke-octave-tablicious-8a1b10f/COPYING000066400000000000000000001045151515050116100201760ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. 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But first, please read . apjanke-octave-tablicious-8a1b10f/DESCRIPTION000066400000000000000000000010701515050116100206410ustar00rootroot00000000000000Name: tablicious Version: 0.4.7 Date: 2026-02-28 Title: Tablicious - Tabular/Relational data for GNU Octave Author: Andrew Janke Maintainer: Andrew Janke Description: Matlab-compatible Octave table class for storing tabular/relational data, with supporting string, datetime, and categorical classes. Also similar to R and Python Pandas DataFrames. License: GPLv3+ Problems: Implementation is not complete. Code quality is mediocre. Url: https://github.com/apjanke/octave-tablicious Categories: data manipulation, statistics apjanke-octave-tablicious-8a1b10f/INDEX000066400000000000000000000017701515050116100177340ustar00rootroot00000000000000Tablicious >> Tablicious Tables table array2table cell2table struct2table tableOuterFillValue vartype istable istimetable istabular tblish.evalWithTableVars tblish.table.grpstats Strings and Categoricals string NaS contains dispstrs categorical iscategorical NaC discretize Dates and Times datetime NaT todatetime localdate isdatetime calendarDuration iscalendarduration calmonths calyears days duration hours isduration milliseconds minutes seconds timezones years Missing Data missing isnanny eqn Validation Functions mustBeA mustBeCellstr mustBeCharvec mustBeFinite mustBeInteger mustBeMember mustBeNonempty mustBeNumeric mustBeReal mustBeSameSize mustBeScalar mustBeScalarLogical mustBeVector Miscellaneous colvecfun dispstrs head isfile isfolder pp scalarexpand size2str splitapply tail vecfun tblish.sizeof2 Example Datasets tblish.datasets tblish.dataset Example Code tblish.examples.coplot tblish.examples.plot_pairs tblish.examples.SpDb apjanke-octave-tablicious-8a1b10f/LICENSE000066400000000000000000001045151515050116100201500ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . apjanke-octave-tablicious-8a1b10f/LICENSES/000077500000000000000000000000001515050116100203425ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/LICENSES/LICENSE-MATPOWER000066400000000000000000000042701515050116100226060ustar00rootroot00000000000000Some of the unit test code in Tablicious, specifically the MP-Test based tests for the table class, is based on code in MATPOWER, with further contributions by the Tablicious developers. The code in MATPOWER is distributed under the 3-clause BSD license below, and the MATPOWER-derived tests in Tablicious are distributed under the same license. Each such file in Tablicious will be noted as such in its contents. The following is the official license notice: -------------------------------------------------------------------------- Copyright (c) 1996-2022, Power Systems Engineering Research Center (PSERC) Copyright (c) 2024, Andrew Janke and individual contributors (see AUTHORS file for details). 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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THE DATA FILES OR SOFTWARE. Except as contained in this notice, the name of a copyright holder shall not be used in advertising or otherwise to promote the sale, use or other dealings in these Data Files or Software without prior written authorization of the copyright holder.apjanke-octave-tablicious-8a1b10f/Makefile000066400000000000000000000176461515050116100206130ustar00rootroot00000000000000# This Makefile is just for when you're hacking on this package inside # its repo. It'll build the octfiles and install them into inst/. # # This only works if the octave and mkoctfile on your path are the ones from # the Octave that you will be using! Otherwise your octfile may crash # Octave. To make this work, set PATH=...:$PATH before your 'make' invocation # to pick up the right commands. Example: # # PATH="/Applications/Octave-8.4.0.app/Contents/Resources/usr/bin:$PATH" # make # # Note: the 'make dist' command generates the dist tarball from the last # git in source control, not the current directory contents. So make sure to # check in your changes if you're testing something out. ## Some basic tools (can be overriden using environment variables) SED ?= sed TAR ?= tar GREP ?= grep CUT ?= cut # Hack to make Octave shut up about X11 warnings export DISPLAY = "bogus" ## Note the use of ':=' (immediate set) and not just '=' (lazy set). ## http://stackoverflow.com/a/448939/1609556 package := $(shell $(GREP) "^Name: " DESCRIPTION | $(CUT) -f2 -d" ") version := $(shell $(GREP) "^Version: " DESCRIPTION | $(CUT) -f2 -d" ") target_dir := ./target release_dir := $(target_dir)/$(package)-$(version) release_tarball := $(target_dir)/$(package)-$(version).tar.gz html_dir := $(target_dir)/$(package)-html html_tarball := $(target_dir)/$(package)-html.tar.gz installation_dir := $(target_dir)/.installation package_list := $(installation_dir)/.octave_packages install_stamp := $(installation_dir)/.install_stamp ## These can be set by environment variables which allow to easily ## test with different Octave versions. ifndef OCTAVE OCTAVE := octave endif OCTAVE := $(OCTAVE) --no-gui --silent --norc MKOCTFILE ?= mkoctfile ## Command used to set permissions before creating tarballs FIX_PERMISSIONS ?= chmod -R a+rX,u+w,go-w,ug-s ## Detect which VCS is used vcs := $(if $(wildcard .hg),hg,$(if $(wildcard .git),git,unknown)) ifeq ($(vcs),hg) release_dir_dep := .hg/dirstate endif ifeq ($(vcs),git) release_dir_dep := .git/index endif .PHONY: help ## make will display the command before runnning them. Use @command ## to not display it (makes specially sense for echo). help: @echo "Targets:" @echo " dist - Create $(release_tarball) for release." @echo " html - Create $(html_tarball) for release." @echo " doc - Create the documentation files in doc/." @echo " local - Build the oct files locally." @echo " release - Create both of the above and show md5sums." @echo " install - Install the package in $(installation_dir), where it is not visible in a normal Octave session." @echo " check - Execute package tests." @echo " doctest - Test the help texts with the doctest package." @echo " run - Run Octave with the package installed in $(installation_dir) in the path." @echo " clean - Remove everything made with this Makefile." ## ## Recipes for release tarballs (package + html) ## ## dist and html targets are only PHONY/alias targets to the release ## and html tarballs. dist: $(release_tarball) html: $(html_tarball) ## An implicit rule with a recipe to build the tarballs correctly. %.tar.gz: % $(TAR) -c -f - --posix -C "$(target_dir)/" "$(notdir $<)" | gzip -9n > "$@" clean-tarballs: -$(RM) $(release_tarball) $(html_tarball) ## Create the unpacked package. ## ## Notes: ## * having ".hg/dirstate" (or ".git/index") as a prerequesite means it is ## only rebuilt if we are at a different commit. ## * the variable RM usually defaults to "rm -f" ## * having this recipe separate from the one that makes the tarball ## makes it easy to have packages in alternative formats (such as zip) ## * note that if a commands needs to be run in a specific directory, ## the command to "cd" needs to be on the same line. Each line restores ## the original working directory. $(release_dir): $(release_dir_dep) -$(RM) -r "$@" ifeq (${vcs},hg) hg archive --exclude ".hg*" --type files "$@" endif ifeq (${vcs},git) git archive --format=tar --prefix="$@/" HEAD | $(TAR) -x $(RM) "$@/.gitignore" endif ## Don't fall back to run the supposed necessary contents of ## 'bootstrap' here. Users are better off if they provide ## 'bootstrap'. Administrators, checking build reproducibility, can ## put in the missing 'bootstrap' file if they feel they know its ## necessary contents. ifneq (,$(wildcard src/bootstrap)) cd "$@/src" && ./bootstrap && $(RM) -r "autom4te.cache" endif run_in_place = $(OCTAVE) --eval ' pkg ("local_list", "$(package_list)"); ' \ --eval ' pkg ("load", "$(package)"); ' #html_options = --eval 'options = get_html_options ("octave-forge");' ## Uncomment this for package documentation. html_options = --eval 'options = get_html_options ("octave-forge");' \ --eval 'options.package_doc = "$(package).texi";' $(html_dir): $(install_stamp) $(RM) -r "$@"; $(run_in_place) \ --eval ' pkg load generate_html; ' \ $(html_options) \ --eval ' generate_package_html ("$(package)", "$@", options); '; $(FIX_PERMISSIONS) "$@"; ## ## Recipes for installing the package. ## .PHONY: install clean-install octave_install_commands = \ ' llist_path = pkg ("local_list"); \ mkdir ("$(installation_dir)"); \ load (llist_path); \ local_packages(cellfun (@ (x) strcmp ("$(package)", x.name), local_packages)) = []; \ save ("$(package_list)", "local_packages"); \ pkg ("local_list", "$(package_list)"); \ pkg ("prefix", "$(installation_dir)", "$(installation_dir)"); \ pkg ("install", "-local", "-verbose", "$(release_tarball)"); ' ## Install unconditionally. Maybe useful for testing installation with ## different versions of Octave. install: $(release_tarball) @echo "Installing package under $(installation_dir) ..." $(OCTAVE) --eval $(octave_install_commands) touch $(install_stamp) ## Install only if installation (under target/...) is not current. $(install_stamp): $(release_tarball) @echo "Installing package under $(installation_dir) ..." $(OCTAVE) --eval $(octave_install_commands) touch $(install_stamp) clean-install: -$(RM) -r $(installation_dir) ## ## Recipes for testing purposes ## .PHONY: run doctest test test: local ./dev-tools/runtests.sh inst ## Start an Octave session with the package directories on the path for ## interactive test of development sources. run: $(install_stamp) $(run_in_place) --persist ## Test example blocks in the documentation. Needs doctest package ## https://octave.sourceforge.io/doctest/index.html doctest: $(install_stamp) $(run_in_place) --eval 'pkg load doctest;' \ --eval "targets = '$(shell (ls inst; ls src | $(GREP) .oct) | $(CUT) -f2 -d@ | $(CUT) -f1 -d.)';" \ --eval "targets = strsplit (targets, ' '); doctest (targets);" ## Test package. octave_test_commands = \ ' args = {"inst", "src"}; \ args(cellfun (@ (x) ! isdir (x), args)) = []; \ if (isempty (args)) error ("no \"inst\" or \"src\" directory"); exit (1); \ else cellfun(@runtests, args); endif ' check: $(install_stamp) $(run_in_place) --eval $(octave_test_commands) ## ## Recipes for local (in-tree) build ## .PHONY: local doc clean-local local: src/__tblish_time_binsearch__.cc octave_tablicious_make_local.m $(OCTAVE) --eval="octave_tablicious_make_local" # Always make docs from clean, bc oure modification tracking isn't quite adequate, # maybe due to transitive dependencies on original source files that aren't captured # in doc/Makefile? doc: doc-clean cd doc && make all doc-clean: cd doc && make clean docs-clean: rm -rf docs/devel/user-guide mkdir -p docs/devel/user-guide gh-pages: docs-clean cp -R doc/tablicious.pdf doc/tablicious.html doc/html docs/devel/user-guide ## ## CLEAN ## clean-local: rm -f inst/*.oct clean-unpacked-release: -$(RM) -r $(release_dir) $(html_dir) clean: clean-local $(RM) -rf $(target_dir) .PHONY: release dist html clean clean-tarballs clean-unpacked-release apjanke-octave-tablicious-8a1b10f/README.md000066400000000000000000000135061515050116100204210ustar00rootroot00000000000000# Tablicious for GNU Octave Tablicious provides tabular/relational data structures for Octave. You can think of it as "pandas for Octave". | WARNING: This library is currently beta quality. Please do not use it in any production or business code! Seriously!! | | ---- | This package attempts to provide a set of mostly-Matlab-compatible implementations of the table class and related structures and functions. It provides: * A `table` array class, and related functions. * (That's kind of our whole thing.) * `datetime`, `string`, and `categorical` array classes. * (Because they're pretty useful when working with tables, and base Octave doesn't have them yet.) * Auxiliary functions related to those classes. It currently does not provide, but we would like to maybe add at some point: * `timetable` * Table I/O, such as `readtable`, `writetable`, and `csvread`/`dlmread` `table` support ## Installation and usage ### Requirements Tablicious 0.4.x requires Octave 7.0 or newer. It may well work on earlier versions, but is not really tested against them. Tablicious does not require the Octave Forge [Statistics package](https://gnu-octave.github.io/packages/statistics/), but you will probably want it, because Statistics supplies functions that are frequently useful for data analysis in a tabular context. In particular, `ismissing()` and the related missing-filling functions are useful, and the Statistics package supplies the general implementation of them. ### Quick start To get started using or testing this project, install it using Octave's `pkg` function: ```octave pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz pkg load tablicious ``` ### Installation for development If you want to hack on the Tablicious code itself, set it up like this: * Clone the repo * `git clone https://github.com/apjanke/octave-tablicious` * Add the `inst/` directory from the cloned repo to your Octave path with `addpath`. That should get everything working except for time zone conversions inside `datetime`, which require a compiled oct-file. See the developer doco in the repo for that. ## Documentation Once you have Tablicious installed, the user manual will show up in the Octave GUI’s documentation browser. You can also run `help ` or `doc ` for any of the classes or functions in Tablicious. The documentation for Tablicious can be viewed online at . That site has the [Tablicious User Guide](https://apjanke.github.io/octave-tablicious/release/v0.3.7/user-guide/html/index.html) containing the API reference, plus some additional general and developer-oriented documentation, and alternate formats of the User Guide. See the [main doco page there](https://apjanke.github.io/octave-tablicious) for details. Developers, especially see the [Developer Notes](https://apjanke.github.io/octave-tablicious/Developer-Notes.html) and [Design and Justification](https://apjanke.github.io/octave-tablicious/Design-and-Justification.html) pages there, which discuss how and why this library is written. There's a [TODO page](https://apjanke.github.io/octave-tablicious/TODO.html) that lists what might be coming. Also see [CONTRIBUTING](CONTRIBUTING.md) if you would like to contribute to this project. Within this repo, you can see all that in the `docs/` directory for notes on this project. It's a local copy of the GitHub Pages site. ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md) for more details. ### No Matlab Usage To avoid issues with the Matlab license's Non-Compete clause, this project needs to be developed entirely using Octave, and not using Matlab at all, including for testing or benchmarking purposes. Please do not submit any Matlab test or benchmark results, or any code produced using Matlab. And if you know anything about how the Matlab internals work, please do not tell me! ## License Tablicious is mostly GNU GPL v3 or later (GPL-3+), with some other Free and Open Source licensed code and files redistributed with it. I believe this is all compatible with redistribution as a GNU GPL licensed package, and conforms with the Debian Free Software Guidelines, but I am not a lawyer. The main code of Tablicious is licensed under the GNU GPL version 3 or later (GPL-3+). See the `LICENSE` file. Certain files in the build tools are licensed under the GNU GPL version 2 or later (GPL-2+). This includes the `mktexi.pl` and `OctTexiDoc.pm` files in the `doc/` directory. The tzinfo database file is Public Domain. The date/time portion of Tablicious includes some Unicode data files licensed under the Unicode License Agreement - Data Files and Software license. See the `LICENSES/LICENSE-Unicode` file. The test suite contains some files, specifically some table-related tests in `inst/t/` using MP-Test, which are BSD 3-Clause licensed (BSD-3-clause), and are adapted from MATPOWER written by Ray Zimmerman. All files under `inst/t/` are BSD-3-clause licensed. See the `LICENSES/LICENSE-MATPOWER` file. The Fisher Iris dataset is Public Domain. ## Author and Acknowledgments Tablicious is created by [Andrew Janke](https://apjanke.net). Thanks to [Polkadot Stingray](https://polkadotstingray-official.jimdo.com/) for [powering](https://www.youtube.com/watch?v=3ad4NsEy1tg) [my](https://www.youtube.com/watch?v=-zlq6eMycLA) [coding](https://www.youtube.com/watch?v=1z4RosaB-UQ) [sessions](https://www.youtube.com/watch?v=p6oVXuLsbxM). Shout out to [Mike Miller](https://mtmxr.com/) for showing me how to properly structure an Octave package repo, and encouraging me to contribute. Thanks to [Sebastian Schöps](https://github.com/schoeps) for getting me more involved in Octave development in the first place, via his [Octave.app](https://octave-app.org) project. 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N%☮-f=E?O BaPd6DbQ8V-FcQv=HdR9$M'JeRd]/LfS9m7NgS}?PhT:%GRiTe6OTjU:VWVkUv_XlV;%geo\nW;w^oW`pX<& bqXf7drY<>ՕfsYw=htZ=&MjuZf]lv[=m/[}px\>'ry\g7tz]9Vv{]wx|^?'z}^gU 4U4U$(=RSVćs HVM ' M d c / iD * L n!C!e#&)+.DZeqf}6zMΚ m Kz"%2.14p79<#>AECFgHKNPS< HLinomntrRGB XYZ  1acspMSFTIEC sRGB-HP cprtP3desclwtptbkptrXYZgXYZ,bXYZ@dmndTpdmddvuedLview$lumimeas $tech0 rTRC< gTRC< bTRC< textCopyright (c) 1998 Hewlett-Packard CompanydescsRGB IEC61966-2.1sRGB IEC61966-2.1XYZ QXYZ XYZ o8XYZ bXYZ $descIEC http://www.iec.chIEC http://www.iec.chdesc.IEC 61966-2.1 Default RGB colour space - sRGB.IEC 61966-2.1 Default RGB colour space - sRGBdesc,Reference Viewing Condition in IEC61966-2.1,Reference Viewing Condition in IEC61966-2.1view_. \XYZ L VPWmeassig CRT curv #(-27;@EJOTY^chmrw| %+28>ELRY`gnu| &/8AKT]gqz !-8COZfr~ -;HUcq~ +:IXgw'7HYj{+=Oat 2FZn  % : O d y  ' = T j " 9 Q i  * C \ u & @ Z t .Id %A^z &Ca~1Om&Ed#Cc'Ij4Vx&IlAe@e Ek*Qw;c*R{Gp@j>i  A l !!H!u!!!"'"U"""# #8#f###$$M$|$$% %8%h%%%&'&W&&&''I'z''( (?(q(())8)k))**5*h**++6+i++,,9,n,,- -A-v--..L.../$/Z///050l0011J1112*2c223 3F3334+4e4455M555676r667$7`7788P8899B999:6:t::;-;k;;<' >`>>?!?a??@#@d@@A)AjAAB0BrBBC:C}CDDGDDEEUEEF"FgFFG5G{GHHKHHIIcIIJ7J}JK KSKKL*LrLMMJMMN%NnNOOIOOP'PqPQQPQQR1R|RSS_SSTBTTU(UuUVV\VVWDWWX/X}XYYiYZZVZZ[E[[\5\\]']x]^^l^__a_``W``aOaabIbbcCccd@dde=eef=ffg=ggh?hhiCiijHjjkOkklWlmm`mnnknooxop+ppq:qqrKrss]sttptu(uuv>vvwVwxxnxy*yyzFz{{c{|!||}A}~~b~#G k͂0WGrׇ;iΉ3dʋ0cʍ1fΏ6n֑?zM _ɖ4 uL$h՛BdҞ@iءG&vVǥ8nRĩ7u\ЭD-u`ֲK³8%yhYѹJº;.! zpg_XQKFAǿ=ȼ:ɹ8ʷ6˶5̵5͵6ζ7ϸ9к<Ѿ?DINU\dlvۀ܊ݖޢ)߯6DScs 2F[p(@Xr4Pm8Ww)Kmapjanke-octave-tablicious-8a1b10f/dev-tools/000077500000000000000000000000001515050116100210515ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/dev-tools/runtests.sh000077500000000000000000000036431515050116100233050ustar00rootroot00000000000000#!/bin/bash # # Test wrapper for Octave tests. # # This exists because Octave's (o)runtests() does not return an error status, so # you can't detect within octave whether your tests have failed. This wrapper # script captures and parses the textual output of running the tests to detect # failures. # # Prerequisite: "make local" must have been run on this package. set -o errexit set -o nounset set -o pipefail if [[ "${TRACE-0}" == "1" ]]; then set -o xtrace; fi # Utility functions THIS_PROGRAM=$(basename $0) function info() { emit "$*"; } function error() { emit "ERROR: $*"; } function emit() { echo >&2 "${THIS_PROGRAM}: $*"; } # Main script code pkg_dir=$(dirname $(realpath $(dirname "$0"))) inst_dir="${pkg_dir}/inst" test_dir="$inst_dir" package='tablicious' #TODO: Should this pull in a defined $OCTAVE? Or will that cause problems with redundant # --norc --silent --no-gui from the Makefile? OCTAVE=(octave --norc --no-gui --path="$inst_dir") TIMESTAMP=$(date '+%Y-%m-%d_%H-%M-%S') outfile="octave-${package}-tests-${TIMESTAMP}.log" info "pkg_dir = ${pkg_dir}" info "inst_dir = ${inst_dir}" info "test_dir = ${test_dir}" # Main test run function run_main_tests() { local oct_test_code info "Running tests for package ${package}" # Run the tests oct_test_code="addpath('$test_dir'); tblish.internal.runtests;" "${OCTAVE[@]}" --eval="${oct_test_code}" 2>&1 | tee "$outfile" # Evaluate test output # The '! test failed' case is for BISTs run as individual files bc oruntests # doesn't discover them. if grep '[^X]FAIL\|! test failed' "$outfile" &>/dev/null; then STATUS=1 info "Some tests FAILED!" else STATUS=0 info "All tests passed." fi echo '' } run_main_tests if [[ $STATUS = 0 ]]; then extra_fail='' else extra_fail='SOME TESTS FAILED!' fi info "Done running tests. Exiting with status ${STATUS}. ${extra_fail}" info "Test output is in file: ${outfile}" echo '' exit "$STATUS" apjanke-octave-tablicious-8a1b10f/dev-tools/tblish_check_stats_overlap.m000066400000000000000000000040201515050116100266130ustar00rootroot00000000000000function tblish_check_stats_overlap (stats_pkg_path) # Check for overlap with the Statistics package's API. if nargin < 1; stats_pkg_path = []; end stats_src_subdirs = {'Classification', 'Clustering', 'datasets', 'dist_fit', 'dist_fun', ... 'dist_stat', 'Regression'}; # Use this Tablicious package tblish_pkg_path = fileparts (fileparts (mfilename ('fullpath'))); # Locate Statistics package for comparison if isempty (stats_pkg_path) # Default to Andrew's conventional repo location stats_pkg_path = fullfile ( getenv ('HOME'), 'repos', 'octave-statistics'); endif stats_probe_file = fullfile (stats_pkg_path, 'inst', 'anova2.m'); if ! isfile (stats_probe_file) error ('Could not find Statistics OF package at: %s', stats_pkg_path) endif stats_inst = fullfile (stats_pkg_path, 'inst'); stats_src_paths = {stats_inst}; for i = 1:numel (stats_src_subdirs) stats_src_paths{end+1} = fullfile (stats_inst, stats_src_subdirs{i}); endfor # Announce emit ('Comparing Tablicious and Statistics package APIs') emit (' Tablicious: %s', tblish_pkg_path) emit (' Statistics: %s', stats_pkg_path) emit ('') # Compare functions tblish_inst = fullfile (tblish_pkg_path, 'inst'); t_fcns = list_fcns_in_dirs (tblish_inst); s_fcns = list_fcns_in_dirs (stats_src_paths); emit('Found functions/classes: Tablicious: %d Statistics: %d', ... numel (t_fcns), numel (s_fcns)) dupe_fcns = intersect (t_fcns, s_fcns); emit ('Found %d duplicate functions', numel (dupe_fcns)) emit (' dupes: %s', strjoin (dupe_fcns, ', ')) emit ('') endfunction function emit (fmt, varargin) fprintf ([fmt '\n'], varargin{:}); endfunction function out = list_mfiles_in_dir (f) files = readdir (f); out = files (endsWith (files, '.m')); endfunction function out = list_fcns_in_dirs (dirs) # Lists functions (or classes) implied by M-files in a set of directories. dirs = cellstr (dirs); out = {}; for i = 1:numel (dirs) d = dirs{i}; mfiles = list_mfiles_in_dir (d); names = regexprep (mfiles, '\.m$', ''); out = [out; names]; endfor endfunction apjanke-octave-tablicious-8a1b10f/doc/000077500000000000000000000000001515050116100177025ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/doc/Makefile000066400000000000000000000144351515050116100213510ustar00rootroot00000000000000# Makefile for Tablicious Octave package documentation # # (Custom Tablicious modification of the standard OF docgen tools.) # # See README.md for more info. # # This setup uses the convention that "*-in.texi" files are the human-editable # input files, and corresponding "*.texi" files are processed intermediate # files, where feasible. # # This Makefile is generic enough that it could be dropped without modification # in the doc/ directory of any package that follows the Octave Forge conventions, # as modified by Tablicious. You need to include its dependencies: mktexi.pl, # OctTexiDoc.pm, and README.md. And then create a src/.qhcp modified for your # package. # # And make sure that your texinfo doc strings follow these conventions: # - each @node must be followed immediately by a @chapter/@section/@subsection/@subsubsection # # Notes: # - You need to have Qt 5.x or newer installed, and its tools available on the path. # - Mac: This requires a newer version of Texinfo that ships with macOS. 4.8 and 5.2 are too # old. 6.x or newer is required; I have tested on 6.6 and 7.0. You must get the new texinfo # tools on your path in front of the system Texinfo before running these make targets. # With Homebrew, you can do `brew install texinfo` and manually add # `$(brew --prefix texinfo)/bin` to the FRONT of your $PATH. I do this by running # `PATH="$(brew --prefix texinfo)/bin:$PATH" when I start working with this code. # - On Ubuntu, don't forget to `apt-get install qt5-defaults`! # - On Mac, `brew install findutils` # # This Makefile originates from the Tablicious package (https://github.com/apjanke/octave-tablicious) # and is based on the one from the Octave Forge communications package # (https://octave.sourceforge.io/communications/index.html). # TODO: # * I'd like to put the generated output files in a subdir, to keep them separated # from the toolchain files in this dir, but I don't know if that works with OF # conventions. ## Some basic tools (can be overriden using environment variables) SED ?= sed TAR ?= tar GREP ?= grep CUT ?= cut ## Note the use of ':=' (immediate set) and not '=' (lazy set). ## http://stackoverflow.com/a/448939/1609556 package := $(shell $(GREP) "^Name: " ../DESCRIPTION | $(CUT) -f2 -d" ") version := $(shell $(GREP) "^Version: " ../DESCRIPTION | $(CUT) -f2 -d" ") DVIPS = dvips LN_S = ln -s PERL = perl ifndef OCTAVE OCTAVE := octave endif OCTAVE := $(OCTAVE) --no-gui --silent --norc UNAME=$(shell uname) ifeq ($(UNAME),Darwin) FIND = gfind else FIND = find endif MAKEINFO = makeinfo TEXI2DVI = texi2dvi TEXI2HTML = makeinfo --html TEXI2PDF = texi2pdf # This discovery of the Qt tools is hacky BS. Help wanted with # a better way to do this. # As of 2023-ish, the plain "qt" brew formula is Qt 6, but Octave still # uses Qt 5, which the "qt@5" versioned formula will give. BREW=$(shell which brew 2>/dev/null) ifneq ($(BREW),) BREW_QT = $(shell brew --prefix qt@5) ifeq ($(wildcard $(BREW_QT)/bin/qhelpgenerator),) QHELPGENERATOR = $(BREW_QT)/bin/qcollectiongenerator else QHELPGENERATOR = $(BREW_QT)/bin/qhelpgenerator endif else ifneq ($(wildcard /usr/bin/qtchooser),) # Probe for actual qhelpgenerator implementation existence # Gotta do this because the /usr/bin/qt{help,collection}generator shim always # exists regardless of whether the installed Qts provide them ifneq ($(shell qhelpgenerator -qt5 2>/dev/null),) QHELPGENERATOR = qhelpgenerator -qt5 else QHELPGENERATOR = qcollectiongenerator -qt5 endif else ifneq ($(shell which qcollectiongenerator 2>/dev/null),) QHELPGENERATOR = qcollectiongenerator else QHELPGENERATOR = qhelpgenerator endif endif # We depend on all files in the input source directories SRCDIRS := ../inst SRCFILES := $(shell find $(SRCDIRS) -type f) INFODOC = $(package).info DVIDOC = $(patsubst %.info,%.dvi,$(INFODOC)) PSDOC = $(patsubst %.info,%.ps,$(INFODOC)) PDFDOC = $(patsubst %.info,%.pdf,$(INFODOC)) TEXIDOC = $(patsubst %.info,%.texi,$(INFODOC)) INDEX = ../INDEX default: all all: info html pdf qhelp info: $(INFODOC) dvi: $(DVIDOC) pdf: $(PDFDOC) ps: $(PSDOC) qhelp: $(package).qhc # TIMESTAMP holds the POSIX time of the last mtime of any source file TIMESTAMP: $(SRCFILES) $(FIND) $(SRCDIRS) -type f -printf '%T@\n' | cut -d '.' -f 1 | sort -n | tail -1 > TIMESTAMP # This target generates .texi and also .qhp as a side effect .PRECIOUS: tablicious.texi tablicious.texi : src/tablicious.texi mktexi.pl $(SRCFILES) TIMESTAMP export SOURCE_DATE_EPOCH=`cat TIMESTAMP`; \ $(PERL) ./mktexi.pl $< $(INDEX) $@ $(package).qhp $(SRCDIRS) check-texinfo-version: $(PERL) ./mktexi.pl --check-texinfo-version %.dvi: %.texi check-texinfo-version $(TEXI2DVI) --quiet --clean -o $@ $< %.info: %.texi check-texinfo-version $(MAKEINFO) --no-split -o $@ $< %.pdf: %.texi TIMESTAMP check-texinfo-version export SOURCE_DATE_EPOCH=`cat TIMESTAMP`; \ $(TEXI2PDF) --quiet --clean -o $@ $< %.ps: %.dvi $(DVIPS) -o $@ $< $(package).qhc: $(package).qhcp $(package).qhp TIMESTAMP $(QHELPGENERATOR) $(package).qhcp -o $(package).qhc $(package).html: $(package).texi $(TEXI2HTML) --no-split -o $(package).html $< # "html" is a phony target, not the actual html/ directory html: $(package).texi $(package).html check-texinfo-version rm -rf html $(TEXI2HTML) -o html $< # Auxiliary make file defines build rules for generated images for the manual -include images.mk images.mk: images.sh $(SHELL) $< > $@ $(DVIDOC): $(IMAGES_EPS) $(PDFDOC): $(IMAGES_PDF) HTMLDIR_IMAGES = $(addprefix $(HTMLDOC)/,$(IMAGES_PNG)) $(HTMLDIR_IMAGES): $(IMAGES_PNG) | $(HTMLDOC) cp $(@F) $@ html: $(HTMLDIR_IMAGES) # The images included in the HTML manual must be present before the makeinfo # command is invoked or it will fall back on incorrect file names. $(HTMLDOC): $(IMAGES_PNG) # The texi2dvi script (used to create both PDF and DVI output formats) # uses some fixed temporary file names. In order to avoid a race condition # the DVI and PDF builds are forced to run serially through a Makefile rule. $(PDFDOC): $(DVIDOC) maintainer-clean: rm -rf html *.dvi *.eps *.html *.info *.pdf *.ps *.png *.texi *.qhp *.qch *.qhc *.tmp images.mk TIMESTAMP # I like a plain "clean" target, bc I keep forgetting that maintainer-clean exists -apj clean: maintainer-clean .PHONY: default all clean maintainer-clean dvi html info pdf ps qhelp check-texinfo-version apjanke-octave-tablicious-8a1b10f/doc/OctTexiDoc.pm000066400000000000000000000567701515050116100222640ustar00rootroot00000000000000# This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program; if not, see . # This program is granted to the public domain. # THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS # OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) # HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY # OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF # SUCH DAMAGE. # # David Bateman Feb 02 2003 # Andrew Janke 2019, 2024 # OctTexiDoc module: common code for mk*.pl in Tablicious Octave package # # Note: In Octave, "package" can mean two different things: either an Octave Forge style # package of software managed by the `pkg` command (like the Tablicious library), or a # namespace in M-code defined by placing source code files in "+" directories # (like the +tblish namespace within Tablicious). For clarity, this code uses "package" # to mean a pkg-managed software package, and "namespace" to mean the "+" # namespacing mechanism. use strict; # A DocSet is a collection of Texinfo doco from Octave source files. # It knows how to read in source directories, accumulating the Texinfo # blocks as it goes. package OctTexiDoc::DocSet; use Moose; use File::Basename; # auto_namespace: whether to auto-prepend namespaces to node names # # Modes: # 'yes' - DocSet will automatically detect the namespace of a source file # based on its location in a "+namespace" directory hierarchy, and prefix the # generated node names with that namespace, prepending it to the identifiers found # in the texinfo items like @deftp and @deftypefn found in that file. # 'no' - the identifiers in those texinfo items are used unmodified as the node names, # and namespaces inferred from dir structure are ignored entirely. # Additional modes may be added later, like 'auto', to add namespaces only if it # looks like the item names in the texinfo look like they don't include it already. has 'auto_namespace' => ( is => 'rw', isa => 'Str', default => 'no' ); # include_internal: whether to include internal-use items. # Currently covers "+internal" namespaces and "____.m" files; can be expanded # to cover other categories like "____" methods/functions inside regular-named # files, etc. Does not consider private/protected access or scope access modifiers; # we consider that to be a different mechanism, where "private" is not a synonym for # "internal". has 'include_internal' => ( is => 'rw', isa => 'Bool', default => 0 ); # include_plain_subdirs: whether to include non-namespace subdirs has 'include_plain_subdirs' => ( is => 'rw', isa => 'Bool', default => 0 ); # docs: top-level hierarchical doc node index # # {$node => {node => $node, block => $block, file => $file, children => [...]}} # Children are also {node => $node, block => $block, file => $file} # where: # $node is the fully-namespace-qualified class or function name # $block is the stripped text of the texinfo block # $file is the path to the file where this block came from has 'docs' => ( is => 'rw', isa => 'HashRef', default => sub { {} } ); # nodes: flat index of all the nodes: {$node => {}} has 'nodes' => ( is => 'rw', isa => 'HashRef', default => sub { {} } ); sub topic_node_names { my $this = shift; return keys %{$this->docs}; } sub all_node_names { my $this = shift; return keys %{$this->nodes}; } sub add_thing { my $self = shift; my ($node_name, $block) = @_; die "Node name was undefined" unless $node_name; die "Node name cannot be empty or blank" if ($node_name eq "" or $node_name eq " "); my $file = $$block{file}; if ($self->docs->{$node_name}) { my $other_file = $self->docs->{$node_name}{file}; printf STDERR "Warning: Node $node_name from file $file is shadowed by previous file $other_file\n"; return; } $self->docs->{$node_name} = $block; $self->nodes->{$node_name} = $block; for my $child (@{$$block{children}}) { $self->nodes->{$$child{node}} = $child; } } sub add_sub_thing { my $self = shift; my ($node_name, $parent_thing_name, $block) = @_; die "Sub-thing node name was undefined" unless $node_name; die "Sub-thing node name cannot be empty or blank" if ($node_name eq "" or $node_name eq " "); my $file = $$block{file}; if ($self->nodes->{$node_name}) { my $other_file = $self->nodes->{$node_name}{file}; printf STDERR "Warning: Node $node_name from file $file is shadowed by previous file $other_file\n"; return; } my $parent_thing = $self->docs->{$parent_thing_name}; if ($parent_thing) { push @{$$parent_thing{children}}, $block; } else { printf STDERR "Warning: Parent thing $parent_thing_name for node $node_name from file $file does not exist\n"; } $self->nodes->{$node_name} = $block; } # Read texinfo from all source files in a directory, possibly recursively sub read_source_dir { my $self = shift; my ($dir, $namespace) = @_; my $eff_namespace; opendir my $dh, $dir or die "Unable to open directory $dir: $!\n"; while (my $file_name = readdir($dh)) { next if $file_name =~ /^\./; my $file = "$dir/$file_name"; if (-d $file) { if ($file_name =~ /^\+/) { # Only include +internal namespaces if asked; they're not part of the public API if ($file_name eq "+internal") { next unless $self->include_internal } $self->read_source_dir ($file, OctTexiDoc::prepend_namespace ($namespace, $file_name)); } elsif ($file_name =~ /^\@/) { $self->read_class_at_dir ($file, $namespace); } elsif ($file_name = "private") { # Always ignore private stuff, bc we don't have a convention for naming the nodes # inside them (identifiers in private dirs are local to the parent dir, but the texinfo # node namespace is a flat global one). } else { # Arbitrary subdirs are conditional. Handles the case where packages arrange their # code in some multi-dir hierarchy where nested dirs may be placed on the path. # May not work right if there are plain-named subdirs under a "+namespace" dir. $self->read_source_dir ($file, $namespace) if $self->include_plain_subdirs } } else { # switch ($self->auto_namespace) { # case 'yes' { $eff_namespace = $namespace } # case 'no' { $eff_namespace = "" } # else { die "Invalid value for DocSet.auto_namespace Must be 'yes' or 'no'." } # } $eff_namespace = $namespace; # "__foo__" functions are internal implementation items. Assume "__foo" is internal too. if ($file_name =~ /^__/) { next unless $self->include_internal } # Need to switch on file type because the manner of embedding texinfo differs between them, # e.g. with different comment-intro characters. if ($file_name =~ /\.m$/) { $self->read_m_source_file ($file, $eff_namespace); } elsif ($file_name =~ /\.cc$/) { $self->read_cxx_source_file ($file, $eff_namespace); } else { # Silently ignore any other type of file. } } } } # Read texinfo from a regular, top-level .m function or classdef source file in to $self sub read_m_source_file { my $self = shift; my ($file, $namespace) = @_; my $blocks = OctTexiDoc::extract_multiple_texinfo_blocks_from_mfile ($file, $namespace, $self->auto_namespace); my $n_blocks = scalar (@$blocks); my $ns_name = $namespace || ""; return unless (scalar (@$blocks)); my $first_block = shift @$blocks; my $node = $$first_block{node}; my $texi = $$first_block{block}; $$first_block{children} = $blocks; $$first_block{file} = $file; $self->add_thing($node, $first_block); } # Read texinfo from a top-level .cxx oct-file source file # # This assumes that oct-files are always in the base namespace/package, or have # fully-qualified names in their texinfo content. We do this because the conventional # OF package layout puts all its oct-files in the root of `src/` and doesn't have a # namespaced layout. sub read_cxx_source_file { my $self = shift; my ($file, $namespace) = @_; my $blocks = OctTexiDoc::extract_texinfo_from_cxxfile ($file); for my $block (@$blocks) { my $node = $$block{node}; my $texi = $$block{block}; $$block{file} = $file; $$block{children} = []; $self->add_thing($node, $block); } } # Read a @ class source directory sub read_class_at_dir { my $self = shift; my ($dir, $namespace) = @_; # Locate and read the main classdef file my $dir_base = basename ($dir); my $class_name = $dir_base; $class_name =~ s/^\@//; my $class_file_name = "$class_name.m"; my $class_file = "$dir/$class_file_name"; unless (-f $class_file) { printf STDERR "Warning: No classdef file found in dir $dir\n"; return; } my $main_blocks = OctTexiDoc::extract_multiple_texinfo_blocks_from_mfile ($class_file, $namespace, $self->auto_namespace); return unless (scalar (@$main_blocks)); my $class_block = shift @$main_blocks; $$class_block{file} = $class_file; $$class_block{children} = $main_blocks; # Then go through the other method files opendir my $dh, $dir or die "Unable to open directory $dir: $!\n"; while (my $file_name = readdir($dh)) { next if $file_name =~ /^\./; next unless $file_name =~ /\.m$/; next if $file_name eq $class_file_name; my $method_file = "$dir/$file_name"; my $blocks = OctTexiDoc::extract_multiple_texinfo_blocks_from_mfile ($method_file, $namespace, $self->auto_namespace); push @{$$class_block{children}}, @$blocks; } # Stash the docs my $node = OctTexiDoc::prepend_namespace($namespace, $class_name); $self->add_thing($node, $class_block); } # Get a given node's full doc text sub get_node_doco { my $self = shift; my ($node_name) = @_; my $block = $self->nodes->{$node_name}; return undef unless $block; my $block_text = $$block{block}; return OctTexiDoc::munge_texi_block_text($block_text); } sub get_node_summary { my $self = shift; my ($node_name) = @_; my $texi = $self->get_node_doco($node_name); return undef unless $texi; return OctTexiDoc::first_sentence($texi); } # Get the first sentence of a given node's doc text package OctTexiDoc; use File::Basename; use IO::File; use IPC::Open3; sub prepend_namespace { my ($namespace, $child) = @_; # convenience hack: strip the "+" used in dir names corresponding to namespaces $child =~ s/^\+//; if ($namespace and $namespace ne "") { return "$namespace.$child"; } else { return $child; } } # Munge texinfo doco blocks into actual texinfo, or whatever it is that # we put in the .texi file. sub munge_texi_block_text { my ($txt) = @_; my $texi = ""; my @lines = split ("\n", $txt); my $in_doctex = 0; for my $line (@lines) { if ($line =~ /\@tex/) { $in_doctex = 1; } if ($in_doctex) { $line =~ s/\\\\/\\/g; } if ($line =~ /\@end tex/) { $in_doctex = 0; } $texi .= "$line\n"; } $texi =~ s/\@seealso\{(.*[^}])\}/See also: $1/g; return $texi; } # Read an INDEX file. Returns hashref: # { # "name" => $toolbox_name, # "long_name" => $long_toolbox_name, # "categories" => \@category_names, # "by_category" => { $category_name => \@category_fcn_names }, # "functions" => \@all_fcn_names, # "descriptions" => { $function_name => $description } # } # # This is based on a really simple understanding of the INDEX file # format, with just a header line, category lines, and function/class # names. # # TODO: Implement full INDEX file format based on spec at # https://octave.org/doc/v4.2.1/The-INDEX-File.html#The-INDEX-File. sub read_index_file { # {{{1 my ($index_file # in path to INDEX file ) = @_; unless ( open(IND, $index_file) ) { die "Error: Could not open INDEX file $index_file: $!\n"; } my ($current_category, @all_functions, @categories, %by_category, %fcn_descrs); my $line = ; $line = while ($line =~ /^\s*(#.*)?$/); # First line is header chomp $line; unless ($line =~ /^\s*(\w+)\s+>>\s+(\S.*\S)\s*$/) { die "Error: Invalid header line in INDEX file $index_file: $line\n"; } my ($toolbox, $toolbox_long_name) = ($1, $2); while (my $line = ) { chomp $line; next if $line =~ /^\s*(#.*)?$/; if ($line =~ /^\S/) { $current_category = $line; push @categories, $current_category unless grep (/^$current_category$/, @categories); $by_category{$current_category} ||= []; } elsif ($line =~ /^(\S+)\s*=\s*(\S.*?)\s*$/) { my ($fcn, $descr) = ($1, $2); $fcn_descrs{$fcn} = $descr; push (@{$by_category{$current_category}}, $fcn); push @all_functions, $fcn; } else { my $txt = substr ($line, 1); my @functions = split /\s+/, $txt; push (@{$by_category{$current_category}}, @functions); push @all_functions, @functions; } } return { "name" => $toolbox, "long_name" => $toolbox_long_name, "categories" => \@categories, "by_category" => \%by_category, "functions" => \@all_functions, "descriptions" => \%fcn_descrs }; } # Extract the entire main documentation comment from an m-file. # This grabs the first comment block after an optional initial copyright block. # It ignores M-code syntax, so if you don't have a file-level comment block, # it may end up grabbing a comment block from inside one of your functions. sub extract_description_from_mfile { my ($mfile) = @_; my $retval = ''; unless (open (IN, $mfile)) { die "Error: Could not open file $mfile: $!\n"; } # Skip leading blank lines while () { last if /\S/; } # First block may be copyright statement; skip it if (m/\s*[%\#][\s\#%]* Copyright/) { while () { last unless /^\s*[%\#]/; } } # Skip everything until the next comment block while (!/^\s*[\#%]+\s/) { $_ = ; last if not defined $_; } # Next comment block is the documentation; strip it and return it while (/^\s*[\#%]+\s/) { s/^\s*[%\#]+\s//; # strip leading comment characters s/[\cM\s]*$//; # strip trailing spaces. $retval .= "$_\n"; $_ = ; last if not defined $_; } close(IN); return $retval; } # Extract all Octave Texinfo documentation comments from an m-file # Returns an arrayref of extracted blocks, with each block being # a hashref with keys "node" and "block". sub extract_multiple_texinfo_blocks_from_mfile { my ($mfile, $namespace, $auto_namespace) = @_; my $retval = ''; my $file_node_name = basename($mfile, ('.m')); my $fh = IO::File->new($mfile, "r") or die "Error: Could not open file $mfile: $!\n"; # Skip leading blank lines while (<$fh>) { last if /\S/; } # First block may be copyright statement; skip it if (m/\s*[%\#][\s\#%]* Copyright/) { while (<$fh>) { last unless /^\s*[%\#]/; } } # Okay, now detect all Texinfo comment blocks # # This is currently broken: I'm using @node lines in the Texinfo to indicate node # starts, but the @node and @section/@subsection/@subsubsection are emitted automatically # by mktexi.pl. my $do_prepend_ns = ($auto_namespace eq 'yes'); my @blocks; my $block; # the current block my $in_block = 0; my $line; my $node; while (<$fh>) { my $line_num = $fh->input_line_number(); if (/^\s*## -\*- texinfo -\*-/) { # block start if ($in_block) { push @blocks, { node => $node, block => $block}; } $block = ""; $in_block = 1; # Grab next line so we can detect node name $line = <$fh>; $line = strip_mfile_block_line ($line); if ($line =~ /^\s*\@node (\S*)/) { my $node_basename = $1; if ($do_prepend_ns) { $node = prepend_namespace($namespace, $node_basename); } else { $node = $node_basename; } } else { # For back-compatibility, the first block may take the file as its node name # Only the first block! Otherwise you'd have collisions. if ((scalar (@blocks)) == 0) { $node = prepend_namespace($namespace, $file_node_name); } else { die "Found non-first block with no \@node statement in file $mfile at line $line_num\n"; } $block .= "$line\n"; } } elsif (/^\s*##\s/) { if ($in_block) { $line = strip_mfile_block_line ($_); $block .= "$line\n"; } } else { if ($in_block) { push @blocks, { node => $node, block => $block}; $block = ""; $in_block = 0; } } } my $line_num = $fh->input_line_number(); if ($in_block) { push @blocks, { node => $node, block => $block}; $block = "-*- texinfo -*-"; $in_block = 0; } my $n_blocks = scalar @blocks; return \@blocks; } sub strip_mfile_block_line { my ($line) = @_; $line =~ s/^\s*##\s?//; # strip leading comment characters $line =~ s/[\cM\s]*$//; # strip trailing spaces. return $line; } # Extract all Octave Texinfo documentation comments from a C++ oct-file # source file. # Returns an arrayref of extracted blocks, with each block being # a hashref with keys "node" and "block". sub extract_texinfo_from_cxxfile { my ($file) = @_; open(IN, $file) or die "Error: Could not open file $file: $!\n"; my @blocks; while () { # skip to the next defined Octave function next unless /^DEFUN_DLD/; # extract function name /\DEFUN_DLD\s*\(\s*(\w+)\s*,/; my $function = $1; # Advance to the comment string in the DEFUN_DLD # The comment string in the DEFUN_DLD is the first line with "..." $_ = until /\"/; my $desc = $_; $desc =~ s/^[^\"]*\"//; # Slurp in C-style implicitly-concatenated strings while ($desc !~ /[^\\]\"\s*\S/ && $desc !~ /^\"/) { # if line ends in '\', chop it and the following '\n' $desc =~ s/\\\s*\n//; # join with the next line $desc .= ; # eliminate consecutive quotes, being careful to ignore # preceding slashes. XXX FIXME XXX what about \\" ? $desc =~ s/([^\\])\"\s*\"/$1/; } $desc = "" if $desc =~ /^\"/; # chop everything if it was "" $desc =~ s/\\n/\n/g; # insert fake line ends $desc =~ s/([^\"])\".*$/$1/; # chop everything after final '"' $desc =~ s/\\\"/\"/; # convert \"; XXX FIXME XXX \\" $desc =~ s/$//g; # chop trailing ... if (!($desc =~ /^\s*-\*- texinfo -\*-/m)) { printf STDERR ("Function %s (file %s) does not contain texinfo help:%s\n", $function, $file); } push @blocks, { node => $function, block => $desc}; } return \@blocks; } sub get_package_metadata_from_description_file { my $description_file = "../DESCRIPTION"; unless (open (IN, $description_file)) { die "Error: Could not open file $description_file: $!\n"; } my ($key, $value, %defn); while () { chomp; next if /^\s*(#.*)?$/; # skip comments if (/^ /) { die "Error: Failed parsing $description_file: found continuation line before any key line: \"$_\"" unless $key; # continuation line my $txt = $_; $txt =~ s/^\s+//; $value .= $txt; } elsif (/^(\S+)\s*:\s*(\S.*?)\s*$/) { $defn{$key} = $value if $key; ($key, $value) = ($1, $2); } else { die "Error: Failed parsing $description_file: Unparseable line: \"$_\""; } } $defn{$key} = $value if $key; return \%defn; } # extract the first "real sentence" from a function (or classdef?) doco texinfo block sub first_sentence { # {{{1 my ($desc) = @_; my $retval = ''; my $line; my $next; my @lines; my $trace = 0; # $trace = 1 if $desc =~ /Levenberg/; return "" unless defined $desc; # Run the texinfo through makeinfo to get plain text my $cmd = "makeinfo --fill-column 1600 --no-warn --no-validate " . "--no-headers --force --ifinfo"; open3(*Writer, *Reader, *Errer, $cmd) or die "Error: Could not run makeinfo: $!"; # I have no idea what this @macro seealso thing is doing -apj 2024-02-05 print Writer "\@macro seealso {args}\n\n\@noindent\nSee also: \\args\\.\n\@end macro\n"; print Writer "$desc"; close (Writer); @lines = ; close (Reader); my @err = ; close (Errer); # I think this &WNOHANG needs 'use POSIX ":sys_wait_h"' to work right? -apj 2020 # waitpid (-1, &WNOHANG); waitpid (-1, 0); # Display source and errors, if any if (@err) { my $n = 1; foreach $line ( split(/\n/,$desc) ) { printf "%2d: %s\n",$n++,$line; } print ">>> @err"; } # Skip prototype and blank lines while (1) { return "" unless @lines; $line = shift @lines; next if $line =~ /^\s*-/; next if $line =~ /^\s*$/; last; } # Try to make a complete sentence, including the '.' if ( "$line " !~ /[^.][.]\s/ && $#lines >= 0) { my $next = $lines[0]; $line =~ s/\s*$//; # trim trailing blanks on last $next =~ s/^\s*//; # trim leading blanks on next $line .= " $next" if "$next " =~ /[^.][.]\s/; # ends the sentence } # Tidy up the sentence. chomp $line; $line =~ s/^\s*//; # trim leading blanks on line $line =~ s/([^.][.])\s.*$/$1/; # trim everything after the sentence print "Skipping:\n$desc---\n" if $line eq ""; # And return it. return $line; } # 1}}} 1; apjanke-octave-tablicious-8a1b10f/doc/README.md000066400000000000000000000303431515050116100211640ustar00rootroot00000000000000# README - Tablicious documentation tools This is the README for the doco toolchain of this Octave package. It goes with the files `mktexi.pl`, `OctTexiDoc.pm`, and `Makefile`, all of which should be found in the `doc/` subdirectory of an Octave package. It is not documentation for the Tablicious Octave package itself. This is Andrew Janke’s summary of how the doco generation tools in this `doc/` directory work. The stuff in here is not the standard Octave Forge ("OF") doco toolchain. This is Andrew Janke’s enhancement of it to support classes and namespaces. It first appeared in Andrew’s Tablicious package in April 2019. Andrew took a copy of it from some other OF package and modified it. ## Requirements Texinfo 6.0+, Perl, and Perl CPAN modules as described below. This toolchain requires Texinfo version 6.0 or newer. Might even need 7.x as of 2023. Texinfo versions 5.x and older will result in a lot of errors complaining about node relationships and whatnot. If you're on macOS and installed the newer Texinfo using Homebrew on a system that also has Texinfo tools from another source (like maybe MacTeX/TeXLive?), you may need to do a `brew link --overwrite texinfo` to force-link them, uninstall the other Texinfo-providing program, or do something else to reconcile that conflict. Perl modules, which must be installed in your perl environment: * `Moose` * `Data::Dumper` * `Date::Parse` * `Text::Wrap` * `IPC::Open3` Sorry for the dependencies. To install these, run `perl -MCPAN -e shell` (you may need `sudo` on that, depending on your Perl installation's setup), and in that shell, run `install Moose Data::Dumper Date::Parse Text::Wrap IPC::Open3`. On macOS, you'll probably want to install a Perl via Homebrew or MacPorts instead of using the system Perl for this. ## How it works ### The files Here’s a list of the files involved in the doco toolchain. In this document, `` means the package name. E.g. `chrono` or `tablicious`. Human-maintained input files: * `../DESCRIPTION` * `../INDEX` * `src/` - human-maintained doco contents * `src/.texi` - texinfo manual parts that aren't generated from the code's inline helptext * `.qhcp` - control file for producing the QHelp files * `../inst/**/*.m` - source files with embedded documentation Doco toolchain script files: * `Makefile` * `mktexi.pl` * `OctTexiDoc.pm` - module with `mktexi.pl`'s implementation Generated intermediate files: * `.texi` - combined Texinfo source * `.log` * `.qhp` * `.dvi` * `TIMESTAMP` Generated final output (user-consumable) target files: * `.html` * `html/*` * `.info` * `.pdf` * `.(qch,qhc)` - QHelp files for Octave GUI's doc browser `html/*` is a final output file because it's used both for packaging up into `.qch` (the QHelp collection file), for optional use in package websites and other locations, and for local reading. Maybe `.texi` should be considered a final output file, so end users can render it to other non-provided formats if they want? The `.{html|info|pdf|qch|qhc}` files are checked in to source control so they are included in the package distribution. Technically, since they are entirely generated from the source and human-maintained index files, they could be re-generated as part of the `pkg install` step for this package. But that would require that users install heavyweight tools like Qt and TeX which are required for their generation, which is undesirable, and also run the doco build to make them usable. I'd rather you be able to run Tablicious and see all the doco directly from just a cloned repo, and not require a build/install step. So we just have the package maintainer generate them at package authoring time and include them in source control and the package distribution file. #### Developer note This toolchain used to have more steps, broken out into `mkdoc.pl`, `mktexi.pl`, and `mkqhp.pl`. `mkdoc.pl` would scan the source files and build a function index with the extracted texinfo blocks; `mktexi.pl` would take that function index and build `.texi`, and `mkqhp.pl` would take `.texi`, `../INDEX`, and the function index and build the `.qhp` file. I changed the code to unify these three steps into a single `mktexi.pl` because I needed to use a multi-level function/topic index that didn’t fit into the existing function index file format, and doing everything in-memory in a single process simplified the code and resulted in faster execution times. ### The process * You launch `make doc` using the `Makefile`. It causes the rest to happen. * `mktexi.pl`: * Scans the source files (in `../inst` and `../src`) and extracts their embedded texinfo blocks * Combines that with `src/.texi`, `../DESCRIPTION`, and `../INDEX` to produce the unified actual Texinfo document `.texi` * Does some substitutions on stuff in `src/.texi` * `@DOCSTRING(...)` gets special handling * `%%%%PACKAGE_VERSION%%%%` gets replaced with the version from `../DESCRIPTION` * Generates a `.qhcp` index file that can be used to build a QHelp collection from the generated HTML help files * The rest of `make doc` uses standard Texinfo and Qt tools to generate target help files in various formats * You check the resulting files in to source control if they’re good ## Writing Octave Texinfo documentation Here’s how to write the variant of Octave Texinfo doco that’s supported by this package’s doco toolchain. Octave Texinfo doco is a way of writing embedded user documentation as part of Octave `.m` or `.cc` source files. It’s like Perldoc or Javadoc. ### Texinfo doco format The Texinfo doco in a source file is comprised of one or more Texinfo comment blocks. A regular function file has exactly one Texinfo block. A classdef file can have multiple Texinfo blocks. A class method file has one Texinfo comment block. Each Texinfo block becomes one topic or subtopic in the generated Texinfo documentation. #### Texinfo in `.m` files In `.m` files, a Texinfo block is comprised of a contiguous run of comment lines each starting with “`## `” (that’s two octothorps (`#`) followed by a space), with optional spaces before the first “`#`”. The first line in a Texinfo block must be “`## -*- texinfo -*-`”; that’s how the parser knows it’s a Texinfo block instead of a regular comment. The Texinfo blocks contain Texinfo markup. Each block should contain the contents of a single node. In a regular function `.m` file, there is a single Texinfo block that documents the function. It should be placed between the initial Copyright header and the `function` line. There is no need to have a `@node` statement in this block; it is generated implicitly. In a classdef `.m` file, there is a main Texinfo block that documents the overall class, and then one or more additional Texinfo blocks that document individual methods, events, or other things within the class. The main (class-level) Texinfo block should precede the `classdef` line. It should not have a `@node` statement. The additional (method-level) Texinfo blocks may be placed anywhere throughout the file, but typically should immediately precede the definition of the method or other thing they are documenting. The additional Texinfo blocks _must_ have a `@node` statement as their first line. The node name must be the class-qualified method name in format `.`. For classes that are within a namespace (package), the node name must _not_ include the namespace as a qualification. For method definition files inside a `@` directory, there should be a single top-level Texinfo block, immediately preceding the method definition. As with a function definition file, it should not have a `@node` statement. Neither the top-level nor additional blocks should include `@section`, `@subsection`, or `@subsubsection` statements. Those are added implicitly by the doco generation tools. Currently, each top-level block is turned into a `@subsection` and each method-level block is turned into a `@subsubsection`, but that may change. #### Texinfo in `.cc` files Texinfo blocks in oct-file `.cc` source files are included in comment strings inside the `DEFUN_DLD` macro call. #### Formatting Texinfo elements These are the conventions that Andrew settled on while documenting the Tablicious package. They seem to work okay. Functions should be documented with `@deftypefn` and optionally `@deftypefnx` statements. Top-level class Texinfo blocks should contain: * a `@deftp {Class} ` that documents the overall class * `@deftypeivar ` items that document the user-visible properties Method-level Texinfo blocks should have `@deftypefn {} ` items that document the method, with optional `@deftypefnx {} ` items to document alternate calling forms. The `` should be: * `{Constructor}` for constructors * `{Method}` for instance methods * `{Static Method}` for static methods Use `@var{...}` for input and ouptut function parameters. Use `@code{...}` for class, method, and function names. For functions that take trailing name/value option pairs, use a `@deftypefnx` with the signature form `(@dots{}, @code{'Option'}, @var{Option})` to document them. The first thing in each function, class, or method doco block should be a one-sentence summary of what it does. This sentence will be extracted and used in other places in the final documentation. In class doco, use “`obj`” as the conventional name for the method dispatch object in the documentation, even though you might use “`this`” or something else as the parameter in the actual code. Documentation is viewed from the caller’s perspective, not the implementation’s perspective, so “`this`” makes less sense. ### Examples #### Example: Function documentation Here’s what documentation of the regular (global) function `foonly` should look like. ```octave ## Copyright (C) 2019 John Doe ## ## [...] ## -*- texinfo -*- ## @deftypefn {Function} {@var{out} =} foonly (@var{x}, @var{y}) ## @deftypefn {Function} {@var{out} =} foonly (@var{x}, @var{y}, @var{dim}) ## @deftypefn {Function} {@var{out} =} foonly (@dots{}, @code{'Format'}, @var{Format}) ## ## Do the foonly computation on given inputs. ## ## Peforms the foonly computation on inputs @var{x} and @var{y}, optionally along ## dimension @var{dim}. ## ## If the option @var{Format} is supplied, it controls the output format. ## ## @end deftypefn function out = foonly (x, y, varargin) % [...] endfunction ``` #### Example: Class documentation Here’s what documentation of a classdef class named `Example` should look like. ```octave ## Copyright (C) 2019 Jane Doe ## ## [...] ## -*- texinfo -*- ## @deftp {Class} datetime ## ## An example class that does nothing. ## ## Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ## ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. ## Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt. ## ## @end deftp ## ## @deftypeivar Example @code{double} x ## ## The x property. Has no semantics. ## ## @end deftypeivar ## ## @deftypeivar Example @code{charvec} format ## ## The format to display @code{x} in. The name of the format this ## defined somewhere else. ## ## @end deftypeivar classdef Example properties x = NaN format = 'default' endproperties methods ## -*- texinfo -*- ## @node Example.Example ## @deftypefn {Constructor} {@var{obj} =} Example () ## ## Constructs a new scalar Example with the default values. ## ## @end deftypefn ## ## @deftypefn {Constructor} {@var{obj} =} Example (@var{x}) ## @deftypefnx {Constructor} {@var{obj} =} Example (@var{x}, @var{format}) ## ## Constructs a new Example with the given @var{x} and @var{format} ## values. ## ## @end deftypefn function this = Example (varargin) % ... endfunction ## -*- texinfo -*- ## @node Example.foo ## @deftypefn {Method} {[@var{a}, @var{b}] =} foo (@var{obj}) ## ## Performs a foo calculation. ## ## @end deftypefn function [a, b] = foo (this) % ... endfunction endmethods endclassdef ``` Note the use of a `@node` line in the method and constructor documentation but _not_ in the class-level documentation. 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tblish.dataset.co2tblish.dataset.co2[apjanke-octave-tablicious-8a1b10f/doc/html/000077500000000000000000000000001515050116100206465ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/doc/html/API-Alphabetically.html000066400000000000000000000135611515050116100250670ustar00rootroot00000000000000 API Alphabetically (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/doc/html/API-Reference.html000066400000000000000000000047401515050116100240460ustar00rootroot00000000000000 API Reference (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/doc/html/API-by-Category.html000066400000000000000000000360501515050116100243340ustar00rootroot00000000000000 API by Category (Tablicious v0.4.7)

    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like.

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/Copying.html000066400000000000000000000110511515050116100231420ustar00rootroot00000000000000 Copying (Tablicious v0.4.7)

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    9 Copying

    Previous: API Reference, Up: Tablicious for GNU Octave   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/Data-Sets-from-R.html000066400000000000000000000056771515050116100245000ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.7)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

    apjanke-octave-tablicious-8a1b10f/doc/html/Date-and-Time-Representation.html000066400000000000000000000060621515050116100270510ustar00rootroot00000000000000 Date and Time Representation (Tablicious v0.4.7)

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/doc/html/Datenum-Compatibility.html000066400000000000000000000074701515050116100257500ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.7)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/doc/html/Defined-Time-Zones.html000066400000000000000000000060161515050116100250650ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.7)

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/doc/html/Durations.html000066400000000000000000000046521515050116100235130ustar00rootroot00000000000000 Durations (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/doc/html/Example-Data-Sets.html000066400000000000000000000102261515050116100247130ustar00rootroot00000000000000 Example Data Sets (Tablicious v0.4.7)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    apjanke-octave-tablicious-8a1b10f/doc/html/Getting-Started.html000066400000000000000000000064201515050116100245430ustar00rootroot00000000000000 Getting Started (Tablicious v0.4.7)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/doc/html/Introduction.html000066400000000000000000000072461515050116100242260ustar00rootroot00000000000000 Introduction (Tablicious v0.4.7)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.7 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

    apjanke-octave-tablicious-8a1b10f/doc/html/Missing-Functionality.html000066400000000000000000000117541515050116100260030ustar00rootroot00000000000000 Missing Functionality (Tablicious v0.4.7)

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support
    • Things omitted for compatibility:
      • ismissing function

    A global ismissing function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. ismissing methods for various classes in Tablicious are defined. Tablicious also supplies an isnanny function with behavior similar to ismissing for clients which which wish to have ismissing-like behavior using a dependency only on Tablicious.

    It is the author’s hope that many of these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

    apjanke-octave-tablicious-8a1b10f/doc/html/NaC.html000066400000000000000000000076401515050116100222040ustar00rootroot00000000000000 NaC (Tablicious v0.4.7)

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/doc/html/NaS.html000066400000000000000000000075251515050116100222260ustar00rootroot00000000000000 NaS (Tablicious v0.4.7)

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

    apjanke-octave-tablicious-8a1b10f/doc/html/NaT.html000066400000000000000000000075711515050116100222300ustar00rootroot00000000000000 NaT (Tablicious v0.4.7)

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

    apjanke-octave-tablicious-8a1b10f/doc/html/Table-Construction.html000066400000000000000000000070421515050116100252560ustar00rootroot00000000000000 Table Construction (Tablicious v0.4.7)

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

    apjanke-octave-tablicious-8a1b10f/doc/html/Table-Representation.html000066400000000000000000000100321515050116100255570ustar00rootroot00000000000000 Table Representation (Tablicious v0.4.7)

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

    apjanke-octave-tablicious-8a1b10f/doc/html/Tables-vs-SQL.html000066400000000000000000000113661515050116100240400ustar00rootroot00000000000000 Tables vs SQL (Tablicious v0.4.7)

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

    apjanke-octave-tablicious-8a1b10f/doc/html/Time-Zones.html000066400000000000000000000141321515050116100235270ustar00rootroot00000000000000 Time Zones (Tablicious v0.4.7)

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/Validation-Functions.html000066400000000000000000000117531515050116100256030ustar00rootroot00000000000000 Validation Functions (Tablicious v0.4.7)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

    Next: Example Data Sets, Previous: Date and Time Representation, Up: Tablicious for GNU Octave   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/array2table.html000066400000000000000000000103041515050116100237420ustar00rootroot00000000000000 array2table (Tablicious v0.4.7)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/doc/html/caldays.html000066400000000000000000000065041515050116100231610ustar00rootroot00000000000000 caldays (Tablicious v0.4.7)

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration-Class.html000066400000000000000000000063161515050116100260640ustar00rootroot00000000000000 calendarDuration Class (Tablicious v0.4.7)

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration.html000066400000000000000000000107151515050116100250170ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.7)

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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002ecalendarDuration.html000066400000000000000000000077751515050116100307610ustar00rootroot00000000000000 calendarDuration.calendarDuration (Tablicious v0.4.7)

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002edatevec.html000066400000000000000000000077011515050116100271020ustar00rootroot00000000000000 calendarDuration.datevec (Tablicious v0.4.7)

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    8.2.3.2 calendarDuration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert a calendarDuration to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this calendarDuration.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002edispstrs.html000066400000000000000000000062011515050116100273340ustar00rootroot00000000000000 calendarDuration.dispstrs (Tablicious v0.4.7)

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    8.2.3.3 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002eismissing.html000066400000000000000000000062451515050116100274760ustar00rootroot00000000000000 calendarDuration.ismissing (Tablicious v0.4.7)

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    8.2.3.4 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002eisnan.html000066400000000000000000000063111515050116100265730ustar00rootroot00000000000000 calendarDuration.isnan (Tablicious v0.4.7)

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    8.2.3.5 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002eminus.html000066400000000000000000000061611515050116100266210ustar00rootroot00000000000000 calendarDuration.minus (Tablicious v0.4.7)

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    8.2.3.6 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002emtimes.html000066400000000000000000000063521515050116100267660ustar00rootroot00000000000000 calendarDuration.mtimes (Tablicious v0.4.7)

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    8.2.3.7 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002eplus.html000066400000000000000000000070301515050116100264450ustar00rootroot00000000000000 calendarDuration.plus (Tablicious v0.4.7)

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    8.2.3.8 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002etimes.html000066400000000000000000000062251515050116100266100ustar00rootroot00000000000000 calendarDuration.times (Tablicious v0.4.7)

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    8.2.3.9 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calendarDuration_002euminus.html000066400000000000000000000056051515050116100270100ustar00rootroot00000000000000 calendarDuration.uminus (Tablicious v0.4.7)

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    8.2.3.10 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

    apjanke-octave-tablicious-8a1b10f/doc/html/calmonths.html000066400000000000000000000065161515050116100235340ustar00rootroot00000000000000 calmonths (Tablicious v0.4.7)

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/calyears.html000066400000000000000000000060241515050116100233410ustar00rootroot00000000000000 calyears (Tablicious v0.4.7)

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical.html000066400000000000000000000177441515050116100240260ustar00rootroot00000000000000 categorical (Tablicious v0.4.7)

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

    Next: cell2table, Previous: calyears, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eaddcats.html000066400000000000000000000060111515050116100260610ustar00rootroot00000000000000 categorical.addcats (Tablicious v0.4.7)

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002ecategorical.html000066400000000000000000000137231515050116100267430ustar00rootroot00000000000000 categorical.categorical (Tablicious v0.4.7)

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002ecategories.html000066400000000000000000000062041515050116100266070ustar00rootroot00000000000000 categorical.categories (Tablicious v0.4.7)

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002ecellstr.html000066400000000000000000000062631515050116100261370ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.7)

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002edispstrs.html000066400000000000000000000062721515050116100263420ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.7)

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002edouble.html000066400000000000000000000067041515050116100257410ustar00rootroot00000000000000 categorical.double (Tablicious v0.4.7)

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eiscategory.html000066400000000000000000000063231515050116100266350ustar00rootroot00000000000000 categorical.iscategory (Tablicious v0.4.7)

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eismissing.html000066400000000000000000000061561515050116100264750ustar00rootroot00000000000000 categorical.ismissing (Tablicious v0.4.7)

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eisnanny.html000066400000000000000000000062611515050116100261440ustar00rootroot00000000000000 categorical.isnanny (Tablicious v0.4.7)

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eisordinal.html000066400000000000000000000061651515050116100264540ustar00rootroot00000000000000 categorical.isordinal (Tablicious v0.4.7)

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eisundefined.html000066400000000000000000000064771515050116100267730ustar00rootroot00000000000000 categorical.isundefined (Tablicious v0.4.7)

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002emergecats.html000066400000000000000000000076321515050116100264420ustar00rootroot00000000000000 categorical.mergecats (Tablicious v0.4.7)

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002emissing.html000066400000000000000000000075071515050116100261420ustar00rootroot00000000000000 categorical.missing (Tablicious v0.4.7)

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eremovecats.html000066400000000000000000000075211515050116100266350ustar00rootroot00000000000000 categorical.removecats (Tablicious v0.4.7)

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002erenamecats.html000066400000000000000000000070311515050116100266030ustar00rootroot00000000000000 categorical.renamecats (Tablicious v0.4.7)

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002ereordercats.html000066400000000000000000000072631515050116100270050ustar00rootroot00000000000000 categorical.reordercats (Tablicious v0.4.7)

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002esetcats.html000066400000000000000000000062751515050116100261400ustar00rootroot00000000000000 categorical.setcats (Tablicious v0.4.7)

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002esqueezecats.html000066400000000000000000000061351515050116100270210ustar00rootroot00000000000000 categorical.squeezecats (Tablicious v0.4.7)

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002estring.html000066400000000000000000000062731515050116100257760ustar00rootroot00000000000000 categorical.string (Tablicious v0.4.7)

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002esummary.html000066400000000000000000000060651515050116100261640ustar00rootroot00000000000000 categorical.summary (Tablicious v0.4.7)

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

    apjanke-octave-tablicious-8a1b10f/doc/html/categorical_002eundefined.html000066400000000000000000000067011515050116100264250ustar00rootroot00000000000000 categorical.undefined (Tablicious v0.4.7)

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

    apjanke-octave-tablicious-8a1b10f/doc/html/cell2table.html000066400000000000000000000102361515050116100235470ustar00rootroot00000000000000 cell2table (Tablicious v0.4.7)

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/doc/html/colvecfun.html000066400000000000000000000065701515050116100235300ustar00rootroot00000000000000 colvecfun (Tablicious v0.4.7)

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

    apjanke-octave-tablicious-8a1b10f/doc/html/contains.html000066400000000000000000000074631515050116100233640ustar00rootroot00000000000000 contains (Tablicious v0.4.7)

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

    apjanke-octave-tablicious-8a1b10f/doc/html/datasets_002edescription.html000066400000000000000000000065321515050116100263440ustar00rootroot00000000000000 datasets.description (Tablicious v0.4.7)

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

    apjanke-octave-tablicious-8a1b10f/doc/html/datasets_002elist.html000066400000000000000000000065501515050116100247740ustar00rootroot00000000000000 datasets.list (Tablicious v0.4.7)

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

    apjanke-octave-tablicious-8a1b10f/doc/html/datasets_002eload.html000066400000000000000000000062751515050116100247440ustar00rootroot00000000000000 datasets.load (Tablicious v0.4.7)

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime-Class.html000066400000000000000000000063731515050116100244040ustar00rootroot00000000000000 datetime Class (Tablicious v0.4.7)

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime.html000066400000000000000000000202661515050116100233360ustar00rootroot00000000000000 datetime (Tablicious v0.4.7)

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

    Next: days, Previous: contains, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eNaT.html000066400000000000000000000075611515050116100244720ustar00rootroot00000000000000 datetime.NaT (Tablicious v0.4.7)

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    8.2.10.26 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ecolon.html000066400000000000000000000117221515050116100251140ustar00rootroot00000000000000 datetime.colon (Tablicious v0.4.7)

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

    Next: datetime.convertDatenumTimeZone, Up: datetime   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002econvertDatenumTimeZone.html000066400000000000000000000070101515050116100304460ustar00rootroot00000000000000 datetime.convertDatenumTimeZone (Tablicious v0.4.7)

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatenum.html000066400000000000000000000060321515050116100254350ustar00rootroot00000000000000 datetime.datenum (Tablicious v0.4.7)

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatenum2posix.html000066400000000000000000000061221515050116100266020ustar00rootroot00000000000000 datetime.datenum2posix (Tablicious v0.4.7)

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatestr.html000066400000000000000000000066421515050116100254550ustar00rootroot00000000000000 datetime.datestr (Tablicious v0.4.7)

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatestrs.html000066400000000000000000000067141515050116100256400ustar00rootroot00000000000000 datetime.datestrs (Tablicious v0.4.7)

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatestruct.html000066400000000000000000000066771515050116100262010ustar00rootroot00000000000000 datetime.datestruct (Tablicious v0.4.7)

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatetime.html000066400000000000000000000136431515050116100256020ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.7)

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edatevec.html000066400000000000000000000071271515050116100254210ustar00rootroot00000000000000 datetime.datevec (Tablicious v0.4.7)

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ediff.html000066400000000000000000000061161515050116100247130ustar00rootroot00000000000000 datetime.diff (Tablicious v0.4.7)

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002edispstrs.html000066400000000000000000000057171515050116100256640ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.7)

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eeq.html000066400000000000000000000062461515050116100244140ustar00rootroot00000000000000 datetime.eq (Tablicious v0.4.7)

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ege.html000066400000000000000000000062611515050116100243770ustar00rootroot00000000000000 datetime.ge (Tablicious v0.4.7)

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002egmtime.html000066400000000000000000000060751515050116100252710ustar00rootroot00000000000000 datetime.gmtime (Tablicious v0.4.7)

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002egt.html000066400000000000000000000062501515050116100244140ustar00rootroot00000000000000 datetime.gt (Tablicious v0.4.7)

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ehms.html000066400000000000000000000063471515050116100246000ustar00rootroot00000000000000 datetime.hms (Tablicious v0.4.7)

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a datetime. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eisbetween.html000066400000000000000000000063651515050116100257760ustar00rootroot00000000000000 datetime.isbetween (Tablicious v0.4.7)

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eismissing.html000066400000000000000000000061621515050116100260110ustar00rootroot00000000000000 datetime.ismissing (Tablicious v0.4.7)

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    8.2.10.18 datetime.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eisnan.html000066400000000000000000000060441515050116100251130ustar00rootroot00000000000000 datetime.isnan (Tablicious v0.4.7)

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    8.2.10.19 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eisnat.html000066400000000000000000000056241515050116100251240ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.7)

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    8.2.10.20 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ele.html000066400000000000000000000063011515050116100243770ustar00rootroot00000000000000 datetime.le (Tablicious v0.4.7)

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    8.2.10.21 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002elinspace.html000066400000000000000000000067351515050116100256100ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.7)

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    8.2.10.22 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002elocaltime.html000066400000000000000000000067331515050116100257610ustar00rootroot00000000000000 datetime.localtime (Tablicious v0.4.7)

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    8.2.10.23 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002elt.html000066400000000000000000000062711515050116100244240ustar00rootroot00000000000000 datetime.lt (Tablicious v0.4.7)

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    8.2.10.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eminus.html000066400000000000000000000070211515050116100251320ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.7)

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    8.2.10.25 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002ene.html000066400000000000000000000062621515050116100244070ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.7)

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    8.2.10.27 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eofDatenum.html000066400000000000000000000060461515050116100257270ustar00rootroot00000000000000 datetime.ofDatenum (Tablicious v0.4.7)

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    8.2.10.28 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eofDatestruct.html000066400000000000000000000063031515050116100264500ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.7)

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    8.2.10.29 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eplus.html000066400000000000000000000071111515050116100247620ustar00rootroot00000000000000 datetime.plus (Tablicious v0.4.7)

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    8.2.10.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eposix2datenum.html000066400000000000000000000062171515050116100266070ustar00rootroot00000000000000 datetime.posix2datenum (Tablicious v0.4.7)

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    8.2.10.31 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eposixtime.html000066400000000000000000000062761515050116100260330ustar00rootroot00000000000000 datetime.posixtime (Tablicious v0.4.7)

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    8.2.10.32 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eproxyKeys.html000066400000000000000000000065131515050116100260210ustar00rootroot00000000000000 datetime.proxyKeys (Tablicious v0.4.7)

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    8.2.10.33 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002etimeofday.html000066400000000000000000000061611515050116100257640ustar00rootroot00000000000000 datetime.timeofday (Tablicious v0.4.7)

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    8.2.10.34 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eweek.html000066400000000000000000000055641515050116100247440ustar00rootroot00000000000000 datetime.week (Tablicious v0.4.7)

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    8.2.10.35 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eymd.html000066400000000000000000000061321515050116100245720ustar00rootroot00000000000000 datetime.ymd (Tablicious v0.4.7)

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    8.2.10.36 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/datetime_002eymdhms.html000066400000000000000000000060671515050116100253110ustar00rootroot00000000000000 datetime.ymdhms (Tablicious v0.4.7)

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    8.2.10.37 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/days.html000066400000000000000000000062471515050116100225050ustar00rootroot00000000000000 days (Tablicious v0.4.7)

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

    apjanke-octave-tablicious-8a1b10f/doc/html/discretize.html000066400000000000000000000122201515050116100236760ustar00rootroot00000000000000 discretize (Tablicious v0.4.7)

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

    apjanke-octave-tablicious-8a1b10f/doc/html/dispstrs.html000066400000000000000000000066111515050116100234130ustar00rootroot00000000000000 dispstrs (Tablicious v0.4.7)

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration-Class.html000066400000000000000000000057651515050116100244410ustar00rootroot00000000000000 duration Class (Tablicious v0.4.7)

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration.html000066400000000000000000000125211515050116100233620ustar00rootroot00000000000000 duration (Tablicious v0.4.7)

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002echar.html000066400000000000000000000056721515050116100247570ustar00rootroot00000000000000 duration.char (Tablicious v0.4.7)

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002edatevec.html000066400000000000000000000101361515050116100254440ustar00rootroot00000000000000 duration.datevec (Tablicious v0.4.7)

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    8.2.14.2 duration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert obj to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002edispstrs.html000066400000000000000000000057321515050116100257120ustar00rootroot00000000000000 duration.dispstrs (Tablicious v0.4.7)

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    8.2.14.3 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002ehms.html000066400000000000000000000061511515050116100246220ustar00rootroot00000000000000 duration.hms (Tablicious v0.4.7)

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    8.2.14.4 duration.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second values of a duration. These are the hour, minute, and second values that when combined are equal to the same duration value.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002ehours.html000066400000000000000000000057731515050116100252040ustar00rootroot00000000000000 duration.hours (Tablicious v0.4.7)

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    8.2.14.5 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002elinspace.html000066400000000000000000000067741515050116100256440ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.7)

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    8.2.14.6 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002emilliseconds.html000066400000000000000000000061011515050116100265130ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.7)

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    8.2.14.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002eminutes.html000066400000000000000000000060551515050116100255220ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.7)

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    8.2.14.8 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002eofDays.html000066400000000000000000000062411515050116100252600ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.7)

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    8.2.14.9 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002eseconds.html000066400000000000000000000057731515050116100255020ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.7)

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    8.2.14.10 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/duration_002eyears.html000066400000000000000000000055461515050116100251650ustar00rootroot00000000000000 duration.years (Tablicious v0.4.7)

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    8.2.14.11 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/eqn.html000066400000000000000000000074611515050116100223270ustar00rootroot00000000000000 eqn (Tablicious v0.4.7)

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/doc/html/head.html000066400000000000000000000075101515050116100224400ustar00rootroot00000000000000 head (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/doc/html/hours.html000066400000000000000000000063121515050116100226760ustar00rootroot00000000000000 hours (Tablicious v0.4.7)

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/doc/html/index.html000066400000000000000000001327631515050116100226570ustar00rootroot00000000000000 Top (Tablicious v0.4.7)

    Next:   [Contents]

    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.7.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/iscalendarduration.html000066400000000000000000000062521515050116100254140ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.7)

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/iscategorical.html000066400000000000000000000062261515050116100243530ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.7)

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/isdatetime.html000066400000000000000000000061361515050116100236720ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.7)

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/isduration.html000066400000000000000000000061021515050116100237140ustar00rootroot00000000000000 isduration (Tablicious v0.4.7)

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/isfile.html000066400000000000000000000044601515050116100230130ustar00rootroot00000000000000 isfile (Tablicious v0.4.7)

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    8.2.22 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/isfolder.html000066400000000000000000000044501515050116100233460ustar00rootroot00000000000000 isfolder (Tablicious v0.4.7)

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    8.2.23 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/isnanny.html000066400000000000000000000100301515050116100232050ustar00rootroot00000000000000 isnanny (Tablicious v0.4.7)

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like.

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.
    3. Octave does not define an ismissing function, but the Octave Forge statistics package does, and adding an ismissing function to Tablicious would conflict with that in hard-to-resolve ways.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/doc/html/istable.html000066400000000000000000000065071515050116100231670ustar00rootroot00000000000000 istable (Tablicious v0.4.7)

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/istabular.html000066400000000000000000000062271515050116100235310ustar00rootroot00000000000000 istabular (Tablicious v0.4.7)

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/istimetable.html000066400000000000000000000066031515050116100240430ustar00rootroot00000000000000 istimetable (Tablicious v0.4.7)

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate.html000066400000000000000000000123071515050116100234670ustar00rootroot00000000000000 localdate (Tablicious v0.4.7)

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002eNaT.html000066400000000000000000000101531515050116100246150ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.7)

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002edatenum.html000066400000000000000000000055131515050116100255740ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.7)

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002edatestr.html000066400000000000000000000066401515050116100256070ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.7)

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002edatestrs.html000066400000000000000000000067421515050116100257750ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.7)

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002edatestruct.html000066400000000000000000000071341515050116100263220ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.7)

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002edispstrs.html000066400000000000000000000060041515050116100260060ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.7)

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002eisnan.html000066400000000000000000000060611515050116100252460ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.7)

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002eisnat.html000066400000000000000000000057051515050116100252600ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.7)

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002elocaldate.html000066400000000000000000000111501515050116100260610ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.7)

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002eposixtime.html000066400000000000000000000063341515050116100261620ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.7)

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/doc/html/localdate_002eymd.html000066400000000000000000000055611515050116100247330ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.7)

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/milliseconds.html000066400000000000000000000064151515050116100242270ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.7)

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/doc/html/minutes.html000066400000000000000000000056261515050116100232310ustar00rootroot00000000000000 minutes (Tablicious v0.4.7)

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing.html000066400000000000000000000075361515050116100232200ustar00rootroot00000000000000 missing (Tablicious v0.4.7)

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing_002edispstrs.html000066400000000000000000000057241515050116100255370ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.7)

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing_002eismissing.html000066400000000000000000000060661515050116100256710ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.7)

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing_002eisnan.html000066400000000000000000000060171515050116100247700ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.7)

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing_002eisnanny.html000066400000000000000000000060301515050116100253320ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.7)

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/missing_002emissing.html000066400000000000000000000054711515050116100253340ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.7)

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeA.html000066400000000000000000000044761515050116100231070ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.7)

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    8.2.32 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeCellstr.html000066400000000000000000000045421515050116100243310ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.7)

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    8.2.33 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeCharvec.html000066400000000000000000000045661515050116100243020ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.7)

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    8.2.34 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeFinite.html000066400000000000000000000045641515050116100241430ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.7)

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    8.2.35 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeInteger.html000066400000000000000000000045621515050116100243200ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.7)

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    8.2.36 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeMember.html000066400000000000000000000045701515050116100241310ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.7)

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    8.2.37 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeNonempty.html000066400000000000000000000045741515050116100245370ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.7)

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    8.2.38 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeNumeric.html000066400000000000000000000045621515050116100243250ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.7)

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    8.2.39 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeReal.html000066400000000000000000000045541515050116100236070ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.7)

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    8.2.40 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeSameSize.html000066400000000000000000000045601515050116100244410ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.7)

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    8.2.41 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeScalar.html000066400000000000000000000046201515050116100241230ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.7)

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    8.2.42 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeScalarLogical.html000066400000000000000000000046261515050116100254240ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.7)

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    8.2.43 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/mustBeVector.html000066400000000000000000000045441515050116100241650ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.7)

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    8.2.44 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/pp.html000066400000000000000000000077771515050116100221750ustar00rootroot00000000000000 pp (Tablicious v0.4.7)

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/doc/html/scalarexpand.html000066400000000000000000000072671515050116100242150ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.7)

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/doc/html/seconds.html000066400000000000000000000063341515050116100232000ustar00rootroot00000000000000 seconds (Tablicious v0.4.7)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/doc/html/size2str.html000066400000000000000000000065321515050116100233270ustar00rootroot00000000000000 size2str (Tablicious v0.4.7)

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/doc/html/splitapply.html000066400000000000000000000114251515050116100237400ustar00rootroot00000000000000 splitapply (Tablicious v0.4.7)

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/doc/html/string.html000066400000000000000000000163061515050116100230500ustar00rootroot00000000000000 string (Tablicious v0.4.7)

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

    Next: struct2table, Previous: splitapply, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002ecell.html000066400000000000000000000057431515050116100244410ustar00rootroot00000000000000 string.cell (Tablicious v0.4.7)

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002ecellstr.html000066400000000000000000000057721515050116100251740ustar00rootroot00000000000000 string.cellstr (Tablicious v0.4.7)

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002echar.html000066400000000000000000000062351515050116100244340ustar00rootroot00000000000000 string.char (Tablicious v0.4.7)

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002ecmp.html000066400000000000000000000075371515050116100243040ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.7)

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002edecode.html000066400000000000000000000062221515050116100247360ustar00rootroot00000000000000 string.decode (Tablicious v0.4.7)

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002edispstrs.html000066400000000000000000000064731515050116100253760ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.7)

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eempty.html000066400000000000000000000061601515050116100246520ustar00rootroot00000000000000 string.empty (Tablicious v0.4.7)

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eencode.html000066400000000000000000000063471515050116100247600ustar00rootroot00000000000000 string.encode (Tablicious v0.4.7)

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eendsWith.html000066400000000000000000000066321515050116100253050ustar00rootroot00000000000000 string.endsWith (Tablicious v0.4.7)

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    8.2.53.9 string.endsWith

    Method: out = endsWith (obj, pattern)
    Method: out = endsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string ends with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eerase.html000066400000000000000000000060441515050116100246140ustar00rootroot00000000000000 string.erase (Tablicious v0.4.7)

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    8.2.53.10 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eismissing.html000066400000000000000000000061651515050116100255260ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.7)

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    8.2.53.11 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eisnanny.html000066400000000000000000000060131515050116100251700ustar00rootroot00000000000000 string.isnanny (Tablicious v0.4.7)

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    8.2.53.12 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eisstring.html000066400000000000000000000057361515050116100253660ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.7)

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    8.2.53.13 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002elower.html000066400000000000000000000062321515050116100246440ustar00rootroot00000000000000 string.lower (Tablicious v0.4.7)

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    8.2.53.14 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002emissing.html000066400000000000000000000061511515050116100251650ustar00rootroot00000000000000 string.missing (Tablicious v0.4.7)

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    8.2.53.15 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eplus.html000066400000000000000000000062761515050116100245070ustar00rootroot00000000000000 string.plus (Tablicious v0.4.7)

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    8.2.53.16 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eregexprep.html000066400000000000000000000070151515050116100255150ustar00rootroot00000000000000 string.regexprep (Tablicious v0.4.7)

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    8.2.53.17 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002ereverse.html000066400000000000000000000062201515050116100251640ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.7)

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    8.2.53.18 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002ereverse_005fbytes.html000066400000000000000000000067541515050116100270010ustar00rootroot00000000000000 string.reverse_bytes (Tablicious v0.4.7)

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    8.2.53.19 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estartsWith.html000066400000000000000000000067351515050116100257000ustar00rootroot00000000000000 string.startsWith (Tablicious v0.4.7)

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    8.2.53.20 string.startsWith

    Method: out = startsWith (obj, pattern)
    Method: out = startsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string starts with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrcat.html000066400000000000000000000062461515050116100250210ustar00rootroot00000000000000 string.strcat (Tablicious v0.4.7)

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    8.2.53.21 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrcmp.html000066400000000000000000000065601515050116100250300ustar00rootroot00000000000000 string.strcmp (Tablicious v0.4.7)

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    8.2.53.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrfind.html000066400000000000000000000070041515050116100251630ustar00rootroot00000000000000 string.strfind (Tablicious v0.4.7)

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    8.2.53.23 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estring.html000066400000000000000000000071311515050116100250210ustar00rootroot00000000000000 string.string (Tablicious v0.4.7)

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    8.2.53.24 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrlength.html000066400000000000000000000072421515050116100255300ustar00rootroot00000000000000 string.strlength (Tablicious v0.4.7)

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    8.2.53.25 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrlength_005fbytes.html000066400000000000000000000066771515050116100273440ustar00rootroot00000000000000 string.strlength_bytes (Tablicious v0.4.7)

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    8.2.53.26 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002estrrep.html000066400000000000000000000072611515050116100250360ustar00rootroot00000000000000 string.strrep (Tablicious v0.4.7)

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    8.2.53.27 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/string_002eupper.html000066400000000000000000000057651515050116100246610ustar00rootroot00000000000000 string.upper (Tablicious v0.4.7)

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    8.2.53.28 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/struct2table.html000066400000000000000000000067701515050116100241640ustar00rootroot00000000000000 struct2table (Tablicious v0.4.7)

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/doc/html/table.html000066400000000000000000000262031515050116100226260ustar00rootroot00000000000000 table (Tablicious v0.4.7)

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tableOuterFillValue.html000066400000000000000000000045321515050116100254520ustar00rootroot00000000000000 tableOuterFillValue (Tablicious v0.4.7)

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    8.2.56 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eaddvars.html000066400000000000000000000077661515050116100247360ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.7)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eantijoin.html000066400000000000000000000070521515050116100251110ustar00rootroot00000000000000 table.antijoin (Tablicious v0.4.7)

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002ecartesian.html000066400000000000000000000077021515050116100252510ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.7)

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002econvertvars.html000066400000000000000000000073451515050116100256570ustar00rootroot00000000000000 table.convertvars (Tablicious v0.4.7)

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002efindgroups.html000066400000000000000000000064171515050116100254620ustar00rootroot00000000000000 table.findgroups (Tablicious v0.4.7)

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002egetvar.html000066400000000000000000000063001515050116100245610ustar00rootroot00000000000000 table.getvar (Tablicious v0.4.7)

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002egetvars.html000066400000000000000000000061731515050116100247540ustar00rootroot00000000000000 table.getvars (Tablicious v0.4.7)

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002egroupby.html000066400000000000000000000075131515050116100247670ustar00rootroot00000000000000 table.groupby (Tablicious v0.4.7)

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eheight.html000066400000000000000000000057631515050116100245550ustar00rootroot00000000000000 table.height (Tablicious v0.4.7)

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002ehorzcat.html000066400000000000000000000063241515050116100247510ustar00rootroot00000000000000 table.horzcat (Tablicious v0.4.7)

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002einnerjoin.html000066400000000000000000000073421515050116100252730ustar00rootroot00000000000000 table.innerjoin (Tablicious v0.4.7)

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eintersect.html000066400000000000000000000065211515050116100252760ustar00rootroot00000000000000 table.intersect (Tablicious v0.4.7)

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eisempty.html000066400000000000000000000057041515050116100247720ustar00rootroot00000000000000 table.isempty (Tablicious v0.4.7)

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eismember.html000066400000000000000000000061361515050116100251030ustar00rootroot00000000000000 table.ismember (Tablicious v0.4.7)

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eismissing.html000066400000000000000000000072721515050116100253070ustar00rootroot00000000000000 table.ismissing (Tablicious v0.4.7)

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eistable.html000066400000000000000000000054721515050116100247250ustar00rootroot00000000000000 table.istable (Tablicious v0.4.7)

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002ejoin.html000066400000000000000000000074661515050116100242460ustar00rootroot00000000000000 table.join (Tablicious v0.4.7)

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002emergevars.html000066400000000000000000000073301515050116100252700ustar00rootroot00000000000000 table.mergevars (Tablicious v0.4.7)

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002emovevars.html000066400000000000000000000070471515050116100251440ustar00rootroot00000000000000 table.movevars (Tablicious v0.4.7)

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002endims.html000066400000000000000000000056351515050116100244150ustar00rootroot00000000000000 table.ndims (Tablicious v0.4.7)

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002enumel.html000066400000000000000000000061211515050116100244120ustar00rootroot00000000000000 table.numel (Tablicious v0.4.7)

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eouterfillvals.html000066400000000000000000000060461515050116100261730ustar00rootroot00000000000000 table.outerfillvals (Tablicious v0.4.7)

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eouterjoin.html000066400000000000000000000076021515050116100253150ustar00rootroot00000000000000 table.outerjoin (Tablicious v0.4.7)

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eprettyprint.html000066400000000000000000000060461515050116100257040ustar00rootroot00000000000000 table.prettyprint (Tablicious v0.4.7)

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erealjoin.html000066400000000000000000000100301515050116100250670ustar00rootroot00000000000000 table.realjoin (Tablicious v0.4.7)

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eremovevars.html000066400000000000000000000061311515050116100254640ustar00rootroot00000000000000 table.removevars (Tablicious v0.4.7)

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erenamevars.html000066400000000000000000000067151515050116100254460ustar00rootroot00000000000000 table.renamevars (Tablicious v0.4.7)

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erepelem.html000066400000000000000000000067011515050116100247270ustar00rootroot00000000000000 table.repelem (Tablicious v0.4.7)

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erepmat.html000066400000000000000000000065131515050116100245670ustar00rootroot00000000000000 table.repmat (Tablicious v0.4.7)

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erestrict.html000066400000000000000000000106041515050116100251320ustar00rootroot00000000000000 table.restrict (Tablicious v0.4.7)

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erowfun.html000066400000000000000000000157061515050116100246230ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.7)

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.rows2vars, Previous: table.restrict, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002erows2vars.html000066400000000000000000000107341515050116100252470ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.7)

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esemijoin.html000066400000000000000000000072311515050116100251120ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.7)

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetDimensionNames.html000066400000000000000000000105551515050116100267250ustar00rootroot00000000000000 table.setDimensionNames (Tablicious v0.4.7)

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetRowNames.html000066400000000000000000000061611515050116100255450ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.7)

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetVariableNames.html000066400000000000000000000076371515050116100265340ustar00rootroot00000000000000 table.setVariableNames (Tablicious v0.4.7)

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetdiff.html000066400000000000000000000063661515050116100247310ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.7)

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetvar.html000066400000000000000000000072021515050116100245770ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.7)

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esetxor.html000066400000000000000000000065501515050116100246240ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.7)

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esize.html000066400000000000000000000072721515050116100242540ustar00rootroot00000000000000 table.size (Tablicious v0.4.7)

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esplitapply.html000066400000000000000000000071331515050116100254770ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.7)

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esplitvars.html000066400000000000000000000076141515050116100253310ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.7)

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esqueeze.html000066400000000000000000000057471515050116100247700ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.7)

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002estack.html000066400000000000000000000072771515050116100244140ustar00rootroot00000000000000 table.stack (Tablicious v0.4.7)

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002esummary.html000066400000000000000000000067701515050116100250010ustar00rootroot00000000000000 table.summary (Tablicious v0.4.7)

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002etable.html000066400000000000000000000126421515050116100243660ustar00rootroot00000000000000 table.table (Tablicious v0.4.7)

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002etable2array.html000066400000000000000000000056061515050116100255110ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.7)

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002etable2cell.html000066400000000000000000000062551515050116100253130ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.7)

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002etable2struct.html000066400000000000000000000072201515050116100257110ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.7)

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002eunion.html000066400000000000000000000064341515050116100244310ustar00rootroot00000000000000 table.union (Tablicious v0.4.7)

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002evarfun.html000066400000000000000000000103541515050116100245760ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.7)

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002evarnames.html000066400000000000000000000064661515050116100251220ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.7)

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002evertcat.html000066400000000000000000000064321515050116100247470ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.7)

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/doc/html/table_002ewidth.html000066400000000000000000000054111515050116100244120ustar00rootroot00000000000000 table.width (Tablicious v0.4.7)

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/doc/html/tail.html000066400000000000000000000076611515050116100224770ustar00rootroot00000000000000 tail (Tablicious v0.4.7)

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset.html000066400000000000000000000244331515050116100251230ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.7)

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eAirPassengers.html000066400000000000000000000076151515050116100303430ustar00rootroot00000000000000 tblish.dataset.AirPassengers (Tablicious v0.4.7)

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eBJsales.html000066400000000000000000000110771515050116100271150ustar00rootroot00000000000000 tblish.dataset.BJsales (Tablicious v0.4.7)

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eBOD.html000066400000000000000000000103211515050116100261650ustar00rootroot00000000000000 tblish.dataset.BOD (Tablicious v0.4.7)

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eChickWeight.html000066400000000000000000000113441515050116100277600ustar00rootroot00000000000000 tblish.dataset.ChickWeight (Tablicious v0.4.7)

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eDNase.html000066400000000000000000000112311515050116100265540ustar00rootroot00000000000000 tblish.dataset.DNase (Tablicious v0.4.7)

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111721515050116100304720ustar00rootroot00000000000000 tblish.dataset.EuStockMarkets (Tablicious v0.4.7)

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114561515050116100301760ustar00rootroot00000000000000 tblish.dataset.Formaldehyde (Tablicious v0.4.7)

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151431515050116100301150ustar00rootroot00000000000000 tblish.dataset.HairEyeColor (Tablicious v0.4.7)

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eHarman23cor.html000066400000000000000000000103311515050116100276410ustar00rootroot00000000000000 tblish.dataset.Harman23cor (Tablicious v0.4.7)

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eHarman74cor.html000066400000000000000000000104061515050116100276520ustar00rootroot00000000000000 tblish.dataset.Harman74cor (Tablicious v0.4.7)

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eIndometh.html000066400000000000000000000110441515050116100273330ustar00rootroot00000000000000 tblish.dataset.Indometh (Tablicious v0.4.7)

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eInsectSprays.html000066400000000000000000000105031515050116100302120ustar00rootroot00000000000000 tblish.dataset.InsectSprays (Tablicious v0.4.7)

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000103151515050116100305410ustar00rootroot00000000000000 tblish.dataset.JohnsonJohnson (Tablicious v0.4.7)

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eLakeHuron.html000066400000000000000000000105331515050116100274560ustar00rootroot00000000000000 tblish.dataset.LakeHuron (Tablicious v0.4.7)

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126651515050116100307700ustar00rootroot00000000000000 tblish.dataset.LifeCycleSavings (Tablicious v0.4.7)

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eLoblolly.html000066400000000000000000000111411515050116100273520ustar00rootroot00000000000000 tblish.dataset.Loblolly (Tablicious v0.4.7)

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eNile.html000066400000000000000000000112541515050116100264560ustar00rootroot00000000000000 tblish.dataset.Nile (Tablicious v0.4.7)

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eOrange.html000066400000000000000000000115611515050116100270030ustar00rootroot00000000000000 tblish.dataset.Orange (Tablicious v0.4.7)

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eOrchardSprays.html000066400000000000000000000133131515050116100303510ustar00rootroot00000000000000 tblish.dataset.OrchardSprays (Tablicious v0.4.7)

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

    Next: tblish.dataset.PlantGrowth, Previous: tblish.dataset.Orange, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102551515050116100300400ustar00rootroot00000000000000 tblish.dataset.PlantGrowth (Tablicious v0.4.7)

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ePuromycin.html000066400000000000000000000125301515050116100275520ustar00rootroot00000000000000 tblish.dataset.Puromycin (Tablicious v0.4.7)

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eTheoph.html000066400000000000000000000143171515050116100270210ustar00rootroot00000000000000 tblish.dataset.Theoph (Tablicious v0.4.7)

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eTitanic.html000066400000000000000000000146421515050116100271660ustar00rootroot00000000000000 tblish.dataset.Titanic (Tablicious v0.4.7)

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eToothGrowth.html000066400000000000000000000115701515050116100300600ustar00rootroot00000000000000 tblish.dataset.ToothGrowth (Tablicious v0.4.7)

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000141361515050116100302340ustar00rootroot00000000000000 tblish.dataset.UCBAdmissions (Tablicious v0.4.7)

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000150021515050116100304060ustar00rootroot00000000000000 tblish.dataset.UKDriverDeaths (Tablicious v0.4.7)

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000111351515050116100300630ustar00rootroot00000000000000 tblish.dataset.UKLungDeaths (Tablicious v0.4.7)

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUKgas.html000066400000000000000000000104261515050116100266010ustar00rootroot00000000000000 tblish.dataset.UKgas (Tablicious v0.4.7)

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000101121515050116100276460ustar00rootroot00000000000000 tblish.dataset.USAccDeaths (Tablicious v0.4.7)

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUSArrests.html000066400000000000000000000140141515050116100274570ustar00rootroot00000000000000 tblish.dataset.USArrests (Tablicious v0.4.7)

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116621515050116100304300ustar00rootroot00000000000000 tblish.dataset.USJudgeRatings (Tablicious v0.4.7)

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111451515050116100320360ustar00rootroot00000000000000 tblish.dataset.USPersonalExpenditure (Tablicious v0.4.7)

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eVADeaths.html000066400000000000000000000113641515050116100272300ustar00rootroot00000000000000 tblish.dataset.VADeaths (Tablicious v0.4.7)

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eWWWusage.html000066400000000000000000000104121515050116100272730ustar00rootroot00000000000000 tblish.dataset.WWWusage (Tablicious v0.4.7)

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eWorldPhones.html000066400000000000000000000105531515050116100300340ustar00rootroot00000000000000 tblish.dataset.WorldPhones (Tablicious v0.4.7)

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eairmiles.html000066400000000000000000000072671515050116100274050ustar00rootroot00000000000000 tblish.dataset.airmiles (Tablicious v0.4.7)

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eairquality.html000066400000000000000000000134011515050116100277470ustar00rootroot00000000000000 tblish.dataset.airquality (Tablicious v0.4.7)

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

    Next: tblish.dataset.anscombe, Previous: tblish.dataset.AirPassengers, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eanscombe.html000066400000000000000000000122041515050116100273520ustar00rootroot00000000000000 tblish.dataset.anscombe (Tablicious v0.4.7)

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eattenu.html000066400000000000000000000115071515050116100270700ustar00rootroot00000000000000 tblish.dataset.attenu (Tablicious v0.4.7)

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eattitude.html000066400000000000000000000111411515050116100274050ustar00rootroot00000000000000 tblish.dataset.attitude (Tablicious v0.4.7)

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eaustres.html000066400000000000000000000102601515050116100272510ustar00rootroot00000000000000 tblish.dataset.austres (Tablicious v0.4.7)

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ebeavers.html000066400000000000000000000107021515050116100272130ustar00rootroot00000000000000 tblish.dataset.beavers (Tablicious v0.4.7)

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ecars.html000066400000000000000000000117301515050116100265160ustar00rootroot00000000000000 tblish.dataset.cars (Tablicious v0.4.7)

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002echickwts.html000066400000000000000000000114611515050116100274060ustar00rootroot00000000000000 tblish.dataset.chickwts (Tablicious v0.4.7)

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eco2.html000066400000000000000000000117241515050116100262540ustar00rootroot00000000000000 tblish.dataset.co2 (Tablicious v0.4.7)

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ecrimtab.html000066400000000000000000000164371515050116100272200ustar00rootroot00000000000000 tblish.dataset.crimtab (Tablicious v0.4.7)

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ecupcake.html000066400000000000000000000105321515050116100272000ustar00rootroot00000000000000 tblish.dataset.cupcake (Tablicious v0.4.7)

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ediscoveries.html000066400000000000000000000106601515050116100301060ustar00rootroot00000000000000 tblish.dataset.discoveries (Tablicious v0.4.7)

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eesoph.html000066400000000000000000000107221515050116100267040ustar00rootroot00000000000000 tblish.dataset.esoph (Tablicious v0.4.7)

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eeuro.html000066400000000000000000000117151515050116100265430ustar00rootroot00000000000000 tblish.dataset.euro (Tablicious v0.4.7)

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eeurodist.html000066400000000000000000000101521515050116100274210ustar00rootroot00000000000000 tblish.dataset.eurodist (Tablicious v0.4.7)

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002efaithful.html000066400000000000000000000121511515050116100273660ustar00rootroot00000000000000 tblish.dataset.faithful (Tablicious v0.4.7)

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002efreeny.html000066400000000000000000000121441515050116100270560ustar00rootroot00000000000000 tblish.dataset.freeny (Tablicious v0.4.7)

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002einfert.html000066400000000000000000000116411515050116100270560ustar00rootroot00000000000000 tblish.dataset.infert (Tablicious v0.4.7)

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eiris.html000066400000000000000000000122141515050116100265320ustar00rootroot00000000000000 tblish.dataset.iris (Tablicious v0.4.7)

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eislands.html000066400000000000000000000104371515050116100272260ustar00rootroot00000000000000 tblish.dataset.islands (Tablicious v0.4.7)

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002elh.html000066400000000000000000000102321515050116100261650ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.7)

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002elongley.html000066400000000000000000000116001515050116100272330ustar00rootroot00000000000000 tblish.dataset.longley (Tablicious v0.4.7)

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002elynx.html000066400000000000000000000113741515050116100265640ustar00rootroot00000000000000 tblish.dataset.lynx (Tablicious v0.4.7)

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002emorley.html000066400000000000000000000121731515050116100270770ustar00rootroot00000000000000 tblish.dataset.morley (Tablicious v0.4.7)

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002emtcars.html000066400000000000000000000124651515050116100270650ustar00rootroot00000000000000 tblish.dataset.mtcars (Tablicious v0.4.7)

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002enhtemp.html000066400000000000000000000105361515050116100270640ustar00rootroot00000000000000 tblish.dataset.nhtemp (Tablicious v0.4.7)

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002enottem.html000066400000000000000000000102001515050116100270630ustar00rootroot00000000000000 tblish.dataset.nottem (Tablicious v0.4.7)

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002enpk.html000066400000000000000000000113431515050116100263560ustar00rootroot00000000000000 tblish.dataset.npk (Tablicious v0.4.7)

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000104221515050116100314500ustar00rootroot00000000000000 tblish.dataset.occupationalStatus (Tablicious v0.4.7)

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eprecip.html000066400000000000000000000104241515050116100270470ustar00rootroot00000000000000 tblish.dataset.precip (Tablicious v0.4.7)

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002epresidents.html000066400000000000000000000112301515050116100277410ustar00rootroot00000000000000 tblish.dataset.presidents (Tablicious v0.4.7)

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002epressure.html000066400000000000000000000112671515050116100274430ustar00rootroot00000000000000 tblish.dataset.pressure (Tablicious v0.4.7)

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002equakes.html000066400000000000000000000121101515050116100270500ustar00rootroot00000000000000 tblish.dataset.quakes (Tablicious v0.4.7)

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002erandu.html000066400000000000000000000111701515050116100266750ustar00rootroot00000000000000 tblish.dataset.randu (Tablicious v0.4.7)

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002erivers.html000066400000000000000000000103221515050116100270740ustar00rootroot00000000000000 tblish.dataset.rivers (Tablicious v0.4.7)

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002erock.html000066400000000000000000000111031515050116100265160ustar00rootroot00000000000000 tblish.dataset.rock (Tablicious v0.4.7)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002esleep.html000066400000000000000000000113441515050116100266770ustar00rootroot00000000000000 tblish.dataset.sleep (Tablicious v0.4.7)

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002estackloss.html000066400000000000000000000142331515050116100275750ustar00rootroot00000000000000 tblish.dataset.stackloss (Tablicious v0.4.7)

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002estate.html000066400000000000000000000130401515050116100267020ustar00rootroot00000000000000 tblish.dataset.state (Tablicious v0.4.7)

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000105111515050116100310750ustar00rootroot00000000000000 tblish.dataset.sunspot_month (Tablicious v0.4.7)

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102511515050116100307110ustar00rootroot00000000000000 tblish.dataset.sunspot_year (Tablicious v0.4.7)

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002esunspots.html000066400000000000000000000105501515050116100274630ustar00rootroot00000000000000 tblish.dataset.sunspots (Tablicious v0.4.7)

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002eswiss.html000066400000000000000000000141301515050116100267330ustar00rootroot00000000000000 tblish.dataset.swiss (Tablicious v0.4.7)

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002etreering.html000066400000000000000000000112321515050116100274020ustar00rootroot00000000000000 tblish.dataset.treering (Tablicious v0.4.7)

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002etrees.html000066400000000000000000000114021515050116100267040ustar00rootroot00000000000000 tblish.dataset.trees (Tablicious v0.4.7)

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002euspop.html000066400000000000000000000103031515050116100267270ustar00rootroot00000000000000 tblish.dataset.uspop (Tablicious v0.4.7)

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002evolcano.html000066400000000000000000000112521515050116100272260ustar00rootroot00000000000000 tblish.dataset.volcano (Tablicious v0.4.7)

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ewarpbreaks.html000066400000000000000000000112341515050116100277260ustar00rootroot00000000000000 tblish.dataset.warpbreaks (Tablicious v0.4.7)

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ewomen.html000066400000000000000000000114041515050116100267110ustar00rootroot00000000000000 tblish.dataset.women (Tablicious v0.4.7)

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edataset_002ezCO2.html000066400000000000000000000105101515050116100263360ustar00rootroot00000000000000 tblish.dataset.zCO2 (Tablicious v0.4.7)

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002edatasets.html000066400000000000000000000064671515050116100253150ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.7)

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002eevalWithTableVars.html000066400000000000000000000076241515050116100270700ustar00rootroot00000000000000 tblish.evalWithTableVars (Tablicious v0.4.7)

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002eexamples_002eSpDb.html000066400000000000000000000100221515050116100266000ustar00rootroot00000000000000 tblish.examples.SpDb (Tablicious v0.4.7)

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002eexamples_002ecoplot.html000066400000000000000000000151211515050116100272550ustar00rootroot00000000000000 tblish.examples.coplot (Tablicious v0.4.7)

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000120361515050116100305260ustar00rootroot00000000000000 tblish.examples.plot_pairs (Tablicious v0.4.7)

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002esizeof2.html000066400000000000000000000124661515050116100250620ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.7)

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/doc/html/tblish_002etable_002egrpstats.html000066400000000000000000000105451515050116100271020ustar00rootroot00000000000000 tblish.table.grpstats (Tablicious v0.4.7)

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/doc/html/timezones.html000066400000000000000000000100401515050116100235440ustar00rootroot00000000000000 timezones (Tablicious v0.4.7)

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/doc/html/todatetime.html000066400000000000000000000070451515050116100237010ustar00rootroot00000000000000 todatetime (Tablicious v0.4.7)

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/doc/html/vartype.html000066400000000000000000000065261515050116100232370ustar00rootroot00000000000000 vartype (Tablicious v0.4.7)

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/doc/html/vecfun.html000066400000000000000000000066651515050116100230370ustar00rootroot00000000000000 vecfun (Tablicious v0.4.7)

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/doc/html/years.html000066400000000000000000000064661515050116100226730ustar00rootroot00000000000000 years (Tablicious v0.4.7)

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    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/doc/mktexi.pl000066400000000000000000000302351515050116100215430ustar00rootroot00000000000000#!/usr/bin/env perl # # David Bateman Feb 02 2003 # Andrew Janke 2019, 2024 # # Extracts the help in texinfo format from Octave source code for use # in documentation and generates .texi and .qhp files from it. # # Usage: # # mktexi.pl [ ...] # # is the input .texi.in template file. # is the main INDEX file at the root of the package repo. # is the output .texi file to generate. # is the output .qhp file to generate. # is an M-code or oct-file source directory. You may specify as many # of them as you want. # # Takes various input files (the package source code, INDEX, DESCRIPTION, .texi.in), # extracts the Texinfo documentation and metadata from them, and generates # the combined .texi help document, along with the .qhp index file # for generating the QHelp collection. # # Emits diagnostic messages to stdout. # # Note: In Octave, "package" can mean two different things: either an Octave Forge style # package of software managed by the `pkg` command (like the Tablicious library), or a # namespace in M-code defined by placing source code files in "+" directories # (like the +tblish namespace within Tablicious). For clarity, this code uses "package" # to mean a pkg-managed software package, and "namespace" to mean the "+" # namespacing mechanism. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program; if not, see . # This program is granted to the public domain. # THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS # OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) # HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY # OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF # SUCH DAMAGE. BEGIN { push @INC, "."; } use strict; use Data::Dumper; use Date::Parse; use File::Find; use File::Basename; use Text::Wrap; use FileHandle; use IPC::Open3; use POSIX ":sys_wait_h"; use POSIX qw(strftime); use OctTexiDoc; if ($ARGV[0] eq "--check-texinfo-version") { my $texi_ver = `texi2any --version | head -1`; $texi_ver =~ m/texi2any \D*(\d+)\.(\d+)/; my $major = $1; my $minor = $2; if ($major < 6) { die "Texinfo version $major.$minor is too old. Need 6.0 or newer.\n"; } printf "Texinfo version $major.$minor is new enough\n"; exit 0; } my $infile = shift @ARGV; my $indexfile = shift @ARGV; my $outfile = shift @ARGV; my $qhpoutfile = shift @ARGV; my @sourcedirs = @ARGV; my $debug = 0; my $verbose = 0; # Extract the Texinfo docs from the source code my $docs = OctTexiDoc::DocSet->new; for my $sourcedir (@sourcedirs) { $docs->read_source_dir($sourcedir); } # Debugging output if ($debug) { open (TMP, ">", "topics.tmp") or die "Error: Could not open output file topics.tmp: $!\n"; print TMP Data::Dumper->Dump([$docs]); close TMP; } open (IN, $infile) or die "Error: Could not open input file $infile: $!\n"; open (OUT, ">", $outfile) or die "Error: Could not open output file $outfile: $!\n"; sub emit { print OUT @_; } # Get metadata from DESCRIPTION file my $pkg_meta = OctTexiDoc::get_package_metadata_from_description_file(); my $pkg_name = $$pkg_meta{"Name"}; my $pkg_version = $$pkg_meta{"Version"}; my $pkg_date_str = $$pkg_meta{"Date"}; my $pkg_date = str2time($pkg_date_str); my $pkg_date_month_str = "Date Unknown"; if ($pkg_date) { my @pkg_date = strptime($pkg_date_str); $pkg_date_month_str = strftime("%B %Y", @pkg_date); } sub unique { my @vals = @_; my %vals = (); for (@vals) { $vals{$_} = 1; } return sort(keys %vals); } sub setdiff { my ($a, $b) = @_; my %in_b = map {$_ => 1} @$b; my @diff = grep {not $in_b{$_}} @$a; return @diff; } sub node_name_to_doc_file_name { my ($node_name) = @_; my $out = $node_name; $out =~ s/_/_005f/g; # Have to do this one first to avoid double-escaping $out =~ s/\./_002e/g; $out =~ s/\//_002f/g; return $out; } # Generate the .texi file my $in_tex = 0; while (my $line = ) { $line =~ s/%%%%PACKAGE_VERSION%%%%/$pkg_version/g; $line =~ s/%%%%PACKAGE_DATE_YEARMONTH%%%%/$pkg_date_month_str/g; if ($line =~ /^\@DOCSTRING/) { $line =~ /^\@DOCSTRING\((.*)\)/; my $fcn_name = $1; $fcn_name =~ /^(.*?),(.*)/; my ($func0, $func1) = ($1, $2); my $fcn_doco = $docs->get_node_doco($func0); emit "$fcn_doco\n"; } elsif ($line =~ /^\@REFERENCE_SECTION/) { $line =~ /^\@REFERENCE_SECTION\((.*?)\)\s*/; my $refsection_name = $1; my $fcn_index = OctTexiDoc::read_index_file ($indexfile); my @all_index_fcns = @{$$fcn_index{"functions"}}; my @topic_nodes = $docs->topic_node_names; my @all_topics = unique(@all_index_fcns, @topic_nodes); @all_topics = sort { lc($a) cmp lc($b) } @all_topics; my %categories = %{$$fcn_index{"by_category"}}; my %descriptions = %{$$fcn_index{"descriptions"}}; # Build "by Category" index based on INDEX listing emit "\@node API by Category\n"; emit "\@section API by Category\n"; my @all_ctg_fcns; for my $category (@{$$fcn_index{"categories"}}) { my @ctg_fcns = @{$categories{$category}}; push @all_ctg_fcns, @ctg_fcns; emit "\@subsection $category\n"; emit "\@table \@asis\n"; for my $fcn (@ctg_fcns) { emit "\@item $fcn\n"; my $description = $descriptions{$fcn} || $docs->get_node_summary($fcn); emit "$description\n"; emit "\n"; emit "\@xref{$fcn}.\n"; } emit "\@end table\n"; } my @uncategorized = setdiff(\@all_topics, \@all_ctg_fcns); if (scalar (@uncategorized)) { emit "\@subsection Uncategorized\n"; emit "\@table \@asis\n"; for my $fcn (@uncategorized) { emit "\@item $fcn\n"; my $description = $descriptions{$fcn} || $docs->get_node_summary($fcn); emit "$description\n"; emit "\n"; emit "\@xref{$fcn}.\n"; } emit "\@end table\n"; } emit "\n"; # Build "Alphabetically" listing based on seen nodes + INDEX listing emit "\@node API Alphabetically\n"; emit "\@section API Alphabetically\n"; emit "\@menu\n"; for my $node (@all_topics) { my $description = $descriptions{$node} || $docs->get_node_summary($node); emit wrap("", "\t\t", "* ${node}::\t$description\n"); } emit "\@end menu\n"; emit "\n"; for my $node (@all_topics) { emit "\@node $node\n"; emit "\@subsection $node\n"; my $node = $docs->docs->{$node}; if ($node) { my $main_doc = OctTexiDoc::munge_texi_block_text($$node{block}); emit "$main_doc\n\n"; my @subnodes = sort { lc($$a{node}) cmp lc($$b{node}) } @{$$node{children}}; for my $subnode (@subnodes) { my $subnode_name = $$subnode{node}; my $subnode_doc = OctTexiDoc::munge_texi_block_text($$subnode{block}); emit "\@node $subnode_name\n"; emit "\@subsubsection $subnode_name\n\n"; emit "$subnode_doc\n"; } } else { emit "\@emph{Not documented}\n"; } } } else { if ($line =~ /\@tex/) { $in_tex = 1; } if ($in_tex) { $line =~ s/\\\\/\\/g; } emit $line; if ($line =~ /\@end tex/) { $in_tex = 0; } } } close IN; close OUT; # Generate the .qhp file my %level_map = ( "top" => 1, "chapter" => 2, "section" => 3, "subsection" => 4, "subsubsection" => 5 ); open (QHP, ">", $qhpoutfile) or die "Error: Could not open output .qhp file $qhpoutfile: $!\n"; sub qhp { print QHP @_; } my $preamble = < octave.community.$pkg_name doc EOS qhp $preamble; # TOC section my @files; my @classes; my @functions; open TEXI, $outfile or die "Could not open generated .texi file for reading: $outfile: $!\n"; my $level = 0; my $indent = " "; while (my $line = ) { chomp $line; next unless ($line =~ /^\s*\@node +(.*?)(,|$)/); my $node_name = $1; my $next_line = ; while ($next_line && $next_line =~ /^\s*$/) { $next_line = ; } chomp $next_line; unless ($next_line =~ /^\s*\@(\S+) +(.*)/) { die "Error: Failed parsing section line for node '$node_name': $next_line"; } my ($section_type, $section_title) = ($1, $2); my $section_level = $level_map{$section_type}; my $section_qhelp_title = $section_title =~ s/@\w+{(.*?)}/\1/rg; my $html_title = $node_name =~ s/\s/-/gr; $html_title = "index" if $html_title eq "Top"; my $html_file_base = node_name_to_doc_file_name ($html_title); my $html_file = "$html_file_base.html"; unshift @files, $html_file; print "Node: '$node_name' ($section_type): \"$section_title\" => \"$section_qhelp_title\"" . " (level $section_level), HTML: $html_file\n" if $verbose; die "Error: Unrecognized section type: $section_type\n" unless $section_level; if ($section_level == $level) { # close last node as sibling qhp $indent . (" " x $level) . "\n"; } elsif ($section_level > $level) { # leave last node open as parent if ($section_level > $level + 1) { die "Error: Discontinuity in section levels at node $node_name ($level to $section_level)"; } } elsif ($section_level < $level) { # close last two nodes my $levels_up = $level - $section_level; while ($level > $section_level) { qhp $indent . (" " x $level--) . "\n"; } qhp $indent . (" " x $level) . "\n"; } qhp $indent . (" " x $section_level) . "
    \n"; qhp $indent . (" " x $section_level) . " \n" if $debug; $level = $section_level; } while ($level > 1) { qhp $indent . (" " x $level--) . "
    \n"; } # Include the all-on-one-page version qhp $indent . (" " x $level) . "
    \n" . "$indent
    \n"; qhp < EOS close TEXI; # Keyword index my $node_index = $docs->nodes; my @node_names = keys %$node_index; @node_names = sort (@node_names); qhp " \n"; for my $node (@node_names) { my $file_base = node_name_to_doc_file_name ($node); qhp " \n"; } qhp " \n"; # Files section qhp " \n"; qhp " $pkg_name.html\n"; foreach my $file (@files) { qhp " html/$file\n"; } qhp " \n"; # Closing qhp <     EOS apjanke-octave-tablicious-8a1b10f/doc/src/000077500000000000000000000000001515050116100204715ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/doc/src/tablicious.texi000066400000000000000000000552261515050116100235340ustar00rootroot00000000000000\input texinfo @c -*-texinfo-*- @c %**start of header @set VERSION %%%%PACKAGE_VERSION%%%% @setfilename tablicious.info @settitle Tablicious v@value{VERSION} @documentencoding UTF-8 @c %**end of header @copying This manual is for Tablicious, version @value{VERSION}. Copyright @copyright{} 2019, 2023, 2024, 2025, 2026 Andrew Janke @quotation Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions. @end quotation @end copying @documentdescription User manual for Tablicious for Octave. @end documentdescription @titlepage @title Tablicious for GNU Octave @subtitle version @value{VERSION}, %%%%PACKAGE_DATE_YEARMONTH%%%% @author Andrew Janke @page @vskip 0pt plus 1filll @insertcopying @end titlepage @dircategory Math @direntry * Tablicious: (tablicious). Tablicious package for Octave @end direntry @shortcontents @contents @ifnottex @node Top, Introduction @top Tablicious for GNU Octave This manual is for Tablicious, version @value{VERSION}. @end ifnottex @menu * Introduction:: * Getting Started:: * Table Representation:: * Date and Time Representation:: * Validation Functions:: * Example Data Sets:: * Data Sets from R:: * Missing Functionality:: * API Reference:: * Copying:: @end menu @node Introduction @chapter Introduction @quotation Time is an illusion. Lunchtime doubly so. @author Douglas Adams @end quotation This is the manual for the Tablicious package version @value{VERSION} for GNU Octave. Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a @code{table} class with support for filtering and join operations; @code{datetime}, @code{duration}, and related classes; Missing Data support; @code{string} and @code{categorical} data types; and other miscellaneous things. This document is a work in progress. You are invited to help improve it and submit patches. Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types. Tablicious was written by Andrew Janke <@email{floss@@apjanke.net}>. Support can be found on the @url{https://github.com/apjanke/octave-tablicious, Tablicious project GitHub page}. @node Getting Started @chapter Getting Started The easiest way to obtain Tablicious is by using Octave's @code{pkg} package manager. To install the development prerelease of Tablicious, run this in Octave: @example pkg install https://github.com/apjanke/octave-tablicious/releases/download/v%%%%PACKAGE_VERSION%%%%/tablicious-%%%%PACKAGE_VERSION%%%%.tar.gz @end example (Check the releases page at @url{https://github.com/apjanke/octave-tablicious/releases} to find out what the actual latest release number is.) For development, you can obtain the source code for Tablicious from the project repo on GitHub at @url{https://github.com/apjanke/octave-tablicious}. Make a local clone of the repo. Then add the @file{inst} directory in the repo to your Octave path. @node Table Representation @chapter Table Representation Tablicious provides the @code{table} class for representing tabular data. A @code{table} is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records. A @code{table} is composed of multiple “variables”, each with a name, which all have the same number of rows. (A @code{table} variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the @var{i}-th element or row of each variable compose a single record or observation. Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a @code{table} is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way. Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using @code{table} arrays. It’s like having your own in-memory SQL engine. @node Table Construction @section Table Construction There are two main ways to construct a @code{table} array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a @code{table}. To build an array from multiple variables, use the @code{table(@dots{})} constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new @code{table} object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the @code{'VariableNames'} option. To convert a tabular-organized array of another type into a @code{table}, use the conversion functions like @code{array2table}, @code{struct2table} and @code{cell2table}. @code{array2table} and @code{cell2table} take each column of the input array and turn it into a separate table variable in the resulting @code{table}. @code{struct2table} takes the fields of a struct and puts them into table variables. @node Tables vs SQL @section Tables vs SQL Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave @code{table} operations. In this table, @code{t} is a variable holding a @code{table} array, and @code{ix} is some indexing expression. @multitable @columnfractions .33 .33 .33 @headitem SQL @tab Relational @tab Octave @code{table} @item SELECT @tab PROJECT @tab @code{subsetvars}, @code{t(:,ix)} @item WHERE @tab RESTRICT @tab @code{subsetrows}, @code{t(ix,:)} @item INNER JOIN @tab JOIN @tab @code{innerjoin} @item OUTER JOIN @tab OUTER JOIN @tab @code{outerjoin} @item FROM @code{@var{table1}, @var{table2}, @dots{}} @tab Cartesian product @tab @code{cartesian} @item GROUP BY @tab SUMMARIZE @tab @code{groupby} @item DISTINCT @tab (automatic) @tab @code{unique(t)} @end multitable Note that there is one big difference between relational algebra and SQL & Octave @code{table}: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit @code{DISTINCT}/@code{unique()} on its results, and there‘s no @code{ORDER BY}/@code{sort()}. This is not the case in SQL or Octave @code{table}. Note for users coming from Matlab: Matlab does not provide a general @code{groupby} function. Instead, you have to variously use @code{rowfun}, @code{grpstats}, @code{groupsummary}, and manual code to accomplish “group by” operations. Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke @node Date and Time Representation @chapter Date and Time Representation Tablicious provides the @code{datetime} class for representing points in time. There’s also @code{duration} and @code{calendarDuration} for representing periods or durations of time. Like vector quantities along the time line, as opposed to @code{datetime} being a point along the time line. @node datetime Class @section @code{datetime} Class A @code{datetime} is an array object that represents points in time in the familiar Gregorian calendar. This is an attempt to reproduce the functionality of Matlab's @code{datetime}. It also contains some Octave-specific extensions. The underlying representation is that of a datenum (a @code{double} containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a @code{datetime} inclues an optional @code{TimeZone} property indicating what time zone the datetimes are in. So, basically, a @code{datetime} is an object wrapper around a datenum array, plus time zone support. @menu * Datenum Compatibility:: @end menu @node Datenum Compatibility @subsection Datenum Compatibility While the underlying data representation of @code{datetime} is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations. This is because of the signature of the @code{datetime} constructor. When combining objects and primitive types like @code{double}, the primitive type is promoted to an object by calling the other object's one-argument constructor on it. However, the one-argument numeric-input consstructor for @code{datetime} does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab's part; for compatibility, Octave does not alter that interface. To combine @code{datetime}s with datenums, you can convert the datenums to @code{datetime}s by calling @code{datetime.ofDatenum} or @code{datetime(x, 'ConvertFrom', 'datenum')}, or you can convert the @code{datetime}s to datenums by accessing its @code{dnums} field with @code{x.dnums}. Examples: @example dt = datetime('2011-03-04') dn = datenum('2017-01-01') [dt dn] @result{} error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND] [dt datetime.ofDatenum(dn)] @result{} 04-Mar-2011 01-Jan-2017 @end example Also, if you have a zoned @code{datetime}, you can’t combine it with a datenum, because datenums do not carry time zone information. @node Time Zones @section Time Zones Tablicious has support for representing dates in time zones and for converting between time zones. A @code{datetime} may be "zoned" or "zoneless". A zoneless @code{datetime} does not have a time zone associated with it. This is represented by an empty @code{TimeZone} property on the @code{datetime} object. A zoneless @code{datetime} represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts). A zoned @code{datetime} is associated with a time zone. It is represented by having the time zone's IANA zone identifier (e.g. @code{'UTC'} or @code{'America/New_York'}) in its @code{TimeZone} property. A zoned @code{datetime} represents the local time in that time zone. By default, the @code{datetime} constructor creates unzoned @code{datetime}s. To make a zoned @code{datetime}, either pass the @code{'TimeZone'} option to the constructor, or set the @code{TimeZone} property after object creation. Setting the @code{TimeZone} property on a zoneless @code{datetime} declares that it's a local time in that time zone. Setting the @code{TimeZone} property on a zoned @code{datetime} turns it back into a zoneless @code{datetime} without changing the local time it represents. You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned @code{datetime}s. @example % Create an unzoned datetime d = datetime('2011-03-04 06:00:00') @result{} 04-Mar-2011 06:00:00 % Create a zoned datetime d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') @result{} 04-Mar-2011 06:00:00 America/New_York % This is equivalent d_ny = datetime('2011-03-04 06:00:00'); d_ny.TimeZone = 'America/New_York' @result{} 04-Mar-2011 06:00:00 America/New_York % Convert it to Chicago time d_chi.TimeZone = 'America/Chicago' @result{} 04-Mar-2011 05:00:00 America/Chicago @end example When you combine two zoned @code{datetime}s via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. @example d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles') d_la - d_ny @result{} 03:00:00 @end example You cannot combine a zoned and an unzoned @code{datetime}. This results in an error being raised. @quotation Warning Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined. @end quotation @menu * Defined Time Zones:: @end menu @node Defined Time Zones @subsection Defined Time Zones Tablicious's time zone data is drawn from the @uref{https://www.iana.org/time-zones, IANA Time Zone Database}, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do. You can use the @code{timezones} function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows). @quotation Note The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for @code{datetime}s outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.) @end quotation @node Durations @section Durations @menu * duration Class:: * calendarDuration Class:: @end menu @node duration Class @subsection @code{duration} Class A @code{duration} represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.) A @code{duration} has a resolution of about a nanosecond for typical dates. The underlying representation is a @code{double} representing the number of days elapsed, similar to a datenum, except it's interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch. You can add or subtract a @code{duration} to a @code{datetime} to get another @code{datetime}. You can also add or subtract @code{durations} to each other. @node calendarDuration Class @subsection @code{calendarDuration} Class A @code{calendarDuration} represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A @code{calendarDuration} does arithmetic with "whole" calendar periods. @code{calendarDuration}s and @code{duration}s cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow @code{duration}s to be interpreted as numbers of days when combined with @code{calendarDuration}s.) @example d = datetime('2011-03-04 00:00:00') @result{} 04-Mar-2011 cdur = calendarDuration(1, 3, 0) @result{} 1y 3mo d2 = d + cdur @result{} 04-Jun-2012 @end example @node Validation Functions @chapter Validation Functions Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs. These validation functions are named following the pattern @code{mustBeXxx}, where @code{Xxx} is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs. The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there. Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider. When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with @code{inputname()}, so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. @example % Validation of a simple variable does not need a label mustBeScalar (x); % Validation of a field or property reference does need a label mustBeScalar (this.foo, 'this.foo'); @end example @node Example Data Sets @chapter Example Data Sets Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the @code{tblish.datasets} and @code{tblish.dataset} classes. To see a list of the available data sets, run @code{tblish.datasets.list()}. Then to load one of the example data sets, run @code{tblish.datasets.load('examplename')}. For example: @example tblish.datasets.list t = tblish.datasets.load('cupcake') @end example You can also load it by calling @code{tblish.dataset.}. This does the same thing. For example: @example t = tblish.dataset.cupcake @end example When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs). Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the @code{doc} command on @code{tblish.dataset.}, where @var{} is the name of the data set. For example: @example doc tblish.dataset.cupcake @end example (The command @code{help tblish.dataset.} ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s @code{help} command.) @node Data Sets from R @section Data Sets from R Many of Tablicious’ example data sets are based on the example datasets found in R’s @code{datasets} package. R can be found at @url{https://www.r-project.org/}, and documentation for its @code{datasets} is at @url{https://rdrr.io/r/datasets/datasets-package.html}. Thanks to the R developers for producing the original data sets here. Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another. Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code. @node Missing Functionality @chapter Missing Functionality Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently: @itemize @item File I/O like @code{readtable()} and @code{writetable()} @item @code{summary()} @code{categorical} @item Assignment to table variables using @code{.}-indexing @item @code{timetable} @item POSIX time zone support for years outside the IANA time zone database coverage @item Week-of-year (ISO calendar) calculations @item Various @code{'ConvertFrom'} forms for @code{datetime} and @code{duration} constructors @item Support for LDML formatting for @code{datetime} @item Various functions: @itemize @item @code{between} @item @code{caldiff} @item @code{dateshift} @item @code{week} @end itemize @item @code{isdst}, @code{isweekend} @item @code{calendarDuration.split} @item @code{duration.Format} support @item Moving window methods in @code{fillmissing} @item @code{UTCOffset} and @code{DSTOffset} fields in the output of @code{timezones()} @item Plotting support @item Things omitted for compatibility: @itemize @item @code{ismissing} function @end itemize @end itemize A global @code{ismissing} function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. @code{ismissing} methods for various classes in Tablicious are defined. Tablicious also supplies an @code{isnanny} function with behavior similar to @code{ismissing} for clients which which wish to have @code{ismissing}-like behavior using a dependency only on Tablicious. It is the author’s hope that many of these will be implemented some day. These areas of missing functionality are tracked on the Tablicious issue tracker at @url{https://github.com/apjanke/octave-tablicious/issues} and @url{https://github.com/users/apjanke/projects/3}. @node API Reference @chapter API Reference @REFERENCE_SECTION(Tablicious) @node Copying @chapter Copying @section Package Copyright Tablicious for GNU Octave is covered by the GNU GPLv3 and other Free and Open Source Software licenses. The main code of Tablicious is licensed under the GNU GPL version 3 or later. Certain files in the build tools are licensed under the GNU GPL version 2 or later. This includes the @code{mktexi.pl} and @code{OctTexiDoc.pm} files in the @code{doc/} directory. The tzinfo database file is Public Domain. The date/time portion of Tablicious includes some Unicode data files licensed under the Unicode License Agreement - Data Files and Software license. The Tablicious test suite contains some files, specifically some table-related tests using MP-Test like @code{t/t_01_table.m}, which are BSD 3-Clause licensed, and are adapted from MATPOWER written by Ray Zimmerman. The Fisher Iris dataset is Public Domain. @section Manual Copyright @insertcopying @bye apjanke-octave-tablicious-8a1b10f/doc/tablicious.html000066400000000000000000027523651515050116100227520ustar00rootroot00000000000000 Tablicious v0.4.7

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.7.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.7 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support
    • Things omitted for compatibility:
      • ismissing function

    A global ismissing function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. ismissing methods for various classes in Tablicious are defined. Tablicious also supplies an isnanny function with behavior similar to ismissing for clients which which wish to have ismissing-like behavior using a dependency only on Tablicious.

    It is the author’s hope that many of these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like.

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.3.2 calendarDuration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert a calendarDuration to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this calendarDuration.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.3.3 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.3.4 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3.5 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3.6 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.3.7 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

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    8.2.3.8 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

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    8.2.3.9 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

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    8.2.3.10 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a datetime. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.10.18 datetime.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum.

    Returns logical array the same size as obj.

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    8.2.10.19 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.10.20 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.10.21 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.22 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.10.23 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.10.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.25 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.10.26 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.10.27 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.28 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.10.29 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.10.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

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    8.2.10.31 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.10.32 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.10.33 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.10.34 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.10.35 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.10.36 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.37 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.14.2 duration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert obj to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.14.3 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.14.4 duration.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second values of a duration. These are the hour, minute, and second values that when combined are equal to the same duration value.

    Returns double arrays the same size as obj.

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    8.2.14.5 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.6 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.8 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.9 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.14.10 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.11 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.22 isfile

    Not documented

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    8.2.23 isfolder

    Not documented

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like.

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.
    3. Octave does not define an ismissing function, but the Octave Forge statistics package does, and adding an ismissing function to Tablicious would conflict with that in hard-to-resolve ways.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.32 mustBeA

    Not documented

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    8.2.33 mustBeCellstr

    Not documented

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    8.2.34 mustBeCharvec

    Not documented

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    8.2.35 mustBeFinite

    Not documented

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    8.2.36 mustBeInteger

    Not documented

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    8.2.37 mustBeMember

    Not documented

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    8.2.38 mustBeNonempty

    Not documented

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    8.2.39 mustBeNumeric

    Not documented

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    8.2.40 mustBeReal

    Not documented

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    8.2.41 mustBeSameSize

    Not documented

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    8.2.42 mustBeScalar

    Not documented

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    8.2.43 mustBeScalarLogical

    Not documented

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    8.2.44 mustBeVector

    Not documented

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.53.9 string.endsWith

    Method: out = endsWith (obj, pattern)
    Method: out = endsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string ends with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

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    8.2.53.10 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.53.11 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.53.12 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.53.13 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.53.14 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53.15 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.53.16 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.53.17 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.53.18 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.53.19 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.53.20 string.startsWith

    Method: out = startsWith (obj, pattern)
    Method: out = startsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string starts with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

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    8.2.53.21 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.53.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.53.23 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.53.24 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.53.25 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.53.26 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.53.27 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.53.28 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.56 tableOuterFillValue

    Not documented

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    Previous: , Up: API Alphabetically   [Contents]

    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    Previous: , Up: Tablicious for GNU Octave   [Contents]

    9 Copying

    apjanke-octave-tablicious-8a1b10f/doc/tablicious.info000066400000000000000000011717351515050116100227340ustar00rootroot00000000000000This is tablicious.info, produced by makeinfo version 7.2 from tablicious.texi. This manual is for Tablicious, version 0.4.7. Copyright © 2019, 2023, 2024, 2025, 2026 Andrew Janke Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions. INFO-DIR-SECTION Math START-INFO-DIR-ENTRY * Tablicious: (tablicious). Tablicious package for Octave END-INFO-DIR-ENTRY  File: tablicious.info, Node: Top, Next: Introduction, Up: (dir) Tablicious for GNU Octave ************************* This manual is for Tablicious, version 0.4.7. * Menu: * Introduction:: * Getting Started:: * Table Representation:: * Date and Time Representation:: * Validation Functions:: * Example Data Sets:: * Data Sets from R:: * Missing Functionality:: * API Reference:: * Copying::  File: tablicious.info, Node: Introduction, Next: Getting Started, Up: Top 1 Introduction ************** Time is an illusion. Lunchtime doubly so. -- _Douglas Adams_ This is the manual for the Tablicious package version 0.4.7 for GNU Octave. Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a ‘table’ class with support for filtering and join operations; ‘datetime’, ‘duration’, and related classes; Missing Data support; ‘string’ and ‘categorical’ data types; and other miscellaneous things. This document is a work in progress. You are invited to help improve it and submit patches. Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types. Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page (https://github.com/apjanke/octave-tablicious).  File: tablicious.info, Node: Getting Started, Next: Table Representation, Prev: Introduction, Up: Top 2 Getting Started ***************** The easiest way to obtain Tablicious is by using Octave's ‘pkg’ package manager. To install the development prerelease of Tablicious, run this in Octave: pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz (Check the releases page at to find out what the actual latest release number is.) For development, you can obtain the source code for Tablicious from the project repo on GitHub at . Make a local clone of the repo. Then add the ‘inst’ directory in the repo to your Octave path.  File: tablicious.info, Node: Table Representation, Next: Date and Time Representation, Prev: Getting Started, Up: Top 3 Table Representation ********************** Tablicious provides the ‘table’ class for representing tabular data. A ‘table’ is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records. A ‘table’ is composed of multiple “variables”, each with a name, which all have the same number of rows. (A ‘table’ variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the I-th element or row of each variable compose a single record or observation. Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a ‘table’ is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way. Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using ‘table’ arrays. It’s like having your own in-memory SQL engine. * Menu: * Table Construction:: * Tables vs SQL::  File: tablicious.info, Node: Table Construction, Next: Tables vs SQL, Up: Table Representation 3.1 Table Construction ====================== There are two main ways to construct a ‘table’ array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a ‘table’. To build an array from multiple variables, use the ‘table(...)’ constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new ‘table’ object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the ‘'VariableNames'’ option. To convert a tabular-organized array of another type into a ‘table’, use the conversion functions like ‘array2table’, ‘struct2table’ and ‘cell2table’. ‘array2table’ and ‘cell2table’ take each column of the input array and turn it into a separate table variable in the resulting ‘table’. ‘struct2table’ takes the fields of a struct and puts them into table variables.  File: tablicious.info, Node: Tables vs SQL, Prev: Table Construction, Up: Table Representation 3.2 Tables vs SQL ================= Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave ‘table’ operations. In this table, ‘t’ is a variable holding a ‘table’ array, and ‘ix’ is some indexing expression. SQL Relational Octave ‘table’ --------------------------------------------------------------------------- SELECT PROJECT ‘subsetvars’, ‘t(:,ix)’ WHERE RESTRICT ‘subsetrows’, ‘t(ix,:)’ INNER JOIN JOIN ‘innerjoin’ OUTER JOIN OUTER JOIN ‘outerjoin’ FROM ‘TABLE1, TABLE2, Cartesian product ‘cartesian’ ...’ GROUP BY SUMMARIZE ‘groupby’ DISTINCT (automatic) ‘unique(t)’ Note that there is one big difference between relational algebra and SQL & Octave ‘table’: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit ‘DISTINCT’/‘unique()’ on its results, and there‘s no ‘ORDER BY’/‘sort()’. This is not the case in SQL or Octave ‘table’. Note for users coming from Matlab: Matlab does not provide a general ‘groupby’ function. Instead, you have to variously use ‘rowfun’, ‘grpstats’, ‘groupsummary’, and manual code to accomplish “group by” operations. Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke  File: tablicious.info, Node: Date and Time Representation, Next: Validation Functions, Prev: Table Representation, Up: Top 4 Date and Time Representation ****************************** Tablicious provides the ‘datetime’ class for representing points in time. There’s also ‘duration’ and ‘calendarDuration’ for representing periods or durations of time. Like vector quantities along the time line, as opposed to ‘datetime’ being a point along the time line. * Menu: * datetime Class:: * Time Zones:: * Durations::  File: tablicious.info, Node: datetime Class, Next: Time Zones, Up: Date and Time Representation 4.1 ‘datetime’ Class ==================== A ‘datetime’ is an array object that represents points in time in the familiar Gregorian calendar. This is an attempt to reproduce the functionality of Matlab's ‘datetime’. It also contains some Octave-specific extensions. The underlying representation is that of a datenum (a ‘double’ containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a ‘datetime’ inclues an optional ‘TimeZone’ property indicating what time zone the datetimes are in. So, basically, a ‘datetime’ is an object wrapper around a datenum array, plus time zone support. * Menu: * Datenum Compatibility::  File: tablicious.info, Node: Datenum Compatibility, Up: datetime Class 4.1.1 Datenum Compatibility --------------------------- While the underlying data representation of ‘datetime’ is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations. This is because of the signature of the ‘datetime’ constructor. When combining objects and primitive types like ‘double’, the primitive type is promoted to an object by calling the other object's one-argument constructor on it. However, the one-argument numeric-input consstructor for ‘datetime’ does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab's part; for compatibility, Octave does not alter that interface. To combine ‘datetime’s with datenums, you can convert the datenums to ‘datetime’s by calling ‘datetime.ofDatenum’ or ‘datetime(x, 'ConvertFrom', 'datenum')’, or you can convert the ‘datetime’s to datenums by accessing its ‘dnums’ field with ‘x.dnums’. Examples: dt = datetime('2011-03-04') dn = datenum('2017-01-01') [dt dn] ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND] [dt datetime.ofDatenum(dn)] ⇒ 04-Mar-2011 01-Jan-2017 Also, if you have a zoned ‘datetime’, you can’t combine it with a datenum, because datenums do not carry time zone information.  File: tablicious.info, Node: Time Zones, Next: Durations, Prev: datetime Class, Up: Date and Time Representation 4.2 Time Zones ============== Tablicious has support for representing dates in time zones and for converting between time zones. A ‘datetime’ may be "zoned" or "zoneless". A zoneless ‘datetime’ does not have a time zone associated with it. This is represented by an empty ‘TimeZone’ property on the ‘datetime’ object. A zoneless ‘datetime’ represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts). A zoned ‘datetime’ is associated with a time zone. It is represented by having the time zone's IANA zone identifier (e.g. ‘'UTC'’ or ‘'America/New_York'’) in its ‘TimeZone’ property. A zoned ‘datetime’ represents the local time in that time zone. By default, the ‘datetime’ constructor creates unzoned ‘datetime’s. To make a zoned ‘datetime’, either pass the ‘'TimeZone'’ option to the constructor, or set the ‘TimeZone’ property after object creation. Setting the ‘TimeZone’ property on a zoneless ‘datetime’ declares that it's a local time in that time zone. Setting the ‘TimeZone’ property on a zoned ‘datetime’ turns it back into a zoneless ‘datetime’ without changing the local time it represents. You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned ‘datetime’s. % Create an unzoned datetime d = datetime('2011-03-04 06:00:00') ⇒ 04-Mar-2011 06:00:00 % Create a zoned datetime d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') ⇒ 04-Mar-2011 06:00:00 America/New_York % This is equivalent d_ny = datetime('2011-03-04 06:00:00'); d_ny.TimeZone = 'America/New_York' ⇒ 04-Mar-2011 06:00:00 America/New_York % Convert it to Chicago time d_chi.TimeZone = 'America/Chicago' ⇒ 04-Mar-2011 05:00:00 America/Chicago When you combine two zoned ‘datetime’s via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles') d_la - d_ny ⇒ 03:00:00 You cannot combine a zoned and an unzoned ‘datetime’. This results in an error being raised. Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined. * Menu: * Defined Time Zones::  File: tablicious.info, Node: Defined Time Zones, Up: Time Zones 4.2.1 Defined Time Zones ------------------------ Tablicious's time zone data is drawn from the IANA Time Zone Database (https://www.iana.org/time-zones), also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do. You can use the ‘timezones’ function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows). Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for ‘datetime’s outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)  File: tablicious.info, Node: Durations, Prev: Time Zones, Up: Date and Time Representation 4.3 Durations ============= * Menu: * duration Class:: * calendarDuration Class::  File: tablicious.info, Node: duration Class, Next: calendarDuration Class, Up: Durations 4.3.1 ‘duration’ Class ---------------------- A ‘duration’ represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.) A ‘duration’ has a resolution of about a nanosecond for typical dates. The underlying representation is a ‘double’ representing the number of days elapsed, similar to a datenum, except it's interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch. You can add or subtract a ‘duration’ to a ‘datetime’ to get another ‘datetime’. You can also add or subtract ‘durations’ to each other.  File: tablicious.info, Node: calendarDuration Class, Prev: duration Class, Up: Durations 4.3.2 ‘calendarDuration’ Class ------------------------------ A ‘calendarDuration’ represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A ‘calendarDuration’ does arithmetic with "whole" calendar periods. ‘calendarDuration’s and ‘duration’s cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow ‘duration’s to be interpreted as numbers of days when combined with ‘calendarDuration’s.) d = datetime('2011-03-04 00:00:00') ⇒ 04-Mar-2011 cdur = calendarDuration(1, 3, 0) ⇒ 1y 3mo d2 = d + cdur ⇒ 04-Jun-2012  File: tablicious.info, Node: Validation Functions, Next: Example Data Sets, Prev: Date and Time Representation, Up: Top 5 Validation Functions ********************** Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs. These validation functions are named following the pattern ‘mustBeXxx’, where ‘Xxx’ is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs. The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there. Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider. When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with ‘inputname()’, so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. % Validation of a simple variable does not need a label mustBeScalar (x); % Validation of a field or property reference does need a label mustBeScalar (this.foo, 'this.foo');  File: tablicious.info, Node: Example Data Sets, Next: Missing Functionality, Prev: Validation Functions, Up: Top 6 Example Data Sets ******************* Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the ‘tblish.datasets’ and ‘tblish.dataset’ classes. To see a list of the available data sets, run ‘tblish.datasets.list()’. Then to load one of the example data sets, run ‘tblish.datasets.load('examplename')’. For example: tblish.datasets.list t = tblish.datasets.load('cupcake') You can also load it by calling ‘tblish.dataset.’. This does the same thing. For example: t = tblish.dataset.cupcake When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs). Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the ‘doc’ command on ‘tblish.dataset.’, where is the name of the data set. For example: doc tblish.dataset.cupcake (The command ‘help tblish.dataset.’ ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s ‘help’ command.) * Menu: * Data Sets from R::  File: tablicious.info, Node: Data Sets from R, Next: Missing Functionality, Prev: Example Data Sets, Up: Example Data Sets 6.1 Data Sets from R ==================== Many of Tablicious’ example data sets are based on the example datasets found in R’s ‘datasets’ package. R can be found at , and documentation for its ‘datasets’ is at . Thanks to the R developers for producing the original data sets here. Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another. Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.  File: tablicious.info, Node: Missing Functionality, Next: API Reference, Prev: Example Data Sets, Up: Top 7 Missing Functionality *********************** Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently: • File I/O like ‘readtable()’ and ‘writetable()’ • ‘summary()’ ‘categorical’ • Assignment to table variables using ‘.’-indexing • ‘timetable’ • POSIX time zone support for years outside the IANA time zone database coverage • Week-of-year (ISO calendar) calculations • Various ‘'ConvertFrom'’ forms for ‘datetime’ and ‘duration’ constructors • Support for LDML formatting for ‘datetime’ • Various functions: • ‘between’ • ‘caldiff’ • ‘dateshift’ • ‘week’ • ‘isdst’, ‘isweekend’ • ‘calendarDuration.split’ • ‘duration.Format’ support • Moving window methods in ‘fillmissing’ • ‘UTCOffset’ and ‘DSTOffset’ fields in the output of ‘timezones()’ • Plotting support • Things omitted for compatibility: • ‘ismissing’ function A global ‘ismissing’ function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. ‘ismissing’ methods for various classes in Tablicious are defined. Tablicious also supplies an ‘isnanny’ function with behavior similar to ‘ismissing’ for clients which which wish to have ‘ismissing’-like behavior using a dependency only on Tablicious. It is the author’s hope that many of these will be implemented some day. These areas of missing functionality are tracked on the Tablicious issue tracker at and .  File: tablicious.info, Node: API Reference, Next: Copying, Prev: Missing Functionality, Up: Top 8 API Reference *************** * Menu: * API by Category:: * API Alphabetically::  File: tablicious.info, Node: API by Category, Next: API Alphabetically, Up: API Reference 8.1 API by Category =================== 8.1.1 Tables ------------ table Tabular data array containing multiple columnar variables. *Note table::. array2table Convert an array to a table. *Note array2table::. cell2table Convert a cell array to a table. *Note cell2table::. struct2table Convert struct to a table. *Note struct2table::. tableOuterFillValue *Note tableOuterFillValue::. vartype Filter by variable type for use in suscripting. *Note vartype::. istable True if input is a ‘table’ array or other table-like type, false otherwise. *Note istable::. istimetable True if input is a ‘timetable’ array or other timetable-like type, false otherwise. *Note istimetable::. istabular True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them. *Note istabular::. tblish.evalWithTableVars Evaluate an expression against a table array’s variables. *Note tblish.evalWithTableVars::. tblish.table.grpstats Statistics by group for a table array. *Note tblish.table.grpstats::. 8.1.2 Strings and Categoricals ------------------------------ string A string array of Unicode strings. *Note string::. NaS “Not-a-String". *Note NaS::. contains Test if strings contain a pattern. *Note contains::. dispstrs Display strings for array. *Note dispstrs::. categorical Categorical variable array. *Note categorical::. iscategorical True if input is a ‘categorical’ array, false otherwise. *Note iscategorical::. NaC “Not-a-Categorical". *Note NaC::. discretize Group data into discrete bins or categories. *Note discretize::. 8.1.3 Dates and Times --------------------- datetime Represents points in time using the Gregorian calendar. *Note datetime::. NaT “Not-a-Time”. *Note NaT::. todatetime Convert input to a Tablicious datetime array, with convenient interface. *Note todatetime::. localdate Represents a complete day using the Gregorian calendar. *Note localdate::. isdatetime True if input is a ‘datetime’ array, false otherwise. *Note isdatetime::. calendarDuration Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. *Note calendarDuration::. iscalendarduration True if input is a ‘calendarDuration’ array, false otherwise. *Note iscalendarduration::. calmonths Create a ‘calendarDuration’ that is a given number of calendar months long. *Note calmonths::. calyears Construct a ‘calendarDuration’ a given number of years long. *Note calyears::. days Duration in days. *Note days::. duration Represents durations or periods of time as an amount of fixed-length time (i.e. *Note duration::. hours Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. *Note hours::. isduration True if input is a ‘duration’ array, false otherwise. *Note isduration::. milliseconds Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X. *Note milliseconds::. minutes Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. *Note minutes::. seconds Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X. *Note seconds::. timezones List all the time zones defined on this system. *Note timezones::. years Create a ‘duration’ X years long, or get the years in a ‘duration’ X. *Note years::. 8.1.4 Missing Data ------------------ missing Generic auto-converting missing value. *Note missing::. isnanny Test if elements are NaN or NaN-like. *Note isnanny::. eqn Determine element-wise equality, treating NaNs as equal *Note eqn::. 8.1.5 Validation Functions -------------------------- mustBeA *Note mustBeA::. mustBeCellstr *Note mustBeCellstr::. mustBeCharvec *Note mustBeCharvec::. mustBeFinite *Note mustBeFinite::. mustBeInteger *Note mustBeInteger::. mustBeMember *Note mustBeMember::. mustBeNonempty *Note mustBeNonempty::. mustBeNumeric *Note mustBeNumeric::. mustBeReal *Note mustBeReal::. mustBeSameSize *Note mustBeSameSize::. mustBeScalar *Note mustBeScalar::. mustBeScalarLogical *Note mustBeScalarLogical::. mustBeVector *Note mustBeVector::. 8.1.6 Miscellaneous ------------------- colvecfun Apply a function to column vectors in array. *Note colvecfun::. dispstrs Display strings for array. *Note dispstrs::. head Get first K rows of an array. *Note head::. isfile *Note isfile::. isfolder *Note isfolder::. pp Alias for prettyprint, for interactive use. *Note pp::. scalarexpand Expand scalar inputs to match size of non-scalar inputs. *Note scalarexpand::. size2str Format an array size for display. *Note size2str::. splitapply Split data into groups and apply function. *Note splitapply::. tail Get last K rows of an array. *Note tail::. vecfun Apply function to vectors in array along arbitrary dimension. *Note vecfun::. tblish.sizeof2 Approximate size of an array in bytes, with object support. *Note tblish.sizeof2::. 8.1.7 Example Datasets ---------------------- tblish.datasets Example dataset collection. *Note tblish.datasets::. tblish.dataset The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious. *Note tblish.dataset::. 8.1.8 Example Code ------------------ tblish.examples.coplot Conditioning plot. *Note tblish.examples.coplot::. tblish.examples.plot_pairs Plot pairs of variables against each other. *Note tblish.examples.plot_pairs::. tblish.examples.SpDb The classic Suppliers-Parts example database. *Note tblish.examples.SpDb::. 8.1.9 Uncategorized ------------------- caldays Create a ‘calendarDuration’ that is a given number of calendar days long. *Note caldays::.  File: tablicious.info, Node: API Alphabetically, Prev: API by Category, Up: API Reference 8.2 API Alphabetically ====================== * Menu: * array2table:: Convert an array to a table. * caldays:: Create a ‘calendarDuration’ that is a given number of calendar days long. * calendarDuration:: Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. * calmonths:: Create a ‘calendarDuration’ that is a given number of calendar months long. * calyears:: Construct a ‘calendarDuration’ a given number of years long. * categorical:: Categorical variable array. * cell2table:: Convert a cell array to a table. * colvecfun:: Apply a function to column vectors in array. * contains:: Test if strings contain a pattern. * datetime:: Represents points in time using the Gregorian calendar. * days:: Duration in days. * discretize:: Group data into discrete bins or categories. * dispstrs:: Display strings for array. * duration:: Represents durations or periods of time as an amount of fixed-length time (i.e. * eqn:: Determine element-wise equality, treating NaNs as equal * head:: Get first K rows of an array. * hours:: Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. * iscalendarduration:: True if input is a ‘calendarDuration’ array, false otherwise. * iscategorical:: True if input is a ‘categorical’ array, false otherwise. * isdatetime:: True if input is a ‘datetime’ array, false otherwise. * isduration:: True if input is a ‘duration’ array, false otherwise. * isfile:: * isfolder:: * isnanny:: Test if elements are NaN or NaN-like. * istable:: True if input is a ‘table’ array or other table-like type, false otherwise. * istabular:: True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them. * istimetable:: True if input is a ‘timetable’ array or other timetable-like type, false otherwise. * localdate:: Represents a complete day using the Gregorian calendar. * milliseconds:: Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X. * minutes:: Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. * missing:: Generic auto-converting missing value. * mustBeA:: * mustBeCellstr:: * mustBeCharvec:: * mustBeFinite:: * mustBeInteger:: * mustBeMember:: * mustBeNonempty:: * mustBeNumeric:: * mustBeReal:: * mustBeSameSize:: * mustBeScalar:: * mustBeScalarLogical:: * mustBeVector:: * NaC:: “Not-a-Categorical". * NaS:: “Not-a-String". * NaT:: “Not-a-Time”. * pp:: Alias for prettyprint, for interactive use. * scalarexpand:: Expand scalar inputs to match size of non-scalar inputs. * seconds:: Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X. * size2str:: Format an array size for display. * splitapply:: Split data into groups and apply function. * string:: A string array of Unicode strings. * struct2table:: Convert struct to a table. * table:: Tabular data array containing multiple columnar variables. * tableOuterFillValue:: * tail:: Get last K rows of an array. * tblish.dataset:: The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious. * tblish.datasets:: Example dataset collection. * tblish.evalWithTableVars:: Evaluate an expression against a table array’s variables. * tblish.examples.coplot:: Conditioning plot. * tblish.examples.plot_pairs:: Plot pairs of variables against each other. * tblish.examples.SpDb:: The classic Suppliers-Parts example database. * tblish.sizeof2:: Approximate size of an array in bytes, with object support. * tblish.table.grpstats:: Statistics by group for a table array. * timezones:: List all the time zones defined on this system. * todatetime:: Convert input to a Tablicious datetime array, with convenient interface. * vartype:: Filter by variable type for use in suscripting. * vecfun:: Apply function to vectors in array along arbitrary dimension. * years:: Create a ‘duration’ X years long, or get the years in a ‘duration’ X.  File: tablicious.info, Node: array2table, Next: caldays, Up: API Alphabetically 8.2.1 array2table ----------------- -- Function: OUT = array2table (C) -- Function: OUT = array2table (..., 'VariableNames', VARIABLENAMES) -- Function: OUT = array2table (..., 'RowNames', ROWNAMES) Convert an array to a table. Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array. You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use ‘cell2table’ instead, columns of the cell array which can be condensed into primitive arrays will be. With ‘array2table’, they won't be. See also: *note cell2table::, *note table::, *note struct2table::  File: tablicious.info, Node: caldays, Next: calendarDuration, Prev: array2table, Up: API Alphabetically 8.2.2 caldays ------------- -- Function File: OUT = caldays (X) Create a ‘calendarDuration’ that is a given number of calendar days long. Input X is a numeric array specifying the number of calendar days. This is a shorthand alternative to calling the ‘calendarDuration’ constructor with ‘calendarDuration(0, 0, x)’. Returns a new ‘calendarDuration’ object of the same size as X. *Note calendarDuration::.  File: tablicious.info, Node: calendarDuration, Next: calmonths, Prev: caldays, Up: API Alphabetically 8.2.3 calendarDuration ---------------------- -- Class: calendarDuration Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.) -- Instance Variable of calendarDuration: char Format The format to display this ‘calendarDuration’ in. Currently unsupported. This is a single value that applies to the whole array. * Menu: * calendarDuration.calendarDuration:: * calendarDuration.datevec:: * calendarDuration.dispstrs:: * calendarDuration.ismissing:: * calendarDuration.isnan:: * calendarDuration.minus:: * calendarDuration.mtimes:: * calendarDuration.plus:: * calendarDuration.times:: * calendarDuration.uminus::  File: tablicious.info, Node: calendarDuration.calendarDuration, Next: calendarDuration.datevec, Up: calendarDuration 8.2.3.1 calendarDuration.calendarDuration ......................................... -- Constructor: OBJ = calendarDuration () Constructs a new scalar ‘calendarDuration’ of zero elapsed time. -- Constructor: OBJ = calendarDuration (Y, M, D) -- Constructor: OBJ = calendarDuration (Y, M, D, H, MI, S) Constructs new ‘calendarDuration’ arrays based on input values.  File: tablicious.info, Node: calendarDuration.datevec, Next: calendarDuration.dispstrs, Prev: calendarDuration.calendarDuration, Up: calendarDuration 8.2.3.2 calendarDuration.datevec ................................ -- Method: [DVEC] = datevec (OBJ) -- Method: [Y, M, D, H, MN, S] = datevec (OBJ) Convert a ‘calendarDuration’ to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this ‘calendarDuration’. When nargout is 0 or 1, returns a double array of size n-by-6, where n is ‘numel(obj)’. When nargout is 2 or more, returns double arrays the same size as OBJ.  File: tablicious.info, Node: calendarDuration.dispstrs, Next: calendarDuration.ismissing, Prev: calendarDuration.datevec, Up: calendarDuration 8.2.3.3 calendarDuration.dispstrs ................................. -- Method: OUT = dispstrs (OBJ) Get display strings for each element of OBJ. Returns a cellstr the same size as OBJ.  File: tablicious.info, Node: calendarDuration.ismissing, Next: calendarDuration.isnan, Prev: calendarDuration.dispstrs, Up: calendarDuration 8.2.3.4 calendarDuration.ismissing .................................. -- Method: OUT = ismissing (OBJ) True if input elements are missing. This is equivalent to ‘ismissing’. Returns logical array the same size as OBJ.  File: tablicious.info, Node: calendarDuration.isnan, Next: calendarDuration.minus, Prev: calendarDuration.ismissing, Up: calendarDuration 8.2.3.5 calendarDuration.isnan .............................. -- Method: OUT = isnan (OBJ) True if input elements are NaN. This is equivalent to ‘ismissing’, and is provided for compatibility and polymorphic programming purposes. Returns logical array the same size as OBJ.  File: tablicious.info, Node: calendarDuration.minus, Next: calendarDuration.mtimes, Prev: calendarDuration.isnan, Up: calendarDuration 8.2.3.6 calendarDuration.minus .............................. -- Method: OUT = times (A, B) Subtraction: Subtracts one ‘calendarDuration’ from another. Returns a ‘calendarDuration’.  File: tablicious.info, Node: calendarDuration.mtimes, Next: calendarDuration.plus, Prev: calendarDuration.minus, Up: calendarDuration 8.2.3.7 calendarDuration.mtimes ............................... -- Method: OUT = times (OBJ, B) Multiplication: Multiplies a ‘calendarDuration’ by a numeric factor. This does not do true matrix multiplication, so at least one of the input arguments must be scalar. Returns a ‘calendarDuration’.  File: tablicious.info, Node: calendarDuration.plus, Next: calendarDuration.times, Prev: calendarDuration.mtimes, Up: calendarDuration 8.2.3.8 calendarDuration.plus ............................. -- Method: OUT = plus (OBJ, B) Addition: add to a ‘calendarDuration’. All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs. B may be a ‘calendarDuration’, ‘duration’, or numeric. If B is numeric, it is converted to a ‘calendarDuration’ using ‘caldays(B)’. Returns a ‘calendarDuration’.  File: tablicious.info, Node: calendarDuration.times, Next: calendarDuration.uminus, Prev: calendarDuration.plus, Up: calendarDuration 8.2.3.9 calendarDuration.times .............................. -- Method: OUT = times (OBJ, B) Multiplication: Multiplies a ‘calendarDuration’ by a numeric factor B. Returns a ‘calendarDuration’.  File: tablicious.info, Node: calendarDuration.uminus, Prev: calendarDuration.times, Up: calendarDuration 8.2.3.10 calendarDuration.uminus ................................ -- Method: OUT = uminus (OBJ) Unary minus. Negates the sign of OBJ, which is equivalent to negating each of its components.  File: tablicious.info, Node: calmonths, Next: calyears, Prev: calendarDuration, Up: API Alphabetically 8.2.4 calmonths --------------- -- Function File: OUT = calmonths (X) Create a ‘calendarDuration’ that is a given number of calendar months long. Input X is a numeric array specifying the number of calendar months. This is a shorthand alternative to calling the ‘calendarDuration’ constructor with ‘calendarDuration(0, x, 0)’. Returns a new ‘calendarDuration’ object of the same size as X. *Note calendarDuration::.  File: tablicious.info, Node: calyears, Next: categorical, Prev: calmonths, Up: API Alphabetically 8.2.5 calyears -------------- -- Function: OUT = calyears (X) Construct a ‘calendarDuration’ a given number of years long. This is a shorthand for calling ‘calendarDuration(X, 0, 0)’. *Note calendarDuration::.  File: tablicious.info, Node: categorical, Next: cell2table, Prev: calyears, Up: API Alphabetically 8.2.6 categorical ----------------- -- Class: categorical Categorical variable array. A ‘categorical’ array represents an array of values of a categorical variable. Each ‘categorical’ array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array). In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a ‘NaN’ value. This class is not fully implemented yet. Missing stuff: • gt, ge, lt, le • Ordinal support in general • countcats • summary -- Instance Variable of categorical: uint16 code The numeric codes of the array element values. These are indexes into the ‘cats’ category list. This is a planar property. -- Instance Variable of categorical: logical tfMissing A logical mask indicating whether each element of the array is missing (that is, undefined). This is a planar property. -- Instance Variable of categorical: cellstr cats The names of the categories in this array. This is the list into which the ‘code’ values are indexes. -- Instance Variable of categorical: scalar_logical isOrdinal A scalar logical indicating whether the categories in this array have an ordinal relationship. * Menu: * categorical.addcats:: * categorical.categorical:: * categorical.categories:: * categorical.cellstr:: * categorical.dispstrs:: * categorical.double:: * categorical.iscategory:: * categorical.ismissing:: * categorical.isnanny:: * categorical.isordinal:: * categorical.isundefined:: * categorical.mergecats:: * categorical.missing:: * categorical.removecats:: * categorical.renamecats:: * categorical.reordercats:: * categorical.setcats:: * categorical.squeezecats:: * categorical.string:: * categorical.summary:: * categorical.undefined::  File: tablicious.info, Node: categorical.addcats, Next: categorical.categorical, Up: categorical 8.2.6.1 categorical.addcats ........................... -- Method: OUT = addcats (OBJ, NEWCATS) Add categories to categorical array. Adds the specified categories to OBJ, without changing any of its values. NEWCATS is a cellstr listing the category names to add to OBJ.  File: tablicious.info, Node: categorical.categorical, Next: categorical.categories, Prev: categorical.addcats, Up: categorical 8.2.6.2 categorical.categorical ............................... -- Constructor: OBJ = categorical () Constructs a new scalar categorical whose value is undefined. -- Constructor: OBJ = categorical (VALS) -- Constructor: OBJ = categorical (VALS, VALUESET) -- Constructor: OBJ = categorical (VALS, VALUESET, CATEGORY_NAMES) -- Constructor: OBJ = categorical (..., 'Ordinal', ORDINAL) -- Constructor: OBJ = categorical (..., 'Protected', PROTECTED) Constructs a new categorical array from the given values. VALS is the array of values to convert to categoricals. VALUESET is the set of all values from which VALS is drawn. If omitted, it defaults to the unique values in VALS. CATEGORY_NAMES is a list of category names corresponding to VALUESET. If omitted, it defaults to VALUESET, converted to strings. ORDINAL is a logical indicating whether the category values in OBJ have a numeric ordering relationship. Defaults to false. PROTECTED indicates whether OBJ should be protected, which prevents the addition of new categories to the array. Defaults to false.  File: tablicious.info, Node: categorical.categories, Next: categorical.cellstr, Prev: categorical.categorical, Up: categorical 8.2.6.3 categorical.categories .............................. -- Method: OUT = categories (OBJ) Get a list of the categories in OBJ. Gets a list of the categories in OBJ, identified by their category names. Returns a cellstr column vector.  File: tablicious.info, Node: categorical.cellstr, Next: categorical.dispstrs, Prev: categorical.categories, Up: categorical 8.2.6.4 categorical.cellstr ........................... -- Method: OUT = cellstr (OBJ) Convert to cellstr. Converts OBJ to a cellstr array. The strings will be the category names for corresponding values, or ‘''’ for undefined values. Returns a cellstr array the same size as OBJ.  File: tablicious.info, Node: categorical.dispstrs, Next: categorical.double, Prev: categorical.cellstr, Up: categorical 8.2.6.5 categorical.dispstrs ............................ -- Method: OUT = dispstrs (OBJ) Display strings. Gets display strings for each element in OBJ. The display strings are either the category string, or ‘''’ for undefined values. Returns a cellstr array the same size as OBJ.  File: tablicious.info, Node: categorical.double, Next: categorical.iscategory, Prev: categorical.dispstrs, Up: categorical 8.2.6.6 categorical.double .......................... -- Method: OUT = double (OBJ) Convert to double array, by getting the underlying code values. Converts OBJ to a string array. The doubles will be the underlying numeric code values of OBJ, or ‘NaN’ for undefined values. The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering. Returns a ‘double’ array the same size as OBJ.  File: tablicious.info, Node: categorical.iscategory, Next: categorical.ismissing, Prev: categorical.double, Up: categorical 8.2.6.7 categorical.iscategory .............................. -- Method: OUT = iscategory (OBJ, CATNAMES) Test whether input is a category on a categorical array. CATNAMES is a cellstr listing the category names to check against OBJ. Returns a logical array the same size as CATNAMES.  File: tablicious.info, Node: categorical.ismissing, Next: categorical.isnanny, Prev: categorical.iscategory, Up: categorical 8.2.6.8 categorical.ismissing ............................. -- Method: OUT = ismissing (OBJ) Test whether elements are missing. For categorical arrays, undefined elements are considered to be missing. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: categorical.isnanny, Next: categorical.isordinal, Prev: categorical.ismissing, Up: categorical 8.2.6.9 categorical.isnanny ........................... -- Method: OUT = isnanny (OBJ) Test whethere elements are NaN-ish. Checks where each element in OBJ is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: categorical.isordinal, Next: categorical.isundefined, Prev: categorical.isnanny, Up: categorical 8.2.6.10 categorical.isordinal .............................. -- Method: OUT = isordinal (OBJ) Whether OBJ is ordinal. Returns true if OBJ is ordinal (as determined by its ‘IsOrdinal’ property), and false otherwise.  File: tablicious.info, Node: categorical.isundefined, Next: categorical.mergecats, Prev: categorical.isordinal, Up: categorical 8.2.6.11 categorical.isundefined ................................ -- Method: OUT = isundefined (OBJ) Test whether elements are undefined. Checks whether each element in OBJ is undefined. "Undefined" is a special value defined by ‘categorical’. It is equivalent to a ‘NaN’ or a ‘missing’ value. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: categorical.mergecats, Next: categorical.missing, Prev: categorical.isundefined, Up: categorical 8.2.6.12 categorical.mergecats .............................. -- Method: OUT = mergecats (OBJ, OLDCATS) -- Method: OUT = mergecats (OBJ, OLDCATS, NEWCAT) Merge multiple categories. Merges the categories OLDCATS into a single category. If NEWCAT is specified, that new category is added if necessary, and all of OLDCATS are merged into it. NEWCAT must be an existing category in OBJ if OBJ is ordinal. If NEWCAT is not provided, all of ODCATS are merged into ‘oldcats{1}’.  File: tablicious.info, Node: categorical.missing, Next: categorical.removecats, Prev: categorical.mergecats, Up: categorical 8.2.6.13 categorical.missing ............................ -- Static Method: OUT = categorical.missing () -- Static Method: OUT = categorical.missing (sz) Create an array of missing (undefined) categoricals. Creates a categorical array whose elements are all missing (). This is a convenience alias for *note categorical.undefined::, so you can call it generically. It returns strictly the same results as calling *note categorical.undefined:: with the same arguments. Returns a categorical array. See also: categorical.undefined  File: tablicious.info, Node: categorical.removecats, Next: categorical.renamecats, Prev: categorical.missing, Up: categorical 8.2.6.14 categorical.removecats ............................... -- Method: OUT = removecats (OBJ) Removes all unused categories from OBJ. This is equivalent to ‘out = squeezecats (obj)’. -- Method: OUT = removecats (OBJ, OLDCATS) Remove categories from categorical array. Removes the specified categories from OBJ. Elements of OBJ whose values belonged to those categories are replaced with undefined. NEWCATS is a cellstr listing the category names to add to OBJ.  File: tablicious.info, Node: categorical.renamecats, Next: categorical.reordercats, Prev: categorical.removecats, Up: categorical 8.2.6.15 categorical.renamecats ............................... -- Method: OUT = renamecats (OBJ, NEWCATS) -- Method: OUT = renamecats (OBJ, OLDCATS, NEWCATS) Rename categories. Renames some or all of the categories in OBJ, without changing any of its values.  File: tablicious.info, Node: categorical.reordercats, Next: categorical.setcats, Prev: categorical.renamecats, Up: categorical 8.2.6.16 categorical.reordercats ................................ -- Method: OUT = reordercats (OBJ) -- Method: OUT = reordercats (OBJ, NEWCATS) Reorder categories. Reorders the categories in OBJ to match NEWCATS. NEWCATS is a cellstr that must be a reordering of OBJ’s existing category list. If NEWCATS is not supplied, sorts the categories in alphabetical order.  File: tablicious.info, Node: categorical.setcats, Next: categorical.squeezecats, Prev: categorical.reordercats, Up: categorical 8.2.6.17 categorical.setcats ............................ -- Method: OUT = setcats (OBJ, NEWCATS) Set categories for categorical array. Sets the categories to use for OBJ. If any current categories are absent from the NEWCATS list, current values of those categories become undefined.  File: tablicious.info, Node: categorical.squeezecats, Next: categorical.string, Prev: categorical.setcats, Up: categorical 8.2.6.18 categorical.squeezecats ................................ -- Method: OUT = squeezecats (OBJ) Remove unused categories. Removes all categories which have no corresponding values in OBJ’s elements. This is currently unimplemented.  File: tablicious.info, Node: categorical.string, Next: categorical.summary, Prev: categorical.squeezecats, Up: categorical 8.2.6.19 categorical.string ........................... -- Method: OUT = string (OBJ) Convert to string array. Converts OBJ to a string array. The strings will be the category names for corresponding values, or for undefined values. Returns a ‘string’ array the same size as OBJ.  File: tablicious.info, Node: categorical.summary, Next: categorical.undefined, Prev: categorical.string, Up: categorical 8.2.6.20 categorical.summary ............................ -- Method: summary (OBJ) Display summary of array’s values. Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.  File: tablicious.info, Node: categorical.undefined, Prev: categorical.summary, Up: categorical 8.2.6.21 categorical.undefined .............................. -- Static Method: OUT = categorical.undefined () -- Static Method: OUT = categorical.undefined (sz) Create an array of undefined categoricals. Creates a categorical array whose elements are all . SZ is the size of the array to create. If omitted or empty, creates a scalar. Returns a categorical array. See also: categorical.missing  File: tablicious.info, Node: cell2table, Next: colvecfun, Prev: categorical, Up: API Alphabetically 8.2.7 cell2table ---------------- -- Function: OUT = cell2table (C) -- Function: OUT = cell2table (..., 'VariableNames', VARIABLENAMES) -- Function: OUT = cell2table (..., 'RowNames', ROWNAMES) Convert a cell array to a table. Converts a 2-dimensional cell matrix into a table. Each column in the input C becomes a variable in OUT. For columns that contain all scalar values of ‘cat’-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type. See also: *note array2table::, *note table::, *note struct2table::  File: tablicious.info, Node: colvecfun, Next: contains, Prev: cell2table, Up: API Alphabetically 8.2.8 colvecfun --------------- -- Function: OUT = colvecfun (FCN, X) Apply a function to column vectors in array. Applies the given function FCN to each column vector in the array X, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array. FCN must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as X. Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.  File: tablicious.info, Node: contains, Next: datetime, Prev: colvecfun, Up: API Alphabetically 8.2.9 contains -------------- -- Function: OUT = colvecfun (STR, PATTERN) -- Function: OUT = colvecfun (..., 'IgnoreCase', IGNORECASE) Test if strings contain a pattern. Tests whether the given strings contain the given pattern(s). STR (char, cellstr, or string) is a list of strings to compare against pattern. PATTERN (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them. Returns a logical array of the same size as the string array represented by STR. See also: startsWith, endsWith  File: tablicious.info, Node: datetime, Next: days, Prev: contains, Up: API Alphabetically 8.2.10 datetime --------------- -- Class: datetime Represents points in time using the Gregorian calendar. The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times. A ‘datetime’ array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array. This is an attempt to reproduce the functionality of Matlab's ‘datetime’. It also contains some Octave-specific extensions. -- Instance Variable of datetime: double dnums The underlying datenums that represent the points in time. These are always in UTC. This is a planar property: the size of ‘dnums’ is the same size as the containing ‘datetime’ array object. -- Instance Variable of datetime: char TimeZone The time zone this ‘datetime’ array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does. Setting the ‘TimeZone’ of a ‘datetime’ array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent. -- Instance Variable of datetime: char Format The format to display this ‘datetime’ in. Currently unsupported. * Menu: * datetime.colon:: * datetime.convertDatenumTimeZone:: * datetime.datenum:: * datetime.datenum2posix:: * datetime.datestr:: * datetime.datestrs:: * datetime.datestruct:: * datetime.datetime:: * datetime.datevec:: * datetime.diff:: * datetime.dispstrs:: * datetime.eq:: * datetime.ge:: * datetime.gmtime:: * datetime.gt:: * datetime.hms:: * datetime.isbetween:: * datetime.ismissing:: * datetime.isnan:: * datetime.isnat:: * datetime.le:: * datetime.linspace:: * datetime.localtime:: * datetime.lt:: * datetime.minus:: * datetime.NaT:: * datetime.ne:: * datetime.ofDatenum:: * datetime.ofDatestruct:: * datetime.plus:: * datetime.posix2datenum:: * datetime.posixtime:: * datetime.proxyKeys:: * datetime.timeofday:: * datetime.week:: * datetime.ymd:: * datetime.ymdhms::  File: tablicious.info, Node: datetime.colon, Next: datetime.convertDatenumTimeZone, Up: datetime 8.2.10.1 datetime.colon ....................... -- Method: OUT = colon (LO, HI) -- Method: OUT = colon (HI, INC, HI) Generate a sequence of uniformly-spaced values. This method implements the behavior for the colon operator (‘lo:hi’ or ‘lo:inc:hi’ calls) for the datetime type. "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time. The INC argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with ‘duration.ofDays (inc)’. The default value for INC, used in the two-arg ‘lo:hi’ is 1, that is, 1 day of exactly 24 hours. Returns a datetime vector. WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if INC is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.  File: tablicious.info, Node: datetime.convertDatenumTimeZone, Next: datetime.datenum, Prev: datetime.colon, Up: datetime 8.2.10.2 datetime.convertDatenumTimeZone ........................................ -- Static Method: OUT = datetime.convertDatenumTimeZone (DNUM, FROMZONEID, TOZONEID) Convert a datenum from one time zone to another. DNUM is a datenum array to convert. FROMZONEID is a charvec containing the IANA Time Zone identifier for the time zone to convert from. TOZONEID is a charvec containing the IANA Time Zone identifier for the time zone to convert to. Returns a datenum array the same size as DNUM.  File: tablicious.info, Node: datetime.datenum, Next: datetime.datenum2posix, Prev: datetime.convertDatenumTimeZone, Up: datetime 8.2.10.3 datetime.datenum ......................... -- Method: OUT = datenum (OBJ) Convert this to datenums that represent the same local time. Returns double array of same size as this.  File: tablicious.info, Node: datetime.datenum2posix, Next: datetime.datestr, Prev: datetime.datenum, Up: datetime 8.2.10.4 datetime.datenum2posix ............................... -- Static Method: OUT = datetime.datenum2posix (DNUMS) Converts Octave datenums to Unix dates. The input datenums are assumed to be in UTC. Returns a double, which may have fractional seconds.  File: tablicious.info, Node: datetime.datestr, Next: datetime.datestrs, Prev: datetime.datenum2posix, Up: datetime 8.2.10.5 datetime.datestr ......................... -- Method: OUT = datestr (OBJ) -- Method: OUT = datestr (OBJ, ...) Format OBJ as date strings. Supports all arguments that core Octave's ‘datestr’ does. Returns date strings as a 2-D char array.  File: tablicious.info, Node: datetime.datestrs, Next: datetime.datestruct, Prev: datetime.datestr, Up: datetime 8.2.10.6 datetime.datestrs .......................... -- Method: OUT = datestrs (OBJ) -- Method: OUT = datestrs (OBJ, ...) Format OBJ as date strings, returning cellstr. Supports all arguments that core Octave's ‘datestr’ does. Returns a cellstr array the same size as OBJ.  File: tablicious.info, Node: datetime.datestruct, Next: datetime.datetime, Prev: datetime.datestrs, Up: datetime 8.2.10.7 datetime.datestruct ............................ -- Method: OUT = datestruct (OBJ) Converts this to a "datestruct" broken-down time structure. A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents. The values in the returned broken-down time are those of the local time in this' defined time zone, if it has one. Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.  File: tablicious.info, Node: datetime.datetime, Next: datetime.datevec, Prev: datetime.datestruct, Up: datetime 8.2.10.8 datetime.datetime .......................... -- Constructor: OBJ = datetime () Constructs a new scalar ‘datetime’ containing the current local time, with no time zone attached. -- Constructor: OBJ = datetime (DATEVEC) -- Constructor: OBJ = datetime (DATESTRS) -- Constructor: OBJ = datetime (IN, 'ConvertFrom', INTYPE) -- Constructor: OBJ = datetime (Y, M, D, H, MI, S) -- Constructor: OBJ = datetime (Y, M, D, H, MI, MS) -- Constructor: OBJ = datetime (..., 'Format', FORMAT, 'InputFormat', INPUTFORMAT, 'Locale', INPUTLOCALE, 'PivotYear', PIVOTYEAR, 'TimeZone', TIMEZONE) Constructs a new ‘datetime’ array based on input values.  File: tablicious.info, Node: datetime.datevec, Next: datetime.diff, Prev: datetime.datetime, Up: datetime 8.2.10.9 datetime.datevec ......................... -- Method: OUT = datevec (OBJ) -- Method: [Y, M, D, H, MN, S] = datevec (OBJ) Convert this to a datevec that represent the same local wall time. When nargout is 0 or 1, returns a double array of size n-by-6, where n is ‘numel(obj)’. When nargout is 2 or more, returns double arrays the same size as OBJ.  File: tablicious.info, Node: datetime.diff, Next: datetime.dispstrs, Prev: datetime.datevec, Up: datetime 8.2.10.10 datetime.diff ....................... -- Method: OUT = diff (OBJ) Differences between elements. Computes the difference between each successive element in OBJ, as a ‘duration’. Returns a ‘duration’ array the same size as OBJ.  File: tablicious.info, Node: datetime.dispstrs, Next: datetime.eq, Prev: datetime.diff, Up: datetime 8.2.10.11 datetime.dispstrs ........................... -- Method: OUT = dispstrs (OBJ) Get display strings for each element of OBJ. Returns a cellstr the same size as OBJ.  File: tablicious.info, Node: datetime.eq, Next: datetime.ge, Prev: datetime.dispstrs, Up: datetime 8.2.10.12 datetime.eq ..................... -- Method: OUT = eq (A, B) True if A is equal to B. This defines the ‘==’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.ge, Next: datetime.gmtime, Prev: datetime.eq, Up: datetime 8.2.10.13 datetime.ge ..................... -- Method: OUT = ge (A, B) True if A is greater than or equal to B. This defines the ‘>=’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.gmtime, Next: datetime.gt, Prev: datetime.ge, Up: datetime 8.2.10.14 datetime.gmtime ......................... -- Method: OUT = gmtime (OBJ) Convert to TM_STRUCT structure in UTC time. Converts OBJ to a TM_STRUCT style structure array. The result is in UTC time. If OBJ is unzoned, it is assumed to be in UTC time. Returns a struct array in TM_STRUCT style.  File: tablicious.info, Node: datetime.gt, Next: datetime.hms, Prev: datetime.gmtime, Up: datetime 8.2.10.15 datetime.gt ..................... -- Method: OUT = gt (A, B) True if A is greater than B. This defines the ‘>’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.hms, Next: datetime.isbetween, Prev: datetime.gt, Up: datetime 8.2.10.16 datetime.hms ...................... -- Method: [H, M, S] = hms (OBJ) Get the Hour, Minute, and Second components of a DATETIME. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result. For zoned ‘datetime’s, these will be local times in the associated time zone. Returns double arrays the same size as ‘obj’.  File: tablicious.info, Node: datetime.isbetween, Next: datetime.ismissing, Prev: datetime.hms, Up: datetime 8.2.10.17 datetime.isbetween ............................ -- Method: OUT = isbetween (OBJ, LOWER, UPPER) Tests whether the elements of OBJ are between LOWER and UPPER. All inputs are implicitly converted to ‘datetime’ arrays, and are subject to scalar expansion. Returns a logical array the same size as the scalar expansion of the inputs.  File: tablicious.info, Node: datetime.ismissing, Next: datetime.isnan, Prev: datetime.isbetween, Up: datetime 8.2.10.18 datetime.ismissing ............................ -- Method: OUT = ismissing (OBJ) Test whether array elements are missing. For ‘string’ arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.isnan, Next: datetime.isnat, Prev: datetime.ismissing, Up: datetime 8.2.10.19 datetime.isnan ........................ -- Method: OUT = isnan (OBJ) True if input elements are NaT. This is an alias for ‘isnat’ to support type compatibility and polymorphic programming. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.isnat, Next: datetime.le, Prev: datetime.isnan, Up: datetime 8.2.10.20 datetime.isnat ........................ -- Method: OUT = isnat (OBJ) True if input elements are NaT. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.le, Next: datetime.linspace, Prev: datetime.isnat, Up: datetime 8.2.10.21 datetime.le ..................... -- Method: OUT = le (A, B) True if A is less than or equal toB. This defines the ‘<=’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.linspace, Next: datetime.localtime, Prev: datetime.le, Up: datetime 8.2.10.22 datetime.linspace ........................... -- Method: OUT = linspace (FROM, TO, N) Linearly-spaced values in date/time space. Constructs a vector of ‘datetime’s that represent linearly spaced points starting at FROM and going up to TO, with N points in the vector. FROM and TO are implicitly converted to ‘datetime’s. N is how many points to use. If omitted, defaults to 100. Returns an N-long ‘datetime’ vector.  File: tablicious.info, Node: datetime.localtime, Next: datetime.lt, Prev: datetime.linspace, Up: datetime 8.2.10.23 datetime.localtime ............................ -- Method: OUT = localtime (OBJ) Convert to TM_STRUCT structure in UTC time. Converts OBJ to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on OBJ. If OBJ is unzoned, it is assumed to be in UTC time. Returns a struct array in TM_STRUCT style. Example: dt = datetime; dt.TimeZone = datetime.SystemTimeZone; tm_struct = localtime (dt);  File: tablicious.info, Node: datetime.lt, Next: datetime.minus, Prev: datetime.localtime, Up: datetime 8.2.10.24 datetime.lt ..................... -- Method: OUT = lt (A, B) True if A is less than B. This defines the ‘<’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.minus, Next: datetime.NaT, Prev: datetime.lt, Up: datetime 8.2.10.25 datetime.minus ........................ -- Method: OUT = minus (A, B) Subtraction (‘-’ operator). Subtracts a ‘duration’, ‘calendarDuration’ or numeric B from a ‘datetime’ A, or subtracts two ‘datetime’s from each other. If both inputs are ‘datetime’, then the output is a ‘duration’. Otherwise, the output is a ‘datetime’. Numeric B inputs are implicitly converted to ‘duration’ using ‘duration.ofDays’. Returns an array the same size as A.  File: tablicious.info, Node: datetime.NaT, Next: datetime.ne, Prev: datetime.minus, Up: datetime 8.2.10.26 datetime.NaT ...................... -- Static Method: OUT = datetime.NaT () -- Static Method: OUT = datetime.NaT (SZ) “Not-a-Time”: Creates NaT-valued arrays. Constructs a new ‘datetime’ array of all ‘NaT’ values of the given size. If no input SZ is given, the result is a scalar ‘NaT’. ‘NaT’ is the ‘datetime’ equivalent of ‘NaN’. It represents a missing or invalid value. ‘NaT’ values never compare equal to, greater than, or less than any value, including other ‘NaT’s. Doing arithmetic with a ‘NaT’ and any other value results in a ‘NaT’.  File: tablicious.info, Node: datetime.ne, Next: datetime.ofDatenum, Prev: datetime.NaT, Up: datetime 8.2.10.27 datetime.ne ..................... -- Method: OUT = ne (A, B) True if A is not equal to B. This defines the ‘!=’ operator for ‘datetime’s. Inputs are implicitly converted to ‘datetime’ using the one-arg constructor or conversion method. Returns logical array the same size as OBJ.  File: tablicious.info, Node: datetime.ofDatenum, Next: datetime.ofDatestruct, Prev: datetime.ne, Up: datetime 8.2.10.28 datetime.ofDatenum ............................ -- Static Method: OBJ = datetime.ofDatenum (DNUMS) Converts a datenum array to a datetime array. Returns an unzoned ‘datetime’ array of the same size as the input.  File: tablicious.info, Node: datetime.ofDatestruct, Next: datetime.plus, Prev: datetime.ofDatenum, Up: datetime 8.2.10.29 datetime.ofDatestruct ............................... -- Static Method: OBJ = datetime.ofDatestruct (DSTRUCT) Converts a datestruct to a datetime array. A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype. Returns an unzoned ‘datetime’ array.  File: tablicious.info, Node: datetime.plus, Next: datetime.posix2datenum, Prev: datetime.ofDatestruct, Up: datetime 8.2.10.30 datetime.plus ....................... -- Method: OUT = plus (A, B) Addition (‘+’ operator). Adds a ‘duration’, ‘calendarDuration’, or numeric B to a ‘datetime’ A. A must be a ‘datetime’. Numeric B inputs are implicitly converted to ‘duration’ using ‘duration.ofDays’. WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy. Returns ‘datetime’ array the same size as A.  File: tablicious.info, Node: datetime.posix2datenum, Next: datetime.posixtime, Prev: datetime.plus, Up: datetime 8.2.10.31 datetime.posix2datenum ................................ -- Static Method: DNUMS = datetime.posix2datenum (PDATES) Converts POSIX (Unix) times to datenums Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.  File: tablicious.info, Node: datetime.posixtime, Next: datetime.proxyKeys, Prev: datetime.posix2datenum, Up: datetime 8.2.10.32 datetime.posixtime ............................ -- Method: OUT = posixtime (OBJ) Converts this to POSIX time values (seconds since the Unix epoch) Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC. Returns double array of same size as this.  File: tablicious.info, Node: datetime.proxyKeys, Next: datetime.timeofday, Prev: datetime.posixtime, Up: datetime 8.2.10.33 datetime.proxyKeys ............................ -- Method: [KEYSA, KEYSB] = proxyKeys (A, B) Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs. This is primarily for Tablicious's internal use; users will typically not need to call it or know how it works. Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.  File: tablicious.info, Node: datetime.timeofday, Next: datetime.week, Prev: datetime.proxyKeys, Up: datetime 8.2.10.34 datetime.timeofday ............................ -- Method: OUT = timeofday (OBJ) Get the time of day (elapsed time since midnight). For zoned ‘datetime’s, these will be local times in the associated time zone. Returns a ‘duration’ array the same size as ‘obj’.  File: tablicious.info, Node: datetime.week, Next: datetime.ymd, Prev: datetime.timeofday, Up: datetime 8.2.10.35 datetime.week ....................... -- Method: OUT = week (OBJ) Get the week of the year. This method is unimplemented.  File: tablicious.info, Node: datetime.ymd, Next: datetime.ymdhms, Prev: datetime.week, Up: datetime 8.2.10.36 datetime.ymd ...................... -- Method: [Y, M, D] = ymd (OBJ) Get the Year, Month, and Day components of OBJ. For zoned ‘datetime’s, these will be local times in the associated time zone. Returns double arrays the same size as ‘obj’.  File: tablicious.info, Node: datetime.ymdhms, Prev: datetime.ymd, Up: datetime 8.2.10.37 datetime.ymdhms ......................... -- Method: [Y, M, D, H, MI, S] = ymdhms (OBJ) Get the Year, Month, Day, Hour, Minute, and Second components of a OBJ. For zoned ‘datetime’s, these will be local times in the associated time zone. Returns double arrays the same size as ‘obj’.  File: tablicious.info, Node: days, Next: discretize, Prev: datetime, Up: API Alphabetically 8.2.11 days ----------- -- Function: OUT = days (X) Duration in days. If X is numeric, then OUT is a ‘duration’ array in units of fixed-length 24-hour days, with the same size as X. If X is a ‘duration’, then returns a ‘double’ array the same size as X indicating the number of fixed-length days that each duration is.  File: tablicious.info, Node: discretize, Next: dispstrs, Prev: days, Up: API Alphabetically 8.2.12 discretize ----------------- -- Function: [Y, E] = discretize (X, N) -- Function: [Y, E] = discretize (X, EDGES) -- Function: [Y, E] = discretize (X, DUR) -- Function: [Y, E] = discretize (..., 'categorical') -- Function: [Y, E] = discretize (..., 'IncludedEdge', INCLUDEDEDGE) Group data into discrete bins or categories. N is the number of bins to group the values into. EDGES is an array of edge values defining the bins. DUR is a ‘duration’ value indicating the length of time of each bin. If ‘'categorical'’ is specified, the resulting values are a ‘categorical’ array instead of a numeric array of bin indexes. Returns: Y - the bin index or category of each value from X E - the list of bin edge values  File: tablicious.info, Node: dispstrs, Next: duration, Prev: discretize, Up: API Alphabetically 8.2.13 dispstrs --------------- -- Function: OUT = dispstrs (X) Display strings for array. Gets the display strings for each element of X. The display strings should be short, one-line, human-presentable strings describing the value of that element. The default implementation of ‘dispstrs’ can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects. This is a polymorphic method that user-defined classes may override with their own custom display that is more informative. Returns a cell array the same size as X.  File: tablicious.info, Node: duration, Next: eqn, Prev: dispstrs, Up: API Alphabetically 8.2.14 duration --------------- -- Class: duration Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time. This is an attempt to reproduce the functionality of Matlab's ‘duration’. It also contains some Octave-specific extensions. Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times. -- Instance Variable of duration: double days The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days. This is a planar property: the size of ‘days’ is the same size as the containing ‘duration’ array object. -- Instance Variable of duration: char Format The format to display this ‘duration’ in. Currently unsupported. * Menu: * duration.char:: * duration.datevec:: * duration.dispstrs:: * duration.hms:: * duration.hours:: * duration.linspace:: * duration.milliseconds:: * duration.minutes:: * duration.ofDays:: * duration.seconds:: * duration.years::  File: tablicious.info, Node: duration.char, Next: duration.datevec, Up: duration 8.2.14.1 duration.char ...................... -- Method: OUT = char (OBJ) Convert to char. The contents of the strings will be the same as returned by ‘dispstrs’. This is primarily a convenience method for use on scalar OBJs. Returns a 2-D char array with one row per element in OBJ.  File: tablicious.info, Node: duration.datevec, Next: duration.dispstrs, Prev: duration.char, Up: duration 8.2.14.2 duration.datevec ......................... -- Method: [DVEC] = datevec (OBJ) -- Method: [Y, M, D, H, MN, S] = datevec (OBJ) Convert OBJ to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31. When nargout is 0 or 1, returns a double array of size n-by-6, where n is ‘numel(obj)’. When nargout is 2 or more, returns double arrays the same size as OBJ.  File: tablicious.info, Node: duration.dispstrs, Next: duration.hms, Prev: duration.datevec, Up: duration 8.2.14.3 duration.dispstrs .......................... -- Method: OUT = duration (OBJ) Get display strings for each element of OBJ. Returns a cellstr the same size as OBJ.  File: tablicious.info, Node: duration.hms, Next: duration.hours, Prev: duration.dispstrs, Up: duration 8.2.14.4 duration.hms ..................... -- Method: [H, M, S] = hms (OBJ) Get the Hour, Minute, and Second values of a DURATION. These are the hour, minute, and second values that when combined are equal to the same duration value. Returns double arrays the same size as ‘obj’.  File: tablicious.info, Node: duration.hours, Next: duration.linspace, Prev: duration.hms, Up: duration 8.2.14.5 duration.hours ....................... -- Method: OUT = hours (OBJ) Equivalent number of hours. Gets the number of fixed-length 60-minute hours that is equivalent to this duration. Returns double array the same size as OBJ.  File: tablicious.info, Node: duration.linspace, Next: duration.milliseconds, Prev: duration.hours, Up: duration 8.2.14.6 duration.linspace .......................... -- Method: OUT = linspace (FROM, TO, N) Linearly-spaced values in time duration space. Constructs a vector of ‘duration’s that represent linearly spaced points starting at FROM and going up to TO, with N points in the vector. FROM and TO are implicitly converted to ‘duration’s. N is how many points to use. If omitted, defaults to 100. Returns an N-long ‘datetime’ vector.  File: tablicious.info, Node: duration.milliseconds, Next: duration.minutes, Prev: duration.linspace, Up: duration 8.2.14.7 duration.milliseconds .............................. -- Method: OUT = milliseconds (OBJ) Equivalent number of milliseconds. Gets the number of milliseconds that is equivalent to this duration. Returns double array the same size as OBJ.  File: tablicious.info, Node: duration.minutes, Next: duration.ofDays, Prev: duration.milliseconds, Up: duration 8.2.14.8 duration.minutes ......................... -- Method: OUT = minutes (OBJ) Equivalent number of minutes. Gets the number of fixed-length 60-second minutes that is equivalent to this duration. Returns double array the same size as OBJ.  File: tablicious.info, Node: duration.ofDays, Next: duration.seconds, Prev: duration.minutes, Up: duration 8.2.14.9 duration.ofDays ........................ -- Static Method: OBJ = duration.ofDays (DNUMS) Converts a double array representing durations in whole and fractional days to a ‘duration’ array. This is the method that is used for implicit conversion of numerics in many cases. Returns a ‘duration’ array of the same size as the input.  File: tablicious.info, Node: duration.seconds, Next: duration.years, Prev: duration.ofDays, Up: duration 8.2.14.10 duration.seconds .......................... -- Method: OUT = seconds (OBJ) Equivalent number of seconds. Gets the number of seconds that is equivalent to this duration. Returns double array the same size as OBJ.  File: tablicious.info, Node: duration.years, Prev: duration.seconds, Up: duration 8.2.14.11 duration.years ........................ -- Method: OUT = years (OBJ) Equivalent number of years. Gets the number of fixed-length 365.2425-day years that is equivalent to this duration. Returns double array the same size as OBJ.  File: tablicious.info, Node: eqn, Next: head, Prev: duration, Up: API Alphabetically 8.2.15 eqn ---------- -- Function: OUT = eqn (A, B) Determine element-wise equality, treating NaNs as equal out = eqn (A, B) ‘eqn’ is just like ‘eq’ (the function that implements the ‘==’ operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of ‘isequaln’. ‘eqn’ uses ‘isnanny’ to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish. Developer's note: the name “‘eqn’” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the ‘isequaln’ pattern of appending an “n” to the corresponding non-NaN-equivocating function. See also: ‘eq’, ‘isequaln’, *note isnanny::  File: tablicious.info, Node: head, Next: hours, Prev: eqn, Up: API Alphabetically 8.2.16 head ----------- -- Function: OUT = head (A) -- Function: OUT = head (A, K) Get first K rows of an array. Returns the array A, subsetted to its first K rows. This means subsetting it to the first ‘(min (k, size (A, 1)))’ elements along dimension 1, and leaving all other dimensions unrestricted. A is the array to subset. K is the number of rows to get. K defaults to 8 if it is omitted or empty. If there are less than K rows in A, returns all rows. Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type. See also: tail  File: tablicious.info, Node: hours, Next: iscalendarduration, Prev: head, Up: API Alphabetically 8.2.17 hours ------------ -- Function File: OUT = hours (X) Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. If input is numeric, returns a ‘duration’ array that is that many hours in time. If input is a ‘duration’, converts the ‘duration’ to a number of hours. Returns an array the same size as X.  File: tablicious.info, Node: iscalendarduration, Next: iscategorical, Prev: hours, Up: API Alphabetically 8.2.18 iscalendarduration ------------------------- -- Function: OUT = iscalendarduration (X) True if input is a ‘calendarDuration’ array, false otherwise. Respects ‘iscalendarduration’ override methods on user-defined classes, even if they do not inherit from ‘calendarDuration’ or were known to Tablicious at authoring time. Returns a scalar logical.  File: tablicious.info, Node: iscategorical, Next: isdatetime, Prev: iscalendarduration, Up: API Alphabetically 8.2.19 iscategorical -------------------- -- Function: OUT = iscategorical (X) True if input is a ‘categorical’ array, false otherwise. Respects ‘iscategorical’ override methods on user-defined classes, even if they do not inherit from ‘categorical’ or were known to Tablicious at authoring time. Returns a scalar logical.  File: tablicious.info, Node: isdatetime, Next: isduration, Prev: iscategorical, Up: API Alphabetically 8.2.20 isdatetime ----------------- -- Function: OUT = isdatetime (X) True if input is a ‘datetime’ array, false otherwise. Respects ‘isdatetime’ override methods on user-defined classes, even if they do not inherit from ‘datetime’ or were known to Tablicious at authoring time. Returns a scalar logical.  File: tablicious.info, Node: isduration, Next: isfile, Prev: isdatetime, Up: API Alphabetically 8.2.21 isduration ----------------- -- Function: OUT = isduration (X) True if input is a ‘duration’ array, false otherwise. Respects ‘isduration’ override methods on user-defined classes, even if they do not inherit from ‘duration’ or were known to Tablicious at authoring time. Returns a scalar logical.  File: tablicious.info, Node: isfile, Next: isfolder, Prev: isduration, Up: API Alphabetically 8.2.22 isfile ------------- _Not documented_  File: tablicious.info, Node: isfolder, Next: isnanny, Prev: isfile, Up: API Alphabetically 8.2.23 isfolder --------------- _Not documented_  File: tablicious.info, Node: isnanny, Next: istable, Prev: isfolder, Up: API Alphabetically 8.2.24 isnanny -------------- -- Function: OUT = isnanny (X) Test if elements are NaN or NaN-like. Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if ‘isnan()’ or ‘isnat()’ returns true, and is false for types that do not support ‘isnan()’ or ‘isnat()’. This function only exists because: a. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and b. isnan() errors out for some types that do not support isnan(), like cells. c. Octave does not define an ‘ismissing’ function, but the Octave Forge statistics package does, and adding an ‘ismissing’ function to Tablicious would conflict with that in hard-to-resolve ways. ‘isnanny()’ smooths over those differences so you can call it polymorphically on any input type. Hopefully. Under normal operation, ‘isnanny()’ should not throw an error for any type or value of input. See also: ismissing, ‘isnan’, ‘isnat’, *note eqn::, ‘isequaln’  File: tablicious.info, Node: istable, Next: istabular, Prev: isnanny, Up: API Alphabetically 8.2.25 istable -------------- -- Function: OUT = istable (X) True if input is a ‘table’ array or other table-like type, false otherwise. Respects ‘istable’ override methods on user-defined classes, even if they do not inherit from ‘table’ or were known to Tablicious at authoring time. User-defined classes should only override ‘istable’ to return true if they conform to the ‘table’ public interface. That interface is not well-defined or documented yet, so maybe you don't want to do that yet. Returns a scalar logical.  File: tablicious.info, Node: istabular, Next: istimetable, Prev: istable, Up: API Alphabetically 8.2.26 istabular ---------------- -- Function: OUT = istabular (X) True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them. Respects ‘istable’ and ‘istimetable’ override methods on user-defined classes, even if they do not inherit from ‘table’ or were known to Tablicious at authoring time. Returns a scalar logical.  File: tablicious.info, Node: istimetable, Next: localdate, Prev: istabular, Up: API Alphabetically 8.2.27 istimetable ------------------ -- Function: OUT = istimetable (X) True if input is a ‘timetable’ array or other timetable-like type, false otherwise. Respects ‘istimetable’ override methods on user-defined classes, even if they do not inherit from ‘table’ or were known to Tablicious at authoring time. User-defined classes should only override ‘istimetable’ to return true if they conform to the ‘table’ public interface. That interface is not well-defined or documented yet, so maybe you don't want to do that yet. Returns a scalar logical.  File: tablicious.info, Node: localdate, Next: milliseconds, Prev: istimetable, Up: API Alphabetically 8.2.28 localdate ---------------- -- Class: localdate Represents a complete day using the Gregorian calendar. This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes. -- Instance Variable of localdate: double dnums The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents. These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component. -- Instance Variable of localdate: char Format The format to display this ‘localdate’ in. Currently unsupported. * Menu: * localdate.datenum:: * localdate.datestr:: * localdate.datestrs:: * localdate.datestruct:: * localdate.dispstrs:: * localdate.isnan:: * localdate.isnat:: * localdate.localdate:: * localdate.NaT:: * localdate.posixtime:: * localdate.ymd::  File: tablicious.info, Node: localdate.datenum, Next: localdate.datestr, Up: localdate 8.2.28.1 localdate.datenum .......................... -- Method: OUT = datenum (OBJ) Convert this to datenums that represent midnight on OBJ’s days. Returns double array of same size as this.  File: tablicious.info, Node: localdate.datestr, Next: localdate.datestrs, Prev: localdate.datenum, Up: localdate 8.2.28.2 localdate.datestr .......................... -- Method: OUT = datestr (OBJ) -- Method: OUT = datestr (OBJ, ...) Format OBJ as date strings. Supports all arguments that core Octave's ‘datestr’ does. Returns date strings as a 2-D char array.  File: tablicious.info, Node: localdate.datestrs, Next: localdate.datestruct, Prev: localdate.datestr, Up: localdate 8.2.28.3 localdate.datestrs ........................... -- Method: OUT = datestrs (OBJ) -- Method: OUT = datestrs (OBJ, ...) Format OBJ as date strings, returning cellstr. Supports all arguments that core Octave's ‘datestr’ does. Returns a cellstr array the same size as OBJ.  File: tablicious.info, Node: localdate.datestruct, Next: localdate.dispstrs, Prev: localdate.datestrs, Up: localdate 8.2.28.4 localdate.datestruct ............................. -- Method: OUT = datestruct (OBJ) Converts this to a “datestruct” broken-down time structure. A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by ‘datetime’ in that it lacks Hour, Minute, and Second components. This is done for efficiency. The values in the returned broken-down time are those of the local time in OBJ’s defined time zone, if it has one. Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.  File: tablicious.info, Node: localdate.dispstrs, Next: localdate.isnan, Prev: localdate.datestruct, Up: localdate 8.2.28.5 localdate.dispstrs ........................... -- Method: OUT = dispstrs (OBJ) Get display strings for each element of OBJ. Returns a cellstr the same size as OBJ.  File: tablicious.info, Node: localdate.isnan, Next: localdate.isnat, Prev: localdate.dispstrs, Up: localdate 8.2.28.6 localdate.isnan ........................ -- Method: OUT = isnan (OBJ) True if input elements are NaT. This is an alias for ‘isnat’ to support type compatibility and polymorphic programming. Returns logical array the same size as OBJ.  File: tablicious.info, Node: localdate.isnat, Next: localdate.localdate, Prev: localdate.isnan, Up: localdate 8.2.28.7 localdate.isnat ........................ -- Method: OUT = isnat (OBJ) True if input elements are NaT. Returns logical array the same size as OBJ.  File: tablicious.info, Node: localdate.localdate, Next: localdate.NaT, Prev: localdate.isnat, Up: localdate 8.2.28.8 localdate.localdate ............................ -- Constructor: OBJ = localdate () Constructs a new scalar ‘localdate’ containing the current local date. -- Constructor: OBJ = localdate (DATENUMS) -- Constructor: OBJ = localdate (DATESTRS) -- Constructor: OBJ = localdate (Y, M, D) -- Constructor: OBJ = localdate (..., 'Format', FORMAT) Constructs a new ‘localdate’ array based on input values.  File: tablicious.info, Node: localdate.NaT, Next: localdate.posixtime, Prev: localdate.localdate, Up: localdate 8.2.28.9 localdate.NaT ...................... -- Static Method: OUT = localdate.NaT () -- Static Method: OUT = localdate.NaT (SZ) “Not-a-Time”: Creates NaT-valued arrays. Constructs a new ‘datetime’ array of all ‘NaT’ values of the given size. If no input SZ is given, the result is a scalar ‘NaT’. ‘NaT’ is the ‘datetime’ equivalent of ‘NaN’. It represents a missing or invalid value. ‘NaT’ values never compare equal to, greater than, or less than any value, including other ‘NaT’s. Doing arithmetic with a ‘NaT’ and any other value results in a ‘NaT’. This static method is provided because the global ‘NaT’ function creates ‘datetime’s, not ‘localdate’s  File: tablicious.info, Node: localdate.posixtime, Next: localdate.ymd, Prev: localdate.NaT, Up: localdate 8.2.28.10 localdate.posixtime ............................. -- Method: OUT = posixtime (OBJ) Converts this to POSIX time values for midnight of OBJ’s days. Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on OBJ’s days. Returns double array of same size as this.  File: tablicious.info, Node: localdate.ymd, Prev: localdate.posixtime, Up: localdate 8.2.28.11 localdate.ymd ....................... -- Method: [Y, M, D] = ymd (OBJ) Get the Year, Month, and Day components of OBJ. Returns double arrays the same size as ‘obj’.  File: tablicious.info, Node: milliseconds, Next: minutes, Prev: localdate, Up: API Alphabetically 8.2.29 milliseconds ------------------- -- Function File: OUT = milliseconds (X) Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X. If input is numeric, returns a ‘duration’ array that is that many milliseconds in time. If input is a ‘duration’, converts the ‘duration’ to a number of milliseconds. Returns an array the same size as X.  File: tablicious.info, Node: minutes, Next: missing, Prev: milliseconds, Up: API Alphabetically 8.2.30 minutes -------------- -- Function File: OUT = hours (X) Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.  File: tablicious.info, Node: missing, Next: mustBeA, Prev: minutes, Up: API Alphabetically 8.2.31 missing -------------- -- Class: missing Generic auto-converting missing value. ‘missing’ is a generic missing value that auto-converts to other types. A ‘missing’ array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations. This class is currently EXPERIMENTAL. Use at your own risk. Note: This class does not actually work for assignment. If you do this: x = 1:5 x(3) = missing It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it. * Menu: * missing.dispstrs:: * missing.ismissing:: * missing.isnan:: * missing.isnanny:: * missing.missing::  File: tablicious.info, Node: missing.dispstrs, Next: missing.ismissing, Up: missing 8.2.31.1 missing.dispstrs ......................... -- Method: OUT = dispstrs (OBJ) Display strings. Gets display strings for each element in OBJ. For ‘missing’, the display strings are always ‘''’. Returns a cellstr the same size as OBJ.  File: tablicious.info, Node: missing.ismissing, Next: missing.isnan, Prev: missing.dispstrs, Up: missing 8.2.31.2 missing.ismissing .......................... -- Method: OUT = ismissing (OBJ) Test whether elements are missing values. ‘ismissing’ is always true for ‘missing’ arrays. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: missing.isnan, Next: missing.isnanny, Prev: missing.ismissing, Up: missing 8.2.31.3 missing.isnan ...................... -- Method: OUT = isnan (OBJ) Test whether elements are NaN. ‘isnan’ is always true for ‘missing’ arrays. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: missing.isnanny, Next: missing.missing, Prev: missing.isnan, Up: missing 8.2.31.4 missing.isnanny ........................ -- Method: OUT = isnanny (OBJ) Test whether elements are NaN-like. ‘isnanny’ is always true for ‘missing’ arrays. Returns a logical array the same size as OBJ.  File: tablicious.info, Node: missing.missing, Prev: missing.isnanny, Up: missing 8.2.31.5 missing.missing ........................ -- Constructor: OBJ = missing () Constructs a scalar ‘missing’ array. The constructor takes no arguments, since there’s only one ‘missing’ value.  File: tablicious.info, Node: mustBeA, Next: mustBeCellstr, Prev: missing, Up: API Alphabetically 8.2.32 mustBeA -------------- _Not documented_  File: tablicious.info, Node: mustBeCellstr, Next: mustBeCharvec, Prev: mustBeA, Up: API Alphabetically 8.2.33 mustBeCellstr -------------------- _Not documented_  File: tablicious.info, Node: mustBeCharvec, Next: mustBeFinite, Prev: mustBeCellstr, Up: API Alphabetically 8.2.34 mustBeCharvec -------------------- _Not documented_  File: tablicious.info, Node: mustBeFinite, Next: mustBeInteger, Prev: mustBeCharvec, Up: API Alphabetically 8.2.35 mustBeFinite ------------------- _Not documented_  File: tablicious.info, Node: mustBeInteger, Next: mustBeMember, Prev: mustBeFinite, Up: API Alphabetically 8.2.36 mustBeInteger -------------------- _Not documented_  File: tablicious.info, Node: mustBeMember, Next: mustBeNonempty, Prev: mustBeInteger, Up: API Alphabetically 8.2.37 mustBeMember ------------------- _Not documented_  File: tablicious.info, Node: mustBeNonempty, Next: mustBeNumeric, Prev: mustBeMember, Up: API Alphabetically 8.2.38 mustBeNonempty --------------------- _Not documented_  File: tablicious.info, Node: mustBeNumeric, Next: mustBeReal, Prev: mustBeNonempty, Up: API Alphabetically 8.2.39 mustBeNumeric -------------------- _Not documented_  File: tablicious.info, Node: mustBeReal, Next: mustBeSameSize, Prev: mustBeNumeric, Up: API Alphabetically 8.2.40 mustBeReal ----------------- _Not documented_  File: tablicious.info, Node: mustBeSameSize, Next: mustBeScalar, Prev: mustBeReal, Up: API Alphabetically 8.2.41 mustBeSameSize --------------------- _Not documented_  File: tablicious.info, Node: mustBeScalar, Next: mustBeScalarLogical, Prev: mustBeSameSize, Up: API Alphabetically 8.2.42 mustBeScalar ------------------- _Not documented_  File: tablicious.info, Node: mustBeScalarLogical, Next: mustBeVector, Prev: mustBeScalar, Up: API Alphabetically 8.2.43 mustBeScalarLogical -------------------------- _Not documented_  File: tablicious.info, Node: mustBeVector, Next: NaC, Prev: mustBeScalarLogical, Up: API Alphabetically 8.2.44 mustBeVector ------------------- _Not documented_  File: tablicious.info, Node: NaC, Next: NaS, Prev: mustBeVector, Up: API Alphabetically 8.2.45 NaC ---------- -- Function: OUT = NaC () -- Function: OUT = NaC (SZ) “Not-a-Categorical". Creates missing-valued categorical arrays. Returns a new ‘categorical’ array of all missing values of the given size. If no input SZ is given, the result is a scalar missing categorical. ‘NaC’ is the ‘categorical’ equivalent of ‘NaN’ or ‘NaT’. It represents a missing, invalid, or null value. ‘NaC’ values never compare equal to any value, including other ‘NaC’s. ‘NaC’ is a convenience function which is strictly a wrapper around ‘categorical.undefined’ and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values. See also: categorical.undefined  File: tablicious.info, Node: NaS, Next: NaT, Prev: NaC, Up: API Alphabetically 8.2.46 NaS ---------- -- Function: OUT = NaS () -- Function: OUT = NaS (SZ) “Not-a-String". Creates missing-valued string arrays. Returns a new ‘string’ array of all missing values of the given size. If no input SZ is given, the result is a scalar missing string. ‘NaS’ is the ‘string’ equivalent of ‘NaN’ or ‘NaT’. It represents a missing, invalid, or null value. ‘NaS’ values never compare equal to any value, including other ‘NaS’s. ‘NaS’ is a convenience function which is strictly a wrapper around ‘string.missing’ and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values. See also: string.missing  File: tablicious.info, Node: NaT, Next: pp, Prev: NaS, Up: API Alphabetically 8.2.47 NaT ---------- -- Function: OUT = NaT () -- Function: OUT = NaT (SZ) “Not-a-Time”. Creates missing-valued datetime arrays. Constructs a new ‘datetime’ array of all ‘NaT’ values of the given size. If no input SZ is given, the result is a scalar ‘NaT’. ‘NaT’ is the ‘datetime’ equivalent of ‘NaN’. It represents a missing or invalid value. ‘NaT’ values never compare equal to, greater than, or less than any value, including other ‘NaT’s. Doing arithmetic with a ‘NaT’ and any other value results in a ‘NaT’. ‘NaT’ currently cannot create NaT arrays of type ‘localdate’. To do that, use *note localdate.NaT:: instead.  File: tablicious.info, Node: pp, Next: scalarexpand, Prev: NaT, Up: API Alphabetically 8.2.48 pp --------- -- Function: pp (X) -- Function: pp (A, B, C, ...) -- Function: pp ('A', 'B', 'C', ...) -- Function: pp A B C ... Alias for prettyprint, for interactive use. This is an alias for prettyprint(), with additional name-conversion magic. If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.  File: tablicious.info, Node: scalarexpand, Next: seconds, Prev: pp, Up: API Alphabetically 8.2.49 scalarexpand ------------------- -- Function: [OUT1, OUT2, ..., OUTN] = scalarexpand (X1, X2, ..., XN) Expand scalar inputs to match size of non-scalar inputs. Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. ‘repmat’ is used to do the expansion. Works on any input types that support ‘size’, ‘isscalar’, and ‘repmat’. It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs. Returns as many output arguments as there were input arguments. Examples: x1 = rand(3); x2 = 42; x3 = magic(3); [x1, x2, x3] = scalarexpand (x1, x2, x3)  File: tablicious.info, Node: seconds, Next: size2str, Prev: scalarexpand, Up: API Alphabetically 8.2.50 seconds -------------- -- Function File: OUT = seconds (X) Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X. If input is numeric, returns a ‘duration’ array that is that many seconds in time. If input is a ‘duration’, converts the ‘duration’ to a number of seconds. Returns an array the same size as X.  File: tablicious.info, Node: size2str, Next: splitapply, Prev: seconds, Up: API Alphabetically 8.2.51 size2str --------------- -- Function: OUT = size2str (SZ) Format an array size for display. Formats the given array size SZ as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by SZ. SZ is an array of dimension sizes, in the format returned by the ‘size’ function. Returns a charvec. Examples: str = size2str (size (magic (4))) ⇒ str = 4-by-4  File: tablicious.info, Node: splitapply, Next: string, Prev: size2str, Up: API Alphabetically 8.2.52 splitapply ----------------- -- Function: OUT = splitapply (FUNC, X, G) -- Function: OUT = splitapply (FUNC, X1, ..., XN, G) -- Function: [Y1, ..., YM] = splitapply (...) Split data into groups and apply function. FUNC is a function handle to call on each group of inputs in turn. X, X1, ..., XN are the input variables that are split into groups for the function calls. If X is a ‘table’, then its contained variables are “popped out” and considered to be the X1 ... XN input variables. G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored. Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture. Returns the concatenated outputs of applying FUNC to each group. See also: *note table.groupby::, *note table.splitapply::  File: tablicious.info, Node: string, Next: struct2table, Prev: splitapply, Up: API Alphabetically 8.2.53 string ------------- -- Class: string A string array of Unicode strings. A string array is an array of strings, where each array element is a single string. The string class represents strings, where: • Each element of a string array is a single string • A single string is a 1-dimensional row vector of Unicode characters • Those characters are encoded in UTF-8 • This last bit depends on the fact that Octave chars are UTF-8 now This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave. String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL. This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.) Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points. This class really serves three roles: 1. It is a type-safe object wrapper around Octave’s base primitive character types. 2. It adds ismissing() semantics. 3. And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars. “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests. TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave's existing char/string-handling functions. TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware. * Menu: * string.cell:: * string.cellstr:: * string.char:: * string.cmp:: * string.decode:: * string.dispstrs:: * string.empty:: * string.encode:: * string.endsWith:: * string.erase:: * string.ismissing:: * string.isnanny:: * string.isstring:: * string.lower:: * string.missing:: * string.plus:: * string.regexprep:: * string.reverse:: * string.reverse_bytes:: * string.startsWith:: * string.strcat:: * string.strcmp:: * string.strfind:: * string.string:: * string.strlength:: * string.strlength_bytes:: * string.strrep:: * string.upper::  File: tablicious.info, Node: string.cell, Next: string.cellstr, Up: string 8.2.53.1 string.cell .................... -- Method: OUT = cell (OBJ) Convert to cell array. Converts this to a cell, which will be a cellstr. Missing values are converted to ‘''’. This method returns the same values as ‘cellstr(obj)’; it is just provided for interface compatibility purposes. Returns a cell array of the same size as OBJ.  File: tablicious.info, Node: string.cellstr, Next: string.char, Prev: string.cell, Up: string 8.2.53.2 string.cellstr ....................... -- Method: OUT = cellstr (OBJ) Convert to cellstr. Converts OBJ to a cellstr. Missing values are converted to ‘''’. Returns a cellstr array of the same size as OBJ.  File: tablicious.info, Node: string.char, Next: string.cmp, Prev: string.cellstr, Up: string 8.2.53.3 string.char .................... -- Method: OUT = char (OBJ) Convert to char array. Converts OBJ to a 2-D char array. It will have as many rows as OBJ has elements. It is an error to convert missing-valued ‘string’ arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.) Returns 2-D char array.  File: tablicious.info, Node: string.cmp, Next: string.decode, Prev: string.char, Up: string 8.2.53.4 string.cmp ................... -- Method: [OUT, OUTA, OUTB] = cmp (A, B) Value ordering comparison, returning -1/0/+1. Compares each element of A and B, returning for each element ‘i’ whether ‘A(i)’ was less than (-1), equal to (0), or greater than (1) the corresponding ‘B(i)’. TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings. Returns a numeric array OUT of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1. Also returns scalar-expanded copies of A and B as OUTA and OUTB, as a programming convenience.  File: tablicious.info, Node: string.decode, Next: string.dispstrs, Prev: string.cmp, Up: string 8.2.53.5 string.decode ...................... -- Static Method: OUT = string.decode (BYTES, CHARSETNAME) Decode encoded text from bytes. Decodes the given encoded text in BYTES according to the specified encoding, given by CHARSETNAME. Returns a scalar string. See also: *note string.encode::  File: tablicious.info, Node: string.dispstrs, Next: string.empty, Prev: string.decode, Up: string 8.2.53.6 string.dispstrs ........................ -- Method: OUT = dispstrs (OBJ) Display strings for array elements. Gets display strings for all the elements in OBJ. These display strings will either be the string contents of the element, enclosed in ‘"..."’, and with CR/LF characters replaced with ‘'\r'’ and ‘'\n'’ escape sequences, or ‘""’ for missing values. Returns a cellstr of the same size as OBJ.  File: tablicious.info, Node: string.empty, Next: string.encode, Prev: string.dispstrs, Up: string 8.2.53.7 string.empty ..................... -- Function: OUT = empty (SZ) Get an empty string array of a specified size. The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0]. The size may also be supplied as multiple arguments containing scalar numerics. Returns an empty string array of the requested size.  File: tablicious.info, Node: string.encode, Next: string.endsWith, Prev: string.empty, Up: string 8.2.53.8 string.encode ...................... -- Method: OUT = encode (OBJ, CHARSETNAME) Encode string in a given character encoding. OBJ must be scalar. CHARSETNAME (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.) Returns the encoded string as a ‘uint8’ vector. See also: *note string.decode::.  File: tablicious.info, Node: string.endsWith, Next: string.erase, Prev: string.encode, Up: string 8.2.53.9 string.endsWith ........................ -- Method: OUT = endsWith (OBJ, PATTERN) -- Method: OUT = endsWith (OBJ, PATTERN, 'IgnoreCase', true/false) Test if string ends with pattern. Returns a logical array the size of the scalar expansion of OBJ and PATTERN.  File: tablicious.info, Node: string.erase, Next: string.ismissing, Prev: string.endsWith, Up: string 8.2.53.10 string.erase ...................... -- Method: OUT = erase (OBJ, MATCH) Erase matching substring. Erases the substrings in OBJ which match the MATCH input. Returns a string array of the same size as OBJ.  File: tablicious.info, Node: string.ismissing, Next: string.isnanny, Prev: string.erase, Up: string 8.2.53.11 string.ismissing .......................... -- Method: OUT = ismissing (OBJ) Test whether array elements are missing. For ‘string’ arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs. Returns a logical array the same size as ‘obj’.  File: tablicious.info, Node: string.isnanny, Next: string.isstring, Prev: string.ismissing, Up: string 8.2.53.12 string.isnanny ........................ -- Method: OUT = isnanny (OBJ) Test whether array elements are NaN-like. Missing values are considered nannish; any other string value is not. Returns a logical array of the same size as OBJ.  File: tablicious.info, Node: string.isstring, Next: string.lower, Prev: string.isnanny, Up: string 8.2.53.13 string.isstring ......................... -- Method: OUT = isstring (OBJ) Test if input is a string array. ‘isstring’ is always true for ‘string’ inputs. Returns a scalar logical.  File: tablicious.info, Node: string.lower, Next: string.missing, Prev: string.isstring, Up: string 8.2.53.14 string.lower ...................... -- Method: OUT = lower (OBJ) Convert to lower case. Converts all the characters in all the strings in OBJ to lower case. This currently delegates to Octave’s own ‘lower()’ function to do the conversion, so whatever character class handling it has, this has. Returns a string array of the same size as OBJ.  File: tablicious.info, Node: string.missing, Next: string.plus, Prev: string.lower, Up: string 8.2.53.15 string.missing ........................ -- Static Method: OUT = string.missing (SZ) Missing string value. Creates a string array of all-missing values of the specified size SZ. If SZ is omitted, creates a scalar missing string. Returns a string array of size SZ or [1 1]. See also: NaS  File: tablicious.info, Node: string.plus, Next: string.regexprep, Prev: string.missing, Up: string 8.2.53.16 string.plus ..................... -- Method: OUT = plus (A, B) String concatenation via plus operator. Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays. The concatenation is done by calling 'strcat' on the inputs, and has the same behavior. Returns a string array the same size as the scalar expansion of its inputs. See also: string.strcat  File: tablicious.info, Node: string.regexprep, Next: string.reverse, Prev: string.plus, Up: string 8.2.53.17 string.regexprep .......................... -- Method: OUT = regexprep (OBJ, PAT, REPSTR) -- Method: OUT = regexprep (..., VARARGIN) Replace based on regular expression matching. Replaces all the substrings matching a given regexp pattern PAT with the given replacement text REPSTR. Returns a string array of the same size as OBJ.  File: tablicious.info, Node: string.reverse, Next: string.reverse_bytes, Prev: string.regexprep, Up: string 8.2.53.18 string.reverse ........................ -- Method: OUT = reverse (OBJ) Reverse string, character-wise. Reverses the characters in each string in OBJ. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output. Returns a string array the same size as OBJ.  File: tablicious.info, Node: string.reverse_bytes, Next: string.startsWith, Prev: string.reverse, Up: string 8.2.53.19 string.reverse_bytes .............................. -- Method: OUT = reverse_bytes (OBJ) Reverse string, byte-wise. Reverses the bytes in each string in OBJ. This operates on bytes (Unicode code units), not characters. This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence. You probably do not want to use this method. You probably want to use ‘string.reverse’ instead. Returns a string array the same size as OBJ. See also: *note string.reverse::  File: tablicious.info, Node: string.startsWith, Next: string.strcat, Prev: string.reverse_bytes, Up: string 8.2.53.20 string.startsWith ........................... -- Method: OUT = startsWith (OBJ, PATTERN) -- Method: OUT = startsWith (OBJ, PATTERN, 'IgnoreCase', true/false) Test if string starts with pattern. Returns a logical array the size of the scalar expansion of OBJ and PATTERN.  File: tablicious.info, Node: string.strcat, Next: string.strcmp, Prev: string.startsWith, Up: string 8.2.53.21 string.strcat ....................... -- Method: OUT = strcat (VARARGIN) String concatenation. Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays. The semantics of concatenating missing strings with non-missing strings has not been determined yet. Returns a string array the same size as the scalar expansion of its inputs.  File: tablicious.info, Node: string.strcmp, Next: string.strfind, Prev: string.strcat, Up: string 8.2.53.22 string.strcmp ....................... -- Method: OUT = strcmp (A, B) String comparison. Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other. This does the same comparison as ‘A == B’, but is not polymorphic. Generally, there is no reason to use ‘strcmp’ instead of ‘==’ or ‘eq’ on string arrays, unless you want to be compatible with cellstr inputs as well. Returns logical array the size of the scalar expansion of A and B.  File: tablicious.info, Node: string.strfind, Next: string.string, Prev: string.strcmp, Up: string 8.2.53.23 string.strfind ........................ -- Method: OUT = strfind (OBJ, PATTERN) -- Method: OUT = strfind (..., VARARGIN) Find pattern in string. Finds the locations where PATTERN occurs in the strings of OBJ. TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out. Returns either an index vector, or a cell array of index vectors.  File: tablicious.info, Node: string.string, Next: string.strlength, Prev: string.strfind, Up: string 8.2.53.24 string.string ....................... -- Constructor: OBJ = string () -- Constructor: OBJ = string (IN) Construct a new string array. The zero-argument constructor creates a new scalar string array whose value is the empty string. The other constructors construct a new string array by converting various types of inputs. • chars and cellstrs are converted via cellstr() • numerics are converted via num2str() • datetimes are converted via datestr()  File: tablicious.info, Node: string.strlength, Next: string.strlength_bytes, Prev: string.string, Up: string 8.2.53.25 string.strlength .......................... -- Method: OUT = strlength (OBJ) String length in characters (actually, UTF-16 code units). Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each. The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility. This is the string length method you probably want to use, not ‘strlength_bytes’. Returns double array of the same size as OBJ. Returns NaNs for missing strings. See also: *note string.strlength_bytes::  File: tablicious.info, Node: string.strlength_bytes, Next: string.strrep, Prev: string.strlength, Up: string 8.2.53.26 string.strlength_bytes ................................ -- Method: OUT = strlength_bytes (OBJ) String length in bytes. Gets the length of each string in OBJ, counted in Unicode UTF-8 code units (bytes). This is the same as ‘numel(str)’ for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want ‘strlength’ instead. Returns double array of the same size as OBJ. Returns NaNs for missing strings. See also: *note string.strlength::  File: tablicious.info, Node: string.strrep, Next: string.upper, Prev: string.strlength_bytes, Up: string 8.2.53.27 string.strrep ....................... -- Method: OUT = strrep (OBJ, MATCH, REPLACEMENT) -- Method: OUT = strrep (..., VARARGIN) Replace occurrences of pattern with other string. Replaces matching substrings in OBJ with a given replacement string. VARARGIN is passed along to the core Octave ‘strrep’ function. This supports whatever options it does. TODO: Maybe document what those options are. Returns a string array of the same size as OBJ.  File: tablicious.info, Node: string.upper, Prev: string.strrep, Up: string 8.2.53.28 string.upper ...................... -- Method: OUT = upper (OBJ) Convert to upper case. Converts all the characters in all the strings in OBJ to upper case. This currently delegates to Octave’s own ‘upper()’ function to do the conversion, so whatever character class handling it has, this has. Returns a string array of the same size as OBJ.  File: tablicious.info, Node: struct2table, Next: table, Prev: string, Up: API Alphabetically 8.2.54 struct2table ------------------- -- Function: OUT = struct2table (S) -- Function: OUT = struct2table (..., 'AsArray', ASARRAY) Convert struct to a table. Converts the input struct S to a ‘table’. S may be a scalar struct or a nonscalar struct array. The ASARRAY option is not implemented yet. Returns a ‘table’.  File: tablicious.info, Node: table, Next: tableOuterFillValue, Prev: struct2table, Up: API Alphabetically 8.2.55 table ------------ -- Class: table Tabular data array containing multiple columnar variables. A ‘table’ is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous. A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas. A table is an array in itself: its size is NROWS-by-NVARIABLES, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2. A note on accessing properties of a ‘table’ array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do ‘t.Properties.’ for a table ‘t’. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a ‘table’ with a variable named ‘Properties’. Try to avoid that.) WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious. We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn't been fully thought through or tested. See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb -- Instance Variable of table: cellstr VariableNames The names of the variables in the table, as a cellstr row vector. -- Instance Variable of table: cell VariableValues A cell vector containing the values for each of the variables. ‘VariableValues(i)’ corresponds to ‘VariableNames(i)’. -- Instance Variable of table: cellstr RowNames An optional list of row names that identify each row in the table. This is a cellstr column vector, if present. -- Instance Variable of table: cellstr DimensionNames Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to ‘{"Row", "Variables"}’. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that's what Matlab uses, so Tablicious uses it too, for Matlab compatibility.) * Menu: * table.addvars:: * table.antijoin:: * table.cartesian:: * table.convertvars:: * table.findgroups:: * table.getvar:: * table.getvars:: * table.groupby:: * table.height:: * table.horzcat:: * table.innerjoin:: * table.intersect:: * table.isempty:: * table.ismember:: * table.ismissing:: * table.istable:: * table.join:: * table.mergevars:: * table.movevars:: * table.ndims:: * table.numel:: * table.outerfillvals:: * table.outerjoin:: * table.prettyprint:: * table.realjoin:: * table.removevars:: * table.renamevars:: * table.repelem:: * table.repmat:: * table.restrict:: * table.rowfun:: * table.rows2vars:: * table.semijoin:: * table.setdiff:: * table.setDimensionNames:: * table.setRowNames:: * table.setvar:: * table.setVariableNames:: * table.setxor:: * table.size:: * table.splitapply:: * table.splitvars:: * table.squeeze:: * table.stack:: * table.summary:: * table.table:: * table.table2array:: * table.table2cell:: * table.table2struct:: * table.union:: * table.varfun:: * table.varnames:: * table.vertcat:: * table.width::  File: tablicious.info, Node: table.addvars, Next: table.antijoin, Up: table 8.2.55.1 table.addvars ...................... -- Method: OUT = addvars (OBJ, VAR1, ..., VARN) -- Method: OUT = addvars (..., 'Before', BEFORE) -- Method: OUT = addvars (..., 'After', AFTER) -- Method: OUT = addvars (..., 'NewVariableNames', NEWVARIABLENAMES) Add variables to table. Adds the specified variables to a table.  File: tablicious.info, Node: table.antijoin, Next: table.cartesian, Prev: table.addvars, Up: table 8.2.55.2 table.antijoin ....................... -- Method: [OUTA, IXA, OUTB, IXB] = antijoin (A, B) Natural antijoin (AKA “semidifference”). Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input. Returns: OUTA - all the rows in A with no matching row in B IXA - the row indexes into A which produced OUTA OUTB - all the rows in B with no matching row in A IXB - the row indexes into B which produced OUTB This is a Tablicious/Octave extension, not defined in the Matlab table interface.  File: tablicious.info, Node: table.cartesian, Next: table.convertvars, Prev: table.antijoin, Up: table 8.2.55.3 table.cartesian ........................ -- Method: [OUT, IXS] = cartesian (A, B) Cartesian product of two tables. Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B. Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do. The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result. This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct. The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance. This is a Tablicious/Octave extension, not defined in the Matlab table interface.  File: tablicious.info, Node: table.convertvars, Next: table.findgroups, Prev: table.cartesian, Up: table 8.2.55.4 table.convertvars .......................... -- Method: OUT = convertvars (OBJ, VARS, DATATYPE) Convert variables to specified data type. Converts the variables in OBJ specified by VARS to the specified data type. VARS is a cellstr or numeric vector specifying which variables to convert. DATATYPE specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables' current types as input, or a conversion method of that name defined on the specified variables' current type. Returns a table with the same variable names as OBJ, but with converted types.  File: tablicious.info, Node: table.findgroups, Next: table.getvar, Prev: table.convertvars, Up: table 8.2.55.5 table.findgroups ......................... -- Method: [G, TID] = findgroups (OBJ) Find groups within a table’s row values. Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements. Returns: G - A double column vector of group numbers created from OBJ. TID - A table containing the row values corresponding to the group numbers.  File: tablicious.info, Node: table.getvar, Next: table.getvars, Prev: table.findgroups, Up: table 8.2.55.6 table.getvar ..................... -- Method: [OUT, NAME] = getvar (OBJ, VARREF) Get value and name for single table variable. VARREF is a variable reference. It may be a name or an index. It may only specify a single table variable. Returns: OUT – the value of the referenced table variable NAME – the name of the referenced table variable  File: tablicious.info, Node: table.getvars, Next: table.groupby, Prev: table.getvar, Up: table 8.2.55.7 table.getvars ...................... -- Method: [OUT1, ...] = getvars (OBJ, VARREF) Get values for one ore more table variables. VARREF is a variable reference in the form of variable names or indexes. Returns as many outputs as VARREF referenced variables. Each output contains the contents of the corresponding table variable.  File: tablicious.info, Node: table.groupby, Next: table.height, Prev: table.getvars, Up: table 8.2.55.8 table.groupby ...................... -- Method: [OUT] = groupby (OBJ, GROUPVARS, AGGCALCS) Find groups in table data and apply functions to variables within groups. This works like an SQL ‘"SELECT ... GROUP BY ..."’ statement. GROUPVARS (cellstr, numeric) is a list of the grouping variables, identified by name or index. AGGCALCS is a specification of the aggregate calculations to perform on them, in the form ‘{’OUT_VAR‘,’ FCN‘,’ IN_VARS‘; ...}’, where: OUT_VAR (char) is the name of the output variable FCN (function handle) is the function to apply to produce it IN_VARS (cellstr) is a list of the input variables to pass to fcn Returns a table. This is a Tablicious/Octave extension, not defined in the Matlab table interface.  File: tablicious.info, Node: table.height, Next: table.horzcat, Prev: table.groupby, Up: table 8.2.55.9 table.height ..................... -- Method: OUT = height (OBJ) Number of rows in table. For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.  File: tablicious.info, Node: table.horzcat, Next: table.innerjoin, Prev: table.height, Up: table 8.2.55.10 table.horzcat ....................... -- Method: OUT = horzcat (VARARGIN) Horizontal concatenation. Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names. Output has the same RowNames as ‘varargin{1}’. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.  File: tablicious.info, Node: table.innerjoin, Next: table.intersect, Prev: table.horzcat, Up: table 8.2.55.11 table.innerjoin ......................... -- Method: [OUT, IXA, IXB] = innerjoin (A, B) -- Method: [...] = innerjoin (A, B, ...) Combine two tables by rows using key variables. Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output. TODO: Document options. Returns: OUT - A table that is the result of joining A and B IX - Indexes into A for each row in out IXB - Indexes into B for each row in out  File: tablicious.info, Node: table.intersect, Next: table.isempty, Prev: table.innerjoin, Up: table 8.2.55.12 table.intersect ......................... -- Method: [C, IA, IB] = intersect (A, B) Set intersection. Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables. Returns: C - A table containing all the unique row values present in both A and B. IA - Row indexes into A of the rows from A included in C. IB - Row indexes into B of the rows from B included in C.  File: tablicious.info, Node: table.isempty, Next: table.ismember, Prev: table.intersect, Up: table 8.2.55.13 table.isempty ....................... -- Method: OUT = isempty (OBJ) Test whether array is empty. For tables, ‘isempty’ is true if the number of rows is 0 or the number of variables is 0.  File: tablicious.info, Node: table.ismember, Next: table.ismissing, Prev: table.isempty, Up: table 8.2.55.14 table.ismember ........................ -- Method: [TF, LOC] = ismember (A, B) Set membership. Finds rows in A that are members of B. Returns: TF - A logical vector indicating whether each A(i,:) was present in B. LOC - Indexes into B of rows that were found.  File: tablicious.info, Node: table.ismissing, Next: table.istable, Prev: table.ismember, Up: table 8.2.55.15 table.ismissing ......................... -- Method: OUT = ismissing (OBJ) -- Method: OUT = ismissing (OBJ, INDICATOR) Find missing values. Finds missing values in OBJ’s variables. If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables. All variables in this must be vectors. (This is due to the requirement that ‘size(out) == size(obj)’.) Returns a logical array the same size as OBJ.  File: tablicious.info, Node: table.istable, Next: table.join, Prev: table.ismissing, Up: table 8.2.55.16 table.istable ....................... -- Method: TF = istable (OBJ) True if input is a table.  File: tablicious.info, Node: table.join, Next: table.mergevars, Prev: table.istable, Up: table 8.2.55.17 table.join .................... -- Method: [C, IB] = join (A, B) -- Method: [C, IB] = join (A, B, ...) Combine two tables by rows using key variables, in a restricted form. This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join. You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead. See also: *note table.innerjoin::, *note table.outerjoin::  File: tablicious.info, Node: table.mergevars, Next: table.movevars, Prev: table.join, Up: table 8.2.55.18 table.mergevars ......................... -- Method: OUT = mergevars (OBJ, VARS) -- Method: OUT = mergevars (..., 'NewVariableName', NEWVARIABLENAME) -- Method: OUT = mergevars (..., 'MergeAsTable', MERGEASTABLE) Merge table variables into a single variable.  File: tablicious.info, Node: table.movevars, Next: table.ndims, Prev: table.mergevars, Up: table 8.2.55.19 table.movevars ........................ -- Method: OUT = movevars (OBJ, VARS, RELLOCATION, LOCATION) Move around variables in a table. VARS is a list of variables to move, specified by name or index. RELLOCATION is ‘'Before'’ or ‘'After'’. LOCATION indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from OBJ, not the original full list of variables in OBJ. Returns a table with the same variables as OBJ, but in a different order.  File: tablicious.info, Node: table.ndims, Next: table.numel, Prev: table.movevars, Up: table 8.2.55.20 table.ndims ..................... -- Method: OUT = ndims (OBJ) Number of dimensions For tables, ‘ndims(obj)’ is always 2, because table arrays are always 2-D (rows-by-columns).  File: tablicious.info, Node: table.numel, Next: table.outerfillvals, Prev: table.ndims, Up: table 8.2.55.21 table.numel ..................... -- Method: OUT = numel (OBJ) Total number of elements in table (actually 1). For compatibility reasons with Octave's OOP interface and subsasgn behavior, table's numel is defined to always return 1. It is not useful for client code to query a table's size using numel. This is an incompatibility with Matlab.  File: tablicious.info, Node: table.outerfillvals, Next: table.outerjoin, Prev: table.numel, Up: table 8.2.55.22 table.outerfillvals ............................. -- Method: OUT = outerfillvals (OBJ) Get fill values for outer join. Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.  File: tablicious.info, Node: table.outerjoin, Next: table.prettyprint, Prev: table.outerfillvals, Up: table 8.2.55.23 table.outerjoin ......................... -- Method: [OUT, IXA, IXB] = outerjoin (A, B) -- Method: [...] = outerjoin (A, B, ...) Combine two tables by rows using key variables, retaining unmatched rows. Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values. TODO: Document options. Returns: OUT - A table that is the result of the outer join of A and B IXA - indexes into A for each row in out IXB - indexes into B for each row in out  File: tablicious.info, Node: table.prettyprint, Next: table.realjoin, Prev: table.outerjoin, Up: table 8.2.55.24 table.prettyprint ........................... -- Method: prettyprint (OBJ) Display table's values in tabular format. This prints the contents of the table in human-readable, tabular form. Variables which contain objects are displayed using the strings returned by their ‘dispstrs’ method, if they define one.  File: tablicious.info, Node: table.realjoin, Next: table.removevars, Prev: table.prettyprint, Up: table 8.2.55.25 table.realjoin ........................ -- Method: [OUT, IXS] = realjoin (A, B) -- Method: [...] = realjoin (A, B, ...) "Real" relational inner join, without key restrictions Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes. Currently does not support tables which have RowNames. This may be added in the future. This is a Tablicious/Octave extension, not defined in the Matlab table interface. Name/value option arguments are: KEYS, LEFTKEYS, RIGHTKEYS, LEFTVARIABLES, RIGHTVARIABLES. FIXME: Document those options. Returns: OUT - A table that is the result of joining A and B IXS - Indexes into A for each row in out  File: tablicious.info, Node: table.removevars, Next: table.renamevars, Prev: table.realjoin, Up: table 8.2.55.26 table.removevars .......................... -- Method: OUT = removevars (OBJ, VARS) Remove variables from table. Deletes the variables specified by VARS from OBJ. VARS may be a char, cellstr, numeric index vector, or logical index vector.  File: tablicious.info, Node: table.renamevars, Next: table.repelem, Prev: table.removevars, Up: table 8.2.55.27 table.renamevars .......................... -- Method: OUT = renamevars (OBJ, RENAMEMAP) Rename variables in a table. Renames selected variables in the table OBJ based on the mapping provided in RENAMEMAP. RENAMEMAP is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column. Variables which are not included in RENAMEMAP are not modified. It is an error if any variables named in the first column of RENAMEMAP are not present in OBJ. Renames  File: tablicious.info, Node: table.repelem, Next: table.repmat, Prev: table.renamevars, Up: table 8.2.55.28 table.repelem ....................... -- Method: OUT = repelem (OBJ, R) -- Method: OUT = repelem (OBJ, R_1, R_2) Replicate elements of matrix. Replicates elements of this table matrix by applying repelem to each of its variables. This Only two dimensions are supported for ‘repelem’ on tables.  File: tablicious.info, Node: table.repmat, Next: table.restrict, Prev: table.repelem, Up: table 8.2.55.29 table.repmat ...................... -- Method: OUT = repmat (OBJ, SZ) Replicate matrix. Repmats a table by repmatting each of its variables vertically. For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table. Returns a new table with the same variable names and types as tbl, but with a possibly different row count.  File: tablicious.info, Node: table.restrict, Next: table.rowfun, Prev: table.repmat, Up: table 8.2.55.30 table.restrict ........................ -- Method: OUT = restrict (OBJ, EXPR) -- Method: OUT = restrict (OBJ, IX) Subset rows using variable expression or index. Subsets a table row-wise, using either an index vector or an expression involving OBJ’s variables. If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with 'subsetrows (this, index)'.) If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with ‘tblish.evalWithTableVars’. The output of expr must be a numeric or logical index vector (This form is a shorthand for ‘out = subsetrows (this, tblish.evalWithTableVars (this, expr))’.) TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra. Examples: [s,p,sp] = tblish.examples.SpDb; prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")')) This is a Tablicious/Octave extension, not defined in the Matlab table interface. See also: tblish.evalWithTableVars  File: tablicious.info, Node: table.rowfun, Next: table.rows2vars, Prev: table.restrict, Up: table 8.2.55.31 table.rowfun ...................... -- Method: OUT = varfun (FUNC, OBJ) -- Method: OUT = varfun (..., 'OptionName', OPTIONVALUE, ...) Apply function to rows in table and collect outputs. This applies the function FUNC to the elements of each row of OBJ’s variables, and collects the concatenated output(s) into the variable(s) of a new table. FUNC is a function handle. It should take as many inputs as there are variables in OBJ. Or, it can take a single input, and you must specify ‘'SeparateInputs', false’ to have the input variables concatenated before being passed to FUNC. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the ‘'NumOutputs'’ or ‘'OutputVariableNames'’ options. Supported name/value options: ‘'OutputVariableNames'’ Names of table variables to store combined function output arguments in. ‘'NumOutputs'’ Number of output arguments to call function with. If omitted, defaults to number of items in OUTPUTVARIABLENAMES if it is supplied, otherwise defaults to 1. ‘'SeparateInputs'’ If true, input variables are passed as separate input arguments to FUNC. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true. ‘'ErrorHandler'’ A function to call as a fallback when calling FUNC results in an error. It is passed the caught exception, along with the original inputs passed to FUNC, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings. ‘'ExtractCellContents'’ Whether to “pop out” the contents of the elements of cell variables in OBJ, or to leave them as cells. True/false; default is false. If you specify this option, then OBJ may not have any multi-column cell-valued variables. ‘'InputVariables'’ If specified, only these variables from OBJ are used as the function inputs, instead of using all variables. ‘'GroupingVariables'’ Not yet implemented. ‘'OutputFormat'’ The format of the output. May be ‘'table'’ (the default), ‘'uniform'’, or ‘'cell'’. If it is ‘'uniform'’ or ‘'cell'’, the output variables are returned in multiple output arguments from ‘'rowfun'’. Returns a ‘table’ whose variables are the collected output arguments of FUNC if OUTPUTFORMAT is ‘'table'’. Otherwise, returns multiple output arguments of whatever type FUNC returned (if OUTPUTFORMAT is ‘'uniform'’) or cells (if OUTPUTFORMAT is ‘'cell'’).  File: tablicious.info, Node: table.rows2vars, Next: table.semijoin, Prev: table.rowfun, Up: table 8.2.55.32 table.rows2vars ......................... -- Method: OUT = rows2vars (OBJ) -- Method: OUT = rows2vars (OBJ, 'VariableNamesSource', VARIABLENAMESSOURCE) -- Method: OUT = rows2vars (..., 'DataVariables', DATAVARIABLES) Reorient table, swapping rows and variables dimensions. This flips the dimensions of the given table OBJ, swapping the orientation of the contained data, and swapping the row names/labels and variable names. The variable names become a new variable named “OriginalVariableNames”. The row names are drawn from the column VARIABLENAMESSOURCE if it is specified. Otherwise, if OBJ has row names, they are used. Otherwise, new variable names in the form “VarN” are generated. If all the variables in OBJ are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.  File: tablicious.info, Node: table.semijoin, Next: table.setdiff, Prev: table.rows2vars, Up: table 8.2.55.33 table.semijoin ........................ -- Method: [OUTA, IXA, OUTB, IXB] = semijoin (A, B) Natural semijoin. Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names. This method also computes the semijoin of B and A, for convenience. Returns: OUTA - all the rows in A with matching row(s) in B IXA - the row indexes into A which produced OUTA OUTB - all the rows in B with matching row(s) in A IXB - the row indexes into B which produced OUTB This is a Tablicious/Octave extension, not defined in the Matlab table interface.  File: tablicious.info, Node: table.setdiff, Next: table.setDimensionNames, Prev: table.semijoin, Up: table 8.2.55.34 table.setdiff ....................... -- Method: [C, IA] = setdiff (A, B) Set difference. Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B. Returns: C - A table containing the unique row values in A that were not in B. IA - Row indexes into A of the rows from A included in C.  File: tablicious.info, Node: table.setDimensionNames, Next: table.setRowNames, Prev: table.setdiff, Up: table 8.2.55.35 table.setDimensionNames ................................. -- Method: OUT = setDimensionNames (OBJ, NAMES) -- Method: OUT = setDimensionNames (OBJ, IX, NAMES) Set dimension names. Sets the ‘DimensionNames’ for this table to a new list of names. NAMES is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned. IX is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in IX may never be higher than 2. This method exists because the ‘obj.Properties.DimensionNames = ...’ assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the ‘obj.Properties.DimensionNames = ...’ assignment form.  File: tablicious.info, Node: table.setRowNames, Next: table.setvar, Prev: table.setDimensionNames, Up: table 8.2.55.36 table.setRowNames ........................... -- Method: OUT = setRowNames (OBJ, NAMES) Set row names. Sets the row names on OBJ to NAMES. NAMES is a cellstr column vector, with the same number of rows as OBJ has.  File: tablicious.info, Node: table.setvar, Next: table.setVariableNames, Prev: table.setRowNames, Up: table 8.2.55.37 table.setvar ...................... -- Method: OUT = setvar (OBJ, VARREF, VALUE) Set value for a variable in table. This sets (adds or replaces) the value for a variable in OBJ. It may be used to change the value of an existing variable, or add a new variable. This method exists primarily because I cannot get ‘obj.foo = value’ to work, apparently due to an issue with Octave's subsasgn support. VARREF is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index. VALUE is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in OBJ.  File: tablicious.info, Node: table.setVariableNames, Next: table.setxor, Prev: table.setvar, Up: table 8.2.55.38 table.setVariableNames ................................ -- Method: OUT = setVariableNames (OBJ, NAMES) -- Method: OUT = setVariableNames (OBJ, IX, NAMES) Set variable names. Sets the ‘VariableNames’ for this table to a new list of names. NAMES is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned. IX is an index vector indicating which variable names to set. If omitted, it sets all of them present in OBJ. This method exists because the ‘obj.Properties.VariableNames = ...’ assignment form does not work, possibly due to an Octave bug.  File: tablicious.info, Node: table.setxor, Next: table.size, Prev: table.setVariableNames, Up: table 8.2.55.39 table.setxor ...................... -- Method: [C, IA, IB] = setxor (A, B) Set exclusive OR. Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both. Returns: C - A table containing all the unique row values in the set XOR of A and B. IA - Row indexes into A of the rows from A included in C. IB - Row indexes into B of the rows from B included in C.  File: tablicious.info, Node: table.size, Next: table.splitapply, Prev: table.setxor, Up: table 8.2.55.40 table.size .................... -- Method: SZ = size (OBJ) -- Method: [NR, NV] = size (OBJ) -- Method: [NR, NV, ...] = size (OBJ) Gets the size of a table. For tables, the size is [number-of-rows x number-of-variables]. This is the same as ‘[height(obj), width(obj)]’.  File: tablicious.info, Node: table.splitapply, Next: table.splitvars, Prev: table.size, Up: table 8.2.55.41 table.splitapply .......................... -- Method: OUT = splitapply (FUNC, OBJ, G) -- Method: [Y1, ..., YM] = splitapply (FUNC, OBJ, G) Split table data into groups and apply function. Performs a splitapply, using the variables in OBJ as the input X variables to the ‘splitapply’ function call. See also: splitapply, table.groupby, tblish.table.grpstats  File: tablicious.info, Node: table.splitvars, Next: table.squeeze, Prev: table.splitapply, Up: table 8.2.55.42 table.splitvars ......................... -- Method: OUT = splitvars (OBJ) -- Method: OUT = splitvars (OBJ, VARS) -- Method: OUT = splitvars (..., 'NewVariableNames', NEWVARIABLENAMES) Split multicolumn table variables. Splits multicolumn table variables into new single-column variables. If VARS is supplied, splits only those variables. If VARS is not supplied, splits all multicolumn variables.  File: tablicious.info, Node: table.squeeze, Next: table.stack, Prev: table.splitvars, Up: table 8.2.55.43 table.squeeze ....................... -- Method: OBJ = squeeze (OBJ) Remove singleton dimensions. For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.  File: tablicious.info, Node: table.stack, Next: table.summary, Prev: table.squeeze, Up: table 8.2.55.44 table.stack ..................... -- Method: OUT = stack (OBJ, VARS) -- Method: OUT = stack (..., 'NewDataVariableName', NEWDATAVARIABLENAME) -- Method: OUT = stack (..., 'IndexVariableName', INDEXVARIABLENAME) Stack multiple table variables into a single variable.  File: tablicious.info, Node: table.summary, Next: table.table, Prev: table.stack, Up: table 8.2.55.45 table.summary ....................... -- Method: summary (OBJ) Display a summary of a table's data. Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use. This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of 'summary', Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.  File: tablicious.info, Node: table.table, Next: table.table2array, Prev: table.summary, Up: table 8.2.55.46 table.table ..................... -- Constructor: OBJ = table () Constructs a new empty (0 rows by 0 variables) table. -- Constructor: OBJ = table (VAR1, VAR2, ..., VARN) Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used. Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value '__tblish_backdoor__', that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious. -- Constructor: OBJ = table ('Size', SZ, 'VariableTypes', VARTYPES) Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type. -- Constructor: OBJ = table (..., 'VariableNames', VARNAMES) -- Constructor: OBJ = table (..., 'RowNames', ROWNAMES) Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.  File: tablicious.info, Node: table.table2array, Next: table.table2cell, Prev: table.table, Up: table 8.2.55.47 table.table2array ........................... -- Method: S = table2struct (OBJ) Converts OBJ to a homogeneous array.  File: tablicious.info, Node: table.table2cell, Next: table.table2struct, Prev: table.table2array, Up: table 8.2.55.48 table.table2cell .......................... -- Method: C = table2cell (OBJ) Converts table to a cell array. Each variable in OBJ becomes one or more columns in the output, depending on how many columns that variable has. Returns a cell array with the same number of rows as OBJ, and with as many or more columns as OBJ has variables.  File: tablicious.info, Node: table.table2struct, Next: table.union, Prev: table.table2cell, Up: table 8.2.55.49 table.table2struct ............................ -- Method: S = table2struct (OBJ) -- Method: S = table2struct (..., 'ToScalar', TRUEORFALSE) Converts OBJ to a scalar structure or structure array. Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, 'ToScalar', true); s.RowNames = tbl.Properties.RowNames; Returns a scalar struct or struct array, depending on the value of the ‘ToScalar’ option.  File: tablicious.info, Node: table.union, Next: table.varfun, Prev: table.table2struct, Up: table 8.2.55.50 table.union ..................... -- Method: [C, IA, IB] = union (A, B) Set union. Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables. Returns: C - A table containing all the unique row values present in A or B. IA - Row indexes into A of the rows from A included in C. IB - Row indexes into B of the rows from B included in C.  File: tablicious.info, Node: table.varfun, Next: table.varnames, Prev: table.union, Up: table 8.2.55.51 table.varfun ...................... -- Method: OUT = varfun (FCN, OBJ) -- Method: OUT = varfun (..., 'OutputFormat', OUTPUTFORMAT) -- Method: OUT = varfun (..., 'InputVariables', VARS) -- Method: OUT = varfun (..., 'ErrorHandler', ERRORFCN) Apply function to table variables. Applies the given function FCN to each variable in OBJ, collecting the output in a table, cell array, or array of another type.  File: tablicious.info, Node: table.varnames, Next: table.vertcat, Prev: table.varfun, Up: table 8.2.55.52 table.varnames ........................ -- Method: OUT = varnames (OBJ) -- Method: OUT = varnames (OBJ, VARNAMES) Get or set variable names for a table. Returns cellstr in the getter form. Returns an updated datetime in the setter form.  File: tablicious.info, Node: table.vertcat, Next: table.width, Prev: table.varnames, Up: table 8.2.55.53 table.vertcat ....................... -- Method: OUT = vertcat (VARARGIN) Vertical concatenation. Combines tables by vertically concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a 'vertcat()' on the variables from the corresponding input tables, in the order they were input in.  File: tablicious.info, Node: table.width, Prev: table.vertcat, Up: table 8.2.55.54 table.width ..................... -- Method: OUT = width (OBJ) Number of variables in table. Note that this is not the sum of the number of columns in each variable. It is just the number of variables.  File: tablicious.info, Node: tableOuterFillValue, Next: tail, Prev: table, Up: API Alphabetically 8.2.56 tableOuterFillValue -------------------------- _Not documented_  File: tablicious.info, Node: tail, Next: tblish.dataset, Prev: tableOuterFillValue, Up: API Alphabetically 8.2.57 tail ----------- -- Function: OUT = tail (A) -- Function: OUT = tail (A, K) Get last K rows of an array. Returns the array A, subsetted to its last K rows. This means subsetting it to the last ‘(min (k, size (A, 1)))’ elements along dimension 1, and leaving all other dimensions unrestricted. A is the array to subset. K is the number of rows to get. K defaults to 8 if it is omitted or empty. If there are less than K rows in A, returns all rows. Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type. See also: head  File: tablicious.info, Node: tblish.dataset, Next: tblish.datasets, Prev: tail, Up: API Alphabetically 8.2.58 tblish.dataset --------------------- -- Class: tblish.dataset The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious. This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list. * Menu: * tblish.dataset.airmiles:: * tblish.dataset.AirPassengers:: * tblish.dataset.airquality:: * tblish.dataset.anscombe:: * tblish.dataset.attenu:: * tblish.dataset.attitude:: * tblish.dataset.austres:: * tblish.dataset.beavers:: * tblish.dataset.BJsales:: * tblish.dataset.BOD:: * tblish.dataset.cars:: * tblish.dataset.ChickWeight:: * tblish.dataset.chickwts:: * tblish.dataset.co2:: * tblish.dataset.crimtab:: * tblish.dataset.cupcake:: * tblish.dataset.discoveries:: * tblish.dataset.DNase:: * tblish.dataset.esoph:: * tblish.dataset.euro:: * tblish.dataset.eurodist:: * tblish.dataset.EuStockMarkets:: * tblish.dataset.faithful:: * tblish.dataset.Formaldehyde:: * tblish.dataset.freeny:: * tblish.dataset.HairEyeColor:: * tblish.dataset.Harman23cor:: * tblish.dataset.Harman74cor:: * tblish.dataset.Indometh:: * tblish.dataset.infert:: * tblish.dataset.InsectSprays:: * tblish.dataset.iris:: * tblish.dataset.islands:: * tblish.dataset.JohnsonJohnson:: * tblish.dataset.LakeHuron:: * tblish.dataset.lh:: * tblish.dataset.LifeCycleSavings:: * tblish.dataset.Loblolly:: * tblish.dataset.longley:: * tblish.dataset.lynx:: * tblish.dataset.morley:: * tblish.dataset.mtcars:: * tblish.dataset.nhtemp:: * tblish.dataset.Nile:: * tblish.dataset.nottem:: * tblish.dataset.npk:: * tblish.dataset.occupationalStatus:: * tblish.dataset.Orange:: * tblish.dataset.OrchardSprays:: * tblish.dataset.PlantGrowth:: * tblish.dataset.precip:: * tblish.dataset.presidents:: * tblish.dataset.pressure:: * tblish.dataset.Puromycin:: * tblish.dataset.quakes:: * tblish.dataset.randu:: * tblish.dataset.rivers:: * tblish.dataset.rock:: * tblish.dataset.sleep:: * tblish.dataset.stackloss:: * tblish.dataset.state:: * tblish.dataset.sunspot_month:: * tblish.dataset.sunspot_year:: * tblish.dataset.sunspots:: * tblish.dataset.swiss:: * tblish.dataset.Theoph:: * tblish.dataset.Titanic:: * tblish.dataset.ToothGrowth:: * tblish.dataset.treering:: * tblish.dataset.trees:: * tblish.dataset.UCBAdmissions:: * tblish.dataset.UKDriverDeaths:: * tblish.dataset.UKgas:: * tblish.dataset.UKLungDeaths:: * tblish.dataset.USAccDeaths:: * tblish.dataset.USArrests:: * tblish.dataset.USJudgeRatings:: * tblish.dataset.USPersonalExpenditure:: * tblish.dataset.uspop:: * tblish.dataset.VADeaths:: * tblish.dataset.volcano:: * tblish.dataset.warpbreaks:: * tblish.dataset.women:: * tblish.dataset.WorldPhones:: * tblish.dataset.WWWusage:: * tblish.dataset.zCO2::  File: tablicious.info, Node: tblish.dataset.airmiles, Next: tblish.dataset.AirPassengers, Up: tblish.dataset 8.2.58.1 tblish.dataset.airmiles ................................ -- Static Method: OUT = airmiles () Passenger Miles on Commercial US Airlines, 1937-1960 Description ........... The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960. Source ...... ‘F.A.A. Statistical Handbook of Aviation’. Examples ........ t = tblish.dataset.airmiles; plot (t.year, t.miles); title ("airmiles data"); xlabel ("Passenger-miles flown by U.S. commercial airlines") ylabel ("airmiles");  File: tablicious.info, Node: tblish.dataset.AirPassengers, Next: tblish.dataset.airquality, Prev: tblish.dataset.airmiles, Up: tblish.dataset 8.2.58.2 tblish.dataset.AirPassengers ..................................... -- Static Method: OUT = AirPassengers () Monthly Airline Passenger Numbers 1949-1960 Description ........... The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960. Source ...... Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). ‘Time Series Analysis, Forecasting and Control’. Third Edition. San Francisco: Holden-Day. Series G. Examples ........ ## TODO: This example needs to be ported from R.  File: tablicious.info, Node: tblish.dataset.airquality, Next: tblish.dataset.anscombe, Prev: tblish.dataset.AirPassengers, Up: tblish.dataset 8.2.58.3 tblish.dataset.airquality .................................. -- Static Method: OUT = airquality () New York Air Quality Measurements from 1973 Description ........... Daily air quality measurements in New York, May to September 1973. Format ...... ‘Ozone’ Ozone concentration (ppb) ‘SolarR’ Solar R (lang) ‘Wind’ Wind (mph) ‘Temp’ Temperature (degrees F) ‘Month’ Month (1-12) ‘Day’ Day of month (1-31) Source ...... New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data). References .......... Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). ‘Graphical Methods for Data Analysis’. Belmont, CA: Wadsworth. Examples ........ t = tblish.dataset.airquality # Plot a scatter-plot plus a fitted line, for each combination of measurements vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"}; n_vars = numel (vars); figure; for i = 1:n_vars for j = 1:n_vars if (i == j) continue endif ix_subplot = (n_vars * (j - 1) + i); hax = subplot (n_vars, n_vars, ix_subplot); var_x = vars{i}; var_y = vars{j}; x = t.(var_x); y = t.(var_y); scatter (hax, x, y, 10); # Fit a cubic line to these points # TODO: Find out exactly what kind of fitted line R's example is using, and # port that. hold on p = polyfit (x, y, 3); x_hat = unique(x); p_y = polyval (p, x_hat); plot (hax, x_hat, p_y, "r"); endfor endfor  File: tablicious.info, Node: tblish.dataset.anscombe, Next: tblish.dataset.attenu, Prev: tblish.dataset.airquality, Up: tblish.dataset 8.2.58.4 tblish.dataset.anscombe ................................ -- Static Method: OUT = anscombe () Anscombe’s Quartet of “Identical” Simple Linear Regressions Description ........... Four sets of x/y pairs which have the same statistical properties, but are very different. Format ...... The data comes in an array of 4 structs, each with fields as follows: ‘x’ The X values for this pair. ‘y’ The Y values for this pair. Source ...... Tufte, Edward R. (1989). ‘The Visual Display of Quantitative Information’. 13–14. Cheshire, CT: Graphics Press. References .......... Anscombe, Francis J. (1973). Graphs in statistical analysis. ‘The American Statistician’, 27, 17–21. Examples ........ data = tblish.dataset.anscombe # Pick good limits for the plots all_x = [data.x]; all_y = [data.y]; x_limits = [min(0, min(all_x)) max(all_x)*1.2]; y_limits = [min(0, min(all_y)) max(all_y)*1.2]; # Do regression on each pair and plot the input and results figure; haxs = NaN (1, 4); for i_pair = 1:4 x = data(i_pair).x; y = data(i_pair).y; # TODO: Port the anova and other characterizations from the R code # TODO: Do a linear regression and plot its line hax = subplot (2, 2, i_pair); haxs(i_pair) = hax; xlabel (sprintf ("x%d", i_pair)); ylabel (sprintf ("y%d", i_pair)); scatter (x, y, "r"); endfor # Fiddle with the plot axes parameters linkaxes (haxs); xlim (haxs(1), x_limits); ylim (haxs(1), y_limits);  File: tablicious.info, Node: tblish.dataset.attenu, Next: tblish.dataset.attitude, Prev: tblish.dataset.anscombe, Up: tblish.dataset 8.2.58.5 tblish.dataset.attenu .............................. -- Static Method: OUT = attenu () Joyner-Boore Earthquake Attenuation Data Description ........... Event data for 23 earthquakes in California, showing peak accelerations. Format ...... ‘event’ Event number ‘mag’ Moment magnitude ‘station’ Station identifier ‘dist’ Station-hypocenter distance (km) ‘accel’ Peak acceleration (g) Source ...... Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA. References .......... Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. ‘Bulletin of the Seismological Society of America’, 72, S269–S268. Examples ........ # TODO: Port the example code from R # It does coplot() and pairs(), which are higher-level plotting tools # than core Octave provides. This could turn into a long example if we # just use base Octave here.  File: tablicious.info, Node: tblish.dataset.attitude, Next: tblish.dataset.austres, Prev: tblish.dataset.attenu, Up: tblish.dataset 8.2.58.6 tblish.dataset.attitude ................................ -- Static Method: OUT = attitude () The Chatterjee-Price Attitude Data Description ........... Aggregated data from a survey of clerical employees at a large financial organization. Format ...... ‘rating’ Overall rating. ‘complaints’ Handling of employee complaints. ‘privileges’ Does not allow special privileges. ‘learning’ Opportunity to learn. ‘raises’ Raises based on performance. ‘critical’ Too critical. ‘advance’ Advancement. Source ...... Chatterjee, S. and Price, B. (1977). ‘Regression Analysis by Example’. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).) Examples ........ t = tblish.dataset.attitude tblish.examples.plot_pairs (t); # TODO: Display table summary # TODO: Whatever those statistical linear-model plots are that R is doing  File: tablicious.info, Node: tblish.dataset.austres, Next: tblish.dataset.beavers, Prev: tblish.dataset.attitude, Up: tblish.dataset 8.2.58.7 tblish.dataset.austres ............................... -- Static Method: OUT = austres () Australian Population Description ........... Numbers of Australian residents measured quarterly from March 1971 to March 1994. Format ...... ‘date’ The month of the observation. ‘residents’ The number of residents. Source ...... Brockwell, P. J. and Davis, R. A. (1996). ‘Introduction to Time Series and Forecasting’. New York: Springer-Verlag. Examples ........ t = tblish.dataset.austres plot (datenum (t.date), t.residents); datetick x xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");  File: tablicious.info, Node: tblish.dataset.beavers, Next: tblish.dataset.BJsales, Prev: tblish.dataset.austres, Up: tblish.dataset 8.2.58.8 tblish.dataset.beavers ............................... -- Static Method: OUT = beavers () Body Temperature Series of Two Beavers Description ........... Body temperature readings for two beavers. Format ...... ‘day’ Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2). ‘time’ Time of observation, in the form 0330 for 3:30am ‘temp’ Measured body temperature in degrees Celsius. ‘activ’ Indicator of activity outside the retreat. Source ...... P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) ‘Case Studies in Biometry’. New York: John Wiley and Sons. Examples ........ # TODO: This example needs to be ported from R.  File: tablicious.info, Node: tblish.dataset.BJsales, Next: tblish.dataset.BOD, Prev: tblish.dataset.beavers, Up: tblish.dataset 8.2.58.9 tblish.dataset.BJsales ............................... -- Static Method: OUT = BJsales () Sales Data with Leading Indicator Description ........... Sales Data with Leading Indicator Format ...... ‘record’ Index of the record. ‘lead’ Leading indicator. ‘sales’ Sales volume. Source ...... The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at . References .......... Box, G. E. P. and Jenkins, G. M. (1976). ‘Time Series Analysis, Forecasting and Control’. San Francisco: Holden-Day. p. 537. Brockwell, P. J. and Davis, R. A. (1991). ‘Time Series: Theory and Methods’, Second edition. New York: Springer-Verlag. p. 414. Examples ........ # TODO: Come up with example code here  File: tablicious.info, Node: tblish.dataset.BOD, Next: tblish.dataset.cars, Prev: tblish.dataset.BJsales, Up: tblish.dataset 8.2.58.10 tblish.dataset.BOD ............................ -- Static Method: OUT = BOD () Biochemical Oxygen Demand Description ........... Contains biochemical oxygen demand versus time in an evaluation of water quality. Format ...... ‘Time’ Time of the measurement (in days). ‘demand’ Biochemical oxygen demand (mg/l). Source ...... Bates, D.M. and Watts, D.G. (1988). ‘Nonlinear Regression Analysis and Its Applications’. New York: John Wiley & Sons. Appendix A1.4. Originally from: Marske (1967). ‘Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface’, M.Sc. Thesis, University of Wisconsin – Madison. Examples ........ # TODO: Port this example from R  File: tablicious.info, Node: tblish.dataset.cars, Next: tblish.dataset.ChickWeight, Prev: tblish.dataset.BOD, Up: tblish.dataset 8.2.58.11 tblish.dataset.cars ............................. -- Static Method: OUT = cars () Speed and Stopping Distances of Cars Description ........... Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s. Format ...... ‘speed’ Speed (mph). ‘dist’ Stopping distance (ft). Source ...... Ezekiel, M. (1930). ‘Methods of Correlation Analysis’. New York: Wiley. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.cars; # TODO: Add Lowess smoothed lines to the plots figure; plot (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars data"); figure; loglog (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars data (logarithmic scales)"); # TODO: Do the linear model plot # Polynomial regression figure; plot (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars polynomial regressions"); hold on xlim ([0 25]); x2 = linspace (0, 25, 200); for degree = 1:4 [P, S, mu] = polyfit (t.speed, t.dist, degree); y2 = polyval(P, x2, [], mu); plot (x2, y2); endfor  File: tablicious.info, Node: tblish.dataset.ChickWeight, Next: tblish.dataset.chickwts, Prev: tblish.dataset.cars, Up: tblish.dataset 8.2.58.12 tblish.dataset.ChickWeight .................................... -- Static Method: OUT = ChickWeight () Weight versus age of chicks on different diets Format ...... ‘weight’ a numeric vector giving the body weight of the chick (gm). ‘Time’ a numeric vector giving the number of days since birth when the measurement was made. ‘Chick’ an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet. ‘Diet’ a factor with levels 1, ..., 4 indicating which experimental diet the chick received. Source ...... Crowder, M. and Hand, D. (1990). ‘Analysis of Repeated Measures’. London: Chapman and Hall. (example 5.3) Hand, D. and Crowder, M. (1996), ‘Practical Longitudinal Data Analysis’. London: Chapman and Hall. (table A.2) Pinheiro, J. C. and Bates, D. M. (2000) ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. Examples ........ t = tblish.dataset.ChickWeight tblish.examples.coplot (t, "Time", "weight", "Chick");  File: tablicious.info, Node: tblish.dataset.chickwts, Next: tblish.dataset.co2, Prev: tblish.dataset.ChickWeight, Up: tblish.dataset 8.2.58.13 tblish.dataset.chickwts ................................. -- Static Method: OUT = chickwts () Chicken Weights by Feed Type Description ........... An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens. Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types. Format ...... ‘weight’ Chick weight at six weeks (gm). ‘feed’ Feed type. Source ...... Anonymous (1948) ‘Biometrika’, 35, 214. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ # This example requires the statistics package from Octave Forge t = tblish.dataset.chickwts # Boxplot by group figure g = groupby (t, "feed", { "weight", @(x) {x}, "weight" }); boxplot (g.weight, 1); xlabel ("feed"); ylabel ("Weight at six weeks (gm)"); xticklabels ([{""} cellstr(g.feed')]); # Linear model # TODO: This linear model thing and anova  File: tablicious.info, Node: tblish.dataset.co2, Next: tblish.dataset.crimtab, Prev: tblish.dataset.chickwts, Up: tblish.dataset 8.2.58.14 tblish.dataset.co2 ............................ -- Static Method: OUT = co2 () Mauna Loa Atmospheric CO2 Concentration Description ........... Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997. Format ...... ‘date’ Date of the month of the observation, as datetime. ‘co2’ CO2 concentration (ppm). Details ....... The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964. Source ...... Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220. . References .......... Cleveland, W. S. (1993). ‘Visualizing Data’. New Jersey: Summit Press. Examples ........ t = tblish.dataset.co2; plot (datenum (t.date), t.co2); datetick ("x"); xlabel ("Time"); ylabel ("Atmospheric concentration of CO2"); title ("co2 data set");  File: tablicious.info, Node: tblish.dataset.crimtab, Next: tblish.dataset.cupcake, Prev: tblish.dataset.co2, Up: tblish.dataset 8.2.58.15 tblish.dataset.crimtab ................................ -- Static Method: OUT = crimtab () Student’s 3000 Criminals Data Description ........... Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales. Format ...... This dataset contains three separate variables. The ‘finger_length’ and ‘body_height’ variables correspond to the rows and columns of the ‘count’ matrix. ‘finger_length’ Midpoints of intervals of finger lengths (cm). ‘body_height’ Body heights (cm). ‘count’ Number of prisoners in this bin. Details ....... Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations: “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.” The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4' 7"9/16 - 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4' 8"). MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.” Source ...... thanks to Jean R. Lobry and Anne-Béatrice Dufour. References .......... Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. ‘The Journal of the Anthropological Institute of Great Britain and Ireland’, 30, 161–198. MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. ‘Biometrika’, 1(2), 177–227. Student (1908). The probable error of a mean. ‘Biometrika’, 6, 1–25. Examples ........ # TODO: Port this from R  File: tablicious.info, Node: tblish.dataset.cupcake, Next: tblish.dataset.discoveries, Prev: tblish.dataset.crimtab, Up: tblish.dataset 8.2.58.16 tblish.dataset.cupcake ................................ -- Static Method: OUT = cupcake () Google Search popularity for "cupcake", 2004-2019 Description ........... Monthly popularity of worldwide Google search results for "cupcake", 2004-2019. Format ...... ‘Month’ Month when searches took place ‘Cupcake’ An indicator of search volume, in unknown units Source ...... Google Trends, , retrieved 2019-05-04 by Andrew Janke. Examples ........ t = tblish.dataset.cupcake plot (datenum (t.Month), t.Cupcake) title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");  File: tablicious.info, Node: tblish.dataset.discoveries, Next: tblish.dataset.DNase, Prev: tblish.dataset.cupcake, Up: tblish.dataset 8.2.58.17 tblish.dataset.discoveries .................................... -- Static Method: OUT = discoveries () Yearly Numbers of Important Discoveries Description ........... The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959. Format ...... ‘year’ Year. ‘discoveries’ Number of “great” discoveries that year. Source ...... ‘The World Almanac and Book of Facts’, 1975 Edition, pages 315–318. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.discoveries; plot (t.year, t.discoveries); xlabel ("Time"); ylabel ("Number of important discoveries"); title ("discoveries data set");  File: tablicious.info, Node: tblish.dataset.DNase, Next: tblish.dataset.esoph, Prev: tblish.dataset.discoveries, Up: tblish.dataset 8.2.58.18 tblish.dataset.DNase .............................. -- Static Method: OUT = DNase () Elisa assay of DNase Description ........... Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum. Format ...... ‘Run’ Ordered ‘categorical’ indicating the assay run. ‘conc’ Known concentration of the protein (ng/ml). ‘density’ Measured optical density in the assay (dimensionless). Source ...... Davidian, M. and Giltinan, D. M. (1995). ‘Nonlinear Models for Repeated Measurement Data’. London: Chapman & Hall. (section 5.2.4, p. 134) Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. Examples ........ t = tblish.dataset.DNase; # TODO: Port this from R tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter); tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ... "PlotArgs", {"o"});  File: tablicious.info, Node: tblish.dataset.esoph, Next: tblish.dataset.euro, Prev: tblish.dataset.DNase, Up: tblish.dataset 8.2.58.19 tblish.dataset.esoph .............................. -- Static Method: OUT = esoph () Smoking, Alcohol and Esophageal Cancer Description ........... Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France. Format ...... ‘item’ Age group (years). ‘alcgp’ Alcohol consumption (gm/day). ‘tobgp’ Tobacco consumption (gm/day). ‘ncases’ Number of cases. ‘ncontrols’ Number of controls Source ...... Breslow, N. E. and Day, N. E. (1980) ‘Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies’. Oxford: IARC Lyon / Oxford University Press. Examples ........ # TODO: Port this from R # TODO: Port the anova output # TODO: Port the fancy plot # This involves a "mosaic plot", which is not supported by Octave, so this will # take some work.  File: tablicious.info, Node: tblish.dataset.euro, Next: tblish.dataset.eurodist, Prev: tblish.dataset.esoph, Up: tblish.dataset 8.2.58.20 tblish.dataset.euro ............................. -- Static Method: OUT = euro () Conversion Rates of Euro Currencies Description ........... Conversion rates between the various Euro currencies. Format ...... This data comes in two separate variables. ‘euro’ An 11-long vector of the value of 1 Euro in all participating currencies. ‘euro_cross’ An 11-by-11 matrix of conversion rates between various Euro currencies. ‘euro_date’ The date upon which these Euro conversion rates were fixed. Details ....... The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits. Source ...... Unknown. This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source. Examples ........ # TODO: Port this from R # TODO: Example conversion # TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa  File: tablicious.info, Node: tblish.dataset.eurodist, Next: tblish.dataset.EuStockMarkets, Prev: tblish.dataset.euro, Up: tblish.dataset 8.2.58.21 tblish.dataset.eurodist ................................. -- Static Method: OUT = eurodist () Distances Between European Cities and Between US Cities Description ........... ‘eurodist’ gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia. ‘UScitiesD’ gives “straight line” distances between 10 cities in the US. Format ...... ‘eurodist’ ????? TODO: Finish this. Source ...... Crystal, D. Ed. (1990). ‘The Cambridge Encyclopaedia’. Cambridge: Cambridge University Press. The US cities distances were provided by Pierre Legendre. Examples ........  File: tablicious.info, Node: tblish.dataset.EuStockMarkets, Next: tblish.dataset.faithful, Prev: tblish.dataset.eurodist, Up: tblish.dataset 8.2.58.22 tblish.dataset.EuStockMarkets ....................................... -- Static Method: OUT = EuStockMarkets () Daily Closing Prices of Major European Stock Indices Description ........... Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted. Format ...... A multivariate time series with 1860 observations on 4 variables. The starting date is the 130th day of 1991, with a frequency of 260 observations per year. Source ...... The data were kindly provided by Erste Bank AG, Vienna, Austria. Examples ........ t = tblish.dataset.EuStockMarkets; # The fact that we're doing this munging means that table might have # been the wrong structure for this data in the first place t2 = removevars (t, "day"); index_names = t2.Properties.VariableNames; day = 1:height (t2); price = table2array (t2); price0 = price(1,:); rel_price = price ./ repmat (price0, [size(price, 1) 1]); figure; plot (day, rel_price); legend (index_names); xlabel ("Business day"); ylabel ("Relative price");  File: tablicious.info, Node: tblish.dataset.faithful, Next: tblish.dataset.Formaldehyde, Prev: tblish.dataset.EuStockMarkets, Up: tblish.dataset 8.2.58.23 tblish.dataset.faithful ................................. -- Static Method: OUT = faithful () Old Faithful Geyser Data Description ........... Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA. Format ...... ‘eruptions’ Eruption time (mins). ‘waiting’ Waiting time to next eruption (mins). Source ...... W. Härdle. References .......... Härdle, W. (1991). ‘Smoothing Techniques with Implementation in S’. New York: Springer. Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. ‘Applied Statistics’, 39, 357–365. Examples ........ t = tblish.dataset.faithful; # Munge the data, rounding eruption time to the second e60 = 60 * t.eruptions; ne60 = round (e60); # TODO: Port zapsmall to Octave eruptions = ne60 / 60; # TODO: Display mean relative difference and bins summary # Histogram of rounded eruption times figure hist (ne60, max (ne60)) xlabel ("Eruption time (sec)") ylabel ("n") title ("faithful data: Eruptions of Old Faithful") # Scatter plot of eruption time vs waiting time figure scatter (t.eruptions, t.waiting) xlabel ("Eruption time (min)") ylabel ("Waiting time to next eruption (min)") title ("faithful data: Eruptions of Old Faithful") # TODO: Port Lowess smoothing to Octave  File: tablicious.info, Node: tblish.dataset.Formaldehyde, Next: tblish.dataset.freeny, Prev: tblish.dataset.faithful, Up: tblish.dataset 8.2.58.24 tblish.dataset.Formaldehyde ..................................... -- Static Method: OUT = Formaldehyde () Determination of Formaldehyde Description ........... These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer. Format ...... ‘record’ Observation record number. ‘carb’ Carbohydrate (ml). ‘optden’ Optical Density Source ...... Bennett, N. A. and N. L. Franklin (1954). ‘Statistical Analysis in Chemistry and the Chemical Industry’. New York: Wiley. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.Formaldehyde; figure scatter (t.carb, t.optden) # TODO: Add a linear model line xlabel ("Carbohydrate (ml)") ylabel ("Optical Density") title ("Formaldehyde data") # TODO: Add linear model summary output # TOD: Add linear model summary plot  File: tablicious.info, Node: tblish.dataset.freeny, Next: tblish.dataset.HairEyeColor, Prev: tblish.dataset.Formaldehyde, Up: tblish.dataset 8.2.58.25 tblish.dataset.freeny ............................... -- Static Method: OUT = freeny () Freeny's Revenue Data Description ........... Freeny’s data on quarterly revenue and explanatory variables. Format ...... Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential). ‘date’ Start date of the quarter for the observation. ‘y’ Observed quarterly revenue. TODO: Determine units (probably millions of USD?) ‘lag_quarterly_revenue’ Quarterly revenue (‘y’), lagged 1 quarter. ‘price_index’ A price index ‘income_level’ ??? TODO: Fill this in ‘market_potential’ ??? TODO: Fill this in Source ...... Freeny, A. E. (1977). ‘A Portable Linear Regression Package with Test Programs’. Bell Laboratories memorandum. References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Examples ........ t = tblish.dataset.freeny; summary (t) tblish.examples.plot_pairs (removevars (t, "date")) # TODO: Create linear model and print summary # TODO: Linear model plot  File: tablicious.info, Node: tblish.dataset.HairEyeColor, Next: tblish.dataset.Harman23cor, Prev: tblish.dataset.freeny, Up: tblish.dataset 8.2.58.26 tblish.dataset.HairEyeColor ..................................... -- Static Method: OUT = HairEyeColor () Hair and Eye Color of Statistics Students Description ........... Distribution of hair and eye color and sex in 592 statistics students. Format ...... This data set comes in multiple variables ‘n’ A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex. ‘hair’ Hair colors for the indexes along dimension 1. ‘eye’ Eye colors for the indexes along dimension 2. ‘sex’ Sexes for the indexes along dimension 3. Details ....... The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes. This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots. Source ...... Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000). References .......... Snee, R. D. (1974). Graphical display of two-way contingency tables. ‘The American Statistician’, 28, 9–12. Friendly, M. (1992a). Graphical methods for categorical data. ‘SAS User Group International Conference Proceedings’, 17, 190–200. Friendly, M. (1992b). Mosaic displays for loglinear models. ‘Proceedings of the Statistical Graphics Section’, American Statistical Association, pp. 61–68. Friendly, M. (2000). ‘Visualizing Categorical Data’. SAS Institute, ISBN 1-58025-660-0. Examples ........ tblish.dataset.HairEyeColor # TODO: Aggregate over sex and display a table of counts # TODO: Port mosaic plot to Octave  File: tablicious.info, Node: tblish.dataset.Harman23cor, Next: tblish.dataset.Harman74cor, Prev: tblish.dataset.HairEyeColor, Up: tblish.dataset 8.2.58.27 tblish.dataset.Harman23cor .................................... -- Static Method: OUT = Harman23cor () Harman Example 2.3 Description ........... A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen. Format ...... ‘cov’ An 8-by-8 correlation matrix. ‘names’ Names of the variables corresponding to the indexes of the correlation matrix’s dimensions. Source ...... Harman, H. H. (1976). ‘Modern Factor Analysis’, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3. Examples ........ tblish.dataset.Harman23cor; # TODO: Port factanal to Octave  File: tablicious.info, Node: tblish.dataset.Harman74cor, Next: tblish.dataset.Indometh, Prev: tblish.dataset.Harman23cor, Up: tblish.dataset 8.2.58.28 tblish.dataset.Harman74cor .................................... -- Static Method: OUT = Harman74cor () Harman Example 7.4 Description ........... A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford. Format ...... ‘cov’ A 2-dimensional correlation matrix. ‘vars’ Names of the variables corresponding to the indexes along the dimensions of ‘cov’. Source ...... Harman, H. H. (1976). ‘Modern Factor Analysis’, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4. Examples ........ tblish.dataset.Harman74cor; # TODO: Port factanal to Octave  File: tablicious.info, Node: tblish.dataset.Indometh, Next: tblish.dataset.infert, Prev: tblish.dataset.Harman74cor, Up: tblish.dataset 8.2.58.29 tblish.dataset.Indometh ................................. -- Static Method: OUT = Indometh () Pharmacokinetics of Indomethacin Description ........... Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’). Format ...... ‘Subject’ Subject identifier. ‘time’ Time since drug administration at which samples were drawn (hours). ‘conc’ Plasma concentration of indomethacin (mcg/ml). Details ....... Each of the six subjects were given an intravenous injection of indometacin. Source ...... Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. ‘Journal of Pharmacokinetics and Biopharmaceutics’ 4, 255–280. Davidian, M. and Giltinan, D. M. (1995). ‘Nonlinear Models for Repeated Measurement Data’. London: Chapman & Hall. (section 5.2.4, p. 129) Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer.  File: tablicious.info, Node: tblish.dataset.infert, Next: tblish.dataset.InsectSprays, Prev: tblish.dataset.Indometh, Up: tblish.dataset 8.2.58.30 tblish.dataset.infert ............................... -- Static Method: OUT = infert () Infertility after Spontaneous and Induced Abortion Description ........... This is a matched case-control study dating from before the availability of conditional logistic regression. Format ...... ‘education’ Index of the record. ‘age’ Age in years of case. ‘parity’ Count. ‘induced’ Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”. ‘case_status’ 0 = control, 1 = case. ‘spontaneous’ Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”. ‘stratum’ Matched set number. ‘pooled_stratum’ Stratum number. Note .... One case with two prior spontaneous abortions and two prior induced abortions is omitted. Source ...... Trichopoulos et al (1976). ‘Br. J. of Obst. and Gynaec.’ 83, 645–650. Examples ........ t = tblish.dataset.infert; # TODO: Port glm() (generalized linear model) stuff to Octave  File: tablicious.info, Node: tblish.dataset.InsectSprays, Next: tblish.dataset.iris, Prev: tblish.dataset.infert, Up: tblish.dataset 8.2.58.31 tblish.dataset.InsectSprays ..................................... -- Static Method: OUT = InsectSprays () Effectiveness of Insect Sprays Description ........... The counts of insects in agricultural experimental units treated with different insecticides. Format ...... ‘spray’ The type of spray. ‘count’ Insect count. Source ...... Beall, G., (1942). The Transformation of data from entomological field experiments. ‘Biometrika’, 29, 243–262. References .......... McNeil, D. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.InsectSprays; # TODO: boxplot # TODO: AOV plots  File: tablicious.info, Node: tblish.dataset.iris, Next: tblish.dataset.islands, Prev: tblish.dataset.InsectSprays, Up: tblish.dataset 8.2.58.32 tblish.dataset.iris ............................. -- Static Method: OUT = iris () The Fisher Iris dataset: measurements of various flowers Description ........... This is the classic Fisher Iris dataset. Format ...... ‘Species’ The species of flower being measured. ‘SepalLength’ Length of sepals, in centimeters. ‘SepalWidth’ Width of sepals, in centimeters. ‘PetalLength’ Length of petals, in centimeters. ‘PetalWidth’ Width of petals, in centimeters. Source ...... References .......... Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in ‘Contributions to Mathematical Statistics’ (John Wiley, NY, 1950). Duda, R.O., & Hart, P.E. (1973). ‘Pattern Classification and Scene Analysis’. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218. The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. ‘Bulletin of the American Iris Society’, 59, 2–5. Examples ........ # TODO: Port this example from R  File: tablicious.info, Node: tblish.dataset.islands, Next: tblish.dataset.JohnsonJohnson, Prev: tblish.dataset.iris, Up: tblish.dataset 8.2.58.33 tblish.dataset.islands ................................ -- Static Method: OUT = islands () Areas of the World's Major Landmasses Description ........... The areas in thousands of square miles of the landmasses which exceed 10,000 square miles. Format ...... ‘name’ The name of the island. ‘area’ The area, in thousands of square miles. Source ...... ‘The World Almanac and Book of Facts’, 1975, page 406. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.islands; # TODO: Port dot chart to Octave  File: tablicious.info, Node: tblish.dataset.JohnsonJohnson, Next: tblish.dataset.LakeHuron, Prev: tblish.dataset.islands, Up: tblish.dataset 8.2.58.34 tblish.dataset.JohnsonJohnson ....................................... -- Static Method: OUT = JohnsonJohnson () Quarterly Earnings per Johnson & Johnson Share Description ........... Quarterly earnings (dollars) per Johnson & Johnson share 1960–80. Format ...... ‘date’ Start date of the quarter. ‘earnings’ Earnings per share (USD). Source ...... Shumway, R. H. and Stoffer, D. S. (2000). ‘Time Series Analysis and its Applications’. Second Edition. New York: Springer. Example 1.1. Examples ........ t = tblish.dataset.JohnsonJohnson # TODO: Yikes, look at all those plots. Port them to Octave.  File: tablicious.info, Node: tblish.dataset.LakeHuron, Next: tblish.dataset.lh, Prev: tblish.dataset.JohnsonJohnson, Up: tblish.dataset 8.2.58.35 tblish.dataset.LakeHuron .................................. -- Static Method: OUT = LakeHuron () Level of Lake Huron 1875-1972 Description ........... Annual measurements of the level, in feet, of Lake Huron 1875–1972. Format ...... ‘year’ Year of the measurement ‘level’ Lake level (ft). Source ...... Brockwell, P. J. and Davis, R. A. (1991). ‘Time Series and Forecasting Methods’. Second edition. New York: Springer. Series A, page 555. Brockwell, P. J. and Davis, R. A. (1996). ‘Introduction to Time Series and Forecasting’. New York: Springer. Sections 5.1 and 7.6. Examples ........ t = tblish.dataset.LakeHuron; plot (t.year, t.level) xlabel ("Year") ylabel ("Lake level (ft)") title ("Level of Lake Huron")  File: tablicious.info, Node: tblish.dataset.lh, Next: tblish.dataset.LifeCycleSavings, Prev: tblish.dataset.LakeHuron, Up: tblish.dataset 8.2.58.36 tblish.dataset.lh ........................... -- Static Method: OUT = lh () Luteinizing Hormone in Blood Samples Description ........... A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples. Format ...... ‘sample’ The number of the observation. ‘lh’ Level of luteinizing hormone. Source ...... P.J. Diggle (1990). ‘Time Series: A Biostatistical Introduction’. Oxford. Table A.1, series 3. Examples ........ t = tblish.dataset.lh; plot (t.sample, t.lh); xlabel ("Sample Number"); ylabel ("lh level");  File: tablicious.info, Node: tblish.dataset.LifeCycleSavings, Next: tblish.dataset.Loblolly, Prev: tblish.dataset.lh, Up: tblish.dataset 8.2.58.37 tblish.dataset.LifeCycleSavings ......................................... -- Static Method: OUT = LifeCycleSavings () Intercountry Life-Cycle Savings Data Description ........... Data on the savings ratio 1960–1970. Format ...... ‘country’ Name of the country. ‘sr’ Aggregate personal savings. ‘pop15’ Percentage of population under 15. ‘pop75’ Percentage of population over 75. ‘dpi’ Real per-capita disposable income. ‘ddpi’ Percent growth rate of dpi. Details ....... Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations. Source ...... The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977). References .......... Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology. Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). ‘Regression Diagnostics’. New York: Wiley. Examples ........ t = tblish.dataset.LifeCycleSavings; # TODO: linear model # TODO: pairs plot with Lowess smoothed line  File: tablicious.info, Node: tblish.dataset.Loblolly, Next: tblish.dataset.longley, Prev: tblish.dataset.LifeCycleSavings, Up: tblish.dataset 8.2.58.38 tblish.dataset.Loblolly ................................. -- Static Method: OUT = Loblolly () Growth of Loblolly pine trees Description ........... Records of the growth of Loblolly pine trees. Format ...... ‘height’ Tree height (ft). ‘age’ Tree age (years). ‘Seed’ Seed source for the tree. Ordering is according to increasing maximum height. Source ...... Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. ‘Proceedings of the Statistical Computing Section’, American Statistical Association, 340–343. Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. Examples ........ t = tblish.dataset.Loblolly; t2 = t(t.Seed == "329",:); scatter (t2.age, t2.height) xlabel ("Tree age (yr)"); ylabel ("Tree height (ft)"); title ("Loblolly data and fitted curve (Seed 329 only)") # TODO: Compute and plot fitted curve  File: tablicious.info, Node: tblish.dataset.longley, Next: tblish.dataset.lynx, Prev: tblish.dataset.Loblolly, Up: tblish.dataset 8.2.58.39 tblish.dataset.longley ................................ -- Static Method: OUT = longley () Longley’s Economic Regression Data Description ........... A macroeconomic data set which provides a well-known example for a highly collinear regression. Format ...... ‘Year’ The year. ‘GNP_deflator’ GNP implicit price deflator (1954=100). ‘GNP’ Gross National Product. ‘Unemployed’ Number of unemployed. ‘Armed_Forces’ Number of people in the armed forces. ‘Population’ “Noninstitutionalized” population ≥ 14 years of age. ‘Employed’ Number of people employed. Source ...... J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. ‘Journal of the American Statistical Association’, 62, 819–841. References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Examples ........ t = tblish.dataset.longley; # TODO: Linear model # TODO: opar plot  File: tablicious.info, Node: tblish.dataset.lynx, Next: tblish.dataset.morley, Prev: tblish.dataset.longley, Up: tblish.dataset 8.2.58.40 tblish.dataset.lynx ............................. -- Static Method: OUT = lynx () Annual Canadian Lynx trappings 1821-1934 Description ........... Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977). Format ...... ‘year’ Year of the record. ‘lynx’ Number of lynx trapped. Source ...... Brockwell, P. J. and Davis, R. A. (1991). ‘Time Series and Forecasting Methods’. Second edition. New York: Springer. Series G (page 557). References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. ‘Journal of the Royal Statistical Society series A’, 140, 411–431. Examples ........ t = tblish.dataset.lynx; plot (t.year, t.lynx); xlabel ("Year"); ylabel ("Lynx Trapped");  File: tablicious.info, Node: tblish.dataset.morley, Next: tblish.dataset.mtcars, Prev: tblish.dataset.lynx, Up: tblish.dataset 8.2.58.41 tblish.dataset.morley ............................... -- Static Method: OUT = morley () Michelson Speed of Light Data Description ........... A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted). Format ...... ‘Expt’ The experiment number, from 1 to 5. ‘Run’ The run number within each experiment. ‘Speed’ Speed-of-light measurement. Details ....... The data is here viewed as a randomized block experiment with ‘experiment’ and ‘run’ as the factors. ‘run’ may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment. Source ...... A. J. Weekes (1986). ‘A Genstat Primer’. London: Edward Arnold. S. M. Stigler (1977). Do robust estimators work with real data? ‘Annals of Statistics’ 5, 1055–1098. (See Table 6.) A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. ‘Astronomic Papers’, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.). Examples ........ t = tblish.dataset.morley; # TODO: Port to Octave  File: tablicious.info, Node: tblish.dataset.mtcars, Next: tblish.dataset.nhtemp, Prev: tblish.dataset.morley, Up: tblish.dataset 8.2.58.42 tblish.dataset.mtcars ............................... -- Static Method: OUT = mtcars () Motor Trend 1974 Car Road Tests Description ........... The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). Format ...... ‘mpg’ Fuel efficiency in miles/gallon ‘cyl’ Number of cylinders ‘disp’ Displacement (cu. in.) ‘hp’ Gross horsepower ‘drat’ Rear axle ratio ‘wt’ Weight (1,000 lbs) ‘qsec’ 1/4 mile time ‘vs’ Engine type (0 = V-shaped, 1 = straight) ‘am’ Transmission type (0 = automatic, 1 = manual) ‘gear’ Number of forward gears ‘carb’ Number of carburetors Note .... Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.” Source ...... Henderson and Velleman (1981). Building multiple regression models interactively. ‘Biometrics’, 37, 391–411. Examples ........ # TODO: Port this example from R  File: tablicious.info, Node: tblish.dataset.nhtemp, Next: tblish.dataset.Nile, Prev: tblish.dataset.mtcars, Up: tblish.dataset 8.2.58.43 tblish.dataset.nhtemp ............................... -- Static Method: OUT = nhtemp () Average Yearly Temperatures in New Haven Description ........... The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971. Format ...... ‘year’ Year of the observation. ‘temp’ Mean annual temperature (degrees F). Source ...... Vaux, J. E. and Brinker, N. B. (1972) ‘Cycles’, 1972, 117–121. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.nhtemp; plot (t.year, t.temp); title ("nhtemp data"); xlabel ("Mean annual temperature in New Haven, CT (deg. F)");  File: tablicious.info, Node: tblish.dataset.Nile, Next: tblish.dataset.nottem, Prev: tblish.dataset.nhtemp, Up: tblish.dataset 8.2.58.44 tblish.dataset.Nile ............................. -- Static Method: OUT = Nile () Flow of the River Nile Description ........... Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249). Format ...... ‘year’ Year of the record. ‘flow’ Annual flow (cubic meters). Source ...... Durbin, J. and Koopman, S. J. (2001). ‘Time Series Analysis by State Space Methods’. Oxford: Oxford University Press. References .......... Balke, N. S. (1993). Detecting level shifts in time series. ‘Journal of Business and Economic Statistics’, 11, 81–92. Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. ‘Biometrika’ 65, 243–51. Examples ........ t = tblish.dataset.Nile; figure plot (t.year, t.flow); # TODO: Port the rest of the example to Octave  File: tablicious.info, Node: tblish.dataset.nottem, Next: tblish.dataset.npk, Prev: tblish.dataset.Nile, Up: tblish.dataset 8.2.58.45 tblish.dataset.nottem ............................... -- Static Method: OUT = nottem () Average Monthly Temperatures at Nottingham, 1920-1939 Description ........... A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years. Format ...... ‘record’ Index of the record. ‘lead’ Leading indicator. ‘sales’ Sales volume. Source ...... Anderson, O. D. (1976). ‘Time Series Analysis and Forecasting: The Box-Jenkins approach’. London: Butterworths. Series R. Examples ........ # TODO: Come up with example code here  File: tablicious.info, Node: tblish.dataset.npk, Next: tblish.dataset.occupationalStatus, Prev: tblish.dataset.nottem, Up: tblish.dataset 8.2.58.46 tblish.dataset.npk ............................ -- Static Method: OUT = npk () Classical N, P, K Factorial Experiment Description ........... A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots. Format ...... ‘block’ Which block (1 to 6). ‘N’ Indicator (0/1) for the application of nitrogen. ‘P’ Indicator (0/1) for the application of phosphate. ‘K’ Indicator (0/1) for the application of potassium. ‘yield’ Yield of peas, in pounds/plot. Plots were 1/70 acre. Source ...... Imperial College, London, M.Sc. exercise sheet. References .......... Venables, W. N. and Ripley, B. D. (2002). ‘Modern Applied Statistics with S’. Fourth edition. New York: Springer. Examples ........ t = tblish.dataset.npk; # TODO: Port aov() and LM to Octave  File: tablicious.info, Node: tblish.dataset.occupationalStatus, Next: tblish.dataset.Orange, Prev: tblish.dataset.npk, Up: tblish.dataset 8.2.58.47 tblish.dataset.occupationalStatus ........................................... -- Static Method: OUT = occupationalStatus () Occupational Status of Fathers and their Sons Description ........... Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status. Format ...... An 8-by-8 matrix of counts, with classifying fators ‘origin’ (father’s occupational status, levels 1:8) and ‘destination’ (son’s occupational status, levels 1:8). Source ...... Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. ‘J. Am. Stat. Assoc.’, 74 (367), 537–552. Examples ........ # TODO: Come up with example code here  File: tablicious.info, Node: tblish.dataset.Orange, Next: tblish.dataset.OrchardSprays, Prev: tblish.dataset.occupationalStatus, Up: tblish.dataset 8.2.58.48 tblish.dataset.Orange ............................... -- Static Method: OUT = Orange () Growth of Orange Trees Description ........... Records of the growth of orange trees. Format ...... ‘Tree’ A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter. ‘age’ Age of the tree (days since 1968-12-31). ‘circumference’ Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry. Source ...... The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at . References .......... Draper, N. R. and Smith, H. (1998). ‘Applied Regression Analysis (3rd ed)’. New York: Wiley. (exercise 24.N). Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. Examples ........ t = tblish.dataset.Orange; # TODO: Port coplot to Octave # TODO: Linear model  File: tablicious.info, Node: tblish.dataset.OrchardSprays, Next: tblish.dataset.PlantGrowth, Prev: tblish.dataset.Orange, Up: tblish.dataset 8.2.58.49 tblish.dataset.OrchardSprays ...................................... -- Static Method: OUT = OrchardSprays () Potency of Orchard Sprays Description ........... An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design. Format ...... ‘rowpos’ Row of the design. ‘colpos’ Column of the design ‘treatment’ Treatment level. ‘decrease’ Response. Details ....... Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur. The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells. An 8 x 8 Latin square design was used and the treatments were coded as follows: A – highest level of lime sulphur B – next highest level of lime sulphur ... G – lowest level of lime sulphur H – no lime sulphur Source ...... Finney, D. J. (1947). ‘Probit Analysis’. Cambridge. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.OrchardSprays; tblish.examples.plot_pairs (t);  File: tablicious.info, Node: tblish.dataset.PlantGrowth, Next: tblish.dataset.precip, Prev: tblish.dataset.OrchardSprays, Up: tblish.dataset 8.2.58.50 tblish.dataset.PlantGrowth .................................... -- Static Method: OUT = PlantGrowth () Results from an Experiment on Plant Growth Description ........... Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions. Format ...... ‘group’ Treatment condition group. ‘weight’ Weight of plants. Source ...... Dobson, A. J. (1983). ‘An Introduction to Statistical Modelling’. London: Chapman and Hall. Examples ........ t = tblish.dataset.PlantGrowth; # TODO: Port anova to Octave  File: tablicious.info, Node: tblish.dataset.precip, Next: tblish.dataset.presidents, Prev: tblish.dataset.PlantGrowth, Up: tblish.dataset 8.2.58.51 tblish.dataset.precip ............................... -- Static Method: OUT = precip () Annual Precipitation in US Cities Description ........... The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities. Format ...... ‘city’ City observed. ‘precip’ Annual precipitation (in). Source ...... ‘Statistical Abstracts of the United States’, 1975. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.precip; # TODO: Port dot plot to Octave  File: tablicious.info, Node: tblish.dataset.presidents, Next: tblish.dataset.pressure, Prev: tblish.dataset.precip, Up: tblish.dataset 8.2.58.52 tblish.dataset.presidents ................................... -- Static Method: OUT = presidents () Quarterly Approval Ratings of US Presidents Description ........... The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974. Format ...... ‘date’ Approximate date of the observation. ‘approval’ Approval rating (%). Details ....... The data are actually a fudged version of the approval ratings. See McNeil’s book for details. Source ...... The Gallup Organisation. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.presidents; figure plot (datenum (t.date), t.approval) datetick ("x") xlabel ("Date") ylabel ("Approval rating (%)") title ("presidents data")  File: tablicious.info, Node: tblish.dataset.pressure, Next: tblish.dataset.Puromycin, Prev: tblish.dataset.presidents, Up: tblish.dataset 8.2.58.53 tblish.dataset.pressure ................................. -- Static Method: OUT = pressure () Vapor Pressure of Mercury as a Function of Temperature Description ........... Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury). Format ...... ‘temperature’ Temperature (deg C). ‘pressure’ Pressure (mm Hg). Source ...... Weast, R. C., ed. (1973). ‘Handbook of Chemistry and Physics’. Cleveland: CRC Press. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.pressure; figure plot (t.temperature, t.pressure) xlabel ("Temperature (deg C)") ylabel ("Pressure (mm of Hg)") title ("pressure data: Vapor Pressure of Mercury") figure semilogy (t.temperature, t.pressure) xlabel ("Temperature (deg C)") ylabel ("Pressure (mm of Hg)") title ("pressure data: Vapor Pressure of Mercury")  File: tablicious.info, Node: tblish.dataset.Puromycin, Next: tblish.dataset.quakes, Prev: tblish.dataset.pressure, Up: tblish.dataset 8.2.58.54 tblish.dataset.Puromycin .................................. -- Static Method: OUT = Puromycin () Reaction Velocity of an Enzymatic Reaction Description ........... Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin. Format ...... ‘state’ Whether the cell was treated. ‘conc’ Substrate concentrations (ppm). ‘rate’ Instantaneous reaction rates (counts/min/min). Details ....... Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated. Source ...... The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at . References .......... Bates, D.M. and Watts, D.G. (1988). ‘Nonlinear Regression Analysis and Its Applications’. New York: Wiley. Appendix A1.3. Treloar, M. A. (1974). ‘Effects of Puromycin on Galactosyltransferase in Golgi Membranes’. M.Sc. Thesis, U. of Toronto. Examples ........ t = tblish.dataset.Puromycin; # TODO: Port example to Octave  File: tablicious.info, Node: tblish.dataset.quakes, Next: tblish.dataset.randu, Prev: tblish.dataset.Puromycin, Up: tblish.dataset 8.2.58.55 tblish.dataset.quakes ............................... -- Static Method: OUT = quakes () Locations of Earthquakes off Fiji Description ........... The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964. Format ...... ‘lat’ Latitude of event. ‘long’ Longitude of event. ‘depth’ Depth (km). ‘mag’ Richter magnitude. ‘stations’ Number of stations reporting. Details ....... There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations. Source ...... This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University. References .......... G. E. P. Box and G. M. Jenkins (1976). ‘Time Series Analysis, Forecasting and Control’. San Francisco: Holden-Day. p. 537. P. J. Brockwell and R. A. Davis (1991). ‘Time Series: Theory and Methods’. Second edition. New York: Springer-Verlag. p. 414. Examples ........ # TODO: Come up with example code here  File: tablicious.info, Node: tblish.dataset.randu, Next: tblish.dataset.rivers, Prev: tblish.dataset.quakes, Up: tblish.dataset 8.2.58.56 tblish.dataset.randu .............................. -- Static Method: OUT = randu () Random Numbers from Congruential Generator RANDU Description ........... 400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5. Format ...... ‘record’ Index of the record. ‘x’ X value of the triple. ‘y’ Y value of the triple. ‘z’ Z value of the triple. Details ....... In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator. These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places. Under VMS versions 2.0 and higher, this problem has been fixed. Source ...... David Donoho Examples ........ t = tblish.dataset.randu;  File: tablicious.info, Node: tblish.dataset.rivers, Next: tblish.dataset.rock, Prev: tblish.dataset.randu, Up: tblish.dataset 8.2.58.57 tblish.dataset.rivers ............................... -- Static Method: OUT = rivers () Lengths of Major North American Rivers Description ........... This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey. Format ...... ‘rivers’ A vector containing 141 observations. Source ...... ‘World Almanac and Book of Facts’, 1975, page 406. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ tblish.dataset.rivers; longest_river = max (rivers) shortest_river = min (rivers)  File: tablicious.info, Node: tblish.dataset.rock, Next: tblish.dataset.sleep, Prev: tblish.dataset.rivers, Up: tblish.dataset 8.2.58.58 tblish.dataset.rock ............................. -- Static Method: OUT = rock () Measurements on Petroleum Rock Samples Description ........... Measurements on 48 rock samples from a petroleum reservoir. Format ...... ‘area’ Area of pores space, in pixels out of 256 by 256. ‘peri’ Perimeter in pixels. ‘shape’ Perimeter/sqrt(area). ‘perm’ Permeability in milli-Darcies. Details ....... Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape. Source ...... Data from BP Research, image analysis by Ronit Katz, U. Oxford. Examples ........ t = tblish.dataset.rock; figure scatter (t.area, t.perm) xlabel ("Area of pores space (pixels out of 256x256)") ylabel ("Permeability (milli-Darcies)")  File: tablicious.info, Node: tblish.dataset.sleep, Next: tblish.dataset.stackloss, Prev: tblish.dataset.rock, Up: tblish.dataset 8.2.58.59 tblish.dataset.sleep .............................. -- Static Method: OUT = sleep () Student’s Sleep Data Description ........... Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients. Format ...... ‘id’ Patient ID. ‘group’ Drug given. ‘extra’ Increase in hours of sleep. Details ....... The ‘group’ variable name may be misleading about the data: They represent measurements on 10 persons, not in groups. Source ...... Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. ‘The Journal of Physiology’, 32, 501–510. Student (1908). The probable error of the mean. ‘Biometrika’, 6, 20. References .......... Scheffé, Henry (1959). ‘The Analysis of Variance’. New York, NY: Wiley. Examples ........ t = tblish.dataset.sleep; # TODO: Port to Octave  File: tablicious.info, Node: tblish.dataset.stackloss, Next: tblish.dataset.state, Prev: tblish.dataset.sleep, Up: tblish.dataset 8.2.58.60 tblish.dataset.stackloss .................................. -- Static Method: OUT = stackloss () Brownlee's Stack Loss Plant Data Description ........... Operational data of a plant for the oxidation of ammonia to nitric acid. Format ...... ‘AirFlow’ Flow of cooling air. ‘WaterTemp’ Cooling Water Inlet temperature. ‘AcidConc’ Concentration of acid (per 1000, minus 500). ‘StackLoss’ Stack loss Details ....... “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.) ‘AirFlow’ represents the rate of operation of the plant. ‘WaterTemp’ is the temperature of cooling water circulated through coils in the absorption tower. ‘AcidConc’ is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. ‘StackLoss’ (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant. Source ...... Brownlee, K. A. (1960, 2nd ed. 1965). ‘Statistical Theory and Methodology in Science and Engineering’. New York: Wiley. pp. 491–500. References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Dodge, Y. (1996). The guinea pig of multiple regression. In: ‘Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday’, 1996, ‘Lecture Notes in Statistics’ 109, Springer-Verlag, New York. Examples ........ t = tblish.dataset.stackloss; # TODO: Create linear model and print summary  File: tablicious.info, Node: tblish.dataset.state, Next: tblish.dataset.sunspot_month, Prev: tblish.dataset.stackloss, Up: tblish.dataset 8.2.58.61 tblish.dataset.state .............................. -- Static Method: OUT = state () US State Facts and Figures Description ........... Data related to the 50 states of the United States of America. Format ...... ‘abb’ State abbreviation. ‘name’ State name. ‘area’ Area (sq mi). ‘lat’ Approximate center (latitude). ‘lon’ Approximate center (longitude). ‘division’ State division. ‘revion’ State region. ‘Population’ Population estimate as of July 1, 1975. ‘Income’ Per capita income (1974). ‘Illiteracy’ Illiteracy as of 1970 (percent of population). ‘LifeExp’ Lfe expectancy in years (1969-71). ‘Murder’ Murder and non-negligent manslaughter rate per 100,000 population (1976). ‘HSGrad’ Percent high-school graduates (1970). ‘Frost’ Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city. Source ...... U.S. Department of Commerce, Bureau of the Census (1977) ‘Statistical Abstract of the United States’. U.S. Department of Commerce, Bureau of the Census (1977) ‘County and City Data Book’. References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Examples ........ t = tblish.dataset.state;  File: tablicious.info, Node: tblish.dataset.sunspot_month, Next: tblish.dataset.sunspot_year, Prev: tblish.dataset.state, Up: tblish.dataset 8.2.58.62 tblish.dataset.sunspot_month ...................................... -- Static Method: OUT = sunspot_month () Monthly Sunspot Data, from 1749 to “Present” Description ........... Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available. Format ...... ‘month’ Month of the observation. ‘sunspots’ Number of sunspots. Source ...... WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at . Examples ........ t = tblish.dataset.sunspot_month;  File: tablicious.info, Node: tblish.dataset.sunspot_year, Next: tblish.dataset.sunspots, Prev: tblish.dataset.sunspot_month, Up: tblish.dataset 8.2.58.63 tblish.dataset.sunspot_year ..................................... -- Static Method: OUT = sunspot_year () Yearly Sunspot Data, 1700-1988 Description ........... Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit). Format ...... ‘year’ Year of the observation. ‘sunspots’ Number of sunspots. Source ...... H. Tong (1996) ‘Non-Linear Time Series’. Clarendon Press, Oxford, p. 471. Examples ........ t = tblish.dataset.sunspot_year; figure plot (t.year, t.sunspots) xlabel ("Year") ylabel ("Sunspots")  File: tablicious.info, Node: tblish.dataset.sunspots, Next: tblish.dataset.swiss, Prev: tblish.dataset.sunspot_year, Up: tblish.dataset 8.2.58.64 tblish.dataset.sunspots ................................. -- Static Method: OUT = sunspots () Monthly Sunspot Numbers, 1749-1983 Description ........... Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory. Format ...... ‘month’ Month of the observation. ‘sunspots’ Number of observed sunspots. Source ...... Andrews, D. F. and Herzberg, A. M. (1985) ‘Data: A Collection of Problems from Many Fields for the Student and Research Worker’. New York: Springer-Verlag. Examples ........ t = tblish.dataset.sunspots; figure plot (datenum (t.month), t.sunspots) datetick ("x") xlabel ("Date") ylabel ("Monthly sunspot numbers") title ("sunspots data")  File: tablicious.info, Node: tblish.dataset.swiss, Next: tblish.dataset.Theoph, Prev: tblish.dataset.sunspots, Up: tblish.dataset 8.2.58.65 tblish.dataset.swiss .............................. -- Static Method: OUT = swiss () Swiss Fertility and Socioeconomic Indicators (1888) Data Description ........... Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888. Format ...... ‘Fertility’ Ig, ‘common standardized fertility measure’. ‘Agriculture’ % of males involved in agriculture as occupation. ‘Examination’ % draftees receiving highest mark on army examination. ‘Education’ % education beyond primary school for draftees. ‘Catholic’ % ‘Catholic’ (as opposed to ‘Protestant’). ‘InfantMortality’ Live births who live less than 1 year. All variables but ‘Fertility’ give proportions of the population. Source ...... (paraphrasing Mosteller and Tukey): Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries. The data collected are for 47 French-speaking “provinces” at about 1888. Here, all variables are scaled to [0, 100], where in the original, all but ‘Catholic’ were scaled to [0, 1]. Note .... Files for all 182 districts in 1888 and other years have been available at . They state that variables ‘Examination’ and ‘Education’ are averages for 1887, 1888 and 1889. References .......... Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). ‘The New S Language’. Monterey: Wadsworth & Brooks/Cole. Examples ........ t = tblish.dataset.swiss; # TODO: Port linear model to Octave  File: tablicious.info, Node: tblish.dataset.Theoph, Next: tblish.dataset.Titanic, Prev: tblish.dataset.swiss, Up: tblish.dataset 8.2.58.66 tblish.dataset.Theoph ............................... -- Static Method: OUT = Theoph () Pharmacokinetics of Theophylline Description ........... An experiment on the pharmacokinetics of theophylline. Format ...... ‘Subject’ Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed. ‘Wt’ Weight of the subject (kg). ‘Dose’ Dose of theophylline administerred orally to the subject (mg/kg). ‘Time’ Time since drug administration when the sample was drawn (hr). ‘conc’ Theophylline concentration in the sample (mg/L). Details ....... Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours. These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available. Source ...... The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at . References .......... Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). ‘NONMEM Users Guide: Part V’. NONMEM Project Group, University of California, San Francisco. Davidian, M. and Giltinan, D. M. (1995). ‘Nonlinear Models for Repeated Measurement Data’. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176) Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. (Appendix A.29) Examples ........ t = tblish.dataset.Theoph; # TODO: Coplot # TODO: Yet another linear model to port to Octave  File: tablicious.info, Node: tblish.dataset.Titanic, Next: tblish.dataset.ToothGrowth, Prev: tblish.dataset.Theoph, Up: tblish.dataset 8.2.58.67 tblish.dataset.Titanic ................................ -- Static Method: OUT = Titanic () Survival of passengers on the Titanic Description ........... This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival. Format ...... ‘n’ is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables: ‘Class’ 1st, 2nd, 3rd, Cre. ‘Sex’ Male, Female. ‘Age’ Child, Adult. ‘Survived’ No, Yes. Details ....... The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger. These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost. Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (). Source ...... Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. ‘Journal of Statistics Education’, 3. The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in: British Board of Trade (1990). ‘Report on the Loss of the ‘Titanic’ (S.S.)’. British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing. Examples ........ tblish.dataset.Titanic; # TODO: Port mosaic plot to Octave # TODO: Check for higher survival rates in children and females  File: tablicious.info, Node: tblish.dataset.ToothGrowth, Next: tblish.dataset.treering, Prev: tblish.dataset.Titanic, Up: tblish.dataset 8.2.58.68 tblish.dataset.ToothGrowth .................................... -- Static Method: OUT = ToothGrowth () The Effect of Vitamin C on Tooth Growth in Guinea Pigs Description ........... The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as ‘VC’). Format ...... ‘supp’ Supplement type. ‘dose’ Dose (mg/day). ‘len’ Tooth length. Source ...... C. I. Bliss (1952). ‘The Statistics of Bioassay’. Academic Press. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. ‘The Journal of Nutrition’, 33(5), 491–504. Examples ........ t = tblish.dataset.ToothGrowth; tblish.examples.coplot (t, "dose", "len", "supp"); # TODO: Port Lowess smoothing to Octave  File: tablicious.info, Node: tblish.dataset.treering, Next: tblish.dataset.trees, Prev: tblish.dataset.ToothGrowth, Up: tblish.dataset 8.2.58.69 tblish.dataset.treering ................................. -- Static Method: OUT = treering () Yearly Treering Data, -6000-1979 Description ........... Contains normalized tree-ring widths in dimensionless units. Format ...... A univariate time series with 7981 observations. Each tree ring corresponds to one year. Details ....... The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California. Source ...... Time Series Data Library: , series ‘CA535.DAT’. References .......... For some photos of Methuselah Walk see . Examples ........ t = tblish.dataset.treering;  File: tablicious.info, Node: tblish.dataset.trees, Next: tblish.dataset.UCBAdmissions, Prev: tblish.dataset.treering, Up: tblish.dataset 8.2.58.70 tblish.dataset.trees .............................. -- Static Method: OUT = trees () Diameter, Height and Volume for Black Cherry Trees Description ........... This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground. Format ...... ‘Girth’ Tree diameter (rather than girth, actually) in inches. ‘Height’ Height in ft. ‘Volume’ Volume of timber in cubic feet. Source ...... Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). ‘The Minitab Student Handbook’. Duxbury Press. References .......... Atkinson, A. C. (1985). ‘Plots, Transformations and Regression’. Oxford: Oxford University Press. Examples ........ t = tblish.dataset.trees; figure tblish.examples.plot_pairs (t); figure loglog (t.Girth, t.Volume) xlabel ("Girth") ylabel ("Volume") # TODO: Transform to log space for the coplot # TODO: Linear model  File: tablicious.info, Node: tblish.dataset.UCBAdmissions, Next: tblish.dataset.UKDriverDeaths, Prev: tblish.dataset.trees, Up: tblish.dataset 8.2.58.71 tblish.dataset.UCBAdmissions ...................................... -- Static Method: OUT = UCBAdmissions () Student Admissions at UC Berkeley Description ........... Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex. Format ...... A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows: ‘Admit’ Admitted, Rejected. ‘Gender’ Male, Female. ‘Dept’ A, B, C, D, E, F. Details ....... This data set is frequently used for illustrating Simpson's paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates). Source ...... The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at . References .......... Bickel, P. J., Hammel, E. A., and O'Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. ‘Science’, 187, 398–403. . Examples ........ tblish.dataset.UCBAdmissions; # TODO: Port mosaic plot to Octave  File: tablicious.info, Node: tblish.dataset.UKDriverDeaths, Next: tblish.dataset.UKgas, Prev: tblish.dataset.UCBAdmissions, Up: tblish.dataset 8.2.58.72 tblish.dataset.UKDriverDeaths ....................................... -- Static Method: OUT = UKDriverDeaths () Road Casualties in Great Britain 1969-84 Description ........... ‘UKDriverDeaths’ is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983. ‘Seatbelts’ is more information on the same problem. Format ...... ‘UKDriverDeaths’ is a table with the following variables: ‘month’ Month of the observation. ‘deaths’ Number of deaths. ‘Seatbelts’ is a table with the following variables: ‘month’ Month of the observation. ‘DriversKilled’ Car drivers killed. ‘drivers’ Same as ‘UKDriverDeaths’ ‘deaths’ count. ‘front’ Front-seat passengers killed or seriously injured. ‘rear’ Rear-seat passengers killed or seriously injured. ‘kms’ Distance driven. ‘PetrolPrice’ Petrol price. ‘VanKilled’ Number of van (“light goods vehicle”) drivers killed. ‘law’ 0/1: was the seatbelt law in effect that month? Source ...... Harvey, A.C. (1989). ‘Forecasting, Structural Time Series Models and the Kalman Filter’. Cambridge: Cambridge University Press. pp. 519–523. Durbin, J. and Koopman, S. J. (2001). ‘Time Series Analysis by State Space Methods’. Oxford: Oxford University Press. References .......... Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. ‘Journal of the Royal Statistical Society’ series A, 149, 187–227. Examples ........ tblish.dataset.UKDriverDeaths; d = UKDriverDeaths; s = Seatbelts; # TODO: Port the model and plots to Octave  File: tablicious.info, Node: tblish.dataset.UKgas, Next: tblish.dataset.UKLungDeaths, Prev: tblish.dataset.UKDriverDeaths, Up: tblish.dataset 8.2.58.73 tblish.dataset.UKgas .............................. -- Static Method: OUT = UKgas () UK Quarterly Gas Consumption Description ........... Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms. Format ...... ‘date’ Quarter of the observation ‘gas’ Gas consumption (MM therms). Source ...... Durbin, J. and Koopman, S. J. (2001). ‘Time Series Analysis by State Space Methods’. Oxford: Oxford University Press. . Examples ........ t = tblish.dataset.UKgas; plot (datenum (t.date), t.gas); datetick ("x") xlabel ("Month") ylabel ("Gas consumption (MM therms)")  File: tablicious.info, Node: tblish.dataset.UKLungDeaths, Next: tblish.dataset.USAccDeaths, Prev: tblish.dataset.UKgas, Up: tblish.dataset 8.2.58.74 tblish.dataset.UKLungDeaths ..................................... -- Static Method: OUT = UKLungDeaths () Monthly Deaths from Lung Diseases in the UK Description ........... Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979. Format ...... ‘date’ Month of the observation. ‘ldeaths’ Total lung deaths. ‘fdeaths’ Lung deaths among females. ‘mdeaths’ Lung deaths among males. Source ...... P. J. Diggle (1990). ‘Time Series: A Biostatistical Introduction’. Oxford. table A.3 Examples ........ t = tblish.dataset.UKLungDeaths; figure plot (datenum (t.date), t.ldeaths); title ("Total UK Lung Deaths") xlabel ("Month") ylabel ("Deaths") figure plot (datenum (t.date), [t.fdeaths t.mdeaths]); title ("UK Lung Deaths buy sex") legend ({"Female", "Male"}) xlabel ("Month") ylabel ("Deaths")  File: tablicious.info, Node: tblish.dataset.USAccDeaths, Next: tblish.dataset.USArrests, Prev: tblish.dataset.UKLungDeaths, Up: tblish.dataset 8.2.58.75 tblish.dataset.USAccDeaths .................................... -- Static Method: OUT = USAccDeaths () Accidental Deaths in the US 1973-1978 Description ........... A time series giving the monthly totals of accidental deaths in the USA. Format ...... ‘month’ Month of the observation. ‘deaths’ Accidental deaths. Source ...... Brockwell, P. J. and Davis, R. A. (1991). ‘Time Series: Theory and Methods’. New York: Springer. Examples ........ t = tblish.dataset.USAccDeaths;  File: tablicious.info, Node: tblish.dataset.USArrests, Next: tblish.dataset.USJudgeRatings, Prev: tblish.dataset.USAccDeaths, Up: tblish.dataset 8.2.58.76 tblish.dataset.USArrests .................................. -- Static Method: OUT = USArrests () Violent Crime Rates by US State Description ........... This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas. Format ...... ‘State’ State name. ‘Murder’ Murder arrests (per 100,000). ‘Assault’ Assault arrests (per 100,000). ‘UrbanPop’ Percent urban population. ‘Rape’ Rape arrests (per 100,000). Note .... ‘USArrests’ contains the data as in McNeil's monograph. For the ‘UrbanPop’ percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona). See the example below on how to correct the error and improve accuracy for the ‘.5’ percentages. Source ...... ‘World Almanac and Book of Facts 1975’. (Crime rates). ‘Statistical Abstracts of the United States 1975’, p.20, (Urban rates), possibly available as . References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.USArrests; summary (t); tblish.examples.plot_pairs (t(:,2:end)); # TODO: Difference between USArrests and its correction # TODO: +/- 0.5 to restore the original .5 percentages  File: tablicious.info, Node: tblish.dataset.USJudgeRatings, Next: tblish.dataset.USPersonalExpenditure, Prev: tblish.dataset.USArrests, Up: tblish.dataset 8.2.58.77 tblish.dataset.USJudgeRatings ....................................... -- Static Method: OUT = USJudgeRatings () Lawyers’ Ratings of State Judges in the US Superior Court Description ........... Lawyers’ ratings of state judges in the US Superior Court. Format ...... ‘CONT’ Number of contacts of lawyer with judge. ‘INTG’ Judicial integrity. ‘DMNR’ Demeanor. ‘DILG’ Diligence. ‘CFMG’ Case flow managing. ‘DECI’ Prompt decisions. ‘PREP’ Preparation for trial. ‘FAMI’ Familiarity with law. ‘ORAL’ Sound oral rulings. ‘WRIT’ Sound written rulings. ‘PHYS’ Physical ability. ‘RTEN’ Worthy of retention. Source ...... New Haven Register, 14 January, 1977 (from John Hartigan). Examples ........ t = tblish.dataset.USJudgeRatings; figure tblish.examples.plot_pairs (t(:,2:end)); title ("USJudgeRatings data")  File: tablicious.info, Node: tblish.dataset.USPersonalExpenditure, Next: tblish.dataset.uspop, Prev: tblish.dataset.USJudgeRatings, Up: tblish.dataset 8.2.58.78 tblish.dataset.USPersonalExpenditure .............................................. -- Static Method: OUT = USPersonalExpenditure () Personal Expenditure Data Description ........... This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960. Format ...... A 2-dimensional matrix ‘x’ with Category along dimension 1 and Year along dimension 2. Source ...... ‘The World Almanac and Book of Facts’, 1962, page 756. References .......... Tukey, J. W. (1977). ‘Exploratory Data Analysis’. Reading, Mass: Addison-Wesley. McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ tblish.dataset.USPersonalExpenditure; # TODO: Port medpolish() from R, whatever that is.  File: tablicious.info, Node: tblish.dataset.uspop, Next: tblish.dataset.VADeaths, Prev: tblish.dataset.USPersonalExpenditure, Up: tblish.dataset 8.2.58.79 tblish.dataset.uspop .............................. -- Static Method: OUT = uspop () Populations Recorded by the US Census Description ........... This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970. Format ...... ‘year’ Year of the census. ‘population’ Population, in millions. Source ...... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.uspop; figure semilogy (t.year, t.population) xlabel ("Year") ylabel ("U.S. Population (millions)")  File: tablicious.info, Node: tblish.dataset.VADeaths, Next: tblish.dataset.volcano, Prev: tblish.dataset.uspop, Up: tblish.dataset 8.2.58.80 tblish.dataset.VADeaths ................................. -- Static Method: OUT = VADeaths () Death Rates in Virginia (1940) Description ........... Death rates per 1000 in Virginia in 1940. Format ...... A 2-dimensional matrix ‘deaths’, with age group along dimension 1 and demographic group along dimension 2. Details ....... The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female. This provides a rather nice 3-way analysis of variance example. Source ...... Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. ‘American Sociological Review’, 12, 525–535. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ tblish.dataset.VADeaths; # TODO: Port to Octave  File: tablicious.info, Node: tblish.dataset.volcano, Next: tblish.dataset.warpbreaks, Prev: tblish.dataset.VADeaths, Up: tblish.dataset 8.2.58.81 tblish.dataset.volcano ................................ -- Static Method: OUT = volcano () Topographic Information on Auckland’s Maunga Whau Volcano Description ........... Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid. Format ...... A matrix ‘volcano’ with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north. Source ...... Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate. References .......... Box, G. E. P. and Jenkins, G. M. (1976). ‘Time Series Analysis, Forecasting and Control’. San Francisco: Holden-Day. p. 537. Brockwell, P. J. and Davis, R. A. (1991). ‘Time Series: Theory and Methods’. Second edition. New York: Springer-Verlag. p. 414. Examples ........ tblish.dataset.volcano; # TODO: Figure out how to do a topo map in Octave. Just a gridded color plot # should be fine. And then maybe do a 3-d mesh plot.  File: tablicious.info, Node: tblish.dataset.warpbreaks, Next: tblish.dataset.women, Prev: tblish.dataset.volcano, Up: tblish.dataset 8.2.58.82 tblish.dataset.warpbreaks ................................... -- Static Method: OUT = warpbreaks () The Number of Breaks in Yarn during Weaving Description ........... This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn. Format ...... ‘wool’ Type of wool (A or B). ‘tension’ The level of tension (L, M, H). ‘breaks’ Number of breaks. There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH). Source ...... Tippett, L. H. C. (1950). ‘Technological Applications of Statistics’. New York: Wiley. Page 106. References .......... Tukey, J. W. (1977). ‘Exploratory Data Analysis’. Reading, Mass: Addison-Wesley. McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.warpbreaks; summary (t) # TODO: Port the plotting code and OPAR to Octave  File: tablicious.info, Node: tblish.dataset.women, Next: tblish.dataset.WorldPhones, Prev: tblish.dataset.warpbreaks, Up: tblish.dataset 8.2.58.83 tblish.dataset.women .............................. -- Static Method: OUT = women () Average Heights and Weights for American Women Description ........... This data set gives the average heights and weights for American women aged 30–39. Format ...... ‘height’ Height (in). ‘weight’ Weight (lbs). Details ....... The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year. The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”. Source ...... ‘The World Almanac and Book of Facts’, 1975. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ t = tblish.dataset.women; figure scatter (t.height, t.weight) xlabel ("Height (in)") ylabel ("Weight (lb") title ("women data: American women aged 30-39")  File: tablicious.info, Node: tblish.dataset.WorldPhones, Next: tblish.dataset.WWWusage, Prev: tblish.dataset.women, Up: tblish.dataset 8.2.58.84 tblish.dataset.WorldPhones .................................... -- Static Method: OUT = WorldPhones () The World’s Telephones Description ........... The number of telephones in various regions of the world (in thousands). Format ...... A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year. The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America. The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961. Source ...... AT&T (1961) ‘The World’s Telephones’. References .......... McNeil, D. R. (1977). ‘Interactive Data Analysis’. New York: Wiley. Examples ........ tblish.dataset.WorldPhones; # TODO: Port matplot() to Octave  File: tablicious.info, Node: tblish.dataset.WWWusage, Next: tblish.dataset.zCO2, Prev: tblish.dataset.WorldPhones, Up: tblish.dataset 8.2.58.85 tblish.dataset.WWWusage ................................. -- Static Method: OUT = WWWusage () WWWusage Description ........... A time series of the numbers of users connected to the Internet through a server every minute. Format ...... A time series of length 100. Source ...... Durbin, J. and Koopman, S. J. (2001). ‘Time Series Analysis by State Space Methods’. Oxford: Oxford University Press. References .......... Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). ‘Forecasting: Methods and Applications’. New York: Wiley. Examples ........ # TODO: Come up with example code here  File: tablicious.info, Node: tblish.dataset.zCO2, Prev: tblish.dataset.WWWusage, Up: tblish.dataset 8.2.58.86 tblish.dataset.zCO2 ............................. -- Static Method: OUT = zCO2 () Carbon Dioxide Uptake in Grass Plants Description ........... The ‘CO2’ data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli. Format ...... Details ....... The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted. Source ...... Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. ‘Ecology’, 71, 1389–1400. Pinheiro, J. C. and Bates, D. M. (2000). ‘Mixed-effects Models in S and S-PLUS’. New York: Springer. Examples ........ t = tblish.dataset.zCO2; # TODO: Coplot # TODO: Port the linear model to Octave  File: tablicious.info, Node: tblish.datasets, Next: tblish.evalWithTableVars, Prev: tblish.dataset, Up: API Alphabetically 8.2.59 tblish.datasets ---------------------- -- Class: tblish.datasets Example dataset collection. ‘tblish.datasets’ is a collection of example datasets to go with the Tablicious package. The ‘tblish.datasets’ class provides methods for listing and loading the example datasets. * Menu: * datasets.description:: * datasets.list:: * datasets.load::  File: tablicious.info, Node: datasets.description, Next: datasets.list, Up: tblish.datasets 8.2.59.1 datasets.description ............................. -- Static Method: description (DATASETNAME) -- Static Method: OUT = description (DATASETNAME) Get or display the description for a dataset. Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.  File: tablicious.info, Node: datasets.list, Next: datasets.load, Prev: datasets.description, Up: tblish.datasets 8.2.59.2 datasets.list ...................... -- Static Method: list () -- Static Method: OUT = list () List all datasets. Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list. Returns a table with variables Name, Description, and possibly more.  File: tablicious.info, Node: datasets.load, Prev: datasets.list, Up: tblish.datasets 8.2.59.3 datasets.load ...................... -- Static Method: load (DATASETNAME) -- Static Method: OUT = load (DATASETNAME) Load a specified dataset. DATASETNAME is the name of the dataset to load, as found in the ‘Name’ column of the dataset list.  File: tablicious.info, Node: tblish.evalWithTableVars, Next: tblish.examples.coplot, Prev: tblish.datasets, Up: API Alphabetically 8.2.60 tblish.evalWithTableVars ------------------------------- -- Function: OUT = tblish.evalWithTableVars (TBL, EXPR) Evaluate an expression against a table array’s variables. Evaluates the M-code expression EXPR in a workspace where all of TBL’s variables have been assigned to workspace variables. EXPR is a charvec containing an Octave expression. As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names. Returns the result of the evaluation. Examples: [s,p,sp] = tblish.examples.SpDb tmp = join (sp, p); shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight") See also: *note table.restrict::  File: tablicious.info, Node: tblish.examples.coplot, Next: tblish.examples.plot_pairs, Prev: tblish.evalWithTableVars, Up: API Alphabetically 8.2.61 tblish.examples.coplot ----------------------------- -- Function: [FIG, HAX] = tblish.examples.coplot (TBL, XVAR, YVAR, GVAR) -- Function: [FIG, HAX] = tblish.examples.coplot (FIG, TBL, XVAR, YVAR, GVAR) -- Function: [FIG, HAX] = tblish.examples.coplot (..., OPTIONNAME, OPTIONVALUE, ...) Conditioning plot. ‘tblish.examples.coplot’ produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot. TBL is a ‘table’ containing the data to plot. XVAR is the name of the table variable within TBL to use as the X values. May be a variable name or index. YVAR is the name of the table variable within TBL to use as the Y values. May be a variable name or index. GVAR is the name of the table variable or variables within TBL to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. GVAR may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored. FIG is the figure handle to plot into. If FIG is not provided, a new figure is created. Name/Value options: ‘PlotFcn’ The plotting function to use, supplied as a function handle. Defaults to ‘@plot’. It must be a function that provides the signature ‘fcn(hax, X, Y, ...)’. ‘PlotArgs’ A cell array of arguments to pass in to the plotting function, following the HAX, X, and Y arguments. Returns: FIG – the figure handle it plotted into HAX – array of axes handles to all the axes for the subplots  File: tablicious.info, Node: tblish.examples.plot_pairs, Next: tblish.examples.SpDb, Prev: tblish.examples.coplot, Up: API Alphabetically 8.2.62 tblish.examples.plot_pairs --------------------------------- -- Function: OUT = tblish.examples.plot_pairs (DATA) -- Function: OUT = tblish.examples.plot_pairs (DATA, PLOT_TYPE) -- Function: OUT = tblish.examples.plot_pairs (FIG, ...) Plot pairs of variables against each other. DATA is the data holding the variables to plot. It may be either a ‘table’ or a struct. Each variable or field in the ‘table’ or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size. PLOT_TYPE is a charvec indicating what plot type to do in each subplot. (‘"scatter"’ is the default.) Valid PLOT_TYPE values are: ‘"scatter"’ A plain scatter plot. ‘"smooth"’ A scatter plot + fitted line, like R's ‘panel.smooth’ does. FIG is an optional figure handle to plot into. If omitted, a new figure is created. Returns the created figure, if the output is captured.  File: tablicious.info, Node: tblish.examples.SpDb, Next: tblish.sizeof2, Prev: tblish.examples.plot_pairs, Up: API Alphabetically 8.2.63 tblish.examples.SpDb --------------------------- -- Function: SPDB = tblish.examples.SpDb () -- Function: [S, P, SP] = tblish.examples.SpDb () The classic Suppliers-Parts example database. Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date's "An Introduction to Database Systems" textbook. Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (S, P, SP).  File: tablicious.info, Node: tblish.sizeof2, Next: tblish.table.grpstats, Prev: tblish.examples.SpDb, Up: API Alphabetically 8.2.64 tblish.sizeof2 --------------------- -- Function: OUT = tblish.sizeof2 (X) Approximate size of an array in bytes, with object support. This is an alternative to Octave's ‘sizeof’ function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions. This may be supplemented or replaced by ‘sizeof’ override methods on Tablicious's classes. I'm not sure whether Octave's ‘sizeof’ supports extension by method overrides, so I'm not doing that yet. If that happens, this ‘sizeof2’ function will stick around in a deprecated state for a while, and it will respect those override methods. For tables, this returns the sum of ‘sizeof’ for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in OBJ. This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious's objects to a separate function. This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases. X is an array of any type. Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported. See also: sizeof  File: tablicious.info, Node: tblish.table.grpstats, Next: timezones, Prev: tblish.sizeof2, Up: API Alphabetically 8.2.65 tblish.table.grpstats ---------------------------- -- Function: [OUT] = tblish.table.grpstats (TBL, GROUPVAR) -- Function: [OUT] = tblish.table.grpstats (..., 'DataVars', DATAVARS) Statistics by group for a table array. This is a table-specific implementation of ‘grpstats’ that works on table arrays. It is supplied as a function in the +TBLISH package to avoid colliding with the global ‘grpstats’ function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its ‘grpstats’ function. This function is supplied as an alternative you can use in an environment where ‘table’ arrays are not supported by the ‘grpstats’ that you have, though you need to make code changes and call it as ‘tblish.table.grpstats(tbl)’ instead of with a plain ‘grpstats(tbl)’. See also: table.groupby, table.findgroups, table.splitapply  File: tablicious.info, Node: timezones, Next: todatetime, Prev: tblish.table.grpstats, Up: API Alphabetically 8.2.66 timezones ---------------- -- Function: OUT = timezones () -- Function: OUT = timezones (AREA) List all the time zones defined on this system. This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from ‘/usr/share/zoneinfo’. On Windows, it will be the database redistributed with the Tablicious package. If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors: ‘Name’ The IANA zone name, as cellstr. ‘Area’ The geographical area the zone is in, as cellstr. Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.  File: tablicious.info, Node: todatetime, Next: vartype, Prev: timezones, Up: API Alphabetically 8.2.67 todatetime ----------------- -- Function: OUT = todatetime (X) Convert input to a Tablicious datetime array, with convenient interface. This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious's author likes better. This mainly exists because datetime's constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab's interface.) I think that's less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that's not the same size as the input. Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".  File: tablicious.info, Node: vartype, Next: vecfun, Prev: todatetime, Up: API Alphabetically 8.2.68 vartype -------------- -- Function: OUT = vartype (TYPE) Filter by variable type for use in suscripting. Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type. TYPE is the name of a type as charvec. This may be anything that the ‘isa’ function accepts, or ‘'cellstr'’ to select cellstrs, as determined by ‘iscellstr’. Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a ‘table’ object.  File: tablicious.info, Node: vecfun, Next: years, Prev: vartype, Up: API Alphabetically 8.2.69 vecfun ------------- -- Function: OUT = vecfun (FCN, X, DIM) Apply function to vectors in array along arbitrary dimension. This function is not implemented yet. Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension. FCN is a function handle to apply. X is an array of arbitrary type which is to be sliced and passed in to FCN. DIM is the dimension along which the vector slices lay. Returns the collected output of the FCN calls, which will be the same size as X, but not necessarily the same type.  File: tablicious.info, Node: years, Prev: vecfun, Up: API Alphabetically 8.2.70 years ------------ -- Function File: OUT = years (X) Create a ‘duration’ X years long, or get the years in a ‘duration’ X. If input is numeric, returns a ‘duration’ array in units of fixed-length years of 365.2425 days each. If input is a ‘duration’, converts the ‘duration’ to a number of fixed-length years as double. Note: ‘years’ creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use ‘calyears’. *Note calyears::.  File: tablicious.info, Node: Copying, Prev: API Reference, Up: Top 9 Copying ********* 9.1 Package Copyright ===================== Tablicious for GNU Octave is covered by the GNU GPLv3 and other Free and Open Source Software licenses. The main code of Tablicious is licensed under the GNU GPL version 3 or later. Certain files in the build tools are licensed under the GNU GPL version 2 or later. This includes the ‘mktexi.pl’ and ‘OctTexiDoc.pm’ files in the ‘doc/’ directory. The tzinfo database file is Public Domain. The date/time portion of Tablicious includes some Unicode data files licensed under the Unicode License Agreement - Data Files and Software license. The Tablicious test suite contains some files, specifically some table-related tests using MP-Test like ‘t/t_01_table.m’, which are BSD 3-Clause licensed, and are adapted from MATPOWER written by Ray Zimmerman. The Fisher Iris dataset is Public Domain. 9.2 Manual Copyright ==================== This manual is for Tablicious, version 0.4.7. Copyright © 2019, 2023, 2024, 2025, 2026 Andrew Janke Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions.  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B|drbr ΎV0LuU;Ճ1y,lƲM粚o}U;_zi_TI&͔[ NśNE'r3>7>̫i$ԣ 3 L1|H|YR:$|t3!pi-2$efV&8W_F^֝P#Wt>MG*} egh(&(:ĘvԚMD;'kFH+;'Vi]5u曣"kv d2UKf v>Rڜ{Ƙ/|!Y '42"WV*&&$1ּYN[muWSTDUSuUenËGiSٱ.|a0oTC>j!Q܍GTV`qEp%\E>T_q^>i/2rCec3@pdeQ d(q4Zfv˵`k2pf[E¨,LJp 'Q]N4[RʹE]fe-gfڶUuUZ/ .AcK:43 B6S09FgvcjbN &c!_s7jW}y<q,JtzsvJW.02ja7f"ߞZ&mj\FP_5Ydwx{ O[S{os]ȐbFC>l/|g/G΍irOMBd#PE2:W|=u]7)[b::DjpzQPuoj4 5jV7/@r,XPeTW {T~mx)CNܐ/K͵@ZUH6A6>Lܾ{jo8z3XP"BGe> stream xXK6"U[EsشM(=peBW:C)K ,!.\E.bw Yd啫zwﮨAHY^}6 uR7pjqO~ܰeO-;Br{OU-ۋi/^ =_'%OKEIP?@sxYvydyɝMJ;7' (q.65؟ՃV:izRZGܓ͔=y5) [ IyF;`í K>;m礚TSg%N74x2︀p"6] ^Lnʂ#Zk[,7 z7;.ҳiC1ٷ|ҲR柌p;w"c\g#€8]|Dwy '{;_BcdpĀu'&hB`ވ w.[T8od>*y$a1d:\l?%E5Vz'OOA `HFfzEV lct]Klqxvb_s,E{\BzBU IIgGI t% niThNg{$W"L-{˪6{E{f:3~3`3v68醳'9!2oۼZj pȈ^OOzLO&dB=Ҋfr2̺4ґ 55!Ws˖haE%6_AտpBb(~y\O11-3ZS1xdbUYNV+#  LF/N *Y7jF_\2X |;S`"(@;إ#RNb*|gEt,^ @W ֚X7>۾!Y3SqDI2ʶ} &toSeZdU8}}>|8gϝx 2 o;Oֳ PI, GAQep}ٲzdh„}٩XTzY:z=ħQQ|. *>(qg?3J&sOaX9OXLuƧ&3@Ǚ%jǑŦ=w>*~USv|dFʚM4]@tډ9ǯ-<ۭ`iŠ endstream endobj 2508 0 obj << /Length 1629 /Filter /FlateDecode >> stream xXKs6W͘ 7`'iǝ&mmRӡ%b"U Eʠi::ŷo`s2$Aea,6GTG7>bf ѫahƜj(jt.u\ HzaDN~?|| (~ gC < 8 =]>Ybým̾AzY~1?r.?pA,6f^E>.r*׭4K!"?CL`"3mKƺ9.PK*+8lS/u\8i;ZT<坫4]j:"-ot?+<D-pR/:Fv!A@P뿯3?!|!kjдȕ\[^f4^OjP :̅,sl1npcxXyeyS+hv|Er=sOɆ`H=|UGM9y&iHramY, 0BA 0nA-70jh`@=V vOݓX b䳕7ŎMUޖҭb9y5epB#`_FniM 8)"/vA=LP_y6GNn?v.agb^3'̘Ssu V8'J[9 PTG#Oo&(o5Ku*%hDJ8w%;[!N!ri@< FkRDn+)Vj}qb-KƮ5ow,?A0;1XKh:Oǔ&🗷Vߌ!.{nZor,ycح^t*ӟz17|\**bpVqjWhIbovs.ܯձ?hS0jP>18vBd/ 8vD,"@hrk[BMl<)gCΛns."By7*CnmM,Id)1/ep?{1sRɓ?/Ms]vt Oj=@u:E4P B̭;yKͮHtIVҟ\ݼphrVm[UԔ,;en%4[!ÓSP,O&iڢ *"yŻC4HQpD $+텕zA0Ec L-cy-hU_qٜhXW ,ftaM,c!uUCPjGE C TZ&XQmk΅ovhnў} X<;H&#h;x y#y{Tp؎L<Sgu1ƈջya&W;uY}a:9/:5 endstream endobj 2512 0 obj << /Length 1615 /Filter /FlateDecode >> stream xYo6_"Mߖ M. aHHB<}Hݑ'Y%i( G&cMLgMBs2u#tI0%XMEtXwV[\M,К,]VxrǮ|[BaMw]Q["Lڧ,Zm W &#t~*ta5mJTFsM,@8gtzD=wmcū$R ug XIqeg#aay] 䱷qB]T4ٽ:4byw{H+wSځ{0q;ܗ@IQ%=e7v(h쫅7ӯJPB^  #Rt:j N#ef6ȗ]r޽6-a2k9^an6#n#fNT1n~ [/֊bqoZn ?Ǚo̓ +@ZGlʆ )YҋdM"l*ᓚrʁ23UA NՔjaHeV&t^5jz >W4eSTovaWZx+fiJ"ΦWwV e(u?@yI ^Nb؅WP՛ݸ-ڇwYe '}+SU߅)fֆ|{Uۊخ$5m=EH((ányJٽ'c>B %^G֙Ib:؛wDl c/Wr+5.3tC)p%V=Xk&QzOڳDb)Ҙ~/.Q>E֗Czot)- ~Pz]Ɇu궼 u}DYp(B .bA燯c6˓]u;o_J*qRV=+MzNCࠁi#R+F%(1a-.G6BEx>@.ƾ Pv! p!b9^~b4p46y7S]P6$ѤZL7hEؿGиSײkm޽ ;6,п^V>fn7o@f۟ҋ8>f1"2pq_ S?FiZ7/U@8h=h+}z:H~umcч kO endstream endobj 2516 0 obj << /Length 1509 /Filter /FlateDecode >> stream xXێ6}߯[)RE],Eri8@Mh+:_)˲v7n&@ș39̅?>[eϒU5^^p=φ`ŏ/p]g.lu34JgY&6l,}dA'hJFVl/|n}Xu = t7it܄FjsH67Ȱ$ YJuw1) $IW$!=R498LنxR6$C0ll-}(jg6y" 㩂uRzbM4)hynBj}qj vN R4x'"Pe0ϱU )Em:z'^`Mji`IUv0G lͱyS1iRJ re u)rɹ4[y A otz)Q%tx"U?<̩KCU1S`& k8ַJ0 [澻/\罀LBD1DAo\mkUIqw q_\Kɸ#;hᯒ{38y)i7)o%vAJЀEu^)tȒM}n+5Unb{$@W\L&tkC첹J$'pS/Ӫ?gM`4ד߂=_*0 endstream endobj 2461 0 obj << /Type /ObjStm /N 100 /First 926 /Length 1422 /Filter /FlateDecode >> stream xYMo7W^(r3$#@@AQC*H}PNH[sN 04KPT]pD]TvQmAőᡚ3g'L.D\4TX.W2!JB&iO9QPԬGfBH 8iw)H.J jP sNURs ^,l*q*X@I4;xМ7y IWsJ ixH9<@2Ij>RK[+}F8FSQ 8fPS# f9 CPMPaM{u.gkdބ8|,^!EJRhx&U@%c[IL؁$bqK :nL-iJA؋HBpdb^jsI,,/o"kQXZu#I.! -sU|ZD,`聀b!ż")a9QCjkiL'6 L"ggsw̨җnǛ?Y {^ܼ]Q8]2'>'F;G7yH½|^obf? %ϡ#]{zbl{{yuUM™WH|ai#[yqO~\zxFJ[_=gHR!TdvZ=R4σ})7$E%] 0'KH uWN2o9M)>χ)@Nc"0%qO+܅]6tYwBI}?xvq{ fݮ>nfx\cXygCr`& ٪5,~mo/wcmumv RAv[M`NFb)I&Q\AzOw" |2zqRh*C|_3:8x=&G/SҀ\s(!y99V?%pH(N@ݑ@Q 2nqvF>0f Y~rlnWng#:}`E!;v8$7tcƒ㜄2o}DPxc&oW.'1M0Yhhx8Wz!&hD8pq($QDhHT\~ endstream endobj 2522 0 obj << /Length 1729 /Filter /FlateDecode >> stream xYK62R=M@"Ch^+d_!g(Q]'E A,I y|{ yY"-\/pGt6O7WE u[lMV7y-ovVliad+\x÷Uʗ{k淫7?Q8Iy"]/qAa2]WmYwS`N¡k' (8P]UFN j; 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s?\{钗/xb(FFQuRC|z v}:З-FR69חi{Z^sN?&>RCZ36F+e!qw9W0ٕqvJ^y$;WXH` '߸UP@;fL!Gݞ+\6uELY'-*zf {1U8-(%:G/B0_]ξG` `/7,<ĉ-?8+PpY6$Y'Gj71Jsg&*^Dy/~>|﷿)5lߍAy~EJ1_4REbTQ9 }>?hzvl3?]{e@1OW-TCckz{5NҩYC}yg^[찺 ZGBdǼvnJ?Bxq`M'n?l<^?ah8@G0ߚ{VT_.Nme)Bqlк`U 4 endstream endobj 2915 0 obj << /Length 1979 /Filter /FlateDecode >> stream xڥn8_a`*SEgEg2%/I%`>~M*z!y7ƋxQE2M*[/fW[ͫ7|GaUb4NUtʲg*+ `IK$$]sLRۀdqfu<,ϕ1%-<. ?(*5L T,O"NAq53\cEh OHH@zNVQ5n6WP x5± L.zBd("55T[xEm?y4WV/5}.q6-L.whqTӞ,&fsW(^)^t}J1( GO,OäLiR)?WR{4|<%Jypoq|b؆Rz[B1jE7ofgT*~ɨrE9IC QpKVZ( >F_*giV$LÍK}k8UO^@ӍԠ*$< Z*E3@JLJJYMi_e{Ȑa2]6B>~jãKHr̘H]!y*-)19AG,S M+$Ys<]`ccEԤ2{n%>҂qТ-ӝθ3GsE+'Dz4ɻYK>Bc\V{OqSx^W!_ehʓr"PetϲC KCoHk[%=j~_qA._?'nòW1OCGP,I t^?2Eh;=C F/yw)kؙW2 #H l+JF"Fj7?Z endstream endobj 2920 0 obj << /Length 1356 /Filter /FlateDecode >> stream xWo6_at/a-ڴhCFfldtlt#wJgl{:~GއL?9+,c^&Ŭڜ~[hr!#狓/t&/E)grvku 8Y,$eg_};T:,K2,^\,F(:J^d"2N^#<+0"kc|EPƬnkݹ5BxSO H~DJnܔ \D5v;i2^!G\덦 쮿 '!K^6鹁ft9=cC>=dBP?Ɨ]W#R͜WL<*bX2|CI I7a}P3!>7}f~%<赝Uj7Tw BzWc{ե6);?1<֯kY*٫:LFN٥#_k>#w|2- z_^6[6(E}#I<09~[HPe7&#ż S*"WX$}BcP_ G.dUpi^|b_1tz8F)I)HEr# ֓),߱ Է C{+x,<./M^ۺ;̂OdS}g|3o;M{1#niVEKʩ[=Z]fl4}ۧ ~6 ɧ0B@)sdL@6SXwfv_'lip[0X?j"l6HS:0̂%dwf-e$<:Nf lq ;jYA#KL̟ssOqb endstream endobj 2926 0 obj << /Length 1416 /Filter /FlateDecode >> stream xڭWo6_@M(K-M"݂bm""KE5:A"x<`X/ˣ(id$rR zPliMNBXD(gbd޻#\[e8I?HAN{Z(x0?O><*8?;νXL#4YBYxJ]6xelD89K+qCgFj3Ws?ڣו,z٥Nc, ÅW^z3ʋΌnl UkXaD%1eyy<E8OCGe mT) /Da,6C ,c*xnLi@Wp$ȭ2Z5~*.3bxa9ᓏO/'LKBgNPʾ>nBa04>!  > zWk!Ouz/#,HehS@u,ɭvag:m|mTWթ{]kRODJÒ`#em`§KT %ߨ%^Ɓ]N~ F8c)<`}_/0> ֣ϙM&Oibz u ~5Zj# Lޮ' endstream endobj 2935 0 obj << /Length 1812 /Filter /FlateDecode >> stream xڍWY6 ~ϯhb al E}AckgZ. &EQ"eQ$8qՌ_HA?\8I Dr?TfwZm~E^~>8.~yC^+4 <)/^-v&aLP楇8IQ xyL6׫q_HDLӯ?͉)H+^׃]$Ͷ0#V?6joo#NOҤH0Ž?ԲF`hM .4t#¼oE>>] )9atLr[] x׺BMQzoy|7cs$`/OW<+|ɸUucźv )c[bTPm9'88K M5v` "n7~`=rw%T!Iv9ȌS&1B8'dHlHa ) Y0đ\W܌gHIJVs j< Bq3 0@~(L I⊨}Ō<;KkroX$߹ 4*s҇ڮD$FFO~KBQE# $; PxPVGi{B.)^ 'ch7 ;7.GC,])mhݷ88at9ޡKNseWA7ݹx#4\efzb ZhcSPImYD\vE5S%.2auk b!lRPG%JvO|L4'<|(!<@xqq"M_mYc> stream xXo6~_al/4P1~Y0 tK%ݽ}e"YI ȣە]4b#>YoE4^Q1׫=l||ƽ]u89yo<^j<=BT "]8NQΤB0MFx_7g/N0<^xD8hK(FﾊzUImB4v4aFy QfRzA- 4|{9KfF, ?YBwWwؼoVhQ^U5 ~ #$ۍ#H4Ȣ3IFCf7@oTԏ8J h#_ I Ĕ=0i/ 4N$ v yE+vql3F>?ޅ< #pS<z-*SBK!6ߪ <.晣&jp%uʕw yx"܍!~~LYKli\=/[,AZ!5fQ=ƈhf-*̥Eb+K<&eUI0OHlzB܈%Zz*\ɢi1ddԫuT(9k}Nz LRee4-"^e`R^Bz4^GCD/RvvMۥJnQpZځkmJ5J^󘸅B Ppi8fgMU5  IB v8-چ;!)վgoa,#4W+tKj&P10ȭW r6QjmmB$! -}i Q=wL|,O휕s~AڑP-N=I=> !nWk?WΒ˜fZVxESO'Mxނ[5 endstream endobj 2946 0 obj << /Length 1236 /Filter /FlateDecode >> stream xVKo8Wً zf4iM6==2c ,;P(E/ ֈ87d ?6H $~<Ȫ=jWl=p#:O/A0`$4a}Wx:Nt^|4~_}Åy/gr8aPq69><^pA y10A(gc,& }„mhɻ,";UC{r5wP&NLSNjR}Aʒ8'1oK&P&d|T4@E,k)gESn{~R.xd] ]Pu:5^6˴ԅlL^h%J^NW(`VL/UZ.,B{SYy3ₛt{79꥚Vm&+n5"3*LQY!5nI+̒Bf~JqY]G`$BsZ-uݣxL$qejNOJBSUr|^.|*{^ ~d%ؽiؐQ<'46Z o+Z/H/duu8} U +~km >q)ӀeYF4td{?l?t2B/_v9VsMssa Nl۵G$A[܌s^Miod|͑9>G~pJ" " x,y;@u,LcFFnj4Qvers7d%FV)B{x'>& 5 Oj endstream endobj 2951 0 obj << /Length 954 /Filter /FlateDecode >> stream xڽV[oH~ϯ@~ >8jtbJGOz>9ŔFu[shXw⭐gNODP9mjT`qoQl<?: @tͦ~P*i"iL7UDFmMڱ LX<q7ܝ;fdJ3P]U0P:}L:06vpltOCfMPH=UŒ.% CrR E5I*u+,Scruc lf?3S& gV37(_@:KР7>wk]jXw6BIr\  endstream endobj 2956 0 obj << /Length 1514 /Filter /FlateDecode >> stream xڭWmo6_al*c1CVvpvhAbm&"(Kx#kP<yo=$Eße7"ţluĝZhpۑzP4_]M,]ߵnKY֪( FA' ԝT1SA D?_?zs1C=1yQD{#dĆiaPOsml.kiUn&RnQ2dqaĢ&I-k=U4(RpF,IBaliYPVE츔Ky'.26*dM^J0d,I@ 0VEV]RxH&ΔU)|m1diSv6yeQP  *u,&B1 tAMJ(0 8?g;r~UXWsUcV&e1xgiPU]t *9C;K;ү `clXKA2 #dPqn  m66"fj 0t($Ô\깲.4^eHA~Zrg>-m##IwD!WR@>sW={ Wm.k*nPcVmdO Ҕ{.ZpA3:bCOJ{J 0z:A7Tta6JpA⿅;Lm-IW g^2YY,aH=nC&K.jj* Y3)3K hD (sq抾a-. 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Copyright @copyright{} 2019, 2023, 2024, 2025, 2026 Andrew Janke @quotation Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions. @end quotation @end copying @documentdescription User manual for Tablicious for Octave. @end documentdescription @titlepage @title Tablicious for GNU Octave @subtitle version @value{VERSION}, February 2026 @author Andrew Janke @page @vskip 0pt plus 1filll @insertcopying @end titlepage @dircategory Math @direntry * Tablicious: (tablicious). Tablicious package for Octave @end direntry @shortcontents @contents @ifnottex @node Top, Introduction @top Tablicious for GNU Octave This manual is for Tablicious, version @value{VERSION}. @end ifnottex @menu * Introduction:: * Getting Started:: * Table Representation:: * Date and Time Representation:: * Validation Functions:: * Example Data Sets:: * Data Sets from R:: * Missing Functionality:: * API Reference:: * Copying:: @end menu @node Introduction @chapter Introduction @quotation Time is an illusion. Lunchtime doubly so. @author Douglas Adams @end quotation This is the manual for the Tablicious package version @value{VERSION} for GNU Octave. Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a @code{table} class with support for filtering and join operations; @code{datetime}, @code{duration}, and related classes; Missing Data support; @code{string} and @code{categorical} data types; and other miscellaneous things. This document is a work in progress. You are invited to help improve it and submit patches. Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types. Tablicious was written by Andrew Janke <@email{floss@@apjanke.net}>. Support can be found on the @url{https://github.com/apjanke/octave-tablicious, Tablicious project GitHub page}. @node Getting Started @chapter Getting Started The easiest way to obtain Tablicious is by using Octave's @code{pkg} package manager. To install the development prerelease of Tablicious, run this in Octave: @example pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz @end example (Check the releases page at @url{https://github.com/apjanke/octave-tablicious/releases} to find out what the actual latest release number is.) For development, you can obtain the source code for Tablicious from the project repo on GitHub at @url{https://github.com/apjanke/octave-tablicious}. Make a local clone of the repo. Then add the @file{inst} directory in the repo to your Octave path. @node Table Representation @chapter Table Representation Tablicious provides the @code{table} class for representing tabular data. A @code{table} is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records. A @code{table} is composed of multiple “variables”, each with a name, which all have the same number of rows. (A @code{table} variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the @var{i}-th element or row of each variable compose a single record or observation. Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a @code{table} is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way. Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using @code{table} arrays. It’s like having your own in-memory SQL engine. @node Table Construction @section Table Construction There are two main ways to construct a @code{table} array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a @code{table}. To build an array from multiple variables, use the @code{table(@dots{})} constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new @code{table} object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the @code{'VariableNames'} option. To convert a tabular-organized array of another type into a @code{table}, use the conversion functions like @code{array2table}, @code{struct2table} and @code{cell2table}. @code{array2table} and @code{cell2table} take each column of the input array and turn it into a separate table variable in the resulting @code{table}. @code{struct2table} takes the fields of a struct and puts them into table variables. @node Tables vs SQL @section Tables vs SQL Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave @code{table} operations. In this table, @code{t} is a variable holding a @code{table} array, and @code{ix} is some indexing expression. @multitable @columnfractions .33 .33 .33 @headitem SQL @tab Relational @tab Octave @code{table} @item SELECT @tab PROJECT @tab @code{subsetvars}, @code{t(:,ix)} @item WHERE @tab RESTRICT @tab @code{subsetrows}, @code{t(ix,:)} @item INNER JOIN @tab JOIN @tab @code{innerjoin} @item OUTER JOIN @tab OUTER JOIN @tab @code{outerjoin} @item FROM @code{@var{table1}, @var{table2}, @dots{}} @tab Cartesian product @tab @code{cartesian} @item GROUP BY @tab SUMMARIZE @tab @code{groupby} @item DISTINCT @tab (automatic) @tab @code{unique(t)} @end multitable Note that there is one big difference between relational algebra and SQL & Octave @code{table}: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit @code{DISTINCT}/@code{unique()} on its results, and there‘s no @code{ORDER BY}/@code{sort()}. This is not the case in SQL or Octave @code{table}. Note for users coming from Matlab: Matlab does not provide a general @code{groupby} function. Instead, you have to variously use @code{rowfun}, @code{grpstats}, @code{groupsummary}, and manual code to accomplish “group by” operations. Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke @node Date and Time Representation @chapter Date and Time Representation Tablicious provides the @code{datetime} class for representing points in time. There’s also @code{duration} and @code{calendarDuration} for representing periods or durations of time. Like vector quantities along the time line, as opposed to @code{datetime} being a point along the time line. @node datetime Class @section @code{datetime} Class A @code{datetime} is an array object that represents points in time in the familiar Gregorian calendar. This is an attempt to reproduce the functionality of Matlab's @code{datetime}. It also contains some Octave-specific extensions. The underlying representation is that of a datenum (a @code{double} containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a @code{datetime} inclues an optional @code{TimeZone} property indicating what time zone the datetimes are in. So, basically, a @code{datetime} is an object wrapper around a datenum array, plus time zone support. @menu * Datenum Compatibility:: @end menu @node Datenum Compatibility @subsection Datenum Compatibility While the underlying data representation of @code{datetime} is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations. This is because of the signature of the @code{datetime} constructor. When combining objects and primitive types like @code{double}, the primitive type is promoted to an object by calling the other object's one-argument constructor on it. However, the one-argument numeric-input consstructor for @code{datetime} does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab's part; for compatibility, Octave does not alter that interface. To combine @code{datetime}s with datenums, you can convert the datenums to @code{datetime}s by calling @code{datetime.ofDatenum} or @code{datetime(x, 'ConvertFrom', 'datenum')}, or you can convert the @code{datetime}s to datenums by accessing its @code{dnums} field with @code{x.dnums}. Examples: @example dt = datetime('2011-03-04') dn = datenum('2017-01-01') [dt dn] @result{} error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND] [dt datetime.ofDatenum(dn)] @result{} 04-Mar-2011 01-Jan-2017 @end example Also, if you have a zoned @code{datetime}, you can’t combine it with a datenum, because datenums do not carry time zone information. @node Time Zones @section Time Zones Tablicious has support for representing dates in time zones and for converting between time zones. A @code{datetime} may be "zoned" or "zoneless". A zoneless @code{datetime} does not have a time zone associated with it. This is represented by an empty @code{TimeZone} property on the @code{datetime} object. A zoneless @code{datetime} represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts). A zoned @code{datetime} is associated with a time zone. It is represented by having the time zone's IANA zone identifier (e.g. @code{'UTC'} or @code{'America/New_York'}) in its @code{TimeZone} property. A zoned @code{datetime} represents the local time in that time zone. By default, the @code{datetime} constructor creates unzoned @code{datetime}s. To make a zoned @code{datetime}, either pass the @code{'TimeZone'} option to the constructor, or set the @code{TimeZone} property after object creation. Setting the @code{TimeZone} property on a zoneless @code{datetime} declares that it's a local time in that time zone. Setting the @code{TimeZone} property on a zoned @code{datetime} turns it back into a zoneless @code{datetime} without changing the local time it represents. You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned @code{datetime}s. @example % Create an unzoned datetime d = datetime('2011-03-04 06:00:00') @result{} 04-Mar-2011 06:00:00 % Create a zoned datetime d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') @result{} 04-Mar-2011 06:00:00 America/New_York % This is equivalent d_ny = datetime('2011-03-04 06:00:00'); d_ny.TimeZone = 'America/New_York' @result{} 04-Mar-2011 06:00:00 America/New_York % Convert it to Chicago time d_chi.TimeZone = 'America/Chicago' @result{} 04-Mar-2011 05:00:00 America/Chicago @end example When you combine two zoned @code{datetime}s via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. @example d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York') d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles') d_la - d_ny @result{} 03:00:00 @end example You cannot combine a zoned and an unzoned @code{datetime}. This results in an error being raised. @quotation Warning Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined. @end quotation @menu * Defined Time Zones:: @end menu @node Defined Time Zones @subsection Defined Time Zones Tablicious's time zone data is drawn from the @uref{https://www.iana.org/time-zones, IANA Time Zone Database}, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do. You can use the @code{timezones} function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows). @quotation Note The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for @code{datetime}s outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.) @end quotation @node Durations @section Durations @menu * duration Class:: * calendarDuration Class:: @end menu @node duration Class @subsection @code{duration} Class A @code{duration} represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.) A @code{duration} has a resolution of about a nanosecond for typical dates. The underlying representation is a @code{double} representing the number of days elapsed, similar to a datenum, except it's interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch. You can add or subtract a @code{duration} to a @code{datetime} to get another @code{datetime}. You can also add or subtract @code{durations} to each other. @node calendarDuration Class @subsection @code{calendarDuration} Class A @code{calendarDuration} represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A @code{calendarDuration} does arithmetic with "whole" calendar periods. @code{calendarDuration}s and @code{duration}s cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow @code{duration}s to be interpreted as numbers of days when combined with @code{calendarDuration}s.) @example d = datetime('2011-03-04 00:00:00') @result{} 04-Mar-2011 cdur = calendarDuration(1, 3, 0) @result{} 1y 3mo d2 = d + cdur @result{} 04-Jun-2012 @end example @node Validation Functions @chapter Validation Functions Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs. These validation functions are named following the pattern @code{mustBeXxx}, where @code{Xxx} is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs. The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there. Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider. When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with @code{inputname()}, so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. @example % Validation of a simple variable does not need a label mustBeScalar (x); % Validation of a field or property reference does need a label mustBeScalar (this.foo, 'this.foo'); @end example @node Example Data Sets @chapter Example Data Sets Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the @code{tblish.datasets} and @code{tblish.dataset} classes. To see a list of the available data sets, run @code{tblish.datasets.list()}. Then to load one of the example data sets, run @code{tblish.datasets.load('examplename')}. For example: @example tblish.datasets.list t = tblish.datasets.load('cupcake') @end example You can also load it by calling @code{tblish.dataset.}. This does the same thing. For example: @example t = tblish.dataset.cupcake @end example When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs). Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the @code{doc} command on @code{tblish.dataset.}, where @var{} is the name of the data set. For example: @example doc tblish.dataset.cupcake @end example (The command @code{help tblish.dataset.} ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s @code{help} command.) @node Data Sets from R @section Data Sets from R Many of Tablicious’ example data sets are based on the example datasets found in R’s @code{datasets} package. R can be found at @url{https://www.r-project.org/}, and documentation for its @code{datasets} is at @url{https://rdrr.io/r/datasets/datasets-package.html}. Thanks to the R developers for producing the original data sets here. Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another. Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code. @node Missing Functionality @chapter Missing Functionality Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently: @itemize @item File I/O like @code{readtable()} and @code{writetable()} @item @code{summary()} @code{categorical} @item Assignment to table variables using @code{.}-indexing @item @code{timetable} @item POSIX time zone support for years outside the IANA time zone database coverage @item Week-of-year (ISO calendar) calculations @item Various @code{'ConvertFrom'} forms for @code{datetime} and @code{duration} constructors @item Support for LDML formatting for @code{datetime} @item Various functions: @itemize @item @code{between} @item @code{caldiff} @item @code{dateshift} @item @code{week} @end itemize @item @code{isdst}, @code{isweekend} @item @code{calendarDuration.split} @item @code{duration.Format} support @item Moving window methods in @code{fillmissing} @item @code{UTCOffset} and @code{DSTOffset} fields in the output of @code{timezones()} @item Plotting support @item Things omitted for compatibility: @itemize @item @code{ismissing} function @end itemize @end itemize A global @code{ismissing} function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. @code{ismissing} methods for various classes in Tablicious are defined. Tablicious also supplies an @code{isnanny} function with behavior similar to @code{ismissing} for clients which which wish to have @code{ismissing}-like behavior using a dependency only on Tablicious. It is the author’s hope that many of these will be implemented some day. These areas of missing functionality are tracked on the Tablicious issue tracker at @url{https://github.com/apjanke/octave-tablicious/issues} and @url{https://github.com/users/apjanke/projects/3}. @node API Reference @chapter API Reference @node API by Category @section API by Category @subsection Tables @table @asis @item table Tabular data array containing multiple columnar variables. @xref{table}. @item array2table Convert an array to a table. @xref{array2table}. @item cell2table Convert a cell array to a table. @xref{cell2table}. @item struct2table Convert struct to a table. @xref{struct2table}. @item tableOuterFillValue @xref{tableOuterFillValue}. @item vartype Filter by variable type for use in suscripting. @xref{vartype}. @item istable True if input is a ‘table’ array or other table-like type, false otherwise. @xref{istable}. @item istimetable True if input is a ‘timetable’ array or other timetable-like type, false otherwise. @xref{istimetable}. @item istabular True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them. @xref{istabular}. @item tblish.evalWithTableVars Evaluate an expression against a table array’s variables. @xref{tblish.evalWithTableVars}. @item tblish.table.grpstats Statistics by group for a table array. @xref{tblish.table.grpstats}. @end table @subsection Strings and Categoricals @table @asis @item string A string array of Unicode strings. @xref{string}. @item NaS “Not-a-String". @xref{NaS}. @item contains Test if strings contain a pattern. @xref{contains}. @item dispstrs Display strings for array. @xref{dispstrs}. @item categorical Categorical variable array. @xref{categorical}. @item iscategorical True if input is a ‘categorical’ array, false otherwise. @xref{iscategorical}. @item NaC “Not-a-Categorical". @xref{NaC}. @item discretize Group data into discrete bins or categories. @xref{discretize}. @end table @subsection Dates and Times @table @asis @item datetime Represents points in time using the Gregorian calendar. @xref{datetime}. @item NaT “Not-a-Time”. @xref{NaT}. @item todatetime Convert input to a Tablicious datetime array, with convenient interface. @xref{todatetime}. @item localdate Represents a complete day using the Gregorian calendar. @xref{localdate}. @item isdatetime True if input is a ‘datetime’ array, false otherwise. @xref{isdatetime}. @item calendarDuration Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. @xref{calendarDuration}. @item iscalendarduration True if input is a ‘calendarDuration’ array, false otherwise. @xref{iscalendarduration}. @item calmonths Create a ‘calendarDuration’ that is a given number of calendar months long. @xref{calmonths}. @item calyears Construct a ‘calendarDuration’ a given number of years long. @xref{calyears}. @item days Duration in days. @xref{days}. @item duration Represents durations or periods of time as an amount of fixed-length time (i.e. @xref{duration}. @item hours Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. @xref{hours}. @item isduration True if input is a ‘duration’ array, false otherwise. @xref{isduration}. @item milliseconds Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X. @xref{milliseconds}. @item minutes Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. @xref{minutes}. @item seconds Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X. @xref{seconds}. @item timezones List all the time zones defined on this system. @xref{timezones}. @item years Create a ‘duration’ X years long, or get the years in a ‘duration’ X. @xref{years}. @end table @subsection Missing Data @table @asis @item missing Generic auto-converting missing value. @xref{missing}. @item isnanny Test if elements are NaN or NaN-like. @xref{isnanny}. @item eqn Determine element-wise equality, treating NaNs as equal @xref{eqn}. @end table @subsection Validation Functions @table @asis @item mustBeA @xref{mustBeA}. @item mustBeCellstr @xref{mustBeCellstr}. @item mustBeCharvec @xref{mustBeCharvec}. @item mustBeFinite @xref{mustBeFinite}. @item mustBeInteger @xref{mustBeInteger}. @item mustBeMember @xref{mustBeMember}. @item mustBeNonempty @xref{mustBeNonempty}. @item mustBeNumeric @xref{mustBeNumeric}. @item mustBeReal @xref{mustBeReal}. @item mustBeSameSize @xref{mustBeSameSize}. @item mustBeScalar @xref{mustBeScalar}. @item mustBeScalarLogical @xref{mustBeScalarLogical}. @item mustBeVector @xref{mustBeVector}. @end table @subsection Miscellaneous @table @asis @item colvecfun Apply a function to column vectors in array. @xref{colvecfun}. @item dispstrs Display strings for array. @xref{dispstrs}. @item head Get first K rows of an array. @xref{head}. @item isfile @xref{isfile}. @item isfolder @xref{isfolder}. @item pp Alias for prettyprint, for interactive use. @xref{pp}. @item scalarexpand Expand scalar inputs to match size of non-scalar inputs. @xref{scalarexpand}. @item size2str Format an array size for display. @xref{size2str}. @item splitapply Split data into groups and apply function. @xref{splitapply}. @item tail Get last K rows of an array. @xref{tail}. @item vecfun Apply function to vectors in array along arbitrary dimension. @xref{vecfun}. @item tblish.sizeof2 Approximate size of an array in bytes, with object support. @xref{tblish.sizeof2}. @end table @subsection Example Datasets @table @asis @item tblish.datasets Example dataset collection. @xref{tblish.datasets}. @item tblish.dataset The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious. @xref{tblish.dataset}. @end table @subsection Example Code @table @asis @item tblish.examples.coplot Conditioning plot. @xref{tblish.examples.coplot}. @item tblish.examples.plot_pairs Plot pairs of variables against each other. @xref{tblish.examples.plot_pairs}. @item tblish.examples.SpDb The classic Suppliers-Parts example database. @xref{tblish.examples.SpDb}. @end table @subsection Uncategorized @table @asis @item caldays Create a ‘calendarDuration’ that is a given number of calendar days long. @xref{caldays}. @end table @node API Alphabetically @section API Alphabetically @menu * array2table:: Convert an array to a table. * caldays:: Create a ‘calendarDuration’ that is a given number of calendar days long. * calendarDuration:: Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. * calmonths:: Create a ‘calendarDuration’ that is a given number of calendar months long. * calyears:: Construct a ‘calendarDuration’ a given number of years long. * categorical:: Categorical variable array. * cell2table:: Convert a cell array to a table. * colvecfun:: Apply a function to column vectors in array. * contains:: Test if strings contain a pattern. * datetime:: Represents points in time using the Gregorian calendar. * days:: Duration in days. * discretize:: Group data into discrete bins or categories. * dispstrs:: Display strings for array. * duration:: Represents durations or periods of time as an amount of fixed-length time (i.e. * eqn:: Determine element-wise equality, treating NaNs as equal * head:: Get first K rows of an array. * hours:: Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. * iscalendarduration:: True if input is a ‘calendarDuration’ array, false otherwise. * iscategorical:: True if input is a ‘categorical’ array, false otherwise. * isdatetime:: True if input is a ‘datetime’ array, false otherwise. * isduration:: True if input is a ‘duration’ array, false otherwise. * isfile:: * isfolder:: * isnanny:: Test if elements are NaN or NaN-like. * istable:: True if input is a ‘table’ array or other table-like type, false otherwise. * istabular:: True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them. * istimetable:: True if input is a ‘timetable’ array or other timetable-like type, false otherwise. * localdate:: Represents a complete day using the Gregorian calendar. * milliseconds:: Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X. * minutes:: Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X. * missing:: Generic auto-converting missing value. * mustBeA:: * mustBeCellstr:: * mustBeCharvec:: * mustBeFinite:: * mustBeInteger:: * mustBeMember:: * mustBeNonempty:: * mustBeNumeric:: * mustBeReal:: * mustBeSameSize:: * mustBeScalar:: * mustBeScalarLogical:: * mustBeVector:: * NaC:: “Not-a-Categorical". * NaS:: “Not-a-String". * NaT:: “Not-a-Time”. * pp:: Alias for prettyprint, for interactive use. * scalarexpand:: Expand scalar inputs to match size of non-scalar inputs. * seconds:: Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X. * size2str:: Format an array size for display. * splitapply:: Split data into groups and apply function. * string:: A string array of Unicode strings. * struct2table:: Convert struct to a table. * table:: Tabular data array containing multiple columnar variables. * tableOuterFillValue:: * tail:: Get last K rows of an array. * tblish.dataset:: The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious. * tblish.datasets:: Example dataset collection. * tblish.evalWithTableVars:: Evaluate an expression against a table array’s variables. * tblish.examples.coplot:: Conditioning plot. * tblish.examples.plot_pairs:: Plot pairs of variables against each other. * tblish.examples.SpDb:: The classic Suppliers-Parts example database. * tblish.sizeof2:: Approximate size of an array in bytes, with object support. * tblish.table.grpstats:: Statistics by group for a table array. * timezones:: List all the time zones defined on this system. * todatetime:: Convert input to a Tablicious datetime array, with convenient interface. * vartype:: Filter by variable type for use in suscripting. * vecfun:: Apply function to vectors in array along arbitrary dimension. * years:: Create a ‘duration’ X years long, or get the years in a ‘duration’ X. @end menu @node array2table @subsection array2table @deftypefn {Function} {@var{out} =} array2table (@var{c}) @deftypefnx {Function} {@var{out} =} array2table (@dots{}, @code{'VariableNames'}, @var{VariableNames}) @deftypefnx {Function} {@var{out} =} array2table (@dots{}, @code{'RowNames'}, @var{RowNames}) Convert an array to a table. Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array. You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use @code{cell2table} instead, columns of the cell array which can be condensed into primitive arrays will be. With @code{array2table}, they won't be. See also: @ref{cell2table}, @ref{table}, @ref{struct2table} @end deftypefn @node caldays @subsection caldays @deftypefn {Function File} {@var{out} =} caldays (@var{x}) Create a @code{calendarDuration} that is a given number of calendar days long. Input @var{x} is a numeric array specifying the number of calendar days. This is a shorthand alternative to calling the @code{calendarDuration} constructor with @code{calendarDuration(0, 0, x)}. Returns a new @code{calendarDuration} object of the same size as @var{x}. @xref{calendarDuration}. @end deftypefn @node calendarDuration @subsection calendarDuration @deftp {Class} calendarDuration Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.) @end deftp @deftypeivar calendarDuration @code{char} Format The format to display this @code{calendarDuration} in. Currently unsupported. This is a single value that applies to the whole array. @end deftypeivar @node calendarDuration.calendarDuration @subsubsection calendarDuration.calendarDuration @deftypefn {Constructor} {@var{obj} =} calendarDuration () Constructs a new scalar @code{calendarDuration} of zero elapsed time. @end deftypefn @deftypefn {Constructor} {@var{obj} =} calendarDuration (@var{Y}, @var{M}, @var{D}) @deftypefnx {Constructor} {@var{obj} =} calendarDuration (@var{Y}, @var{M}, @ @var{D}, @var{H}, @var{MI}, @var{S}) Constructs new @code{calendarDuration} arrays based on input values. @end deftypefn @node calendarDuration.datevec @subsubsection calendarDuration.datevec @deftypefn {Method} {[@var{dvec}] =} datevec (@var{obj}) @deftypefnx {Method} {[@var{Y}, @var{M}, @var{D}, @var{H}, @var{MN}, @var{S}] =} datevec (@var{obj}) Convert a @code{calendarDuration} to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this @code{calendarDuration}. When nargout is 0 or 1, returns a double array of size n-by-6, where n is @code{numel(obj)}. When nargout is 2 or more, returns double arrays the same size as @var{obj}. @end deftypefn @node calendarDuration.dispstrs @subsubsection calendarDuration.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Get display strings for each element of @var{obj}. Returns a cellstr the same size as @var{obj}. @end deftypefn @node calendarDuration.ismissing @subsubsection calendarDuration.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) True if input elements are missing. This is equivalent to @code{ismissing}. Returns logical array the same size as @var{obj}. @end deftypefn @node calendarDuration.isnan @subsubsection calendarDuration.isnan @deftypefn {Method} {@var{out} =} isnan (@var{obj}) True if input elements are NaN. This is equivalent to @code{ismissing}, and is provided for compatibility and polymorphic programming purposes. Returns logical array the same size as @var{obj}. @end deftypefn @node calendarDuration.minus @subsubsection calendarDuration.minus @deftypefn {Method} {@var{out} =} times (@var{A}, @var{B}) Subtraction: Subtracts one @code{calendarDuration} from another. Returns a @code{calendarDuration}. @end deftypefn @node calendarDuration.mtimes @subsubsection calendarDuration.mtimes @deftypefn {Method} {@var{out} =} times (@var{obj}, @var{B}) Multiplication: Multiplies a @code{calendarDuration} by a numeric factor. This does not do true matrix multiplication, so at least one of the input arguments must be scalar. Returns a @code{calendarDuration}. @end deftypefn @node calendarDuration.plus @subsubsection calendarDuration.plus @deftypefn {Method} {@var{out} =} plus (@var{obj}, @var{B}) Addition: add to a @code{calendarDuration}. All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs. @var{B} may be a @code{calendarDuration}, @code{duration}, or numeric. If @var{B} is numeric, it is converted to a @code{calendarDuration} using @code{caldays(B)}. Returns a @code{calendarDuration}. @end deftypefn @node calendarDuration.times @subsubsection calendarDuration.times @deftypefn {Method} {@var{out} =} times (@var{obj}, @var{B}) Multiplication: Multiplies a @code{calendarDuration} by a numeric factor @var{B}. Returns a @code{calendarDuration}. @end deftypefn @node calendarDuration.uminus @subsubsection calendarDuration.uminus @deftypefn {Method} {@var{out} =} uminus (@var{obj}) Unary minus. Negates the sign of @var{obj}, which is equivalent to negating each of its components. @end deftypefn @node calmonths @subsection calmonths @deftypefn {Function File} {@var{out} =} calmonths (@var{x}) Create a @code{calendarDuration} that is a given number of calendar months long. Input @var{x} is a numeric array specifying the number of calendar months. This is a shorthand alternative to calling the @code{calendarDuration} constructor with @code{calendarDuration(0, x, 0)}. Returns a new @code{calendarDuration} object of the same size as @var{x}. @xref{calendarDuration}. @end deftypefn @node calyears @subsection calyears @deftypefn {Function} {@var{out} =} calyears (@var{x}) Construct a @code{calendarDuration} a given number of years long. This is a shorthand for calling @code{calendarDuration(@var{x}, 0, 0)}. @xref{calendarDuration}. @end deftypefn @node categorical @subsection categorical @deftp {Class} categorical Categorical variable array. A @code{categorical} array represents an array of values of a categorical variable. Each @code{categorical} array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array). In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a @code{NaN} value. This class is not fully implemented yet. Missing stuff: @itemize @bullet @item gt, ge, lt, le @item Ordinal support in general @item countcats @item summary @end itemize @end deftp @deftypeivar categorical @code{uint16} code The numeric codes of the array element values. These are indexes into the @code{cats} category list. This is a planar property. @end deftypeivar @deftypeivar categorical @code{logical} tfMissing A logical mask indicating whether each element of the array is missing (that is, undefined). This is a planar property. @end deftypeivar @deftypeivar categorical @code{cellstr} cats The names of the categories in this array. This is the list into which the @code{code} values are indexes. @end deftypeivar @deftypeivar categorical @code{scalar_logical} isOrdinal A scalar logical indicating whether the categories in this array have an ordinal relationship. @end deftypeivar @node categorical.addcats @subsubsection categorical.addcats @deftypefn {Method} {@var{out} =} addcats (@var{obj}, @var{newcats}) Add categories to categorical array. Adds the specified categories to @var{obj}, without changing any of its values. @var{newcats} is a cellstr listing the category names to add to @var{obj}. @end deftypefn @node categorical.categorical @subsubsection categorical.categorical @deftypefn {Constructor} {@var{obj} =} categorical () Constructs a new scalar categorical whose value is undefined. @end deftypefn @deftypefn {Constructor} {@var{obj} =} categorical (@var{vals}) @deftypefnx {Constructor} {@var{obj} =} categorical (@var{vals}, @var{valueset}) @deftypefnx {Constructor} {@var{obj} =} categorical (@var{vals}, @var{valueset}, @var{category_names}) @deftypefnx {Constructor} {@var{obj} =} categorical (@dots{}, @code{'Ordinal'}, @var{Ordinal}) @deftypefnx {Constructor} {@var{obj} =} categorical (@dots{}, @code{'Protected'}, @var{Protected}) Constructs a new categorical array from the given values. @var{vals} is the array of values to convert to categoricals. @var{valueset} is the set of all values from which @var{vals} is drawn. If omitted, it defaults to the unique values in @var{vals}. @var{category_names} is a list of category names corresponding to @var{valueset}. If omitted, it defaults to @var{valueset}, converted to strings. @var{Ordinal} is a logical indicating whether the category values in @var{obj} have a numeric ordering relationship. Defaults to false. @var{Protected} indicates whether @var{obj} should be protected, which prevents the addition of new categories to the array. Defaults to false. @end deftypefn @node categorical.categories @subsubsection categorical.categories @deftypefn {Method} {@var{out} =} categories (@var{obj}) Get a list of the categories in @var{obj}. Gets a list of the categories in @var{obj}, identified by their category names. Returns a cellstr column vector. @end deftypefn @node categorical.cellstr @subsubsection categorical.cellstr @deftypefn {Method} {@var{out} =} cellstr (@var{obj}) Convert to cellstr. Converts @var{obj} to a cellstr array. The strings will be the category names for corresponding values, or @code{''} for undefined values. Returns a cellstr array the same size as @var{obj}. @end deftypefn @node categorical.dispstrs @subsubsection categorical.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Display strings. Gets display strings for each element in @var{obj}. The display strings are either the category string, or @code{''} for undefined values. Returns a cellstr array the same size as @var{obj}. @end deftypefn @node categorical.double @subsubsection categorical.double @deftypefn {Method} {@var{out} =} double (@var{obj}) Convert to double array, by getting the underlying code values. Converts @var{obj} to a string array. The doubles will be the underlying numeric code values of @var{obj}, or @code{NaN} for undefined values. The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering. Returns a @code{double} array the same size as @var{obj}. @end deftypefn @node categorical.iscategory @subsubsection categorical.iscategory @deftypefn {Method} {@var{out} =} iscategory (@var{obj}, @var{catnames}) Test whether input is a category on a categorical array. @var{catnames} is a cellstr listing the category names to check against @var{obj}. Returns a logical array the same size as @var{catnames}. @end deftypefn @node categorical.ismissing @subsubsection categorical.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) Test whether elements are missing. For categorical arrays, undefined elements are considered to be missing. Returns a logical array the same size as @var{obj}. @end deftypefn @node categorical.isnanny @subsubsection categorical.isnanny @deftypefn {Method} {@var{out} =} isnanny (@var{obj}) Test whethere elements are NaN-ish. Checks where each element in @var{obj} is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not. Returns a logical array the same size as @var{obj}. @end deftypefn @node categorical.isordinal @subsubsection categorical.isordinal @deftypefn {Method} {@var{out} =} isordinal (@var{obj}) Whether @var{obj} is ordinal. Returns true if @var{obj} is ordinal (as determined by its @code{IsOrdinal} property), and false otherwise. @end deftypefn @node categorical.isundefined @subsubsection categorical.isundefined @deftypefn {Method} {@var{out} =} isundefined (@var{obj}) Test whether elements are undefined. Checks whether each element in @var{obj} is undefined. "Undefined" is a special value defined by @code{categorical}. It is equivalent to a @code{NaN} or a @code{missing} value. Returns a logical array the same size as @var{obj}. @end deftypefn @node categorical.mergecats @subsubsection categorical.mergecats @deftypefn {Method} {@var{out} =} mergecats (@var{obj}, @var{oldcats}) @deftypefnx {Method} {@var{out} =} mergecats (@var{obj}, @var{oldcats}, @var{newcat}) Merge multiple categories. Merges the categories @var{oldcats} into a single category. If @var{newcat} is specified, that new category is added if necessary, and all of @var{oldcats} are merged into it. @var{newcat} must be an existing category in @var{obj} if @var{obj} is ordinal. If @var{newcat} is not provided, all of @var{odcats} are merged into @code{oldcats@{1@}}. @end deftypefn @node categorical.missing @subsubsection categorical.missing @deftypefn {Static Method} {@var{out} =} categorical.missing () @deftypefnx {Static Method} {@var{out} =} categorical.missing (sz) Create an array of missing (undefined) categoricals. Creates a categorical array whose elements are all missing (). This is a convenience alias for @ref{categorical.undefined}, so you can call it generically. It returns strictly the same results as calling @ref{categorical.undefined} with the same arguments. Returns a categorical array. See also: categorical.undefined @end deftypefn @node categorical.removecats @subsubsection categorical.removecats @deftypefn {Method} {@var{out} =} removecats (@var{obj}) Removes all unused categories from @var{obj}. This is equivalent to @code{out = squeezecats (obj)}. @end deftypefn @deftypefn {Method} {@var{out} =} removecats (@var{obj}, @var{oldcats}) Remove categories from categorical array. Removes the specified categories from @var{obj}. Elements of @var{obj} whose values belonged to those categories are replaced with undefined. @var{newcats} is a cellstr listing the category names to add to @var{obj}. @end deftypefn @node categorical.renamecats @subsubsection categorical.renamecats @deftypefn {Method} {@var{out} =} renamecats (@var{obj}, @var{newcats}) @deftypefnx {Method} {@var{out} =} renamecats (@var{obj}, @var{oldcats}, @var{newcats}) Rename categories. Renames some or all of the categories in @var{obj}, without changing any of its values. @end deftypefn @node categorical.reordercats @subsubsection categorical.reordercats @deftypefn {Method} {@var{out} =} reordercats (@var{obj}) @deftypefnx {Method} {@var{out} =} reordercats (@var{obj}, @var{newcats}) Reorder categories. Reorders the categories in @var{obj} to match @var{newcats}. @var{newcats} is a cellstr that must be a reordering of @var{obj}’s existing category list. If @var{newcats} is not supplied, sorts the categories in alphabetical order. @end deftypefn @node categorical.setcats @subsubsection categorical.setcats @deftypefn {Method} {@var{out} =} setcats (@var{obj}, @var{newcats}) Set categories for categorical array. Sets the categories to use for @var{obj}. If any current categories are absent from the @var{newcats} list, current values of those categories become undefined. @end deftypefn @node categorical.squeezecats @subsubsection categorical.squeezecats @deftypefn {Method} {@var{out} =} squeezecats (@var{obj}) Remove unused categories. Removes all categories which have no corresponding values in @var{obj}’s elements. This is currently unimplemented. @end deftypefn @node categorical.string @subsubsection categorical.string @deftypefn {Method} {@var{out} =} string (@var{obj}) Convert to string array. Converts @var{obj} to a string array. The strings will be the category names for corresponding values, or for undefined values. Returns a @code{string} array the same size as @var{obj}. @end deftypefn @node categorical.summary @subsubsection categorical.summary @deftypefn {Method} {} summary (@var{obj}) Display summary of array’s values. Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category. @end deftypefn @node categorical.undefined @subsubsection categorical.undefined @deftypefn {Static Method} {@var{out} =} categorical.undefined () @deftypefnx {Static Method} {@var{out} =} categorical.undefined (sz) Create an array of undefined categoricals. Creates a categorical array whose elements are all . @var{sz} is the size of the array to create. If omitted or empty, creates a scalar. Returns a categorical array. See also: categorical.missing @end deftypefn @node cell2table @subsection cell2table @deftypefn {Function} {@var{out} =} cell2table (@var{c}) @deftypefnx {Function} {@var{out} =} cell2table (@dots{}, @code{'VariableNames'}, @var{VariableNames}) @deftypefnx {Function} {@var{out} =} cell2table (@dots{}, @code{'RowNames'}, @var{RowNames}) Convert a cell array to a table. Converts a 2-dimensional cell matrix into a table. Each column in the input @var{c} becomes a variable in @var{out}. For columns that contain all scalar values of @code{cat}-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type. See also: @ref{array2table}, @ref{table}, @ref{struct2table} @end deftypefn @node colvecfun @subsection colvecfun @deftypefn {Function} {@var{out} =} colvecfun (@var{fcn}, @var{x}) Apply a function to column vectors in array. Applies the given function @var{fcn} to each column vector in the array @var{x}, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array. @var{fcn} must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as @var{x}. Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x. @end deftypefn @node contains @subsection contains @deftypefn {Function} {@var{out} =} colvecfun (@var{str}, @var{pattern}) @deftypefnx {Function} {@var{out} =} colvecfun (@dots{}, @code{'IgnoreCase'}, @var{IgnoreCase}) Test if strings contain a pattern. Tests whether the given strings contain the given pattern(s). @var{str} (char, cellstr, or string) is a list of strings to compare against pattern. @var{pattern} (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them. Returns a logical array of the same size as the string array represented by @var{str}. See also: startsWith, endsWith @end deftypefn @node datetime @subsection datetime @deftp {Class} datetime Represents points in time using the Gregorian calendar. The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times. A @code{datetime} array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array. This is an attempt to reproduce the functionality of Matlab's @code{datetime}. It also contains some Octave-specific extensions. @end deftp @deftypeivar datetime @code{double} dnums The underlying datenums that represent the points in time. These are always in UTC. This is a planar property: the size of @code{dnums} is the same size as the containing @code{datetime} array object. @end deftypeivar @deftypeivar datetime @code{char} TimeZone The time zone this @code{datetime} array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does. Setting the @code{TimeZone} of a @code{datetime} array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent. @end deftypeivar @deftypeivar datetime @code{char} Format The format to display this @code{datetime} in. Currently unsupported. @end deftypeivar @node datetime.colon @subsubsection datetime.colon @deftypefn {Method} {@var{out} =} colon (@var{lo}, @var{hi}) @deftypefnx {Method} {@var{out} =} colon (@var{hi}, @var{inc}, @var{hi}) Generate a sequence of uniformly-spaced values. This method implements the behavior for the colon operator (@code{lo:hi} or @code{lo:inc:hi} calls) for the datetime type. "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time. The @var{inc} argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with @code{duration.ofDays (inc)}. The default value for @var{inc}, used in the two-arg @code{lo:hi} is 1, that is, 1 day of exactly 24 hours. Returns a datetime vector. WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if @var{inc} is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious. @end deftypefn @node datetime.convertDatenumTimeZone @subsubsection datetime.convertDatenumTimeZone @deftypefn {Static Method} {@var{out} =} datetime.convertDatenumTimeZone @ (@var{dnum}, @var{fromZoneId}, @var{toZoneId}) Convert a datenum from one time zone to another. @var{dnum} is a datenum array to convert. @var{fromZoneId} is a charvec containing the IANA Time Zone identifier for the time zone to convert from. @var{toZoneId} is a charvec containing the IANA Time Zone identifier for the time zone to convert to. Returns a datenum array the same size as @var{dnum}. @end deftypefn @node datetime.datenum @subsubsection datetime.datenum @deftypefn {Method} {@var{out} =} datenum (@var{obj}) Convert this to datenums that represent the same local time. Returns double array of same size as this. @end deftypefn @node datetime.datenum2posix @subsubsection datetime.datenum2posix @deftypefn {Static Method} {@var{out} =} datetime.datenum2posix (@var{dnums}) Converts Octave datenums to Unix dates. The input datenums are assumed to be in UTC. Returns a double, which may have fractional seconds. @end deftypefn @node datetime.datestr @subsubsection datetime.datestr @deftypefn {Method} {@var{out} =} datestr (@var{obj}) @deftypefnx {Method} {@var{out} =} datestr (@var{obj}, @dots{}) Format @var{obj} as date strings. Supports all arguments that core Octave's @code{datestr} does. Returns date strings as a 2-D char array. @end deftypefn @node datetime.datestrs @subsubsection datetime.datestrs @deftypefn {Method} {@var{out} =} datestrs (@var{obj}) @deftypefnx {Method} {@var{out} =} datestrs (@var{obj}, @dots{}) Format @var{obj} as date strings, returning cellstr. Supports all arguments that core Octave's @code{datestr} does. Returns a cellstr array the same size as @var{obj}. @end deftypefn @node datetime.datestruct @subsubsection datetime.datestruct @deftypefn {Method} {@var{out} =} datestruct (@var{obj}) Converts this to a "datestruct" broken-down time structure. A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents. The values in the returned broken-down time are those of the local time in this' defined time zone, if it has one. Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this. @end deftypefn @node datetime.datetime @subsubsection datetime.datetime @deftypefn {Constructor} {@var{obj} =} datetime () Constructs a new scalar @code{datetime} containing the current local time, with no time zone attached. @end deftypefn @deftypefn {Constructor} {@var{obj} =} datetime (@var{datevec}) @deftypefnx {Constructor} {@var{obj} =} datetime (@var{datestrs}) @deftypefnx {Constructor} {@var{obj} =} datetime (@var{in}, @code{'ConvertFrom'}, @var{inType}) @deftypefnx {Constructor} {@var{obj} =} datetime @ (@var{Y}, @var{M}, @var{D}, @var{H}, @var{MI}, @var{S}) @deftypefnx {Constructor} {@var{obj} =} datetime @ (@var{Y}, @var{M}, @var{D}, @var{H}, @var{MI}, @var{MS}) @deftypefnx {Constructor} {@var{obj} =} datetime @ (@dots{}, @code{'Format'}, @var{Format}, @code{'InputFormat'}, @var{InputFormat}, @ @code{'Locale'}, @var{InputLocale}, @code{'PivotYear'}, @var{PivotYear}, @ @code{'TimeZone'}, @var{TimeZone}) Constructs a new @code{datetime} array based on input values. @end deftypefn @node datetime.datevec @subsubsection datetime.datevec @deftypefn {Method} {@var{out} =} datevec (@var{obj}) @deftypefnx {Method} {[@var{Y}, @var{M}, @var{D}, @var{H}, @var{MN}, @var{S}] =} datevec (@var{obj}) Convert this to a datevec that represent the same local wall time. When nargout is 0 or 1, returns a double array of size n-by-6, where n is @code{numel(obj)}. When nargout is 2 or more, returns double arrays the same size as @var{obj}. @end deftypefn @node datetime.diff @subsubsection datetime.diff @deftypefn {Method} {@var{out} =} diff (@var{obj}) Differences between elements. Computes the difference between each successive element in @var{obj}, as a @code{duration}. Returns a @code{duration} array the same size as @var{obj}. @end deftypefn @node datetime.dispstrs @subsubsection datetime.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Get display strings for each element of @var{obj}. Returns a cellstr the same size as @var{obj}. @end deftypefn @node datetime.eq @subsubsection datetime.eq @deftypefn {Method} {@var{out} =} eq (@var{A}, @var{B}) True if @var{A} is equal to @var{B}. This defines the @code{==} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.ge @subsubsection datetime.ge @deftypefn {Method} {@var{out} =} ge (@var{A}, @var{B}) True if @var{A} is greater than or equal to @var{B}. This defines the @code{>=} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.gmtime @subsubsection datetime.gmtime @deftypefn {Method} {@var{out} =} gmtime (@var{obj}) Convert to TM_STRUCT structure in UTC time. Converts @var{obj} to a TM_STRUCT style structure array. The result is in UTC time. If @var{obj} is unzoned, it is assumed to be in UTC time. Returns a struct array in TM_STRUCT style. @end deftypefn @node datetime.gt @subsubsection datetime.gt @deftypefn {Method} {@var{out} =} gt (@var{A}, @var{B}) True if @var{A} is greater than @var{B}. This defines the @code{>} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.hms @subsubsection datetime.hms @deftypefn {Method} {[@var{h}, @var{m}, @var{s}] =} hms (@var{obj}) Get the Hour, Minute, and Second components of a @var{datetime}. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result. For zoned @code{datetime}s, these will be local times in the associated time zone. Returns double arrays the same size as @code{obj}. @end deftypefn @node datetime.isbetween @subsubsection datetime.isbetween @deftypefn {Method} {@var{out} =} isbetween (@var{obj}, @var{lower}, @var{upper}) Tests whether the elements of @var{obj} are between @var{lower} and @var{upper}. All inputs are implicitly converted to @code{datetime} arrays, and are subject to scalar expansion. Returns a logical array the same size as the scalar expansion of the inputs. @end deftypefn @node datetime.ismissing @subsubsection datetime.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) Test whether array elements are missing. For @code{string} arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.isnan @subsubsection datetime.isnan @deftypefn {Method} {@var{out} =} isnan (@var{obj}) True if input elements are NaT. This is an alias for @code{isnat} to support type compatibility and polymorphic programming. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.isnat @subsubsection datetime.isnat @deftypefn {Method} {@var{out} =} isnat (@var{obj}) True if input elements are NaT. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.le @subsubsection datetime.le @deftypefn {Method} {@var{out} =} le (@var{A}, @var{B}) True if @var{A} is less than or equal to@var{B}. This defines the @code{<=} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.linspace @subsubsection datetime.linspace @deftypefn {Method} {@var{out} =} linspace (@var{from}, @var{to}, @var{n}) Linearly-spaced values in date/time space. Constructs a vector of @code{datetime}s that represent linearly spaced points starting at @var{from} and going up to @var{to}, with @var{n} points in the vector. @var{from} and @var{to} are implicitly converted to @code{datetime}s. @var{n} is how many points to use. If omitted, defaults to 100. Returns an @var{n}-long @code{datetime} vector. @end deftypefn @node datetime.localtime @subsubsection datetime.localtime @deftypefn {Method} {@var{out} =} localtime (@var{obj}) Convert to TM_STRUCT structure in UTC time. Converts @var{obj} to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on @var{obj}. If @var{obj} is unzoned, it is assumed to be in UTC time. Returns a struct array in TM_STRUCT style. Example: @example dt = datetime; dt.TimeZone = datetime.SystemTimeZone; tm_struct = localtime (dt); @end example @end deftypefn @node datetime.lt @subsubsection datetime.lt @deftypefn {Method} {@var{out} =} lt (@var{A}, @var{B}) True if @var{A} is less than @var{B}. This defines the @code{<} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.minus @subsubsection datetime.minus @deftypefn {Method} {@var{out} =} minus (@var{A}, @var{B}) Subtraction (@code{-} operator). Subtracts a @code{duration}, @code{calendarDuration} or numeric @var{B} from a @code{datetime} @var{A}, or subtracts two @code{datetime}s from each other. If both inputs are @code{datetime}, then the output is a @code{duration}. Otherwise, the output is a @code{datetime}. Numeric @var{B} inputs are implicitly converted to @code{duration} using @code{duration.ofDays}. Returns an array the same size as @var{A}. @end deftypefn @node datetime.NaT @subsubsection datetime.NaT @deftypefn {Static Method} {@var{out} =} datetime.NaT () @deftypefnx {Static Method} {@var{out} =} datetime.NaT (@var{sz}) “Not-a-Time”: Creates NaT-valued arrays. Constructs a new @code{datetime} array of all @code{NaT} values of the given size. If no input @var{sz} is given, the result is a scalar @code{NaT}. @code{NaT} is the @code{datetime} equivalent of @code{NaN}. It represents a missing or invalid value. @code{NaT} values never compare equal to, greater than, or less than any value, including other @code{NaT}s. Doing arithmetic with a @code{NaT} and any other value results in a @code{NaT}. @end deftypefn @node datetime.ne @subsubsection datetime.ne @deftypefn {Method} {@var{out} =} ne (@var{A}, @var{B}) True if @var{A} is not equal to @var{B}. This defines the @code{!=} operator for @code{datetime}s. Inputs are implicitly converted to @code{datetime} using the one-arg constructor or conversion method. Returns logical array the same size as @var{obj}. @end deftypefn @node datetime.ofDatenum @subsubsection datetime.ofDatenum @deftypefn {Static Method} {@var{obj} =} datetime.ofDatenum (@var{dnums}) Converts a datenum array to a datetime array. Returns an unzoned @code{datetime} array of the same size as the input. @end deftypefn @node datetime.ofDatestruct @subsubsection datetime.ofDatestruct @deftypefn {Static Method} {@var{obj} =} datetime.ofDatestruct (@var{dstruct}) Converts a datestruct to a datetime array. A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype. Returns an unzoned @code{datetime} array. @end deftypefn @node datetime.plus @subsubsection datetime.plus @deftypefn {Method} {@var{out} =} plus (@var{A}, @var{B}) Addition (@code{+} operator). Adds a @code{duration}, @code{calendarDuration}, or numeric @var{B} to a @code{datetime} @var{A}. @var{A} must be a @code{datetime}. Numeric @var{B} inputs are implicitly converted to @code{duration} using @code{duration.ofDays}. WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy. Returns @code{datetime} array the same size as @var{A}. @end deftypefn @node datetime.posix2datenum @subsubsection datetime.posix2datenum @deftypefn {Static Method} {@var{dnums} =} datetime.posix2datenum (@var{pdates}) Converts POSIX (Unix) times to datenums Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC. @end deftypefn @node datetime.posixtime @subsubsection datetime.posixtime @deftypefn {Method} {@var{out} =} posixtime (@var{obj}) Converts this to POSIX time values (seconds since the Unix epoch) Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC. Returns double array of same size as this. @end deftypefn @node datetime.proxyKeys @subsubsection datetime.proxyKeys @deftypefn {Method} {[@var{keysA}, @var{keysB}] =} proxyKeys (@var{a}, @var{b}) Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs. This is primarily for Tablicious's internal use; users will typically not need to call it or know how it works. Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input. @end deftypefn @node datetime.timeofday @subsubsection datetime.timeofday @deftypefn {Method} {@var{out} =} timeofday (@var{obj}) Get the time of day (elapsed time since midnight). For zoned @code{datetime}s, these will be local times in the associated time zone. Returns a @code{duration} array the same size as @code{obj}. @end deftypefn @node datetime.week @subsubsection datetime.week @deftypefn {Method} {@var{out} =} week (@var{obj}) Get the week of the year. This method is unimplemented. @end deftypefn @node datetime.ymd @subsubsection datetime.ymd @deftypefn {Method} {[@var{y}, @var{m}, @var{d}] =} ymd (@var{obj}) Get the Year, Month, and Day components of @var{obj}. For zoned @code{datetime}s, these will be local times in the associated time zone. Returns double arrays the same size as @code{obj}. @end deftypefn @node datetime.ymdhms @subsubsection datetime.ymdhms @deftypefn {Method} {[@var{y}, @var{m}, @var{d}, @var{h}, @var{mi}, @var{s}] =} ymdhms @ (@var{obj}) Get the Year, Month, Day, Hour, Minute, and Second components of a @var{obj}. For zoned @code{datetime}s, these will be local times in the associated time zone. Returns double arrays the same size as @code{obj}. @end deftypefn @node days @subsection days @deftypefn {Function} {@var{out} =} days (@var{x}) Duration in days. If @var{x} is numeric, then @var{out} is a @code{duration} array in units of fixed-length 24-hour days, with the same size as @var{x}. If @var{x} is a @code{duration}, then returns a @code{double} array the same size as @var{x} indicating the number of fixed-length days that each duration is. @end deftypefn @node discretize @subsection discretize @deftypefn {Function} {[@var{Y}, @var{E}] =} discretize (@var{X}, @var{n}) @deftypefnx {Function} {[@var{Y}, @var{E}] =} discretize (@var{X}, @var{edges}) @deftypefnx {Function} {[@var{Y}, @var{E}] =} discretize (@var{X}, @var{dur}) @deftypefnx {Function} {[@var{Y}, @var{E}] =} discretize (@dots{}, @code{'categorical'}) @deftypefnx {Function} {[@var{Y}, @var{E}] =} discretize (@dots{}, @code{'IncludedEdge'}, @var{IncludedEdge}) Group data into discrete bins or categories. @var{n} is the number of bins to group the values into. @var{edges} is an array of edge values defining the bins. @var{dur} is a @code{duration} value indicating the length of time of each bin. If @code{'categorical'} is specified, the resulting values are a @code{categorical} array instead of a numeric array of bin indexes. Returns: @var{Y} - the bin index or category of each value from @var{X} @var{E} - the list of bin edge values @end deftypefn @node dispstrs @subsection dispstrs @deftypefn {Function} {@var{out} =} dispstrs (@var{x}) Display strings for array. Gets the display strings for each element of @var{x}. The display strings should be short, one-line, human-presentable strings describing the value of that element. The default implementation of @code{dispstrs} can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects. This is a polymorphic method that user-defined classes may override with their own custom display that is more informative. Returns a cell array the same size as @var{x}. @end deftypefn @node duration @subsection duration @deftp {Class} duration Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time. This is an attempt to reproduce the functionality of Matlab's @code{duration}. It also contains some Octave-specific extensions. Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times. @end deftp @deftypeivar duration @code{double} days The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days. This is a planar property: the size of @code{days} is the same size as the containing @code{duration} array object. @end deftypeivar @deftypeivar duration @code{char} Format The format to display this @code{duration} in. Currently unsupported. @end deftypeivar @node duration.char @subsubsection duration.char @deftypefn {Method} {@var{out} =} char (@var{obj}) Convert to char. The contents of the strings will be the same as returned by @code{dispstrs}. This is primarily a convenience method for use on scalar @var{obj}s. Returns a 2-D char array with one row per element in @var{obj}. @end deftypefn @node duration.datevec @subsubsection duration.datevec @deftypefn {Method} {[@var{dvec}] =} datevec (@var{obj}) @deftypefnx {Method} {[@var{Y}, @var{M}, @var{D}, @var{H}, @var{MN}, @var{S}] =} datevec (@var{obj}) Convert @var{obj} to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31. When nargout is 0 or 1, returns a double array of size n-by-6, where n is @code{numel(obj)}. When nargout is 2 or more, returns double arrays the same size as @var{obj}. @end deftypefn @node duration.dispstrs @subsubsection duration.dispstrs @deftypefn {Method} {@var{out} =} duration (@var{obj}) Get display strings for each element of @var{obj}. Returns a cellstr the same size as @var{obj}. @end deftypefn @node duration.hms @subsubsection duration.hms @deftypefn {Method} {[@var{h}, @var{m}, @var{s}] =} hms (@var{obj}) Get the Hour, Minute, and Second values of a @var{duration}. These are the hour, minute, and second values that when combined are equal to the same duration value. Returns double arrays the same size as @code{obj}. @end deftypefn @node duration.hours @subsubsection duration.hours @deftypefn {Method} {@var{out} =} hours (@var{obj}) Equivalent number of hours. Gets the number of fixed-length 60-minute hours that is equivalent to this duration. Returns double array the same size as @var{obj}. @end deftypefn @node duration.linspace @subsubsection duration.linspace @deftypefn {Method} {@var{out} =} linspace (@var{from}, @var{to}, @var{n}) Linearly-spaced values in time duration space. Constructs a vector of @code{duration}s that represent linearly spaced points starting at @var{from} and going up to @var{to}, with @var{n} points in the vector. @var{from} and @var{to} are implicitly converted to @code{duration}s. @var{n} is how many points to use. If omitted, defaults to 100. Returns an @var{n}-long @code{datetime} vector. @end deftypefn @node duration.milliseconds @subsubsection duration.milliseconds @deftypefn {Method} {@var{out} =} milliseconds (@var{obj}) Equivalent number of milliseconds. Gets the number of milliseconds that is equivalent to this duration. Returns double array the same size as @var{obj}. @end deftypefn @node duration.minutes @subsubsection duration.minutes @deftypefn {Method} {@var{out} =} minutes (@var{obj}) Equivalent number of minutes. Gets the number of fixed-length 60-second minutes that is equivalent to this duration. Returns double array the same size as @var{obj}. @end deftypefn @node duration.ofDays @subsubsection duration.ofDays @deftypefn {Static Method} {@var{obj} =} duration.ofDays (@var{dnums}) Converts a double array representing durations in whole and fractional days to a @code{duration} array. This is the method that is used for implicit conversion of numerics in many cases. Returns a @code{duration} array of the same size as the input. @end deftypefn @node duration.seconds @subsubsection duration.seconds @deftypefn {Method} {@var{out} =} seconds (@var{obj}) Equivalent number of seconds. Gets the number of seconds that is equivalent to this duration. Returns double array the same size as @var{obj}. @end deftypefn @node duration.years @subsubsection duration.years @deftypefn {Method} {@var{out} =} years (@var{obj}) Equivalent number of years. Gets the number of fixed-length 365.2425-day years that is equivalent to this duration. Returns double array the same size as @var{obj}. @end deftypefn @node eqn @subsection eqn @deftypefn {Function} {@var{out} =} eqn (@var{A}, @var{B}) Determine element-wise equality, treating NaNs as equal out = eqn (A, B) @code{eqn} is just like @code{eq} (the function that implements the @code{==} operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of @code{isequaln}. @code{eqn} uses @code{isnanny} to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish. Developer's note: the name “@code{eqn}” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the @code{isequaln} pattern of appending an “n” to the corresponding non-NaN-equivocating function. See also: @code{eq}, @code{isequaln}, @ref{isnanny} @end deftypefn @node head @subsection head @deftypefn {Function} {@var{out} =} head (@var{A}) @deftypefnx {Function} {@var{out} =} head (@var{A}, @var{k}) Get first K rows of an array. Returns the array @var{A}, subsetted to its first @var{k} rows. This means subsetting it to the first @code{(min (k, size (A, 1)))} elements along dimension 1, and leaving all other dimensions unrestricted. @var{A} is the array to subset. @var{k} is the number of rows to get. @var{k} defaults to 8 if it is omitted or empty. If there are less than @var{k} rows in @var{A}, returns all rows. Returns an array of the same type as @var{A}, unless ()-indexing @var{A} produces an array of a different type, in which case it returns that type. See also: tail @end deftypefn @node hours @subsection hours @deftypefn {Function File} {@var{out} =} hours (@var{x}) Create a @code{duration} @var{x} hours long, or get the hours in a @code{duration} @var{x}. If input is numeric, returns a @code{duration} array that is that many hours in time. If input is a @code{duration}, converts the @code{duration} to a number of hours. Returns an array the same size as @var{x}. @end deftypefn @node iscalendarduration @subsection iscalendarduration @deftypefn {Function} {@var{out} =} iscalendarduration (@var{x}) True if input is a @code{calendarDuration} array, false otherwise. Respects @code{iscalendarduration} override methods on user-defined classes, even if they do not inherit from @code{calendarDuration} or were known to Tablicious at authoring time. Returns a scalar logical. @end deftypefn @node iscategorical @subsection iscategorical @deftypefn {Function} {@var{out} =} iscategorical (@var{x}) True if input is a @code{categorical} array, false otherwise. Respects @code{iscategorical} override methods on user-defined classes, even if they do not inherit from @code{categorical} or were known to Tablicious at authoring time. Returns a scalar logical. @end deftypefn @node isdatetime @subsection isdatetime @deftypefn {Function} {@var{out} =} isdatetime (@var{x}) True if input is a @code{datetime} array, false otherwise. Respects @code{isdatetime} override methods on user-defined classes, even if they do not inherit from @code{datetime} or were known to Tablicious at authoring time. Returns a scalar logical. @end deftypefn @node isduration @subsection isduration @deftypefn {Function} {@var{out} =} isduration (@var{x}) True if input is a @code{duration} array, false otherwise. Respects @code{isduration} override methods on user-defined classes, even if they do not inherit from @code{duration} or were known to Tablicious at authoring time. Returns a scalar logical. @end deftypefn @node isfile @subsection isfile @emph{Not documented} @node isfolder @subsection isfolder @emph{Not documented} @node isnanny @subsection isnanny @deftypefn {Function} {@var{out} =} isnanny (@var{X}) Test if elements are NaN or NaN-like. Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if @code{isnan()} or @code{isnat()} returns true, and is false for types that do not support @code{isnan()} or @code{isnat()}. This function only exists because: @enumerate a @item Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and @item isnan() errors out for some types that do not support isnan(), like cells. @item Octave does not define an @code{ismissing} function, but the Octave Forge statistics package does, and adding an @code{ismissing} function to Tablicious would conflict with that in hard-to-resolve ways. @end enumerate @code{isnanny()} smooths over those differences so you can call it polymorphically on any input type. Hopefully. Under normal operation, @code{isnanny()} should not throw an error for any type or value of input. See also: ismissing, @code{isnan}, @code{isnat}, @ref{eqn}, @code{isequaln} @end deftypefn @node istable @subsection istable @deftypefn {Function} {@var{out} =} istable (@var{x}) True if input is a @code{table} array or other table-like type, false otherwise. Respects @code{istable} override methods on user-defined classes, even if they do not inherit from @code{table} or were known to Tablicious at authoring time. User-defined classes should only override @code{istable} to return true if they conform to the @code{table} public interface. That interface is not well-defined or documented yet, so maybe you don't want to do that yet. Returns a scalar logical. @end deftypefn @node istabular @subsection istabular @deftypefn {Function} {@var{out} =} istabular (@var{x}) True if input is eitehr a @code{table} or @code{timetable} array, or an object like them. Respects @code{istable} and @code{istimetable} override methods on user-defined classes, even if they do not inherit from @code{table} or were known to Tablicious at authoring time. Returns a scalar logical. @end deftypefn @node istimetable @subsection istimetable @deftypefn {Function} {@var{out} =} istimetable (@var{x}) True if input is a @code{timetable} array or other timetable-like type, false otherwise. Respects @code{istimetable} override methods on user-defined classes, even if they do not inherit from @code{table} or were known to Tablicious at authoring time. User-defined classes should only override @code{istimetable} to return true if they conform to the @code{table} public interface. That interface is not well-defined or documented yet, so maybe you don't want to do that yet. Returns a scalar logical. @end deftypefn @node localdate @subsection localdate @deftp {Class} localdate Represents a complete day using the Gregorian calendar. This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes. @end deftp @deftypeivar localdate @code{double} dnums The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents. These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component. @end deftypeivar @deftypeivar localdate @code{char} Format The format to display this @code{localdate} in. Currently unsupported. @end deftypeivar @node localdate.datenum @subsubsection localdate.datenum @deftypefn {Method} {@var{out} =} datenum (@var{obj}) Convert this to datenums that represent midnight on @var{obj}’s days. Returns double array of same size as this. @end deftypefn @node localdate.datestr @subsubsection localdate.datestr @deftypefn {Method} {@var{out} =} datestr (@var{obj}) @deftypefnx {Method} {@var{out} =} datestr (@var{obj}, @dots{}) Format @var{obj} as date strings. Supports all arguments that core Octave's @code{datestr} does. Returns date strings as a 2-D char array. @end deftypefn @node localdate.datestrs @subsubsection localdate.datestrs @deftypefn {Method} {@var{out} =} datestrs (@var{obj}) @deftypefnx {Method} {@var{out} =} datestrs (@var{obj}, @dots{}) Format @var{obj} as date strings, returning cellstr. Supports all arguments that core Octave's @code{datestr} does. Returns a cellstr array the same size as @var{obj}. @end deftypefn @node localdate.datestruct @subsubsection localdate.datestruct @deftypefn {Method} {@var{out} =} datestruct (@var{obj}) Converts this to a “datestruct” broken-down time structure. A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by @code{datetime} in that it lacks Hour, Minute, and Second components. This is done for efficiency. The values in the returned broken-down time are those of the local time in @var{obj}’s defined time zone, if it has one. Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this. @end deftypefn @node localdate.dispstrs @subsubsection localdate.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Get display strings for each element of @var{obj}. Returns a cellstr the same size as @var{obj}. @end deftypefn @node localdate.isnan @subsubsection localdate.isnan @deftypefn {Method} {@var{out} =} isnan (@var{obj}) True if input elements are NaT. This is an alias for @code{isnat} to support type compatibility and polymorphic programming. Returns logical array the same size as @var{obj}. @end deftypefn @node localdate.isnat @subsubsection localdate.isnat @deftypefn {Method} {@var{out} =} isnat (@var{obj}) True if input elements are NaT. Returns logical array the same size as @var{obj}. @end deftypefn @node localdate.localdate @subsubsection localdate.localdate @deftypefn {Constructor} {@var{obj} =} localdate () Constructs a new scalar @code{localdate} containing the current local date. @end deftypefn @deftypefn {Constructor} {@var{obj} =} localdate (@var{datenums}) @deftypefnx {Constructor} {@var{obj} =} localdate (@var{datestrs}) @deftypefnx {Constructor} {@var{obj} =} localdate (@var{Y}, @var{M}, @var{D}) @deftypefnx {Constructor} {@var{obj} =} localdate (@dots{}, @code{'Format'}, @var{Format}) Constructs a new @code{localdate} array based on input values. @end deftypefn @node localdate.NaT @subsubsection localdate.NaT @deftypefn {Static Method} {@var{out} =} localdate.NaT () @deftypefnx {Static Method} {@var{out} =} localdate.NaT (@var{sz}) “Not-a-Time”: Creates NaT-valued arrays. Constructs a new @code{datetime} array of all @code{NaT} values of the given size. If no input @var{sz} is given, the result is a scalar @code{NaT}. @code{NaT} is the @code{datetime} equivalent of @code{NaN}. It represents a missing or invalid value. @code{NaT} values never compare equal to, greater than, or less than any value, including other @code{NaT}s. Doing arithmetic with a @code{NaT} and any other value results in a @code{NaT}. This static method is provided because the global @code{NaT} function creates @code{datetime}s, not @code{localdate}s @end deftypefn @node localdate.posixtime @subsubsection localdate.posixtime @deftypefn {Method} {@var{out} =} posixtime (@var{obj}) Converts this to POSIX time values for midnight of @var{obj}’s days. Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on @var{obj}’s days. Returns double array of same size as this. @end deftypefn @node localdate.ymd @subsubsection localdate.ymd @deftypefn {Method} {[@var{y}, @var{m}, @var{d}] =} ymd (@var{obj}) Get the Year, Month, and Day components of @var{obj}. Returns double arrays the same size as @code{obj}. @end deftypefn @node milliseconds @subsection milliseconds @deftypefn {Function File} {@var{out} =} milliseconds (@var{x}) Create a @code{duration} @var{x} milliseconds long, or get the milliseconds in a @code{duration} @var{x}. If input is numeric, returns a @code{duration} array that is that many milliseconds in time. If input is a @code{duration}, converts the @code{duration} to a number of milliseconds. Returns an array the same size as @var{x}. @end deftypefn @node minutes @subsection minutes @deftypefn {Function File} {@var{out} =} hours (@var{x}) Create a @code{duration} @var{x} hours long, or get the hours in a @code{duration} @var{x}. @end deftypefn @node missing @subsection missing @deftp {Class} missing Generic auto-converting missing value. @code{missing} is a generic missing value that auto-converts to other types. A @code{missing} array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations. This class is currently EXPERIMENTAL. Use at your own risk. Note: This class does not actually work for assignment. If you do this: @example x = 1:5 x(3) = missing @end example It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it. @end deftp @node missing.dispstrs @subsubsection missing.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Display strings. Gets display strings for each element in @var{obj}. For @code{missing}, the display strings are always @code{''}. Returns a cellstr the same size as @var{obj}. @end deftypefn @node missing.ismissing @subsubsection missing.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) Test whether elements are missing values. @code{ismissing} is always true for @code{missing} arrays. Returns a logical array the same size as @var{obj}. @end deftypefn @node missing.isnan @subsubsection missing.isnan @deftypefn {Method} {@var{out} =} isnan (@var{obj}) Test whether elements are NaN. @code{isnan} is always true for @code{missing} arrays. Returns a logical array the same size as @var{obj}. @end deftypefn @node missing.isnanny @subsubsection missing.isnanny @deftypefn {Method} {@var{out} =} isnanny (@var{obj}) Test whether elements are NaN-like. @code{isnanny} is always true for @code{missing} arrays. Returns a logical array the same size as @var{obj}. @end deftypefn @node missing.missing @subsubsection missing.missing @deftypefn {Constructor} {@var{obj} =} missing () Constructs a scalar @code{missing} array. The constructor takes no arguments, since there’s only one @code{missing} value. @end deftypefn @node mustBeA @subsection mustBeA @emph{Not documented} @node mustBeCellstr @subsection mustBeCellstr @emph{Not documented} @node mustBeCharvec @subsection mustBeCharvec @emph{Not documented} @node mustBeFinite @subsection mustBeFinite @emph{Not documented} @node mustBeInteger @subsection mustBeInteger @emph{Not documented} @node mustBeMember @subsection mustBeMember @emph{Not documented} @node mustBeNonempty @subsection mustBeNonempty @emph{Not documented} @node mustBeNumeric @subsection mustBeNumeric @emph{Not documented} @node mustBeReal @subsection mustBeReal @emph{Not documented} @node mustBeSameSize @subsection mustBeSameSize @emph{Not documented} @node mustBeScalar @subsection mustBeScalar @emph{Not documented} @node mustBeScalarLogical @subsection mustBeScalarLogical @emph{Not documented} @node mustBeVector @subsection mustBeVector @emph{Not documented} @node NaC @subsection NaC @deftypefn {Function} {@var{out} =} NaC () @deftypefnx {Function} {@var{out} =} NaC (@var{sz}) “Not-a-Categorical". Creates missing-valued categorical arrays. Returns a new @code{categorical} array of all missing values of the given size. If no input @var{sz} is given, the result is a scalar missing categorical. @code{NaC} is the @code{categorical} equivalent of @code{NaN} or @code{NaT}. It represents a missing, invalid, or null value. @code{NaC} values never compare equal to any value, including other @code{NaC}s. @code{NaC} is a convenience function which is strictly a wrapper around @code{categorical.undefined} and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values. See also: categorical.undefined @end deftypefn @node NaS @subsection NaS @deftypefn {Function} {@var{out} =} NaS () @deftypefnx {Function} {@var{out} =} NaS (@var{sz}) “Not-a-String". Creates missing-valued string arrays. Returns a new @code{string} array of all missing values of the given size. If no input @var{sz} is given, the result is a scalar missing string. @code{NaS} is the @code{string} equivalent of @code{NaN} or @code{NaT}. It represents a missing, invalid, or null value. @code{NaS} values never compare equal to any value, including other @code{NaS}s. @code{NaS} is a convenience function which is strictly a wrapper around @code{string.missing} and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values. See also: string.missing @end deftypefn @node NaT @subsection NaT @deftypefn {Function} {@var{out} =} NaT () @deftypefnx {Function} {@var{out} =} NaT (@var{sz}) “Not-a-Time”. Creates missing-valued datetime arrays. Constructs a new @code{datetime} array of all @code{NaT} values of the given size. If no input @var{sz} is given, the result is a scalar @code{NaT}. @code{NaT} is the @code{datetime} equivalent of @code{NaN}. It represents a missing or invalid value. @code{NaT} values never compare equal to, greater than, or less than any value, including other @code{NaT}s. Doing arithmetic with a @code{NaT} and any other value results in a @code{NaT}. @code{NaT} currently cannot create NaT arrays of type @code{localdate}. To do that, use @ref{localdate.NaT} instead. @end deftypefn @node pp @subsection pp @deftypefn {Function} {} pp (@var{X}) @deftypefnx {Function} {} pp (@var{A}, @var{B}, @var{C}, @dots{}) @deftypefnx {Function} {} pp (@code{'A'}, @code{'B'}, @code{'C'}, @dots{}) @deftypefnx {Function} {} pp @code{A} @code{B} @code{C} @dots{} Alias for prettyprint, for interactive use. This is an alias for prettyprint(), with additional name-conversion magic. If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line. @end deftypefn @node scalarexpand @subsection scalarexpand @deftypefn {Function} {[@var{out1}, @var{out2}, @dots{}, @var{outN}] =} scalarexpand @ (@var{x1}, @var{x2}, @dots{}, @var{xN}) Expand scalar inputs to match size of non-scalar inputs. Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. @code{repmat} is used to do the expansion. Works on any input types that support @code{size}, @code{isscalar}, and @code{repmat}. It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs. Returns as many output arguments as there were input arguments. Examples: @example x1 = rand(3); x2 = 42; x3 = magic(3); [x1, x2, x3] = scalarexpand (x1, x2, x3) @end example @end deftypefn @node seconds @subsection seconds @deftypefn {Function File} {@var{out} =} seconds (@var{x}) Create a @code{duration} @var{x} seconds long, or get the seconds in a @code{duration} @var{x}. If input is numeric, returns a @code{duration} array that is that many seconds in time. If input is a @code{duration}, converts the @code{duration} to a number of seconds. Returns an array the same size as @var{x}. @end deftypefn @node size2str @subsection size2str @deftypefn {Function} {@var{out} =} size2str (@var{sz}) Format an array size for display. Formats the given array size @var{sz} as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the @var{N} dimensions represented by @var{sz}. @var{sz} is an array of dimension sizes, in the format returned by the @code{size} function. Returns a charvec. Examples: @example str = size2str (size (magic (4))) @result{} str = 4-by-4 @end example @end deftypefn @node splitapply @subsection splitapply @deftypefn {Function} {@var{out} =} splitapply (@var{func}, @var{X}, @var{G}) @deftypefnx {Function} {@var{out} =} splitapply (@var{func}, @var{X1}, @dots{}, @var{XN}, @var{G}) @deftypefnx {Function} {[@var{Y1}, @dots{}, @var{YM}] =} splitapply (@dots{}) Split data into groups and apply function. @var{func} is a function handle to call on each group of inputs in turn. @var{X}, @var{X1}, @dots{}, @var{XN} are the input variables that are split into groups for the function calls. If @var{X} is a @code{table}, then its contained variables are “popped out” and considered to be the @var{X1} @dots{} @var{XN} input variables. @var{G} is the grouping variable vector. It contains a list of integers that identify which group each element of the @var{X} input variables belongs to. NaNs in @var{G} mean that element is ignored. Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture. Returns the concatenated outputs of applying @var{func} to each group. See also: @ref{table.groupby}, @ref{table.splitapply} @end deftypefn @node string @subsection string @deftp {Class} string A string array of Unicode strings. A string array is an array of strings, where each array element is a single string. The string class represents strings, where: @itemize @bullet @item Each element of a string array is a single string @item A single string is a 1-dimensional row vector of Unicode characters @item Those characters are encoded in UTF-8 @itemize @bullet @item This last bit depends on the fact that Octave chars are UTF-8 now @end itemize @end itemize This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave. String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL. This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.) Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points. This class really serves three roles: @enumerate @item It is a type-safe object wrapper around Octave’s base primitive character types. @item It adds ismissing() semantics. @item And it introduces Unicode support. @end enumerate Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars. “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests. TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave's existing char/string-handling functions. TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware. @end deftp @node string.cell @subsubsection string.cell @deftypefn {Method} {@var{out} =} cell (@var{obj}) Convert to cell array. Converts this to a cell, which will be a cellstr. Missing values are converted to @code{''}. This method returns the same values as @code{cellstr(obj)}; it is just provided for interface compatibility purposes. Returns a cell array of the same size as @var{obj}. @end deftypefn @node string.cellstr @subsubsection string.cellstr @deftypefn {Method} {@var{out} =} cellstr (@var{obj}) Convert to cellstr. Converts @var{obj} to a cellstr. Missing values are converted to @code{''}. Returns a cellstr array of the same size as @var{obj}. @end deftypefn @node string.char @subsubsection string.char @deftypefn {Method} {@var{out} =} char (@var{obj}) Convert to char array. Converts @var{obj} to a 2-D char array. It will have as many rows as @var{obj} has elements. It is an error to convert missing-valued @code{string} arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.) Returns 2-D char array. @end deftypefn @node string.cmp @subsubsection string.cmp @deftypefn {Method} {[@var{out}, @var{outA}, @var{outB}] =} cmp (@var{A}, @var{B}) Value ordering comparison, returning -1/0/+1. Compares each element of @var{A} and @var{B}, returning for each element @code{i} whether @code{A(i)} was less than (-1), equal to (0), or greater than (1) the corresponding @code{B(i)}. TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings. Returns a numeric array @var{out} of the same size as the scalar expansion of @var{A} and @var{B}. Each value in it will be -1, 0, or 1. Also returns scalar-expanded copies of @var{A} and @var{B} as @var{outA} and @var{outB}, as a programming convenience. @end deftypefn @node string.decode @subsubsection string.decode @deftypefn {Static Method} {@var{out} =} string.decode (@var{bytes}, @var{charsetName}) Decode encoded text from bytes. Decodes the given encoded text in @var{bytes} according to the specified encoding, given by @var{charsetName}. Returns a scalar string. See also: @ref{string.encode} @end deftypefn @node string.dispstrs @subsubsection string.dispstrs @deftypefn {Method} {@var{out} =} dispstrs (@var{obj}) Display strings for array elements. Gets display strings for all the elements in @var{obj}. These display strings will either be the string contents of the element, enclosed in @code{"..."}, and with CR/LF characters replaced with @code{'\r'} and @code{'\n'} escape sequences, or @code{""} for missing values. Returns a cellstr of the same size as @var{obj}. @end deftypefn @node string.empty @subsubsection string.empty @deftypefn {Function} {@var{out} =} empty (@var{sz}) Get an empty string array of a specified size. The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0]. The size may also be supplied as multiple arguments containing scalar numerics. Returns an empty string array of the requested size. @end deftypefn @node string.encode @subsubsection string.encode @deftypefn {Method} {@var{out} =} encode (@var{obj}, @var{charsetName}) Encode string in a given character encoding. @var{obj} must be scalar. @var{charsetName} (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.) Returns the encoded string as a @code{uint8} vector. See also: @ref{string.decode}. @end deftypefn @node string.endsWith @subsubsection string.endsWith @deftypefn {Method} {@var{out} =} endsWith (@var{obj}, @var{pattern}) @deftypefnx {Method} {@var{out} =} endsWith (@var{obj}, @var{pattern}, 'IgnoreCase', true/false) Test if string ends with pattern. Returns a logical array the size of the scalar expansion of @var{obj} and @var{pattern}. @end deftypefn @node string.erase @subsubsection string.erase @deftypefn {Method} {@var{out} =} erase (@var{obj}, @var{match}) Erase matching substring. Erases the substrings in @var{obj} which match the @var{match} input. Returns a string array of the same size as @var{obj}. @end deftypefn @node string.ismissing @subsubsection string.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) Test whether array elements are missing. For @code{string} arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs. Returns a logical array the same size as @code{obj}. @end deftypefn @node string.isnanny @subsubsection string.isnanny @deftypefn {Method} {@var{out} =} isnanny (@var{obj}) Test whether array elements are NaN-like. Missing values are considered nannish; any other string value is not. Returns a logical array of the same size as @var{obj}. @end deftypefn @node string.isstring @subsubsection string.isstring @deftypefn {Method} {@var{out} =} isstring (@var{obj}) Test if input is a string array. @code{isstring} is always true for @code{string} inputs. Returns a scalar logical. @end deftypefn @node string.lower @subsubsection string.lower @deftypefn {Method} {@var{out} =} lower (@var{obj}) Convert to lower case. Converts all the characters in all the strings in @var{obj} to lower case. This currently delegates to Octave’s own @code{lower()} function to do the conversion, so whatever character class handling it has, this has. Returns a string array of the same size as @var{obj}. @end deftypefn @node string.missing @subsubsection string.missing @deftypefn {Static Method} {@var{out} = } string.missing (@var{sz}) Missing string value. Creates a string array of all-missing values of the specified size @var{sz}. If @var{sz} is omitted, creates a scalar missing string. Returns a string array of size @var{sz} or [1 1]. See also: NaS @end deftypefn @node string.plus @subsubsection string.plus @deftypefn {Method} {@var{out} =} plus (@var{a}, @var{b}) String concatenation via plus operator. Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays. The concatenation is done by calling `strcat` on the inputs, and has the same behavior. Returns a string array the same size as the scalar expansion of its inputs. See also: string.strcat @end deftypefn @node string.regexprep @subsubsection string.regexprep @deftypefn {Method} {@var{out} =} regexprep (@var{obj}, @var{pat}, @var{repstr}) @deftypefnx {Method} {@var{out} =} regexprep (@dots{}, @var{varargin}) Replace based on regular expression matching. Replaces all the substrings matching a given regexp pattern @var{pat} with the given replacement text @var{repstr}. Returns a string array of the same size as @var{obj}. @end deftypefn @node string.reverse @subsubsection string.reverse @deftypefn {Method} {@var{out} =} reverse (@var{obj}) Reverse string, character-wise. Reverses the characters in each string in @var{obj}. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output. Returns a string array the same size as @var{obj}. @end deftypefn @node string.reverse_bytes @subsubsection string.reverse_bytes @deftypefn {Method} {@var{out} =} reverse_bytes (@var{obj}) Reverse string, byte-wise. Reverses the bytes in each string in @var{obj}. This operates on bytes (Unicode code units), not characters. This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence. You probably do not want to use this method. You probably want to use @code{string.reverse} instead. Returns a string array the same size as @var{obj}. See also: @ref{string.reverse} @end deftypefn @node string.startsWith @subsubsection string.startsWith @deftypefn {Method} {@var{out} =} startsWith (@var{obj}, @var{pattern}) @deftypefnx {Method} {@var{out} =} startsWith (@var{obj}, @var{pattern}, 'IgnoreCase', true/false) Test if string starts with pattern. Returns a logical array the size of the scalar expansion of @var{obj} and @var{pattern}. @end deftypefn @node string.strcat @subsubsection string.strcat @deftypefn {Method} {@var{out} =} strcat (@var{varargin}) String concatenation. Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays. The semantics of concatenating missing strings with non-missing strings has not been determined yet. Returns a string array the same size as the scalar expansion of its inputs. @end deftypefn @node string.strcmp @subsubsection string.strcmp @deftypefn {Method} {@var{out} =} strcmp (@var{A}, @var{B}) String comparison. Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other. This does the same comparison as @code{A == B}, but is not polymorphic. Generally, there is no reason to use @code{strcmp} instead of @code{==} or @code{eq} on string arrays, unless you want to be compatible with cellstr inputs as well. Returns logical array the size of the scalar expansion of A and B. @end deftypefn @node string.strfind @subsubsection string.strfind @deftypefn {Method} {@var{out} =} strfind (@var{obj}, @var{pattern}) @deftypefnx {Method} {@var{out} =} strfind (@dots{}, @var{varargin}) Find pattern in string. Finds the locations where @var{pattern} occurs in the strings of @var{obj}. TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out. Returns either an index vector, or a cell array of index vectors. @end deftypefn @node string.string @subsubsection string.string @deftypefn {Constructor} {@var{obj} =} string () @deftypefnx {Constructor} {@var{obj} =} string (@var{in}) Construct a new string array. The zero-argument constructor creates a new scalar string array whose value is the empty string. The other constructors construct a new string array by converting various types of inputs. @itemize @item chars and cellstrs are converted via cellstr() @item numerics are converted via num2str() @item datetimes are converted via datestr() @end itemize @end deftypefn @node string.strlength @subsubsection string.strlength @deftypefn {Method} {@var{out} =} strlength (@var{obj}) String length in characters (actually, UTF-16 code units). Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each. The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility. This is the string length method you probably want to use, not @code{strlength_bytes}. Returns double array of the same size as @var{obj}. Returns NaNs for missing strings. See also: @ref{string.strlength_bytes} @end deftypefn @node string.strlength_bytes @subsubsection string.strlength_bytes @deftypefn {Method} {@var{out} =} strlength_bytes (@var{obj}) String length in bytes. Gets the length of each string in @var{obj}, counted in Unicode UTF-8 code units (bytes). This is the same as @code{numel(str)} for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want @code{strlength} instead. Returns double array of the same size as @var{obj}. Returns NaNs for missing strings. See also: @ref{string.strlength} @end deftypefn @node string.strrep @subsubsection string.strrep @deftypefn {Method} {@var{out} =} strrep (@var{obj}, @var{match}, @var{replacement}) @deftypefnx {Method} {@var{out} =} strrep (@dots{}, @var{varargin}) Replace occurrences of pattern with other string. Replaces matching substrings in @var{obj} with a given replacement string. @var{varargin} is passed along to the core Octave @code{strrep} function. This supports whatever options it does. TODO: Maybe document what those options are. Returns a string array of the same size as @var{obj}. @end deftypefn @node string.upper @subsubsection string.upper @deftypefn {Method} {@var{out} =} upper (@var{obj}) Convert to upper case. Converts all the characters in all the strings in @var{obj} to upper case. This currently delegates to Octave’s own @code{upper()} function to do the conversion, so whatever character class handling it has, this has. Returns a string array of the same size as @var{obj}. @end deftypefn @node struct2table @subsection struct2table @deftypefn {Function} {@var{out} =} struct2table (@var{s}) @deftypefnx {Function} {@var{out} =} struct2table (@dots{}, @code{'AsArray'}, @var{AsArray}) Convert struct to a table. Converts the input struct @var{s} to a @code{table}. @var{s} may be a scalar struct or a nonscalar struct array. The @var{AsArray} option is not implemented yet. Returns a @code{table}. @end deftypefn @node table @subsection table @deftp {Class} table Tabular data array containing multiple columnar variables. A @code{table} is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous. A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas. A table is an array in itself: its size is @var{nrows}-by-@var{nvariables}, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2. A note on accessing properties of a @code{table} array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do @code{t.Properties.} for a table @code{t}. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a @code{table} with a variable named @code{Properties}. Try to avoid that.) WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious. We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn't been fully thought through or tested. See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb @end deftp @deftypeivar table @code{cellstr} VariableNames The names of the variables in the table, as a cellstr row vector. @end deftypeivar @deftypeivar table @code{cell} VariableValues A cell vector containing the values for each of the variables. @code{VariableValues(i)} corresponds to @code{VariableNames(i)}. @end deftypeivar @deftypeivar table @code{cellstr} RowNames An optional list of row names that identify each row in the table. This is a cellstr column vector, if present. @end deftypeivar @deftypeivar table @code{cellstr} DimensionNames Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to @code{@{"Row", "Variables"@}}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that's what Matlab uses, so Tablicious uses it too, for Matlab compatibility.) @end deftypeivar @node table.addvars @subsubsection table.addvars @deftypefn {Method} {@var{out} =} addvars (@var{obj}, @var{var1}, @dots{}, @var{varN}) @deftypefnx {Method} {@var{out} =} addvars (@dots{}, @code{'Before'}, @var{Before}) @deftypefnx {Method} {@var{out} =} addvars (@dots{}, @code{'After'}, @var{After}) @deftypefnx {Method} {@var{out} =} addvars (@dots{}, @ @code{'NewVariableNames'}, @var{NewVariableNames}) Add variables to table. Adds the specified variables to a table. @end deftypefn @node table.antijoin @subsubsection table.antijoin @deftypefn {Method} {[@var{outA}, @var{ixA}, @var{outB}, @var{ixB}] =} antijoin @ (@var{A}, @var{B}) Natural antijoin (AKA “semidifference”). Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input. Returns: @var{outA} - all the rows in A with no matching row in B @var{ixA} - the row indexes into A which produced @var{outA} @var{outB} - all the rows in B with no matching row in A @var{ixB} - the row indexes into B which produced @var{outB} This is a Tablicious/Octave extension, not defined in the Matlab table interface. @end deftypefn @node table.cartesian @subsubsection table.cartesian @deftypefn {Method} {[@var{out}, @var{ixs}] =} cartesian (@var{A}, @var{B}) Cartesian product of two tables. Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B. Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do. The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result. This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct. The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance. This is a Tablicious/Octave extension, not defined in the Matlab table interface. @end deftypefn @node table.convertvars @subsubsection table.convertvars @deftypefn {Method} {@var{out} =} convertvars (@var{obj}, @var{vars}, @var{dataType}) Convert variables to specified data type. Converts the variables in @var{obj} specified by @var{vars} to the specified data type. @var{vars} is a cellstr or numeric vector specifying which variables to convert. @var{dataType} specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables' current types as input, or a conversion method of that name defined on the specified variables' current type. Returns a table with the same variable names as @var{obj}, but with converted types. @end deftypefn @node table.findgroups @subsubsection table.findgroups @deftypefn {Method} {[@var{G}, @var{TID}] =} findgroups (@var{obj}) Find groups within a table’s row values. Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements. Returns: @var{G} - A double column vector of group numbers created from @var{obj}. @var{TID} - A table containing the row values corresponding to the group numbers. @end deftypefn @node table.getvar @subsubsection table.getvar @deftypefn {Method} {[@var{out}, @var{name}]} = getvar (@var{obj}, @var{varRef}) Get value and name for single table variable. @var{varRef} is a variable reference. It may be a name or an index. It may only specify a single table variable. Returns: @var{out} – the value of the referenced table variable @var{name} – the name of the referenced table variable @end deftypefn @node table.getvars @subsubsection table.getvars @deftypefn {Method} {[@var{out1}, @dots{}]} = getvars (@var{obj}, @var{varRef}) Get values for one ore more table variables. @var{varRef} is a variable reference in the form of variable names or indexes. Returns as many outputs as @var{varRef} referenced variables. Each output contains the contents of the corresponding table variable. @end deftypefn @node table.groupby @subsubsection table.groupby @deftypefn {Method} {[@var{out}] =} groupby (@var{obj}, @var{groupvars}, @var{aggcalcs}) Find groups in table data and apply functions to variables within groups. This works like an SQL @code{"SELECT ... GROUP BY ..."} statement. @var{groupvars} (cellstr, numeric) is a list of the grouping variables, identified by name or index. @var{aggcalcs} is a specification of the aggregate calculations to perform on them, in the form @code{@{}@var{out_var}@code{,} @var{fcn}@code{,} @var{in_vars}@code{; ...@}}, where: @var{out_var} (char) is the name of the output variable @var{fcn} (function handle) is the function to apply to produce it @var{in_vars} (cellstr) is a list of the input variables to pass to fcn Returns a table. This is a Tablicious/Octave extension, not defined in the Matlab table interface. @end deftypefn @node table.height @subsubsection table.height @deftypefn {Method} {@var{out} =} height (@var{obj}) Number of rows in table. For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows. @end deftypefn @node table.horzcat @subsubsection table.horzcat @deftypefn {Method} {@var{out} =} horzcat (@var{varargin}) Horizontal concatenation. Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names. Output has the same RowNames as @code{varargin@{1@}}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs. @end deftypefn @node table.innerjoin @subsubsection table.innerjoin @deftypefn {Method} {[@var{out}, @var{ixa}, @var{ixb}] =} innerjoin (@var{A}, @var{B}) @deftypefnx {Method} {[@dots{}] =} innerjoin (@var{A}, @var{B}, @dots{}) Combine two tables by rows using key variables. Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output. TODO: Document options. Returns: @var{out} - A table that is the result of joining A and B @var{ix} - Indexes into A for each row in out @var{ixb} - Indexes into B for each row in out @end deftypefn @node table.intersect @subsubsection table.intersect @deftypefn {Method} {[@var{C}, @var{ia}, @var{ib}] =} intersect (@var{A}, @var{B}) Set intersection. Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables. Returns: @var{C} - A table containing all the unique row values present in both A and B. @var{ia} - Row indexes into A of the rows from A included in C. @var{ib} - Row indexes into B of the rows from B included in C. @end deftypefn @node table.isempty @subsubsection table.isempty @deftypefn {Method} {@var{out} =} isempty (@var{obj}) Test whether array is empty. For tables, @code{isempty} is true if the number of rows is 0 or the number of variables is 0. @end deftypefn @node table.ismember @subsubsection table.ismember @deftypefn {Method} {[@var{tf}, @var{loc}] =} ismember (@var{A}, @var{B}) Set membership. Finds rows in A that are members of B. Returns: @var{tf} - A logical vector indicating whether each A(i,:) was present in B. @var{loc} - Indexes into B of rows that were found. @end deftypefn @node table.ismissing @subsubsection table.ismissing @deftypefn {Method} {@var{out} =} ismissing (@var{obj}) @deftypefnx {Method} {@var{out} =} ismissing (@var{obj}, @var{indicator}) Find missing values. Finds missing values in @var{obj}’s variables. If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables. All variables in this must be vectors. (This is due to the requirement that @code{size(out) == size(obj)}.) Returns a logical array the same size as @var{obj}. @end deftypefn @node table.istable @subsubsection table.istable @deftypefn {Method} {@var{tf} =} istable (@var{obj}) True if input is a table. @end deftypefn @node table.join @subsubsection table.join @deftypefn {Method} {[@var{C}, @var{ib}] =} join (@var{A}, @var{B}) @deftypefnx {Method} {[@var{C}, @var{ib}] =} join (@var{A}, @var{B}, @dots{}) Combine two tables by rows using key variables, in a restricted form. This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join. You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead. See also: @ref{table.innerjoin}, @ref{table.outerjoin} @end deftypefn @node table.mergevars @subsubsection table.mergevars @deftypefn {Method} {@var{out} =} mergevars (@var{obj}, @var{vars}) @deftypefnx {Method} {@var{out} =} mergevars (@dots{}, @ @code{'NewVariableName'}, @var{NewVariableName}) @deftypefnx {Method} {@var{out} =} mergevars (@dots{}, @ @code{'MergeAsTable'}, @var{MergeAsTable}) Merge table variables into a single variable. @end deftypefn @node table.movevars @subsubsection table.movevars @deftypefn {Method} {@var{out} =} movevars (@var{obj}, @var{vars}, @var{relLocation}, @var{location}) Move around variables in a table. @var{vars} is a list of variables to move, specified by name or index. @var{relLocation} is @code{'Before'} or @code{'After'}. @var{location} indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from @var{obj}, not the original full list of variables in @var{obj}. Returns a table with the same variables as @var{obj}, but in a different order. @end deftypefn @node table.ndims @subsubsection table.ndims @deftypefn {Method} {@var{out} =} ndims (@var{obj}) Number of dimensions For tables, @code{ndims(obj)} is always 2, because table arrays are always 2-D (rows-by-columns). @end deftypefn @node table.numel @subsubsection table.numel @deftypefn {Method} {@var{out} =} numel (@var{obj}) Total number of elements in table (actually 1). For compatibility reasons with Octave's OOP interface and subsasgn behavior, table's numel is defined to always return 1. It is not useful for client code to query a table's size using numel. This is an incompatibility with Matlab. @end deftypefn @node table.outerfillvals @subsubsection table.outerfillvals @deftypefn {Method} {@var{out} =} outerfillvals (@var{obj}) Get fill values for outer join. Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join. @end deftypefn @node table.outerjoin @subsubsection table.outerjoin @deftypefn {Method} {[@var{out}, @var{ixa}, @var{ixb}] =} outerjoin (@var{A}, @var{B}) @deftypefnx {Method} {[@dots{}] =} outerjoin (@var{A}, @var{B}, @dots{}) Combine two tables by rows using key variables, retaining unmatched rows. Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values. TODO: Document options. Returns: @var{out} - A table that is the result of the outer join of A and B @var{ixa} - indexes into A for each row in out @var{ixb} - indexes into B for each row in out @end deftypefn @node table.prettyprint @subsubsection table.prettyprint @deftypefn {Method} {} prettyprint (@var{obj}) Display table's values in tabular format. This prints the contents of the table in human-readable, tabular form. Variables which contain objects are displayed using the strings returned by their @code{dispstrs} method, if they define one. @end deftypefn @node table.realjoin @subsubsection table.realjoin @deftypefn {Method} {[@var{out}, @var{ixs}] =} realjoin (@var{A}, @var{B}) @deftypefnx {Method} {[@dots{}] =} realjoin (@var{A}, @var{B}, @dots{}) "Real" relational inner join, without key restrictions Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes. Currently does not support tables which have RowNames. This may be added in the future. This is a Tablicious/Octave extension, not defined in the Matlab table interface. Name/value option arguments are: @var{Keys}, @var{LeftKeys}, @var{RightKeys}, @var{LeftVariables}, @var{RightVariables}. FIXME: Document those options. Returns: @var{out} - A table that is the result of joining A and B @var{ixs} - Indexes into A for each row in out @end deftypefn @node table.removevars @subsubsection table.removevars @deftypefn {Method} {@var{out} =} removevars (@var{obj}, @var{vars}) Remove variables from table. Deletes the variables specified by @var{vars} from @var{obj}. @var{vars} may be a char, cellstr, numeric index vector, or logical index vector. @end deftypefn @node table.renamevars @subsubsection table.renamevars @deftypefn {Method} {@var{out} =} renamevars (@var{obj}, @var{renameMap}) Rename variables in a table. Renames selected variables in the table @var{obj} based on the mapping provided in @var{renameMap}. @var{renameMap} is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column. Variables which are not included in @var{renameMap} are not modified. It is an error if any variables named in the first column of @var{renameMap} are not present in @var{obj}. Renames @end deftypefn @node table.repelem @subsubsection table.repelem @deftypefn {Method} {@var{out} =} repelem (@var{obj}, @var{R}) @deftypefnx {Method} {@var{out} =} repelem (@var{obj}, @var{R_1}, @var{R_2}) Replicate elements of matrix. Replicates elements of this table matrix by applying repelem to each of its variables. This Only two dimensions are supported for @code{repelem} on tables. @end deftypefn @node table.repmat @subsubsection table.repmat @deftypefn {Method} {@var{out} =} repmat (@var{obj}, @var{sz}) Replicate matrix. Repmats a table by repmatting each of its variables vertically. For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table. Returns a new table with the same variable names and types as tbl, but with a possibly different row count. @end deftypefn @node table.restrict @subsubsection table.restrict @deftypefn {Method} {@var{out} =} restrict (@var{obj}, @var{expr}) @deftypefnx {Method} {@var{out} =} restrict (@var{obj}, @var{ix}) Subset rows using variable expression or index. Subsets a table row-wise, using either an index vector or an expression involving @var{obj}’s variables. If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with `subsetrows (this, index)`.) If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with @code{tblish.evalWithTableVars}. The output of expr must be a numeric or logical index vector (This form is a shorthand for @code{out = subsetrows (this, tblish.evalWithTableVars (this, expr))}.) TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra. Examples: @example [s,p,sp] = tblish.examples.SpDb; prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")')) @end example This is a Tablicious/Octave extension, not defined in the Matlab table interface. See also: tblish.evalWithTableVars @end deftypefn @node table.rowfun @subsubsection table.rowfun @deftypefn {Method} {@var{out} =} varfun (@var{func}, @var{obj}) @deftypefnx {Method} {@var{out} =} varfun (@dots{}, @code{'OptionName'}, @var{OptionValue}, @dots{}) Apply function to rows in table and collect outputs. This applies the function @var{func} to the elements of each row of @var{obj}’s variables, and collects the concatenated output(s) into the variable(s) of a new table. @var{func} is a function handle. It should take as many inputs as there are variables in @var{obj}. Or, it can take a single input, and you must specify @code{'SeparateInputs', false} to have the input variables concatenated before being passed to @var{func}. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the @code{'NumOutputs'} or @code{'OutputVariableNames'} options. Supported name/value options: @table @code @item 'OutputVariableNames' Names of table variables to store combined function output arguments in. @item 'NumOutputs' Number of output arguments to call function with. If omitted, defaults to number of items in @var{OutputVariableNames} if it is supplied, otherwise defaults to 1. @item 'SeparateInputs' If true, input variables are passed as separate input arguments to @var{func}. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true. @item 'ErrorHandler' A function to call as a fallback when calling @var{func} results in an error. It is passed the caught exception, along with the original inputs passed to @var{func}, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings. @item 'ExtractCellContents' Whether to “pop out” the contents of the elements of cell variables in @var{obj}, or to leave them as cells. True/false; default is false. If you specify this option, then @var{obj} may not have any multi-column cell-valued variables. @item 'InputVariables' If specified, only these variables from @var{obj} are used as the function inputs, instead of using all variables. @item 'GroupingVariables' Not yet implemented. @item 'OutputFormat' The format of the output. May be @code{'table'} (the default), @code{'uniform'}, or @code{'cell'}. If it is @code{'uniform'} or @code{'cell'}, the output variables are returned in multiple output arguments from @code{'rowfun'}. @end table Returns a @code{table} whose variables are the collected output arguments of @var{func} if @var{OutputFormat} is @code{'table'}. Otherwise, returns multiple output arguments of whatever type @var{func} returned (if @var{OutputFormat} is @code{'uniform'}) or cells (if @var{OutputFormat} is @code{'cell'}). @end deftypefn @node table.rows2vars @subsubsection table.rows2vars @deftypefn {Method} {@var{out} =} rows2vars (@var{obj}) @deftypefnx {Method} {@var{out} =} rows2vars (@var{obj}, @ @code{'VariableNamesSource'}, @var{VariableNamesSource}) @deftypefnx {Method} {@var{out} =} rows2vars (@dots{}, @ @code{'DataVariables'}, @var{DataVariables}) Reorient table, swapping rows and variables dimensions. This flips the dimensions of the given table @var{obj}, swapping the orientation of the contained data, and swapping the row names/labels and variable names. The variable names become a new variable named “OriginalVariableNames”. The row names are drawn from the column @var{VariableNamesSource} if it is specified. Otherwise, if @var{obj} has row names, they are used. Otherwise, new variable names in the form “VarN” are generated. If all the variables in @var{obj} are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values. @end deftypefn @node table.semijoin @subsubsection table.semijoin @deftypefn {Method} {[@var{outA}, @var{ixA}, @var{outB}, @var{ixB}] =} semijoin @ (@var{A}, @var{B}) Natural semijoin. Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names. This method also computes the semijoin of B and A, for convenience. Returns: @var{outA} - all the rows in A with matching row(s) in B @var{ixA} - the row indexes into A which produced @var{outA} @var{outB} - all the rows in B with matching row(s) in A @var{ixB} - the row indexes into B which produced @var{outB} This is a Tablicious/Octave extension, not defined in the Matlab table interface. @end deftypefn @node table.setdiff @subsubsection table.setdiff @deftypefn {Method} {[@var{C}, @var{ia}] =} setdiff (@var{A}, @var{B}) Set difference. Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B. Returns: @var{C} - A table containing the unique row values in A that were not in B. @var{ia} - Row indexes into A of the rows from A included in C. @end deftypefn @node table.setDimensionNames @subsubsection table.setDimensionNames @deftypefn {Method} {@var{out} =} setDimensionNames (@var{obj}, @var{names}) @deftypefnx {Method} {@var{out} =} setDimensionNames (@var{obj}, @var{ix}, @var{names}) Set dimension names. Sets the @code{DimensionNames} for this table to a new list of names. @var{names} is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned. @var{ix} is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in @var{ix} may never be higher than 2. This method exists because the @code{obj.Properties.DimensionNames = @dots{}} assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the @code{obj.Properties.DimensionNames = @dots{}} assignment form. @end deftypefn @node table.setRowNames @subsubsection table.setRowNames @deftypefn {Method} {@var{out} =} setRowNames (@var{obj}, @var{names}) Set row names. Sets the row names on @var{obj} to @var{names}. @var{names} is a cellstr column vector, with the same number of rows as @var{obj} has. @end deftypefn @node table.setvar @subsubsection table.setvar @deftypefn {Method} {@var{out} =} setvar (@var{obj}, @var{varRef}, @var{value}) Set value for a variable in table. This sets (adds or replaces) the value for a variable in @var{obj}. It may be used to change the value of an existing variable, or add a new variable. This method exists primarily because I cannot get @code{obj.foo = value} to work, apparently due to an issue with Octave's subsasgn support. @var{varRef} is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index. @var{value} is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in @var{obj}. @end deftypefn @node table.setVariableNames @subsubsection table.setVariableNames @deftypefn {Method} {@var{out} =} setVariableNames (@var{obj}, @var{names}) @deftypefnx {Method} {@var{out} =} setVariableNames (@var{obj}, @var{ix}, @var{names}) Set variable names. Sets the @code{VariableNames} for this table to a new list of names. @var{names} is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned. @var{ix} is an index vector indicating which variable names to set. If omitted, it sets all of them present in @var{obj}. This method exists because the @code{obj.Properties.VariableNames = @dots{}} assignment form does not work, possibly due to an Octave bug. @end deftypefn @node table.setxor @subsubsection table.setxor @deftypefn {Method} {[@var{C}, @var{ia}, @var{ib}] =} setxor (@var{A}, @var{B}) Set exclusive OR. Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both. Returns: @var{C} - A table containing all the unique row values in the set XOR of A and B. @var{ia} - Row indexes into A of the rows from A included in C. @var{ib} - Row indexes into B of the rows from B included in C. @end deftypefn @node table.size @subsubsection table.size @deftypefn {Method} {@var{sz} =} size (@var{obj}) @deftypefnx {Method} {[@var{nr}, @var{nv}] =} size (@var{obj}) @deftypefnx {Method} {[@var{nr}, @var{nv}, @dots{}] =} size (@var{obj}) Gets the size of a table. For tables, the size is [number-of-rows x number-of-variables]. This is the same as @code{[height(obj), width(obj)]}. @end deftypefn @node table.splitapply @subsubsection table.splitapply @deftypefn {Method} {@var{out} =} splitapply (@var{func}, @var{obj}, @var{G}) @deftypefnx {Method} {[@var{Y1}, @dots{}, @var{YM}] =} splitapply (@var{func}, @var{obj}, @var{G}) Split table data into groups and apply function. Performs a splitapply, using the variables in @var{obj} as the input X variables to the @code{splitapply} function call. See also: splitapply, table.groupby, tblish.table.grpstats @end deftypefn @node table.splitvars @subsubsection table.splitvars @deftypefn {Method} {@var{out} =} splitvars (@var{obj}) @deftypefnx {Method} {@var{out} =} splitvars (@var{obj}, @var{vars}) @deftypefnx {Method} {@var{out} =} splitvars (@dots{}, @ @code{'NewVariableNames'}, @var{NewVariableNames}) Split multicolumn table variables. Splits multicolumn table variables into new single-column variables. If @var{vars} is supplied, splits only those variables. If @var{vars} is not supplied, splits all multicolumn variables. @end deftypefn @node table.squeeze @subsubsection table.squeeze @deftypefn {Method} {@var{obj} =} squeeze (@var{obj}) Remove singleton dimensions. For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze. @end deftypefn @node table.stack @subsubsection table.stack @deftypefn {Method} {@var{out} =} stack (@var{obj}, @var{vars}) @deftypefnx {Method} {@var{out} =} stack (@dots{}, @ @code{'NewDataVariableName'}, @var{NewDataVariableName}) @deftypefnx {Method} {@var{out} =} stack (@dots{}, @ @code{'IndexVariableName'}, @var{IndexVariableName}) Stack multiple table variables into a single variable. @end deftypefn @node table.summary @subsubsection table.summary @deftypefn {Method} summary (@var{obj}) Display a summary of a table's data. Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use. This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of `summary`, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output. @end deftypefn @node table.table @subsubsection table.table @deftypefn {Constructor} {@var{obj} =} table () Constructs a new empty (0 rows by 0 variables) table. @end deftypefn @deftypefn {Constructor} {@var{obj} =} table (@var{var1}, @var{var2}, @dots{}, @var{varN}) Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used. Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value '__tblish_backdoor__', that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious. @end deftypefn @deftypefn {Constructor} {@var{obj} =} table (@code{'Size'}, @var{sz}, @ @code{'VariableTypes'}, @var{varTypes}) Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type. @end deftypefn @deftypefn {Constructor} {@var{obj} =} table (@dots{}, @code{'VariableNames'}, @var{varNames}) @deftypefnx {Constructor} {@var{obj} =} table (@dots{}, @code{'RowNames'}, @var{rowNames}) Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names. @end deftypefn @node table.table2array @subsubsection table.table2array @deftypefn {Method} {@var{s} =} table2struct (@var{obj}) Converts @var{obj} to a homogeneous array. @end deftypefn @node table.table2cell @subsubsection table.table2cell @deftypefn {Method} {@var{c} =} table2cell (@var{obj}) Converts table to a cell array. Each variable in @var{obj} becomes one or more columns in the output, depending on how many columns that variable has. Returns a cell array with the same number of rows as @var{obj}, and with as many or more columns as @var{obj} has variables. @end deftypefn @node table.table2struct @subsubsection table.table2struct @deftypefn {Method} {@var{s} =} table2struct (@var{obj}) @deftypefnx {Method} {@var{s} =} table2struct (@dots{}, @code{'ToScalar'}, @var{trueOrFalse}) Converts @var{obj} to a scalar structure or structure array. Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, 'ToScalar', true); s.RowNames = tbl.Properties.RowNames; Returns a scalar struct or struct array, depending on the value of the @code{ToScalar} option. @end deftypefn @node table.union @subsubsection table.union @deftypefn {Method} {[@var{C}, @var{ia}, @var{ib}] =} union (@var{A}, @var{B}) Set union. Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables. Returns: @var{C} - A table containing all the unique row values present in A or B. @var{ia} - Row indexes into A of the rows from A included in C. @var{ib} - Row indexes into B of the rows from B included in C. @end deftypefn @node table.varfun @subsubsection table.varfun @deftypefn {Method} {@var{out} =} varfun (@var{fcn}, @var{obj}) @deftypefnx {Method} {@var{out} =} varfun (@dots{}, @code{'OutputFormat'}, @var{outputFormat}) @deftypefnx {Method} {@var{out} =} varfun (@dots{}, @code{'InputVariables'}, @var{vars}) @deftypefnx {Method} {@var{out} =} varfun (@dots{}, @code{'ErrorHandler'}, @var{errorFcn}) Apply function to table variables. Applies the given function @var{fcn} to each variable in @var{obj}, collecting the output in a table, cell array, or array of another type. @end deftypefn @node table.varnames @subsubsection table.varnames @deftypefn {Method} {@var{out} =} varnames (@var{obj}) @deftypefnx {Method} {@var{out} =} varnames (@var{obj}, @var{varNames}) Get or set variable names for a table. Returns cellstr in the getter form. Returns an updated datetime in the setter form. @end deftypefn @node table.vertcat @subsubsection table.vertcat @deftypefn {Method} {@var{out} =} vertcat (@var{varargin}) Vertical concatenation. Combines tables by vertically concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a `vertcat()` on the variables from the corresponding input tables, in the order they were input in. @end deftypefn @node table.width @subsubsection table.width @deftypefn {Method} {@var{out} =} width (@var{obj}) Number of variables in table. Note that this is not the sum of the number of columns in each variable. It is just the number of variables. @end deftypefn @node tableOuterFillValue @subsection tableOuterFillValue @emph{Not documented} @node tail @subsection tail @deftypefn {Function} {@var{out} =} tail (@var{A}) @deftypefnx {Function} {@var{out} =} tail (@var{A}, @var{k}) Get last K rows of an array. Returns the array @var{A}, subsetted to its last @var{k} rows. This means subsetting it to the last @code{(min (k, size (A, 1)))} elements along dimension 1, and leaving all other dimensions unrestricted. @var{A} is the array to subset. @var{k} is the number of rows to get. @var{k} defaults to 8 if it is omitted or empty. If there are less than @var{k} rows in @var{A}, returns all rows. Returns an array of the same type as @var{A}, unless ()-indexing @var{A} produces an array of a different type, in which case it returns that type. See also: head @end deftypefn @node tblish.dataset @subsection tblish.dataset @deftp {Class} tblish.dataset The @code{tblish.dataset} class provides convenient access to the various datasets included with Tablicious. This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list. @end deftp @node tblish.dataset.airmiles @subsubsection tblish.dataset.airmiles @deftypefn {Static Method} {@var{out} =} airmiles () Passenger Miles on Commercial US Airlines, 1937-1960 @subsubheading Description The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960. @subsubheading Source @cite{F.A.A. Statistical Handbook of Aviation}. @subsubheading Examples @example t = tblish.dataset.airmiles; plot (t.year, t.miles); title ("airmiles data"); xlabel ("Passenger-miles flown by U.S. commercial airlines") ylabel ("airmiles"); @end example @end deftypefn @node tblish.dataset.AirPassengers @subsubsection tblish.dataset.AirPassengers @deftypefn {Static Method} {@var{out} =} AirPassengers () Monthly Airline Passenger Numbers 1949-1960 @subsubheading Description The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960. @subsubheading Source Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). @cite{Time Series Analysis, Forecasting and Control}. Third Edition. San Francisco: Holden-Day. Series G. @subsubheading Examples @example ## TODO: This example needs to be ported from R. @end example @end deftypefn @node tblish.dataset.airquality @subsubsection tblish.dataset.airquality @deftypefn {Static Method} {@var{out} =} airquality () New York Air Quality Measurements from 1973 @subsubheading Description Daily air quality measurements in New York, May to September 1973. @subsubheading Format @table @code @item Ozone Ozone concentration (ppb) @item SolarR Solar R (lang) @item Wind Wind (mph) @item Temp Temperature (degrees F) @item Month Month (1-12) @item Day Day of month (1-31) @end table @subsubheading Source New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data). @subsubheading References Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). @cite{Graphical Methods for Data Analysis}. Belmont, CA: Wadsworth. @subsubheading Examples @example t = tblish.dataset.airquality # Plot a scatter-plot plus a fitted line, for each combination of measurements vars = @{"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"@}; n_vars = numel (vars); figure; for i = 1:n_vars for j = 1:n_vars if (i == j) continue endif ix_subplot = (n_vars * (j - 1) + i); hax = subplot (n_vars, n_vars, ix_subplot); var_x = vars@{i@}; var_y = vars@{j@}; x = t.(var_x); y = t.(var_y); scatter (hax, x, y, 10); # Fit a cubic line to these points # TODO: Find out exactly what kind of fitted line R's example is using, and # port that. hold on p = polyfit (x, y, 3); x_hat = unique(x); p_y = polyval (p, x_hat); plot (hax, x_hat, p_y, "r"); endfor endfor @end example @end deftypefn @node tblish.dataset.anscombe @subsubsection tblish.dataset.anscombe @deftypefn {Static Method} {@var{out} =} anscombe () Anscombe’s Quartet of “Identical” Simple Linear Regressions @subsubheading Description Four sets of x/y pairs which have the same statistical properties, but are very different. @subsubheading Format The data comes in an array of 4 structs, each with fields as follows: @table @code @item x The X values for this pair. @item y The Y values for this pair. @end table @subsubheading Source Tufte, Edward R. (1989). @cite{The Visual Display of Quantitative Information}. 13–14. Cheshire, CT: Graphics Press. @subsubheading References Anscombe, Francis J. (1973). Graphs in statistical analysis. @cite{The American Statistician}, 27, 17–21. @subsubheading Examples @example data = tblish.dataset.anscombe # Pick good limits for the plots all_x = [data.x]; all_y = [data.y]; x_limits = [min(0, min(all_x)) max(all_x)*1.2]; y_limits = [min(0, min(all_y)) max(all_y)*1.2]; # Do regression on each pair and plot the input and results figure; haxs = NaN (1, 4); for i_pair = 1:4 x = data(i_pair).x; y = data(i_pair).y; # TODO: Port the anova and other characterizations from the R code # TODO: Do a linear regression and plot its line hax = subplot (2, 2, i_pair); haxs(i_pair) = hax; xlabel (sprintf ("x%d", i_pair)); ylabel (sprintf ("y%d", i_pair)); scatter (x, y, "r"); endfor # Fiddle with the plot axes parameters linkaxes (haxs); xlim (haxs(1), x_limits); ylim (haxs(1), y_limits); @end example @end deftypefn @node tblish.dataset.attenu @subsubsection tblish.dataset.attenu @deftypefn {Static Method} {@var{out} =} attenu () Joyner-Boore Earthquake Attenuation Data @subsubheading Description Event data for 23 earthquakes in California, showing peak accelerations. @subsubheading Format @table @code @item event Event number @item mag Moment magnitude @item station Station identifier @item dist Station-hypocenter distance (km) @item accel Peak acceleration (g) @end table @subsubheading Source Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA. @subsubheading References Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. @cite{Bulletin of the Seismological Society of America}, 72, S269–S268. @subsubheading Examples @example # TODO: Port the example code from R # It does coplot() and pairs(), which are higher-level plotting tools # than core Octave provides. This could turn into a long example if we # just use base Octave here. @end example @end deftypefn @node tblish.dataset.attitude @subsubsection tblish.dataset.attitude @deftypefn {Static Method} {@var{out} =} attitude () The Chatterjee-Price Attitude Data @subsubheading Description Aggregated data from a survey of clerical employees at a large financial organization. @subsubheading Format @table @code @item rating Overall rating. @item complaints Handling of employee complaints. @item privileges Does not allow special privileges. @item learning Opportunity to learn. @item raises Raises based on performance. @item critical Too critical. @item advance Advancement. @end table @subsubheading Source Chatterjee, S. and Price, B. (1977). @cite{Regression Analysis by Example}. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).) @subsubheading Examples @example t = tblish.dataset.attitude tblish.examples.plot_pairs (t); # TODO: Display table summary # TODO: Whatever those statistical linear-model plots are that R is doing @end example @end deftypefn @node tblish.dataset.austres @subsubsection tblish.dataset.austres @deftypefn {Static Method} {@var{out} =} austres () Australian Population @subsubheading Description Numbers of Australian residents measured quarterly from March 1971 to March 1994. @subsubheading Format @table @code @item date The month of the observation. @item residents The number of residents. @end table @subsubheading Source Brockwell, P. J. and Davis, R. A. (1996). @cite{Introduction to Time Series and Forecasting}. New York: Springer-Verlag. @subsubheading Examples @example t = tblish.dataset.austres plot (datenum (t.date), t.residents); datetick x xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents"); @end example @end deftypefn @node tblish.dataset.beavers @subsubsection tblish.dataset.beavers @deftypefn {Static Method} {@var{out} =} beavers () Body Temperature Series of Two Beavers @subsubheading Description Body temperature readings for two beavers. @subsubheading Format @table @code @item day Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2). @item time Time of observation, in the form 0330 for 3:30am @item temp Measured body temperature in degrees Celsius. @item activ Indicator of activity outside the retreat. @end table @subsubheading Source P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) @cite{Case Studies in Biometry}. New York: John Wiley and Sons. @subsubheading Examples @example # TODO: This example needs to be ported from R. @end example @end deftypefn @node tblish.dataset.BJsales @subsubsection tblish.dataset.BJsales @deftypefn {Static Method} {@var{out} =} BJsales () Sales Data with Leading Indicator @subsubheading Description Sales Data with Leading Indicator @subsubheading Format @table @code @item record Index of the record. @item lead Leading indicator. @item sales Sales volume. @end table @subsubheading Source The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}. @subsubheading References Box, G. E. P. and Jenkins, G. M. (1976). @cite{Time Series Analysis, Forecasting and Control}. San Francisco: Holden-Day. p. 537. Brockwell, P. J. and Davis, R. A. (1991). @cite{Time Series: Theory and Methods}, Second edition. New York: Springer-Verlag. p. 414. @subsubheading Examples @example # TODO: Come up with example code here @end example @end deftypefn @node tblish.dataset.BOD @subsubsection tblish.dataset.BOD @deftypefn {Static Method} {@var{out} =} BOD () Biochemical Oxygen Demand @subsubheading Description Contains biochemical oxygen demand versus time in an evaluation of water quality. @subsubheading Format @table @code @item Time Time of the measurement (in days). @item demand Biochemical oxygen demand (mg/l). @end table @subsubheading Source Bates, D.M. and Watts, D.G. (1988). @cite{Nonlinear Regression Analysis and Its Applications}. New York: John Wiley & Sons. Appendix A1.4. Originally from: Marske (1967). @cite{Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface}, M.Sc. Thesis, University of Wisconsin – Madison. @subsubheading Examples @example # TODO: Port this example from R @end example @end deftypefn @node tblish.dataset.cars @subsubsection tblish.dataset.cars @deftypefn {Static Method} {@var{out} =} cars () Speed and Stopping Distances of Cars @subsubheading Description Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s. @subsubheading Format @table @code @item speed Speed (mph). @item dist Stopping distance (ft). @end table @subsubheading Source Ezekiel, M. (1930). @cite{Methods of Correlation Analysis}. New York: Wiley. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.cars; # TODO: Add Lowess smoothed lines to the plots figure; plot (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars data"); figure; loglog (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars data (logarithmic scales)"); # TODO: Do the linear model plot # Polynomial regression figure; plot (t.speed, t.dist, "o"); xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)"); title ("cars polynomial regressions"); hold on xlim ([0 25]); x2 = linspace (0, 25, 200); for degree = 1:4 [P, S, mu] = polyfit (t.speed, t.dist, degree); y2 = polyval(P, x2, [], mu); plot (x2, y2); endfor @end example @end deftypefn @node tblish.dataset.ChickWeight @subsubsection tblish.dataset.ChickWeight @deftypefn {Static Method} {@var{out} =} ChickWeight () Weight versus age of chicks on different diets @subsubheading Format @table @code @item weight a numeric vector giving the body weight of the chick (gm). @item Time a numeric vector giving the number of days since birth when the measurement was made. @item Chick an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet. @item Diet a factor with levels 1, ..., 4 indicating which experimental diet the chick received. @end table @subsubheading Source Crowder, M. and Hand, D. (1990). @cite{Analysis of Repeated Measures}. London: Chapman and Hall. (example 5.3) Hand, D. and Crowder, M. (1996), @cite{Practical Longitudinal Data Analysis}. London: Chapman and Hall. (table A.2) Pinheiro, J. C. and Bates, D. M. (2000) @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.ChickWeight tblish.examples.coplot (t, "Time", "weight", "Chick"); @end example @end deftypefn @node tblish.dataset.chickwts @subsubsection tblish.dataset.chickwts @deftypefn {Static Method} {@var{out} =} chickwts () Chicken Weights by Feed Type @subsubheading Description An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens. Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types. @subsubheading Format @table @code @item weight Chick weight at six weeks (gm). @item feed Feed type. @end table @subsubheading Source Anonymous (1948) @cite{Biometrika}, 35, 214. @subsubheading References McNeil, D. R. (1977). @code{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example # This example requires the statistics package from Octave Forge t = tblish.dataset.chickwts # Boxplot by group figure g = groupby (t, "feed", @{ "weight", @@(x) @{x@}, "weight" @}); boxplot (g.weight, 1); xlabel ("feed"); ylabel ("Weight at six weeks (gm)"); xticklabels ([@{""@} cellstr(g.feed')]); # Linear model # TODO: This linear model thing and anova @end example @end deftypefn @node tblish.dataset.co2 @subsubsection tblish.dataset.co2 @deftypefn {Static Method} {@var{out} =} co2 () Mauna Loa Atmospheric CO2 Concentration @subsubheading Description Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997. @subsubheading Format @table @code @item date Date of the month of the observation, as datetime. @item co2 CO2 concentration (ppm). @end table @subsubheading Details The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964. @subsubheading Source Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220. @url{ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2}. @subsubheading References Cleveland, W. S. (1993). @code{Visualizing Data}. New Jersey: Summit Press. @subsubheading Examples @example t = tblish.dataset.co2; plot (datenum (t.date), t.co2); datetick ("x"); xlabel ("Time"); ylabel ("Atmospheric concentration of CO2"); title ("co2 data set"); @end example @end deftypefn @node tblish.dataset.crimtab @subsubsection tblish.dataset.crimtab @deftypefn {Static Method} {@var{out} =} crimtab () Student’s 3000 Criminals Data @subsubheading Description Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales. @subsubheading Format This dataset contains three separate variables. The @code{finger_length} and @code{body_height} variables correspond to the rows and columns of the @code{count} matrix. @table @code @item finger_length Midpoints of intervals of finger lengths (cm). @item body_height Body heights (cm). @item count Number of prisoners in this bin. @end table @subsubheading Details Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations: “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.” The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4' 7"9/16 -- 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4' 8"). MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.” @subsubheading Source @url{http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt} thanks to Jean R. Lobry and Anne-Béatrice Dufour. @subsubheading References Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. @cite{The Journal of the Anthropological Institute of Great Britain and Ireland}, 30, 161–198. MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. @cite{Biometrika}, 1(2), 177–227. Student (1908). The probable error of a mean. @code{Biometrika}, 6, 1–25. @subsubheading Examples @example # TODO: Port this from R @end example @end deftypefn @node tblish.dataset.cupcake @subsubsection tblish.dataset.cupcake @deftypefn {Static Method} {@var{out} =} cupcake () Google Search popularity for "cupcake", 2004-2019 @subsubheading Description Monthly popularity of worldwide Google search results for "cupcake", 2004-2019. @subsubheading Format @table @code @item Month Month when searches took place @item Cupcake An indicator of search volume, in unknown units @end table @subsubheading Source Google Trends, @url{https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all}, retrieved 2019-05-04 by Andrew Janke. @subsubheading Examples @example t = tblish.dataset.cupcake plot (datenum (t.Month), t.Cupcake) title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric"); @end example @end deftypefn @node tblish.dataset.discoveries @subsubsection tblish.dataset.discoveries @deftypefn {Static Method} {@var{out} =} discoveries () Yearly Numbers of Important Discoveries @subsubheading Description The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959. @subsubheading Format @table @code @item year Year. @item discoveries Number of “great” discoveries that year. @end table @subsubheading Source @cite{The World Almanac and Book of Facts}, 1975 Edition, pages 315–318. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.discoveries; plot (t.year, t.discoveries); xlabel ("Time"); ylabel ("Number of important discoveries"); title ("discoveries data set"); @end example @end deftypefn @node tblish.dataset.DNase @subsubsection tblish.dataset.DNase @deftypefn {Static Method} {@var{out} =} DNase () Elisa assay of DNase @subsubheading Description Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum. @subsubheading Format @table @code @item Run Ordered @code{categorical} indicating the assay run. @item conc Known concentration of the protein (ng/ml). @item density Measured optical density in the assay (dimensionless). @end table @subsubheading Source Davidian, M. and Giltinan, D. M. (1995). @cite{Nonlinear Models for Repeated Measurement Data}. London: Chapman & Hall. (section 5.2.4, p. 134) Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.DNase; # TODO: Port this from R tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @@scatter); tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @@loglog, ... "PlotArgs", @{"o"@}); @end example @end deftypefn @node tblish.dataset.esoph @subsubsection tblish.dataset.esoph @deftypefn {Static Method} {@var{out} =} esoph () Smoking, Alcohol and Esophageal Cancer @subsubheading Description Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France. @subsubheading Format @table @code @item item Age group (years). @item alcgp Alcohol consumption (gm/day). @item tobgp Tobacco consumption (gm/day). @item ncases Number of cases. @item ncontrols Number of controls @end table @subsubheading Source Breslow, N. E. and Day, N. E. (1980) @cite{Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies}. Oxford: IARC Lyon / Oxford University Press. @subsubheading Examples @example # TODO: Port this from R # TODO: Port the anova output # TODO: Port the fancy plot # This involves a "mosaic plot", which is not supported by Octave, so this will # take some work. @end example @end deftypefn @node tblish.dataset.euro @subsubsection tblish.dataset.euro @deftypefn {Static Method} {@var{out} =} euro () Conversion Rates of Euro Currencies @subsubheading Description Conversion rates between the various Euro currencies. @subsubheading Format This data comes in two separate variables. @table @code @item euro An 11-long vector of the value of 1 Euro in all participating currencies. @item euro_cross An 11-by-11 matrix of conversion rates between various Euro currencies. @item euro_date The date upon which these Euro conversion rates were fixed. @end table @subsubheading Details The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits. @subsubheading Source Unknown. This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source. @subsubheading Examples @example # TODO: Port this from R # TODO: Example conversion # TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa @end example @end deftypefn @node tblish.dataset.eurodist @subsubsection tblish.dataset.eurodist @deftypefn {Static Method} {@var{out} =} eurodist () Distances Between European Cities and Between US Cities @subsubheading Description @code{eurodist} gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia. @code{UScitiesD} gives “straight line” distances between 10 cities in the US. @subsubheading Format @table @code @item eurodist ????? @end table TODO: Finish this. @subsubheading Source Crystal, D. Ed. (1990). @cite{The Cambridge Encyclopaedia}. Cambridge: Cambridge University Press. The US cities distances were provided by Pierre Legendre. @subsubheading Examples @end deftypefn @node tblish.dataset.EuStockMarkets @subsubsection tblish.dataset.EuStockMarkets @deftypefn {Static Method} {@var{out} =} EuStockMarkets () Daily Closing Prices of Major European Stock Indices @subsubheading Description Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted. @subsubheading Format A multivariate time series with 1860 observations on 4 variables. The starting date is the 130th day of 1991, with a frequency of 260 observations per year. @subsubheading Source The data were kindly provided by Erste Bank AG, Vienna, Austria. @subsubheading Examples @example t = tblish.dataset.EuStockMarkets; # The fact that we're doing this munging means that table might have # been the wrong structure for this data in the first place t2 = removevars (t, "day"); index_names = t2.Properties.VariableNames; day = 1:height (t2); price = table2array (t2); price0 = price(1,:); rel_price = price ./ repmat (price0, [size(price, 1) 1]); figure; plot (day, rel_price); legend (index_names); xlabel ("Business day"); ylabel ("Relative price"); @end example @end deftypefn @node tblish.dataset.faithful @subsubsection tblish.dataset.faithful @deftypefn {Static Method} {@var{out} =} faithful () Old Faithful Geyser Data @subsubheading Description Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA. @subsubheading Format @table @code @item eruptions Eruption time (mins). @item waiting Waiting time to next eruption (mins). @end table @subsubheading Source W. Härdle. @subsubheading References Härdle, W. (1991). @cite{Smoothing Techniques with Implementation in S}. New York: Springer. Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. @cite{Applied Statistics}, 39, 357–365. @subsubheading Examples @example t = tblish.dataset.faithful; # Munge the data, rounding eruption time to the second e60 = 60 * t.eruptions; ne60 = round (e60); # TODO: Port zapsmall to Octave eruptions = ne60 / 60; # TODO: Display mean relative difference and bins summary # Histogram of rounded eruption times figure hist (ne60, max (ne60)) xlabel ("Eruption time (sec)") ylabel ("n") title ("faithful data: Eruptions of Old Faithful") # Scatter plot of eruption time vs waiting time figure scatter (t.eruptions, t.waiting) xlabel ("Eruption time (min)") ylabel ("Waiting time to next eruption (min)") title ("faithful data: Eruptions of Old Faithful") # TODO: Port Lowess smoothing to Octave @end example @end deftypefn @node tblish.dataset.Formaldehyde @subsubsection tblish.dataset.Formaldehyde @deftypefn {Static Method} {@var{out} =} Formaldehyde () Determination of Formaldehyde @subsubheading Description These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer. @subsubheading Format @table @code @item record Observation record number. @item carb Carbohydrate (ml). @item optden Optical Density @end table @subsubheading Source Bennett, N. A. and N. L. Franklin (1954). @cite{Statistical Analysis in Chemistry and the Chemical Industry}. New York: Wiley. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.Formaldehyde; figure scatter (t.carb, t.optden) # TODO: Add a linear model line xlabel ("Carbohydrate (ml)") ylabel ("Optical Density") title ("Formaldehyde data") # TODO: Add linear model summary output # TOD: Add linear model summary plot @end example @end deftypefn @node tblish.dataset.freeny @subsubsection tblish.dataset.freeny @deftypefn {Static Method} {@var{out} =} freeny () Freeny's Revenue Data @subsubheading Description Freeny’s data on quarterly revenue and explanatory variables. @subsubheading Format Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential). @table @code @item date Start date of the quarter for the observation. @item y Observed quarterly revenue. TODO: Determine units (probably millions of USD?) @item lag_quarterly_revenue Quarterly revenue (@code{y}), lagged 1 quarter. @item price_index A price index @item income_level ??? TODO: Fill this in @item market_potential ??? TODO: Fill this in @end table @subsubheading Source Freeny, A. E. (1977). @cite{A Portable Linear Regression Package with Test Programs}. Bell Laboratories memorandum. @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. @subsubheading Examples @example t = tblish.dataset.freeny; summary (t) tblish.examples.plot_pairs (removevars (t, "date")) # TODO: Create linear model and print summary # TODO: Linear model plot @end example @end deftypefn @node tblish.dataset.HairEyeColor @subsubsection tblish.dataset.HairEyeColor @deftypefn {Static Method} {@var{out} =} HairEyeColor () Hair and Eye Color of Statistics Students @subsubheading Description Distribution of hair and eye color and sex in 592 statistics students. @subsubheading Format This data set comes in multiple variables @table @code @item n A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex. @item hair Hair colors for the indexes along dimension 1. @item eye Eye colors for the indexes along dimension 2. @item sex Sexes for the indexes along dimension 3. @end table @subsubheading Details The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes. This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots. @subsubheading Source @url{http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas} Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000). @subsubheading References Snee, R. D. (1974). Graphical display of two-way contingency tables. @cite{The American Statistician}, 28, 9–12. Friendly, M. (1992a). Graphical methods for categorical data. @cite{SAS User Group International Conference Proceedings}, 17, 190–200. @url{http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html} Friendly, M. (1992b). Mosaic displays for loglinear models. @cite{Proceedings of the Statistical Graphics Section}, American Statistical Association, pp. 61–68. @url{http://www.math.yorku.ca/SCS/Papers/asa92.html} Friendly, M. (2000). @cite{Visualizing Categorical Data}. SAS Institute, ISBN 1-58025-660-0. @subsubheading Examples @example tblish.dataset.HairEyeColor # TODO: Aggregate over sex and display a table of counts # TODO: Port mosaic plot to Octave @end example @end deftypefn @node tblish.dataset.Harman23cor @subsubsection tblish.dataset.Harman23cor @deftypefn {Static Method} {@var{out} =} Harman23cor () Harman Example 2.3 @subsubheading Description A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen. @subsubheading Format @table @code @item cov An 8-by-8 correlation matrix. @item names Names of the variables corresponding to the indexes of the correlation matrix’s dimensions. @end table @subsubheading Source Harman, H. H. (1976). @cite{Modern Factor Analysis}, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3. @subsubheading Examples @example tblish.dataset.Harman23cor; # TODO: Port factanal to Octave @end example @end deftypefn @node tblish.dataset.Harman74cor @subsubsection tblish.dataset.Harman74cor @deftypefn {Static Method} {@var{out} =} Harman74cor () Harman Example 7.4 @subsubheading Description A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford. @subsubheading Format @table @code @item cov A 2-dimensional correlation matrix. @item vars Names of the variables corresponding to the indexes along the dimensions of @code{cov}. @end table @subsubheading Source Harman, H. H. (1976). @cite{Modern Factor Analysis}, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4. @subsubheading Examples @example tblish.dataset.Harman74cor; # TODO: Port factanal to Octave @end example @end deftypefn @node tblish.dataset.Indometh @subsubsection tblish.dataset.Indometh @deftypefn {Static Method} {@var{out} =} Indometh () Pharmacokinetics of Indomethacin @subsubheading Description Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’). @subsubheading Format @table @code @item Subject Subject identifier. @item time Time since drug administration at which samples were drawn (hours). @item conc Plasma concentration of indomethacin (mcg/ml). @end table @subsubheading Details Each of the six subjects were given an intravenous injection of indometacin. @subsubheading Source Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. @cite{Journal of Pharmacokinetics and Biopharmaceutics} 4, 255–280. Davidian, M. and Giltinan, D. M. (1995). @cite{Nonlinear Models for Repeated Measurement Data}. London: Chapman & Hall. (section 5.2.4, p. 129) Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @end deftypefn @node tblish.dataset.infert @subsubsection tblish.dataset.infert @deftypefn {Static Method} {@var{out} =} infert () Infertility after Spontaneous and Induced Abortion @subsubheading Description This is a matched case-control study dating from before the availability of conditional logistic regression. @subsubheading Format @table @code @item education Index of the record. @item age Age in years of case. @item parity Count. @item induced Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”. @item case_status 0 = control, 1 = case. @item spontaneous Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”. @item stratum Matched set number. @item pooled_stratum Stratum number. @end table @subsubheading Note One case with two prior spontaneous abortions and two prior induced abortions is omitted. @subsubheading Source Trichopoulos et al (1976). @cite{Br. J. of Obst. and Gynaec.} 83, 645–650. @subsubheading Examples @example t = tblish.dataset.infert; # TODO: Port glm() (generalized linear model) stuff to Octave @end example @end deftypefn @node tblish.dataset.InsectSprays @subsubsection tblish.dataset.InsectSprays @deftypefn {Static Method} {@var{out} =} InsectSprays () Effectiveness of Insect Sprays @subsubheading Description The counts of insects in agricultural experimental units treated with different insecticides. @subsubheading Format @table @code @item spray The type of spray. @item count Insect count. @end table @subsubheading Source Beall, G., (1942). The Transformation of data from entomological field experiments. @cite{Biometrika}, 29, 243–262. @subsubheading References McNeil, D. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.InsectSprays; # TODO: boxplot # TODO: AOV plots @end example @end deftypefn @node tblish.dataset.iris @subsubsection tblish.dataset.iris @deftypefn {Static Method} {@var{out} =} iris () The Fisher Iris dataset: measurements of various flowers @subsubheading Description This is the classic Fisher Iris dataset. @subsubheading Format @table @code @item Species The species of flower being measured. @item SepalLength Length of sepals, in centimeters. @item SepalWidth Width of sepals, in centimeters. @item PetalLength Length of petals, in centimeters. @item PetalWidth Width of petals, in centimeters. @end table @subsubheading Source @url{http://archive.ics.uci.edu/ml/datasets/Iris} @subsubheading References @url{https://en.wikipedia.org/wiki/Iris_flower_data_set} Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in @cite{Contributions to Mathematical Statistics} (John Wiley, NY, 1950). Duda, R.O., & Hart, P.E. (1973). @cite{Pattern Classification and Scene Analysis}. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218. The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. @cite{Bulletin of the American Iris Society}, 59, 2–5. @subsubheading Examples @example # TODO: Port this example from R @end example @end deftypefn @node tblish.dataset.islands @subsubsection tblish.dataset.islands @deftypefn {Static Method} {@var{out} =} islands () Areas of the World's Major Landmasses @subsubheading Description The areas in thousands of square miles of the landmasses which exceed 10,000 square miles. @subsubheading Format @table @code @item name The name of the island. @item area The area, in thousands of square miles. @end table @subsubheading Source @cite{The World Almanac and Book of Facts}, 1975, page 406. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.islands; # TODO: Port dot chart to Octave @end example @end deftypefn @node tblish.dataset.JohnsonJohnson @subsubsection tblish.dataset.JohnsonJohnson @deftypefn {Static Method} {@var{out} =} JohnsonJohnson () Quarterly Earnings per Johnson & Johnson Share @subsubheading Description Quarterly earnings (dollars) per Johnson & Johnson share 1960–80. @subsubheading Format @table @code @item date Start date of the quarter. @item earnings Earnings per share (USD). @end table @subsubheading Source Shumway, R. H. and Stoffer, D. S. (2000). @cite{Time Series Analysis and its Applications}. Second Edition. New York: Springer. Example 1.1. @subsubheading Examples @example t = tblish.dataset.JohnsonJohnson # TODO: Yikes, look at all those plots. Port them to Octave. @end example @end deftypefn @node tblish.dataset.LakeHuron @subsubsection tblish.dataset.LakeHuron @deftypefn {Static Method} {@var{out} =} LakeHuron () Level of Lake Huron 1875-1972 @subsubheading Description Annual measurements of the level, in feet, of Lake Huron 1875–1972. @subsubheading Format @table @code @item year Year of the measurement @item level Lake level (ft). @end table @subsubheading Source Brockwell, P. J. and Davis, R. A. (1991). @cite{Time Series and Forecasting Methods}. Second edition. New York: Springer. Series A, page 555. Brockwell, P. J. and Davis, R. A. (1996). @cite{Introduction to Time Series and Forecasting}. New York: Springer. Sections 5.1 and 7.6. @subsubheading Examples @example t = tblish.dataset.LakeHuron; plot (t.year, t.level) xlabel ("Year") ylabel ("Lake level (ft)") title ("Level of Lake Huron") @end example @end deftypefn @node tblish.dataset.lh @subsubsection tblish.dataset.lh @deftypefn {Static Method} {@var{out} =} lh () Luteinizing Hormone in Blood Samples @subsubheading Description A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples. @subsubheading Format @table @code @item sample The number of the observation. @item lh Level of luteinizing hormone. @end table @subsubheading Source P.J. Diggle (1990). @cite{Time Series: A Biostatistical Introduction}. Oxford. Table A.1, series 3. @subsubheading Examples @example t = tblish.dataset.lh; plot (t.sample, t.lh); xlabel ("Sample Number"); ylabel ("lh level"); @end example @end deftypefn @node tblish.dataset.LifeCycleSavings @subsubsection tblish.dataset.LifeCycleSavings @deftypefn {Static Method} {@var{out} =} LifeCycleSavings () Intercountry Life-Cycle Savings Data @subsubheading Description Data on the savings ratio 1960–1970. @subsubheading Format @table @code @item country Name of the country. @item sr Aggregate personal savings. @item pop15 Percentage of population under 15. @item pop75 Percentage of population over 75. @item dpi Real per-capita disposable income. @item ddpi Percent growth rate of dpi. @end table @subsubheading Details Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations. @subsubheading Source The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977). @subsubheading References Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology. Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). @cite{Regression Diagnostics}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.LifeCycleSavings; # TODO: linear model # TODO: pairs plot with Lowess smoothed line @end example @end deftypefn @node tblish.dataset.Loblolly @subsubsection tblish.dataset.Loblolly @deftypefn {Static Method} {@var{out} =} Loblolly () Growth of Loblolly pine trees @subsubheading Description Records of the growth of Loblolly pine trees. @subsubheading Format @table @code @item height Tree height (ft). @item age Tree age (years). @item Seed Seed source for the tree. Ordering is according to increasing maximum height. @end table @subsubheading Source Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. @cite{Proceedings of the Statistical Computing Section}, American Statistical Association, 340–343. Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.Loblolly; t2 = t(t.Seed == "329",:); scatter (t2.age, t2.height) xlabel ("Tree age (yr)"); ylabel ("Tree height (ft)"); title ("Loblolly data and fitted curve (Seed 329 only)") # TODO: Compute and plot fitted curve @end example @end deftypefn @node tblish.dataset.longley @subsubsection tblish.dataset.longley @deftypefn {Static Method} {@var{out} =} longley () Longley’s Economic Regression Data @subsubheading Description A macroeconomic data set which provides a well-known example for a highly collinear regression. @subsubheading Format @table @code @item Year The year. @item GNP_deflator GNP implicit price deflator (1954=100). @item GNP Gross National Product. @item Unemployed Number of unemployed. @item Armed_Forces Number of people in the armed forces. @item Population “Noninstitutionalized” population ≥ 14 years of age. @item Employed Number of people employed. @end table @subsubheading Source J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. @cite{Journal of the American Statistical Association}, 62, 819–841. @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. @subsubheading Examples @example t = tblish.dataset.longley; # TODO: Linear model # TODO: opar plot @end example @end deftypefn @node tblish.dataset.lynx @subsubsection tblish.dataset.lynx @deftypefn {Static Method} {@var{out} =} lynx () Annual Canadian Lynx trappings 1821-1934 @subsubheading Description Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977). @subsubheading Format @table @code @item year Year of the record. @item lynx Number of lynx trapped. @end table @subsubheading Source Brockwell, P. J. and Davis, R. A. (1991). @cite{Time Series and Forecasting Methods}. Second edition. New York: Springer. Series G (page 557). @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. @cite{Journal of the Royal Statistical Society series A}, 140, 411–431. @subsubheading Examples @example t = tblish.dataset.lynx; plot (t.year, t.lynx); xlabel ("Year"); ylabel ("Lynx Trapped"); @end example @end deftypefn @node tblish.dataset.morley @subsubsection tblish.dataset.morley @deftypefn {Static Method} {@var{out} =} morley () Michelson Speed of Light Data @subsubheading Description A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted). @subsubheading Format @table @code @item Expt The experiment number, from 1 to 5. @item Run The run number within each experiment. @item Speed Speed-of-light measurement. @end table @subsubheading Details The data is here viewed as a randomized block experiment with @code{experiment} and @code{run} as the factors. @code{run} may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment. @subsubheading Source A. J. Weekes (1986). @cite{A Genstat Primer}. London: Edward Arnold. S. M. Stigler (1977). Do robust estimators work with real data? @cite{Annals of Statistics} 5, 1055–1098. (See Table 6.) A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. @cite{Astronomic Papers}, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.). @subsubheading Examples @example t = tblish.dataset.morley; # TODO: Port to Octave @end example @end deftypefn @node tblish.dataset.mtcars @subsubsection tblish.dataset.mtcars @deftypefn {Static Method} {@var{out} =} mtcars () Motor Trend 1974 Car Road Tests @subsubheading Description The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). @subsubheading Format @table @code @item mpg Fuel efficiency in miles/gallon @item cyl Number of cylinders @item disp Displacement (cu. in.) @item hp Gross horsepower @item drat Rear axle ratio @item wt Weight (1,000 lbs) @item qsec 1/4 mile time @item vs Engine type (0 = V-shaped, 1 = straight) @item am Transmission type (0 = automatic, 1 = manual) @item gear Number of forward gears @item carb Number of carburetors @end table @subsubheading Note Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.” @subsubheading Source Henderson and Velleman (1981). Building multiple regression models interactively. @cite{Biometrics}, 37, 391–411. @subsubheading Examples @example # TODO: Port this example from R @end example @end deftypefn @node tblish.dataset.nhtemp @subsubsection tblish.dataset.nhtemp @deftypefn {Static Method} {@var{out} =} nhtemp () Average Yearly Temperatures in New Haven @subsubheading Description The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971. @subsubheading Format @table @code @item year Year of the observation. @item temp Mean annual temperature (degrees F). @end table @subsubheading Source Vaux, J. E. and Brinker, N. B. (1972) @cite{Cycles}, 1972, 117–121. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.nhtemp; plot (t.year, t.temp); title ("nhtemp data"); xlabel ("Mean annual temperature in New Haven, CT (deg. F)"); @end example @end deftypefn @node tblish.dataset.Nile @subsubsection tblish.dataset.Nile @deftypefn {Static Method} {@var{out} =} Nile () Flow of the River Nile @subsubheading Description Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249). @subsubheading Format @table @code @item year Year of the record. @item flow Annual flow (cubic meters). @end table @subsubheading Source Durbin, J. and Koopman, S. J. (2001). @cite{Time Series Analysis by State Space Methods}. Oxford: Oxford University Press. @url{http://www.ssfpack.com/DKbook.html} @subsubheading References Balke, N. S. (1993). Detecting level shifts in time series. @cite{Journal of Business and Economic Statistics}, 11, 81–92. Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. @cite{Biometrika} 65, 243–51. @subsubheading Examples @example t = tblish.dataset.Nile; figure plot (t.year, t.flow); # TODO: Port the rest of the example to Octave @end example @end deftypefn @node tblish.dataset.nottem @subsubsection tblish.dataset.nottem @deftypefn {Static Method} {@var{out} =} nottem () Average Monthly Temperatures at Nottingham, 1920-1939 @subsubheading Description A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years. @subsubheading Format @table @code @item record Index of the record. @item lead Leading indicator. @item sales Sales volume. @end table @subsubheading Source Anderson, O. D. (1976). @cite{Time Series Analysis and Forecasting: The Box-Jenkins approach}. London: Butterworths. Series R. @subsubheading Examples @example # TODO: Come up with example code here @end example @end deftypefn @node tblish.dataset.npk @subsubsection tblish.dataset.npk @deftypefn {Static Method} {@var{out} =} npk () Classical N, P, K Factorial Experiment @subsubheading Description A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots. @subsubheading Format @table @code @item block Which block (1 to 6). @item N Indicator (0/1) for the application of nitrogen. @item P Indicator (0/1) for the application of phosphate. @item K Indicator (0/1) for the application of potassium. @item yield Yield of peas, in pounds/plot. Plots were 1/70 acre. @end table @subsubheading Source Imperial College, London, M.Sc. exercise sheet. @subsubheading References Venables, W. N. and Ripley, B. D. (2002). @cite{Modern Applied Statistics with S}. Fourth edition. New York: Springer. @subsubheading Examples @example t = tblish.dataset.npk; # TODO: Port aov() and LM to Octave @end example @end deftypefn @node tblish.dataset.occupationalStatus @subsubsection tblish.dataset.occupationalStatus @deftypefn {Static Method} {@var{out} =} occupationalStatus () Occupational Status of Fathers and their Sons @subsubheading Description Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status. @subsubheading Format An 8-by-8 matrix of counts, with classifying fators @code{origin} (father’s occupational status, levels 1:8) and @code{destination} (son’s occupational status, levels 1:8). @subsubheading Source Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. @cite{J. Am. Stat. Assoc.}, 74 (367), 537–552. @subsubheading Examples @example # TODO: Come up with example code here @end example @end deftypefn @node tblish.dataset.Orange @subsubsection tblish.dataset.Orange @deftypefn {Static Method} {@var{out} =} Orange () Growth of Orange Trees @subsubheading Description Records of the growth of orange trees. @subsubheading Format @table @code @item Tree A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter. @item age Age of the tree (days since 1968-12-31). @item circumference Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry. @end table @subsubheading Source The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}. @subsubheading References Draper, N. R. and Smith, H. (1998). @cite{Applied Regression Analysis (3rd ed)}. New York: Wiley. (exercise 24.N). Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.Orange; # TODO: Port coplot to Octave # TODO: Linear model @end example @end deftypefn @node tblish.dataset.OrchardSprays @subsubsection tblish.dataset.OrchardSprays @deftypefn {Static Method} {@var{out} =} OrchardSprays () Potency of Orchard Sprays @subsubheading Description An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design. @subsubheading Format @table @code @item rowpos Row of the design. @item colpos Column of the design @item treatment Treatment level. @item decrease Response. @end table @subsubheading Details Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur. The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells. An 8 x 8 Latin square design was used and the treatments were coded as follows: A – highest level of lime sulphur B – next highest level of lime sulphur @dots{} G – lowest level of lime sulphur H – no lime sulphur @subsubheading Source Finney, D. J. (1947). @cite{Probit Analysis}. Cambridge. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.OrchardSprays; tblish.examples.plot_pairs (t); @end example @end deftypefn @node tblish.dataset.PlantGrowth @subsubsection tblish.dataset.PlantGrowth @deftypefn {Static Method} {@var{out} =} PlantGrowth () Results from an Experiment on Plant Growth @subsubheading Description Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions. @subsubheading Format @table @code @item group Treatment condition group. @item weight Weight of plants. @end table @subsubheading Source Dobson, A. J. (1983). @cite{An Introduction to Statistical Modelling}. London: Chapman and Hall. @subsubheading Examples @example t = tblish.dataset.PlantGrowth; # TODO: Port anova to Octave @end example @end deftypefn @node tblish.dataset.precip @subsubsection tblish.dataset.precip @deftypefn {Static Method} {@var{out} =} precip () Annual Precipitation in US Cities @subsubheading Description The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities. @subsubheading Format @table @code @item city City observed. @item precip Annual precipitation (in). @end table @subsubheading Source @cite{Statistical Abstracts of the United States}, 1975. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.precip; # TODO: Port dot plot to Octave @end example @end deftypefn @node tblish.dataset.presidents @subsubsection tblish.dataset.presidents @deftypefn {Static Method} {@var{out} =} presidents () Quarterly Approval Ratings of US Presidents @subsubheading Description The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974. @subsubheading Format @table @code @item date Approximate date of the observation. @item approval Approval rating (%). @end table @subsubheading Details The data are actually a fudged version of the approval ratings. See McNeil’s book for details. @subsubheading Source The Gallup Organisation. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.presidents; figure plot (datenum (t.date), t.approval) datetick ("x") xlabel ("Date") ylabel ("Approval rating (%)") title ("presidents data") @end example @end deftypefn @node tblish.dataset.pressure @subsubsection tblish.dataset.pressure @deftypefn {Static Method} {@var{out} =} pressure () Vapor Pressure of Mercury as a Function of Temperature @subsubheading Description Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury). @subsubheading Format @table @code @item temperature Temperature (deg C). @item pressure Pressure (mm Hg). @end table @subsubheading Source Weast, R. C., ed. (1973). @cite{Handbook of Chemistry and Physics}. Cleveland: CRC Press. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.pressure; figure plot (t.temperature, t.pressure) xlabel ("Temperature (deg C)") ylabel ("Pressure (mm of Hg)") title ("pressure data: Vapor Pressure of Mercury") figure semilogy (t.temperature, t.pressure) xlabel ("Temperature (deg C)") ylabel ("Pressure (mm of Hg)") title ("pressure data: Vapor Pressure of Mercury") @end example @end deftypefn @node tblish.dataset.Puromycin @subsubsection tblish.dataset.Puromycin @deftypefn {Static Method} {@var{out} =} Puromycin () Reaction Velocity of an Enzymatic Reaction @subsubheading Description Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin. @subsubheading Format @table @code @item state Whether the cell was treated. @item conc Substrate concentrations (ppm). @item rate Instantaneous reaction rates (counts/min/min). @end table @subsubheading Details Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated. @subsubheading Source The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}. @subsubheading References Bates, D.M. and Watts, D.G. (1988). @cite{Nonlinear Regression Analysis and Its Applications}. New York: Wiley. Appendix A1.3. Treloar, M. A. (1974). @cite{Effects of Puromycin on Galactosyltransferase in Golgi Membranes}. M.Sc. Thesis, U. of Toronto. @subsubheading Examples @example t = tblish.dataset.Puromycin; # TODO: Port example to Octave @end example @end deftypefn @node tblish.dataset.quakes @subsubsection tblish.dataset.quakes @deftypefn {Static Method} {@var{out} =} quakes () Locations of Earthquakes off Fiji @subsubheading Description The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964. @subsubheading Format @table @code @item lat Latitude of event. @item long Longitude of event. @item depth Depth (km). @item mag Richter magnitude. @item stations Number of stations reporting. @end table @subsubheading Details There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations. @subsubheading Source This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University. @subsubheading References G. E. P. Box and G. M. Jenkins (1976). @cite{Time Series Analysis, Forecasting and Control}. San Francisco: Holden-Day. p. 537. P. J. Brockwell and R. A. Davis (1991). @cite{Time Series: Theory and Methods}. Second edition. New York: Springer-Verlag. p. 414. @subsubheading Examples @example # TODO: Come up with example code here @end example @end deftypefn @node tblish.dataset.randu @subsubsection tblish.dataset.randu @deftypefn {Static Method} {@var{out} =} randu () Random Numbers from Congruential Generator RANDU @subsubheading Description 400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5. @subsubheading Format @table @code @item record Index of the record. @item x X value of the triple. @item y Y value of the triple. @item z Z value of the triple. @end table @subsubheading Details In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator. These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places. Under VMS versions 2.0 and higher, this problem has been fixed. @subsubheading Source David Donoho @subsubheading Examples @example t = tblish.dataset.randu; @end example @end deftypefn @node tblish.dataset.rivers @subsubsection tblish.dataset.rivers @deftypefn {Static Method} {@var{out} =} rivers () Lengths of Major North American Rivers @subsubheading Description This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey. @subsubheading Format @table @code @item rivers A vector containing 141 observations. @end table @subsubheading Source @cite{World Almanac and Book of Facts}, 1975, page 406. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example tblish.dataset.rivers; longest_river = max (rivers) shortest_river = min (rivers) @end example @end deftypefn @node tblish.dataset.rock @subsubsection tblish.dataset.rock @deftypefn {Static Method} {@var{out} =} rock () Measurements on Petroleum Rock Samples @subsubheading Description Measurements on 48 rock samples from a petroleum reservoir. @subsubheading Format @table @code @item area Area of pores space, in pixels out of 256 by 256. @item peri Perimeter in pixels. @item shape Perimeter/sqrt(area). @item perm Permeability in milli-Darcies. @end table @subsubheading Details Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape. @subsubheading Source Data from BP Research, image analysis by Ronit Katz, U. Oxford. @subsubheading Examples @example t = tblish.dataset.rock; figure scatter (t.area, t.perm) xlabel ("Area of pores space (pixels out of 256x256)") ylabel ("Permeability (milli-Darcies)") @end example @end deftypefn @node tblish.dataset.sleep @subsubsection tblish.dataset.sleep @deftypefn {Static Method} {@var{out} =} sleep () Student’s Sleep Data @subsubheading Description Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients. @subsubheading Format @table @code @item id Patient ID. @item group Drug given. @item extra Increase in hours of sleep. @end table @subsubheading Details The @code{group} variable name may be misleading about the data: They represent measurements on 10 persons, not in groups. @subsubheading Source Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. @cite{The Journal of Physiology}, 32, 501–510. Student (1908). The probable error of the mean. @cite{Biometrika}, 6, 20. @subsubheading References Scheffé, Henry (1959). @cite{The Analysis of Variance}. New York, NY: Wiley. @subsubheading Examples @example t = tblish.dataset.sleep; # TODO: Port to Octave @end example @end deftypefn @node tblish.dataset.stackloss @subsubsection tblish.dataset.stackloss @deftypefn {Static Method} {@var{out} =} stackloss () Brownlee's Stack Loss Plant Data @subsubheading Description Operational data of a plant for the oxidation of ammonia to nitric acid. @subsubheading Format @table @code @item AirFlow Flow of cooling air. @item WaterTemp Cooling Water Inlet temperature. @item AcidConc Concentration of acid (per 1000, minus 500). @item StackLoss Stack loss @end table @subsubheading Details “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.) @code{AirFlow} represents the rate of operation of the plant. @code{WaterTemp} is the temperature of cooling water circulated through coils in the absorption tower. @code{AcidConc} is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. @code{StackLoss} (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant. @subsubheading Source Brownlee, K. A. (1960, 2nd ed. 1965). @cite{Statistical Theory and Methodology in Science and Engineering}. New York: Wiley. pp. 491–500. @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. Dodge, Y. (1996). The guinea pig of multiple regression. In: @cite{Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday}, 1996, @cite{Lecture Notes in Statistics} 109, Springer-Verlag, New York. @subsubheading Examples @example t = tblish.dataset.stackloss; # TODO: Create linear model and print summary @end example @end deftypefn @node tblish.dataset.state @subsubsection tblish.dataset.state @deftypefn {Static Method} {@var{out} =} state () US State Facts and Figures @subsubheading Description Data related to the 50 states of the United States of America. @subsubheading Format @table @code @item abb State abbreviation. @item name State name. @item area Area (sq mi). @item lat Approximate center (latitude). @item lon Approximate center (longitude). @item division State division. @item revion State region. @item Population Population estimate as of July 1, 1975. @item Income Per capita income (1974). @item Illiteracy Illiteracy as of 1970 (percent of population). @item LifeExp Lfe expectancy in years (1969-71). @item Murder Murder and non-negligent manslaughter rate per 100,000 population (1976). @item HSGrad Percent high-school graduates (1970). @item Frost Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city. @end table @subsubheading Source U.S. Department of Commerce, Bureau of the Census (1977) @cite{Statistical Abstract of the United States}. U.S. Department of Commerce, Bureau of the Census (1977) @cite{County and City Data Book}. @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. @subsubheading Examples @example t = tblish.dataset.state; @end example @end deftypefn @node tblish.dataset.sunspot_month @subsubsection tblish.dataset.sunspot_month @deftypefn {Static Method} {@var{out} =} sunspot_month () Monthly Sunspot Data, from 1749 to “Present” @subsubheading Description Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available. @subsubheading Format @table @code @item month Month of the observation. @item sunspots Number of sunspots. @end table @subsubheading Source WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at @url{http://www.sidc.be/silso/datafiles}. @subsubheading Examples @example t = tblish.dataset.sunspot_month; @end example @end deftypefn @node tblish.dataset.sunspot_year @subsubsection tblish.dataset.sunspot_year @deftypefn {Static Method} {@var{out} =} sunspot_year () Yearly Sunspot Data, 1700-1988 @subsubheading Description Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit). @subsubheading Format @table @code @item year Year of the observation. @item sunspots Number of sunspots. @end table @subsubheading Source H. Tong (1996) @cite{Non-Linear Time Series}. Clarendon Press, Oxford, p. 471. @subsubheading Examples @example t = tblish.dataset.sunspot_year; figure plot (t.year, t.sunspots) xlabel ("Year") ylabel ("Sunspots") @end example @end deftypefn @node tblish.dataset.sunspots @subsubsection tblish.dataset.sunspots @deftypefn {Static Method} {@var{out} =} sunspots () Monthly Sunspot Numbers, 1749-1983 @subsubheading Description Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory. @subsubheading Format @table @code @item month Month of the observation. @item sunspots Number of observed sunspots. @end table @subsubheading Source Andrews, D. F. and Herzberg, A. M. (1985) @cite{Data: A Collection of Problems from Many Fields for the Student and Research Worker}. New York: Springer-Verlag. @subsubheading Examples @example t = tblish.dataset.sunspots; figure plot (datenum (t.month), t.sunspots) datetick ("x") xlabel ("Date") ylabel ("Monthly sunspot numbers") title ("sunspots data") @end example @end deftypefn @node tblish.dataset.swiss @subsubsection tblish.dataset.swiss @deftypefn {Static Method} {@var{out} =} swiss () Swiss Fertility and Socioeconomic Indicators (1888) Data @subsubheading Description Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888. @subsubheading Format @table @code @item Fertility Ig, ‘common standardized fertility measure’. @item Agriculture % of males involved in agriculture as occupation. @item Examination % draftees receiving highest mark on army examination. @item Education % education beyond primary school for draftees. @item Catholic % ‘Catholic’ (as opposed to ‘Protestant’). @item InfantMortality Live births who live less than 1 year. @end table All variables but ‘Fertility’ give proportions of the population. @subsubheading Source (paraphrasing Mosteller and Tukey): Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries. The data collected are for 47 French-speaking “provinces” at about 1888. Here, all variables are scaled to [0, 100], where in the original, all but @code{Catholic} were scaled to [0, 1]. @subsubheading Note Files for all 182 districts in 1888 and other years have been available at @url{https://opr.princeton.edu/archive/pefp/switz.aspx}. They state that variables @code{Examination} and @code{Education} are averages for 1887, 1888 and 1889. @subsubheading References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). @cite{The New S Language}. Monterey: Wadsworth & Brooks/Cole. @subsubheading Examples @example t = tblish.dataset.swiss; # TODO: Port linear model to Octave @end example @end deftypefn @node tblish.dataset.Theoph @subsubsection tblish.dataset.Theoph @deftypefn {Static Method} {@var{out} =} Theoph () Pharmacokinetics of Theophylline @subsubheading Description An experiment on the pharmacokinetics of theophylline. @subsubheading Format @table @code @item Subject Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed. @item Wt Weight of the subject (kg). @item Dose Dose of theophylline administerred orally to the subject (mg/kg). @item Time Time since drug administration when the sample was drawn (hr). @item conc Theophylline concentration in the sample (mg/L). @end table @subsubheading Details Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours. These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available. @subsubheading Source The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}. @subsubheading References Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). @cite{NONMEM Users Guide: Part V}. NONMEM Project Group, University of California, San Francisco. Davidian, M. and Giltinan, D. M. (1995). @cite{Nonlinear Models for Repeated Measurement Data}. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176) Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. (Appendix A.29) @subsubheading Examples @example t = tblish.dataset.Theoph; # TODO: Coplot # TODO: Yet another linear model to port to Octave @end example @end deftypefn @node tblish.dataset.Titanic @subsubsection tblish.dataset.Titanic @deftypefn {Static Method} {@var{out} =} Titanic () Survival of passengers on the Titanic @subsubheading Description This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival. @subsubheading Format @code{n} is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables: @table @code @item Class 1st, 2nd, 3rd, Cre. @item Sex Male, Female. @item Age Child, Adult. @item Survived No, Yes. @end table @subsubheading Details The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger. These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost. Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (@url{https://www.encyclopedia-titanica.org/}). @subsubheading Source Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. @cite{Journal of Statistics Education}, 3. The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in: British Board of Trade (1990). @cite{Report on the Loss of the ‘Titanic’ (S.S.)}. British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing. @subsubheading Examples @example tblish.dataset.Titanic; # TODO: Port mosaic plot to Octave # TODO: Check for higher survival rates in children and females @end example @end deftypefn @node tblish.dataset.ToothGrowth @subsubsection tblish.dataset.ToothGrowth @deftypefn {Static Method} {@var{out} =} ToothGrowth () The Effect of Vitamin C on Tooth Growth in Guinea Pigs @subsubheading Description The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as @code{VC}). @subsubheading Format @table @code @item supp Supplement type. @item dose Dose (mg/day). @item len Tooth length. @end table @subsubheading Source C. I. Bliss (1952). @cite{The Statistics of Bioassay}. Academic Press. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. @cite{The Journal of Nutrition}, 33(5), 491–504. @subsubheading Examples @example t = tblish.dataset.ToothGrowth; tblish.examples.coplot (t, "dose", "len", "supp"); # TODO: Port Lowess smoothing to Octave @end example @end deftypefn @node tblish.dataset.treering @subsubsection tblish.dataset.treering @deftypefn {Static Method} {@var{out} =} treering () Yearly Treering Data, -6000-1979 @subsubheading Description Contains normalized tree-ring widths in dimensionless units. @subsubheading Format A univariate time series with 7981 observations. Each tree ring corresponds to one year. @subsubheading Details The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California. @subsubheading Source Time Series Data Library: @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}, series ‘CA535.DAT’. @subsubheading References For some photos of Methuselah Walk see @url{https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html}. @subsubheading Examples @example t = tblish.dataset.treering; @end example @end deftypefn @node tblish.dataset.trees @subsubsection tblish.dataset.trees @deftypefn {Static Method} {@var{out} =} trees () Diameter, Height and Volume for Black Cherry Trees @subsubheading Description This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground. @subsubheading Format @table @code @item Girth Tree diameter (rather than girth, actually) in inches. @item Height Height in ft. @item Volume Volume of timber in cubic feet. @end table @subsubheading Source Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). @cite{The Minitab Student Handbook}. Duxbury Press. @subsubheading References Atkinson, A. C. (1985). @cite{Plots, Transformations and Regression}. Oxford: Oxford University Press. @subsubheading Examples @example t = tblish.dataset.trees; figure tblish.examples.plot_pairs (t); figure loglog (t.Girth, t.Volume) xlabel ("Girth") ylabel ("Volume") # TODO: Transform to log space for the coplot # TODO: Linear model @end example @end deftypefn @node tblish.dataset.UCBAdmissions @subsubsection tblish.dataset.UCBAdmissions @deftypefn {Static Method} {@var{out} =} UCBAdmissions () Student Admissions at UC Berkeley @subsubheading Description Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex. @subsubheading Format A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows: @table @code @item Admit Admitted, Rejected. @item Gender Male, Female. @item Dept A, B, C, D, E, F. @end table @subsubheading Details This data set is frequently used for illustrating Simpson's paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates). @subsubheading Source The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at @url{http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/}. @subsubheading References Bickel, P. J., Hammel, E. A., and O'Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. @cite{Science}, 187, 398–403. @url{http://www.jstor.org/stable/1739581}. @subsubheading Examples @example tblish.dataset.UCBAdmissions; # TODO: Port mosaic plot to Octave @end example @end deftypefn @node tblish.dataset.UKDriverDeaths @subsubsection tblish.dataset.UKDriverDeaths @deftypefn {Static Method} {@var{out} =} UKDriverDeaths () Road Casualties in Great Britain 1969-84 @subsubheading Description @code{UKDriverDeaths} is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983. @code{Seatbelts} is more information on the same problem. @subsubheading Format @code{UKDriverDeaths} is a table with the following variables: @table @code @item month Month of the observation. @item deaths Number of deaths. @end table @code{Seatbelts} is a table with the following variables: @table @code @item month Month of the observation. @item DriversKilled Car drivers killed. @item drivers Same as @code{UKDriverDeaths} @code{deaths} count. @item front Front-seat passengers killed or seriously injured. @item rear Rear-seat passengers killed or seriously injured. @item kms Distance driven. @item PetrolPrice Petrol price. @item VanKilled Number of van (“light goods vehicle”) drivers killed. @item law 0/1: was the seatbelt law in effect that month? @end table @subsubheading Source Harvey, A.C. (1989). @cite{Forecasting, Structural Time Series Models and the Kalman Filter}. Cambridge: Cambridge University Press. pp. 519–523. Durbin, J. and Koopman, S. J. (2001). @cite{Time Series Analysis by State Space Methods}. Oxford: Oxford University Press. @url{http://www.ssfpack.com/dkbook/} @subsubheading References Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. @cite{Journal of the Royal Statistical Society} series A, 149, 187–227. @subsubheading Examples @example tblish.dataset.UKDriverDeaths; d = UKDriverDeaths; s = Seatbelts; # TODO: Port the model and plots to Octave @end example @end deftypefn @node tblish.dataset.UKgas @subsubsection tblish.dataset.UKgas @deftypefn {Static Method} {@var{out} =} UKgas () UK Quarterly Gas Consumption @subsubheading Description Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms. @subsubheading Format @table @code @item date Quarter of the observation @item gas Gas consumption (MM therms). @end table @subsubheading Source Durbin, J. and Koopman, S. J. (2001). @cite{Time Series Analysis by State Space Methods}. Oxford: Oxford University Press. @url{http://www.ssfpack.com/dkbook/}. @subsubheading Examples @example t = tblish.dataset.UKgas; plot (datenum (t.date), t.gas); datetick ("x") xlabel ("Month") ylabel ("Gas consumption (MM therms)") @end example @end deftypefn @node tblish.dataset.UKLungDeaths @subsubsection tblish.dataset.UKLungDeaths @deftypefn {Static Method} {@var{out} =} UKLungDeaths () Monthly Deaths from Lung Diseases in the UK @subsubheading Description Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979. @subsubheading Format @table @code @item date Month of the observation. @item ldeaths Total lung deaths. @item fdeaths Lung deaths among females. @item mdeaths Lung deaths among males. @end table @subsubheading Source P. J. Diggle (1990). @cite{Time Series: A Biostatistical Introduction}. Oxford. table A.3 @subsubheading Examples @example t = tblish.dataset.UKLungDeaths; figure plot (datenum (t.date), t.ldeaths); title ("Total UK Lung Deaths") xlabel ("Month") ylabel ("Deaths") figure plot (datenum (t.date), [t.fdeaths t.mdeaths]); title ("UK Lung Deaths buy sex") legend (@{"Female", "Male"@}) xlabel ("Month") ylabel ("Deaths") @end example @end deftypefn @node tblish.dataset.USAccDeaths @subsubsection tblish.dataset.USAccDeaths @deftypefn {Static Method} {@var{out} =} USAccDeaths () Accidental Deaths in the US 1973-1978 @subsubheading Description A time series giving the monthly totals of accidental deaths in the USA. @subsubheading Format @table @code @item month Month of the observation. @item deaths Accidental deaths. @end table @subsubheading Source Brockwell, P. J. and Davis, R. A. (1991). @cite{Time Series: Theory and Methods}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.USAccDeaths; @end example @end deftypefn @node tblish.dataset.USArrests @subsubsection tblish.dataset.USArrests @deftypefn {Static Method} {@var{out} =} USArrests () Violent Crime Rates by US State @subsubheading Description This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas. @subsubheading Format @table @code @item State State name. @item Murder Murder arrests (per 100,000). @item Assault Assault arrests (per 100,000). @item UrbanPop Percent urban population. @item Rape Rape arrests (per 100,000). @end table @subsubheading Note @code{USArrests} contains the data as in McNeil's monograph. For the @code{UrbanPop} percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona). See the example below on how to correct the error and improve accuracy for the ‘.5’ percentages. @subsubheading Source @cite{World Almanac and Book of Facts 1975}. (Crime rates). @cite{Statistical Abstracts of the United States 1975}, p.20, (Urban rates), possibly available as @url{https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20}. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.USArrests; summary (t); tblish.examples.plot_pairs (t(:,2:end)); # TODO: Difference between USArrests and its correction # TODO: +/- 0.5 to restore the original .5 percentages @end example @end deftypefn @node tblish.dataset.USJudgeRatings @subsubsection tblish.dataset.USJudgeRatings @deftypefn {Static Method} {@var{out} =} USJudgeRatings () Lawyers’ Ratings of State Judges in the US Superior Court @subsubheading Description Lawyers’ ratings of state judges in the US Superior Court. @subsubheading Format @table @code @item CONT Number of contacts of lawyer with judge. @item INTG Judicial integrity. @item DMNR Demeanor. @item DILG Diligence. @item CFMG Case flow managing. @item DECI Prompt decisions. @item PREP Preparation for trial. @item FAMI Familiarity with law. @item ORAL Sound oral rulings. @item WRIT Sound written rulings. @item PHYS Physical ability. @item RTEN Worthy of retention. @end table @subsubheading Source New Haven Register, 14 January, 1977 (from John Hartigan). @subsubheading Examples @example t = tblish.dataset.USJudgeRatings; figure tblish.examples.plot_pairs (t(:,2:end)); title ("USJudgeRatings data") @end example @end deftypefn @node tblish.dataset.USPersonalExpenditure @subsubsection tblish.dataset.USPersonalExpenditure @deftypefn {Static Method} {@var{out} =} USPersonalExpenditure () Personal Expenditure Data @subsubheading Description This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960. @subsubheading Format A 2-dimensional matrix @code{x} with Category along dimension 1 and Year along dimension 2. @subsubheading Source @cite{The World Almanac and Book of Facts}, 1962, page 756. @subsubheading References Tukey, J. W. (1977). @cite{Exploratory Data Analysis}. Reading, Mass: Addison-Wesley. McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example tblish.dataset.USPersonalExpenditure; # TODO: Port medpolish() from R, whatever that is. @end example @end deftypefn @node tblish.dataset.uspop @subsubsection tblish.dataset.uspop @deftypefn {Static Method} {@var{out} =} uspop () Populations Recorded by the US Census @subsubheading Description This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970. @subsubheading Format @table @code @item year Year of the census. @item population Population, in millions. @end table @subsubheading Source McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.uspop; figure semilogy (t.year, t.population) xlabel ("Year") ylabel ("U.S. Population (millions)") @end example @end deftypefn @node tblish.dataset.VADeaths @subsubsection tblish.dataset.VADeaths @deftypefn {Static Method} {@var{out} =} VADeaths () Death Rates in Virginia (1940) @subsubheading Description Death rates per 1000 in Virginia in 1940. @subsubheading Format A 2-dimensional matrix @code{deaths}, with age group along dimension 1 and demographic group along dimension 2. @subsubheading Details The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female. This provides a rather nice 3-way analysis of variance example. @subsubheading Source Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. @cite{American Sociological Review}, 12, 525–535. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example tblish.dataset.VADeaths; # TODO: Port to Octave @end example @end deftypefn @node tblish.dataset.volcano @subsubsection tblish.dataset.volcano @deftypefn {Static Method} {@var{out} =} volcano () Topographic Information on Auckland’s Maunga Whau Volcano @subsubheading Description Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid. @subsubheading Format A matrix @code{volcano} with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north. @subsubheading Source Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate. @subsubheading References Box, G. E. P. and Jenkins, G. M. (1976). @cite{Time Series Analysis, Forecasting and Control}. San Francisco: Holden-Day. p. 537. Brockwell, P. J. and Davis, R. A. (1991). @cite{Time Series: Theory and Methods}. Second edition. New York: Springer-Verlag. p. 414. @subsubheading Examples @example tblish.dataset.volcano; # TODO: Figure out how to do a topo map in Octave. Just a gridded color plot # should be fine. And then maybe do a 3-d mesh plot. @end example @end deftypefn @node tblish.dataset.warpbreaks @subsubsection tblish.dataset.warpbreaks @deftypefn {Static Method} {@var{out} =} warpbreaks () The Number of Breaks in Yarn during Weaving @subsubheading Description This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn. @subsubheading Format @table @code @item wool Type of wool (A or B). @item tension The level of tension (L, M, H). @item breaks Number of breaks. @end table There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH). @subsubheading Source Tippett, L. H. C. (1950). @cite{Technological Applications of Statistics}. New York: Wiley. Page 106. @subsubheading References Tukey, J. W. (1977). @cite{Exploratory Data Analysis}. Reading, Mass: Addison-Wesley. McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.warpbreaks; summary (t) # TODO: Port the plotting code and OPAR to Octave @end example @end deftypefn @node tblish.dataset.women @subsubsection tblish.dataset.women @deftypefn {Static Method} {@var{out} =} women () Average Heights and Weights for American Women @subsubheading Description This data set gives the average heights and weights for American women aged 30–39. @subsubheading Format @table @code @item height Height (in). @item weight Weight (lbs). @end table @subsubheading Details The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year. The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”. @subsubheading Source @cite{The World Almanac and Book of Facts}, 1975. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example t = tblish.dataset.women; figure scatter (t.height, t.weight) xlabel ("Height (in)") ylabel ("Weight (lb") title ("women data: American women aged 30-39") @end example @end deftypefn @node tblish.dataset.WorldPhones @subsubsection tblish.dataset.WorldPhones @deftypefn {Static Method} {@var{out} =} WorldPhones () The World’s Telephones @subsubheading Description The number of telephones in various regions of the world (in thousands). @subsubheading Format A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year. The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America. The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961. @subsubheading Source AT&T (1961) @cite{The World’s Telephones}. @subsubheading References McNeil, D. R. (1977). @cite{Interactive Data Analysis}. New York: Wiley. @subsubheading Examples @example tblish.dataset.WorldPhones; # TODO: Port matplot() to Octave @end example @end deftypefn @node tblish.dataset.WWWusage @subsubsection tblish.dataset.WWWusage @deftypefn {Static Method} {@var{out} =} WWWusage () WWWusage @subsubheading Description A time series of the numbers of users connected to the Internet through a server every minute. @subsubheading Format A time series of length 100. @subsubheading Source Durbin, J. and Koopman, S. J. (2001). @cite{Time Series Analysis by State Space Methods}. Oxford: Oxford University Press. @url{http://www.ssfpack.com/dkbook/} @subsubheading References Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). @cite{Forecasting: Methods and Applications}. New York: Wiley. @subsubheading Examples @example # TODO: Come up with example code here @end example @end deftypefn @node tblish.dataset.zCO2 @subsubsection tblish.dataset.zCO2 @deftypefn {Static Method} {@var{out} =} zCO2 () Carbon Dioxide Uptake in Grass Plants @subsubheading Description The @code{CO2} data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli. @subsubheading Format @subsubheading Details The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted. @subsubheading Source Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. @cite{Ecology}, 71, 1389–1400. Pinheiro, J. C. and Bates, D. M. (2000). @cite{Mixed-effects Models in S and S-PLUS}. New York: Springer. @subsubheading Examples @example t = tblish.dataset.zCO2; # TODO: Coplot # TODO: Port the linear model to Octave @end example @end deftypefn @node tblish.datasets @subsection tblish.datasets @deftp {Class} tblish.datasets Example dataset collection. @code{tblish.datasets} is a collection of example datasets to go with the Tablicious package. The @code{tblish.datasets} class provides methods for listing and loading the example datasets. @end deftp @node datasets.description @subsubsection datasets.description @deftypefn {Static Method} description (@var{datasetName}) @deftypefnx {Static Method} {@var{out} =} description (@var{datasetName}) Get or display the description for a dataset. Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console. @end deftypefn @node datasets.list @subsubsection datasets.list @deftypefn {Static Method} {} list () @deftypefnx {Static Method} {@var{out} =} list () List all datasets. Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list. Returns a table with variables Name, Description, and possibly more. @end deftypefn @node datasets.load @subsubsection datasets.load @deftypefn {Static Method} load (@var{datasetName}) @deftypefnx {Static Method} {@var{out} =} load (@var{datasetName}) Load a specified dataset. @var{datasetName} is the name of the dataset to load, as found in the @code{Name} column of the dataset list. @end deftypefn @node tblish.evalWithTableVars @subsection tblish.evalWithTableVars @deftypefn {Function} {@var{out} =} tblish.evalWithTableVars (@var{tbl}, @var{expr}) Evaluate an expression against a table array’s variables. Evaluates the M-code expression @var{expr} in a workspace where all of @var{tbl}’s variables have been assigned to workspace variables. @var{expr} is a charvec containing an Octave expression. As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names. Returns the result of the evaluation. Examples: @example [s,p,sp] = tblish.examples.SpDb tmp = join (sp, p); shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight") @end example See also: @ref{table.restrict} @end deftypefn @node tblish.examples.coplot @subsection tblish.examples.coplot @deftypefn {Function} {[@var{fig}, @var{hax}] =} tblish.examples.coplot (@var{tbl}, @var{xvar}, @var{yvar}, @var{gvar}) @deftypefnx {Function} {[@var{fig}, @var{hax}] =} tblish.examples.coplot (@var{fig}, @var{tbl}, @var{xvar}, @var{yvar}, @var{gvar}) @deftypefnx {Function} {[@var{fig}, @var{hax}] =} tblish.examples.coplot (@dots{}, @var{OptionName}, @var{OptionValue}, @dots{}) Conditioning plot. @code{tblish.examples.coplot} produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot. @var{tbl} is a @code{table} containing the data to plot. @var{xvar} is the name of the table variable within @var{tbl} to use as the X values. May be a variable name or index. @var{yvar} is the name of the table variable within @var{tbl} to use as the Y values. May be a variable name or index. @var{gvar} is the name of the table variable or variables within @var{tbl} to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. @var{gvar} may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored. @var{fig} is the figure handle to plot into. If @var{fig} is not provided, a new figure is created. Name/Value options: @table @code @item PlotFcn The plotting function to use, supplied as a function handle. Defaults to @code{@@plot}. It must be a function that provides the signature @code{fcn(hax, X, Y, @dots{})}. @item PlotArgs A cell array of arguments to pass in to the plotting function, following the @var{hax}, @var{x}, and @var{y} arguments. @end table Returns: @var{fig} – the figure handle it plotted into @var{hax} – array of axes handles to all the axes for the subplots @end deftypefn @node tblish.examples.plot_pairs @subsection tblish.examples.plot_pairs @deftypefn {Function} {@var{out} =} tblish.examples.plot_pairs (@var{data}) @deftypefnx {Function} {@var{out} =} tblish.examples.plot_pairs (@var{data}, @var{plot_type}) @deftypefnx {Function} {@var{out} =} tblish.examples.plot_pairs (@var{fig}, @dots{}) Plot pairs of variables against each other. @var{data} is the data holding the variables to plot. It may be either a @code{table} or a struct. Each variable or field in the @code{table} or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size. @var{plot_type} is a charvec indicating what plot type to do in each subplot. (@code{"scatter"} is the default.) Valid @var{plot_type} values are: @table @code @item "scatter" A plain scatter plot. @item "smooth" A scatter plot + fitted line, like R's @code{panel.smooth} does. @end table @var{fig} is an optional figure handle to plot into. If omitted, a new figure is created. Returns the created figure, if the output is captured. @end deftypefn @node tblish.examples.SpDb @subsection tblish.examples.SpDb @deftypefn {Function} {@var{spdb} =} tblish.examples.SpDb () @deftypefnx {Function} {[@var{s}, @var{p}, @var{sp}] =} tblish.examples.SpDb () The classic Suppliers-Parts example database. Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date's "An Introduction to Database Systems" textbook. Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (@var{s}, @var{p}, @var{sp}). @end deftypefn @node tblish.sizeof2 @subsection tblish.sizeof2 @deftypefn {Function} {@var{out} =} tblish.sizeof2 (@var{x}) Approximate size of an array in bytes, with object support. This is an alternative to Octave's @code{sizeof} function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions. This may be supplemented or replaced by @code{sizeof} override methods on Tablicious's classes. I'm not sure whether Octave's @code{sizeof} supports extension by method overrides, so I'm not doing that yet. If that happens, this @code{sizeof2} function will stick around in a deprecated state for a while, and it will respect those override methods. For tables, this returns the sum of @code{sizeof} for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in @var{obj}. This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious's objects to a separate function. This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases. @var{x} is an array of any type. Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported. See also: sizeof @end deftypefn @node tblish.table.grpstats @subsection tblish.table.grpstats @deftypefn {Function} {[@var{out}] =} tblish.table.grpstats (@var{tbl}, @var{groupvar}) @deftypefnx {Function} {[@var{out}] =} tblish.table.grpstats (@dots{}, @code{'DataVars'}, @var{DataVars}) Statistics by group for a table array. This is a table-specific implementation of @code{grpstats} that works on table arrays. It is supplied as a function in the @var{+tblish} package to avoid colliding with the global @code{grpstats} function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its @code{grpstats} function. This function is supplied as an alternative you can use in an environment where @code{table} arrays are not supported by the @code{grpstats} that you have, though you need to make code changes and call it as @code{tblish.table.grpstats(tbl)} instead of with a plain @code{grpstats(tbl)}. See also: table.groupby, table.findgroups, table.splitapply @end deftypefn @node timezones @subsection timezones @deftypefn {Function} {@var{out} =} timezones () @deftypefnx {Function} {@var{out} =} timezones (@var{area}) List all the time zones defined on this system. This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from @file{/usr/share/zoneinfo}. On Windows, it will be the database redistributed with the Tablicious package. If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors: @table @code @item Name The IANA zone name, as cellstr. @item Area The geographical area the zone is in, as cellstr. @end table Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented. @end deftypefn @node todatetime @subsection todatetime @deftypefn {Function} {@var{out} =} todatetime (@var{x}) Convert input to a Tablicious datetime array, with convenient interface. This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious's author likes better. This mainly exists because datetime's constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab's interface.) I think that's less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that's not the same size as the input. Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents". @end deftypefn @node vartype @subsection vartype @deftypefn {Function} {@var{out} =} vartype (@var{type}) Filter by variable type for use in suscripting. Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type. @var{type} is the name of a type as charvec. This may be anything that the @code{isa} function accepts, or @code{'cellstr'} to select cellstrs, as determined by @code{iscellstr}. Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a @code{table} object. @end deftypefn @node vecfun @subsection vecfun @deftypefn {Function} {@var{out} =} vecfun (@var{fcn}, @var{x}, @var{dim}) Apply function to vectors in array along arbitrary dimension. This function is not implemented yet. Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension. @var{fcn} is a function handle to apply. @var{x} is an array of arbitrary type which is to be sliced and passed in to @var{fcn}. @var{dim} is the dimension along which the vector slices lay. Returns the collected output of the @var{fcn} calls, which will be the same size as @var{x}, but not necessarily the same type. @end deftypefn @node years @subsection years @deftypefn {Function File} {@var{out} =} years (@var{x}) Create a @code{duration} @var{x} years long, or get the years in a @code{duration} @var{x}. If input is numeric, returns a @code{duration} array in units of fixed-length years of 365.2425 days each. If input is a @code{duration}, converts the @code{duration} to a number of fixed-length years as double. Note: @code{years} creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use @code{calyears}. @xref{calyears}. @end deftypefn @node Copying @chapter Copying @section Package Copyright Tablicious for GNU Octave is covered by the GNU GPLv3 and other Free and Open Source Software licenses. The main code of Tablicious is licensed under the GNU GPL version 3 or later. Certain files in the build tools are licensed under the GNU GPL version 2 or later. This includes the @code{mktexi.pl} and @code{OctTexiDoc.pm} files in the @code{doc/} directory. The tzinfo database file is Public Domain. The date/time portion of Tablicious includes some Unicode data files licensed under the Unicode License Agreement - Data Files and Software license. The Tablicious test suite contains some files, specifically some table-related tests using MP-Test like @code{t/t_01_table.m}, which are BSD 3-Clause licensed, and are adapted from MATPOWER written by Ray Zimmerman. The Fisher Iris dataset is Public Domain. @section Manual Copyright @insertcopying @bye apjanke-octave-tablicious-8a1b10f/docs/000077500000000000000000000000001515050116100200655ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/Design-and-Justification.md000066400000000000000000000304441515050116100251760ustar00rootroot00000000000000# Tablicious Design and Justifications This document describes the design choices I made in Tablicious, and some justifications for them and why I think they should be allowed into core Octave, or at least an Octave Forge package. ## Objects ### Copy constructors Each data object has a copy constructor calling form. The Matlab trick of doing this: ```octave function this = Foo(x) if isa (x, 'Foo') this = x; return end [...] end ``` doesn’t seem to be supported in Octave; or at least it seemed to lead to some instability when I tried it. Besides, it introduces the problem that the output of the constructor might be a subclass instead of an object of the exact type of the constructor. So I wrote out copy-constructor forms for most of the classes. The rest should be filled in, too; their absence is a bug. ### Planar-organized classes The data classes like `datetime` are implemented as planar-organized classes for efficiency. This lets them represent their arrays as arrays of primitives on which vectorized operations can be performed. “Planar-organized” is opposed to “struct-organized”, which is the default organization for Octave/Matlab classes. In struct-organized classes, arrays are represented by arrays of struct-y objects, each of which contains a single element/record’s data in its fields. Planar-organized classes are the only way to go fast with objects in Octave/Matlab. For example, an M-by-N `datetime` array is a single object wrapper around an M-by-N double array in the `dnums` property. Instead of it being an M-by-N array of blessed structs, each of which holds a scalar `dnums`. The downside is that implementing a planar-organized class requires you to write a _lot_ of boilerplate code. I went ahead and did that, though, because the performance gain is worth it. ## Namespaces ### The `+internal` namespace Any namespace with `+internal` as one of its components is considered to be an internal, private, implementation detail of a library itself. The language doesn’t enforce any access rules on this; it’s just a naming convention. But I think it’s become a reasonably well-established convention in other languages: Java supports it, and I think Matlab does too. I think Octave would do well to adopt it. Tablicious’ doco generation tools support `+internal` by excluding any `+internal` namespace from the generation of the API Reference section of the manual. So code in there is hidden from the user view at the documentation level. ### The `+octave/+examples` namespace I’m sticking example code in the `octave.examples` namespace. This makes it readily available to anyone with the package loaded (as opposed to requiring them to `cd` into a specific `examples/` directory or manually add it to the path), but keeps them out of the global namespace and the Octave and Tablicious public APIs. The `+examples` namespace is a suitable place to throw code that might be useful for users, but we don’t want in the actual package API for some reason. This includes: * Collecting code that’s used in multiple examples, like in the `octave.datasets` example data sets, into one place * Making available code that users can model their own code on, or reuse directly, but isn’t good enough for “production” code yet * Throwing examples where we don’t know what we want the permanent API to look like The stuff in `+examples` can be lower quality than main code, in the sense that: * It can be “toy” code that gets the happy path right, but doesn’t bother doing input validation or error handling * They can go without tests * Bugs here are lower impact * No need to keep things from changing between versions; no need to preserve back-compatibility ## `datetime` ### `datetime` constructor for numeric inputs The single-arg `datetime(x)` constructor, when called on numeric inputs, treats them as a datevec instead of as datenums. I think that’s a design mistake; datenums are closer to the “canonical” representation for dates in pre-`datetime` Matlab/Octave. And datenums seem to me to be what you‘d want the implicit conversion behavior to do in the case of mixed-type arithmetic, concatenation, or other combining operations. But datevecs is what Matlab picked, so we’re stuck with that, for compatibility. I’m thinking about adding a global `todatetime(x)` function that will treat its numeric inputs as datenums, for convenience. ## Example Datasets ### API The `octave.datasets` class provides functions for listing and loading all the example datasets this library provides. The `octave.dataset` class provides a list of all the individual examples, so you can use tab-completion on them (or at least you’ll be able to, once Octave supports tab completion for static methods on classes inside namespaces). This class is also how the Texinfo doco for the datasets finds its way into the generated user manual. This is done as two separate classes to keep the “list of data sets” separate from the “API for accessing datasets”. I think that makes it easier for users to find what they’re looking for; otherwise the generic methods in `octave.datasets` would get lost in the big list of individual example datasets. You run `octave.internal.generate_datasets_list` to generate the `octave.dataset` file from the individual example classes. This only needs to be done at code authoring time, not by the user. ### Organization The Example code sections in the `description.texi` for the example datasets are stored separately as scripts so that you can view them as M-code in an editor (with syntax highlighting, indentation assistance, and so on) and conveniently run them to test them (instead of having to copy-and-paste them from a view of the helptext, which would require you to go through the doco building cycle each time you made a change and wanted to run the code to test it). I’m thinking about breaking the `load` step out into separate `load_raw` and `munge` steps, to make it easier for users to view the example dataset code and see how the data-packaging classes like `table` are used. ## Justification ### Why does `table` display differently than in Matlab? In Matlab, doing `disp()` on a `table` will result in its printing the entire table contents in tabular form, as it does with primitives and cells. I think that, for any more than a few rows, this spams the console, and is not useful output. In interactive use, it’s much nicer to get a short summary of the table as its default display: tables have enough structure that a short summary is still useful, and it won’t spam your scrollback. I’ve spent years working with `table`-style objects that operate each way, and found the summary style to be much more usable. I think this is fine because `disp()` is mostly used for interactive use, and is not frequently used to produce actual program output, so this shouldn't be a real compatibility issue. ### Justify all these new top-level functions Tablicious defines several new top-level (global, not in namespaces or `private/` directories) functions. Many of them are ports of Matlab functions, which should be self-justifying. But some of them are not. Here are reasons for the new ones I made up. I call the top-level namespace that’s not in any explicit namespace the “global” namespace. I don’t know if that’s actually the correct term. ### Justify `colvecfun` Could easily go in the `+octave` namespace; there’s no real reason for it to be global, aside from easy discovery and analogy with the global `cellfun`, `arrayfun`, and so on. ### Justify `dispstr` and `dispstrs` Many modern programming languages provide a generic, polymorphic way of converting an arbitrary input to a string for user/developer presentation. E.g. Java has `toString`, Ruby has `to_str`, and Python has `str` and `repr`. Octave could really use one of those. Needs to be global so user-defined classes can override it with object methods. Because of Octave’s pervasive array nature, we need both a variant for “one string describing the entire array” (`dispstr`) and “N strings describing each of the array’s elements” (`dispstrs`). This dispstr stuff is all based on the [Dispstr API](http://dispstr.janklab.net/) I created for use with Matlab. ### Justify `eqn` This is a straightforward extension if `isequaln` behavior to the element-wise case. Needs to be global so user-defined classes can override it with object methods. ### Justify `isnanny` For some reason, Matlab chose to name the NaN values for `datetime` objects “NaT” instead, and define a new `isnat()` function to work with them. So you can’t write generic polymorphic code that works on both numerics and `datetime`s when testing for NaN-ness. Sigh. The new `ismissing()` function doesn’t fit the bill, because it’s too broad: it also considers empty strings and blank characters to be missing, and you may not want that broadness in a NaN-ness test. `isnanny()` smooths over the NaN/NaT difference without becoming as broad as `ismissing()`. UPDATE 2021-03-14: I've changed my mind. I now think that `ismissing` probably is a fine generalization of `isnan`/`isnat`, and we should just use that, and `isnanny` should go away. I made this a global function so that user-defined classes could override it with object methods. Without `methods(klass)` working for classdef objects, it’d be hard to detect whether a user-defined object supports `isnan()` and/or `isnat()`, so classes might want to override `isnanny` itself. This is not ideal; I'm still thinking about whether this is the best approach. ### Justify these extra `mustBeXxx` functions Many of the `mustBeXxx()` functions are just ports of Matlab’s existing validator functions. But I added a few new ones: * `mustBeA` * `mustBeCellstr` * `mustBeCharvec` * `mustBeSameSize` * `mustBeScalar` * `mustBeScalarLogical` * `mustBeVector` * `makeItBeA` These all see a lot of use in my experience, so they’d be useful to provide. And I think their semantics and implementation are obvious enough that we don’t need to be trepidatious about adding them to the public API. ### Justify `pp` If you end up working with tables or other complex structures that support `prettyprint`, you end up calling `prettyprint` a lot, so it’s nice to have a shortcut for it, especially one with the char-interpreting command style. Needs to be have a super short name so users can quickly bang it out at the command line. Namespace would make this inconvenient. However, `pp` is only ever called directly by end users; it doesn’t need to be available to other code in the library. So this could just be provided as an example function, and require users to copy it into their default path (like at `~/Documents/octave`) to have it take effect. ### Justify `proxykeysForMatrixes` This one is ugly to have hanging around in the global namespace, but it’s necessary in order to allow user-defined classes to override it with object methods. ### Justify `scalarexpand` This one is really useful. I use it in the input handling for a lot of functions, and I think it should exist. This one could probably go in the `+octave` namespace, though. It does not need to be overridden by anything. ### Justify `size2str` I use this one a lot in generating error and progress messages. It gets used often enough that I think it could belong in core Octave. It's trivial to implement. But it's useful enough that I think it deserves a home in a library or core Octave. This would make it both convenient for users to use, and easier for code readers to recognize and understand if there were a single core, conventional name for this operation. Its behavior is simple enough that its interface is unlikely to change. Though the format could change: maybe you’d want to do “AxBxC” instead of “A-by-B-by-C”, or something like that. In which case that’s another reason to prefer an established, central function to do this. ### Justify `tableOuterFillValue` Having this be global is necessary in order to allow user-defined classes to override it with object methods. This ugliness could probably be hacked around by not having a global function, and just having `table.outerjoin` test its variable values to see if they are objects which define a `tableOuterFillValue`, and only call that if they are defined. But this will be hard because Octave currently doesn’t support calling `methods` on user-defined classes. apjanke-octave-tablicious-8a1b10f/docs/Developer-Notes.md000066400000000000000000000143561515050116100234330ustar00rootroot00000000000000# Tablicious Developer Notes See also: * [Design and Justification](Design-and-Justification.html) for info on the design of this library. * [Join Behavior](Join-Behavior.html) for a description of the "proxy keys" and other stuff related to join semantics and implementation. * [TODO](TODO.html) * [Release Checklist](Release-Checklist.html) ## Code Tablicious is a decent-sized package, though not as large as some other Octave Forge packages. As of February 2024, it's about 39,000 lines of code and documentation. To get familiar with the library, you probably want to start with the main `table` class, and then `datetime` and `string`, and at least skim the documentation (the User Manual plus the `*.md` files in this directory) before diving in to reading the code. ## Code Style and Principles * Standard [GNU Octave code style](https://wiki.octave.org/Octave_style_guide) * In general, use reallyLongDescriptiveNames for non-standard-Matlab-or-Octave functions you are adding, to reduce chance of name collisions. ## No Matlab usage! To avoid issues with the Matlab license's Non-Compete clause, this project needs to be developed entirely using Octave, and not using Matlab at all, including for testing or benchmarking purposes. Please do not submit any Matlab test or benchmark results, or any code produced using Matlab. And if you know anything about how the Matlab internals work, please do not tell me! ## Our Documentation ### Building the Docs The texinfo format we use requires Texinfo 6.0 or newer. This is newer than the Texinfo that comes with macOS, which ships a 5.x Texinfo. To build the docs on Mac, you will need to install a newer Texinfo (e.g. with Homebrew) and then make sure that Texinfo is on your path ahead of the system Texinfo. ### Texinfo notes The `mktexi.pl` tool we use doesn't have great support for classdefs; it is more function-oriented. You need to work around that a little bit in the texinfo documentation you write. For methods in classes, including the constructor, you need to manually add a `@node .` line in the texinfo block, right before the `@deftypefn` line. That's because mktexi.pl will auto-generate `@node` lines for free functions, but not for methods inside functions. Other things – free functions, classdefs, and scripts, including those inside packages (namespaces) – should not have explicit `@node` lines in the texinfo; those will be added automatically by `mktexi.pl`. For functions and classes inside packages (namespaces), you should fully qualify their names in the `@deftypefn` or `@deftp` lines. This will get their node name generated correctly, and IMHO is also how they should look in the presented help, because that's their full name, and they need to be addressed that way in the code (with either a fully-qualified name, or an `import` statement). And it makes it unambiguous what you're looking at, in the case of multiple functions or classes with the same non-namespace-qualified name. This approach also makes it so in the texinfo we're not relying on implicit package qualification via the file's location in the repo directory tree, and these things can be placed anywhere in the source tree with the same contents, and not have to be edited manually when they change, and not have to have special support in the `mktexi.pl` tooling for package directories. The support for packages (namespaces) and classes in `mktexi.pl` is a Tablicious customization we added, after copying that tool from another OF package. `mktexi.pl` is heavily modified in other ways, and doesn't closely match the "standard" one used by other OF packages. You need TeX installed, along with some TeX packages like those supplied in the TeXLive distribution. I don't know specifically which packages. They're some packages (or other things) supplied by the full MacTeX TeXLive distribution, but not by the BasicTeX version. If you don't have them, then the `texi2*` calls inside the `make doc` target may fail with errors like `/usr/local/bin/texi2dvi: texinfo.tex appears to be broken. ... ! I can't find file 'texinfo.tex'`. ## Unit Tests Tablicious doesn't have great unit test coverage, but it has some. It uses a combination of regular Octave style BISTs and [MP-Test](https://github.com/MATPOWER/mptest) ("mptest") tests. The BISTs and mptest suites are integrated with each other, in `tblish.internal.runtests`, so you can run them together as a suite. But the result reporting is a little disjoint; you need to read the textual output to evaluate the full results. To run the full Tablicious unit test suite, run `tblish.internal.runtests()` while Tablicious is loaded. It will skip the MP-Test tests if MP-Test is not also loaded. The BISTs are embedded in the various `*.m` source code files. The MP-Test tests are in `inst/t`. To run them, you'll need MP-Test, which you get separately from [the MP-Test GitHub repo](https://github.com/MATPOWER/mptest). (I don't think it's available as an Octave Forge package.) Add the `lib/` dir from the MP-Test repo to your Octave path. Then run `tblish.internal.runtests()`, and it will include the MP-Test tests in its test suite. Or, add Tablicious' `inst/t/` dir to your path (that's not done as part of loading the Tablicious package), and run `tblish_mptest_tablicious()` to run them all. Or, call one of the test functions like `t_01_table()` to run a single test. The single tests show detailed output by default; the combined suite does not. The MP-Test test suites from Tablicious and other packages may interfere with each other, so you should only have one package's MP-Test test suite on the Octave path at a time. ## Distribution Tablicious is indexed in the [GNU Octave Packages](https://gnu-octave.github.io/packages) index at . The index entry is in the [gnu-octave/packages repo](https://github.com/gnu-octave/packages) at `packages/tablicious.yaml`. It is supported by the `DESCRIPTION` and `octave-tablicious.metainfo.xml` files in this repo. Debian is considering picking up Tablicious, at . [Octave Online](https://octave-online.net) includes Tablicious. It's my intent to support all these downstream packagers (to the extent that's convenient), but Tablicious has no formal relationship with them. apjanke-octave-tablicious-8a1b10f/docs/FAQ.md000066400000000000000000000055321515050116100210230ustar00rootroot00000000000000# Tablicious FAQ ## General ### Is this code ready for production use? *NO!* This is beta quality code. Do *NOT* use this for your business or production code needs yet! ## Build issues ### I'm getting Texinfo errors Like this? ```text $ make doc master ✱ cd doc && make maintainer-clean && make all rm -rf *.tmp rm -rf *.dvi *.eps *.html *.info *.pdf *.ps *.png *.texi *.qhp *.qch *.qhc images.mk perl ./mkdoc.pl DOCSTRINGS.texi.tmp ../inst ../src perl ./mktexi.pl src/tablicious.texi DOCSTRINGS.texi.tmp ../INDEX chrono.texi makeinfo --no-split -o chrono.info chrono.texi chrono.texi:10: warning: unrecognized encoding name `UTF-8'. chrono.texi:31: warning: undefined flag: VERSION. chrono.texi:53: warning: undefined flag: VERSION. chrono.texi:79: warning: undefined flag: VERSION. chrono.texi:729: warning: undefined flag: VERSION. /Users/janke/local/repos/octave-chrono/doc//chrono.texi:389: `Functions Alphabetically' has no Up field (perhaps incorrect sectioning?). /Users/janke/local/repos/octave-chrono/doc//chrono.texi:335: `Funtions by Category' has no Up field (perhaps incorrect sectioning?). /Users/janke/local/repos/octave-chrono/doc//chrono.texi:243: `Defined Time Zones' has no Up field (perhaps incorrect sectioning?). /Users/janke/local/repos/octave-chrono/doc//chrono.texi:148: `datenum Compatibility' has no Up field (perhaps incorrect sectioning?). /Users/janke/local/repos/octave-chrono/doc//chrono.texi:335: warning: unreferenced node `Funtions by Category'. /Users/janke/local/repos/octave-chrono/doc//chrono.texi:243: warning: unreferenced node `Defined Time Zones'. /Users/janke/local/repos/octave-chrono/doc//chrono.texi:148: warning: unreferenced node `datenum Compatibility'. makeinfo: Removing output file `chrono.info' due to errors; use --force to preserve. make[1]: *** [chrono.info] Error 1 make: *** [doc] Error 2 ``` Your Texinfo is too old. Get a newer one. On macOS, you can do this with Homebrew. ```bash brew install texinfo PATH="$(brew --prefix texinfo)/bin:$PATH" make doc ``` ### I'm getting weird `sed: ...@documenten...` warnings in the `make doc` step? Like this? ```text $ make doc [...] /usr/local/opt/texinfo/bin/texi2pdf --quiet --clean -o chrono.pdf chrono.texi sed: 2: "s/\(^\|.* \)@documenten ...": whitespace after branch sed: 4: "s/\(^\|.* \)@documenten ...": whitespace after label sed: 6: "s/\(^\|.* \)@documenten ...": undefined label 'found [...] ``` Those warnings are produced by older Texinfo programs, like Texinfo 4.8, which is the default on macOS 10.13 and 10.14. [Install a newer Texinfo using Homebrew](https://github.com/apjanke/octave-chrono/issues/17) and pull that in explicitly for your build. ```bash brew install texinfo PATH="$(brew --prefix texinfo)/bin:$PATH" make doc ``` apjanke-octave-tablicious-8a1b10f/docs/Gemfile000066400000000000000000000024521515050116100213630ustar00rootroot00000000000000source 'https://rubygems.org' # Hello! This is where you manage which Jekyll version is used to run. # When you want to use a different version, change it below, save the # file and run `bundle install`. Run Jekyll with `bundle exec`, like so: # # bundle exec jekyll serve # # This will help ensure the proper Jekyll version is running. # Happy Jekylling! gem 'jekyll' #, '~> 3.8.5' gem 'minima' #, '~> 2.0' gem 'kramdown-parser-gfm' gem 'github-pages' group :jekyll_plugins do gem 'jekyll-theme-cayman' gem 'jekyll-asciidoc' gem 'jekyll-coffeescript' gem 'jekyll-default-layout' gem 'jekyll-gist' gem 'jekyll-github-metadata' gem 'jekyll-paginate' gem 'jekyll-readme-index' gem 'jekyll-relative-links' gem 'jekyll-titles-from-headings' gem 'jekyll-feed' gem 'jekyll-seo-tag' gem 'jekyll-theme-slate' gem 'jekyll-last-modified-at' gem 'jekyll-toc' gem 'jekyll-sitemap' gem 'jekyll-optional-front-matter' end # Asciidoctor uses pygments for source highlighting gem 'pygments.rb' # Windows does not include zoneinfo files, so bundle the tzinfo-data gem gem 'tzinfo-data', platforms: [:mingw, :mswin, :x64_mingw, :jruby] # Performance-booster for watching directories on Windows gem 'wdm', '~> 0.1.0' if Gem.win_platform? # I don't know why we need these gem 'rexml' gem 'webrick' apjanke-octave-tablicious-8a1b10f/docs/Join-Behavior.md000066400000000000000000000305631515050116100230520ustar00rootroot00000000000000# Tablicious Join Behavior This page describes the semantics and implementation of join-related stuff in Tablicious's `table` class, including its special "proxy keys" trick. ## Supported variable data types Variables in `table` arrays may be of just about any Octave data type, including user-defined classes. For basic structural operations, the type just needs to itself support basic structural operations: * `()`-indexing * `size` and `numel` * `vertcat` For relational operations, such as joins, `unique`, and other `table` methods to work, the variables used as keys in these operations must support the following small set of operations: * `unique` * `ismember` * `eq` * `isequal` * `ismissing`, under certain conditions * Maybe `(:)`-indexing to reshape to a column vector A user-defined class only needs to define an `ismissing` method if it implements missing or NaN-like semantics, in which case it needs to define an `ismissing` that is consistent with its `eq`, `ismember`, and `unique` methods. An exception is made for `table` itself: `table` does not support `(:)` for conversion to a column vector, and its `unique` method violates the general contract of the `unique` function, because it operates row-wise even when you do not specify its `'rows'` option. This is handled with special-case code for nested `table`s inside `table` itself. The following functions or methods are optional, but if your type supports them, then `table` will respect that and enhance its behavior to use them. In particular, this allows you to define custom display formats for how your type is displayed when it is used as a variable in a `table`: * `dispstrs()` and `reprstrs()` TBD: We may require support for the `'rows'` option of `unique`. I'm still undecided on this. If we require this, then the requirement for `(:)` reshaping goes away. TBD: We may require support for conversion from `missing` or some other way of specifing a default fill value. This is needed to support outer joins. Right now we're doing it with tricks using array expansion via `()`-indexing; unclear whether that's sufficient. ## Document conventions In this document, we'll talk about example `table`s, which will be named `tbl`, `tblA`, `tblB`, and so on. nA is the number of rows in `tblA`; nB is the number of rows in `tblB`, and so on. Many people may think of the data items in a `table` array as "columns" or "variables" interchangeably. But each variable in a `table` may actually have multiple columns. So in here, I'm careful to distinguish between "variables", which are the named things in a `table`, and "columns", which are the columns within a variable. A "tuple" is a set of named values. It's the formal term from relational algebra for a row or record within a SQL table or result set, or any similar structure. A row in a `table` array can be considered a tuple. The "cardinality" of an array is the number of unique values in it. Each missing or NaN value counts as a separate value. For example, the cardinality of `[1 1 2 2 NaN NaN NaN]` is 5. The cardinality of a `table` is the number of distinct tuple values in it. ## "Proxy Keys" ### Background "Proxy keys" is a trick I came up with around 2004, and have used successfully in a couple implementations of `table` in Matlab back in the day before Matlab had its own `table` class. I think that's a good indication that it will work in Octave. ### What we need The basic problem here is how to efficiently compute matches and ordering for heterogeneously-typed multi-column record-oriented data structures in Octave. The logic for most table operations, like `sort`, `unique`, and all the joins, boils down to doing ordering or equality tests on multi-column values that may have columns of heterogeneous types: a `table` array may have variables of any type, so the variables used as keys may have mixed types. We need to compare the value of "tuples" which are composed of the i-th rows from one or more variables, and either sort the tuples within a single `table`, or produce a list of matches between the tuples in two different `table`s. For example, let's say you have this `tblA` and `tblB`: `tblA`: | k1 | k2 | v1 | | -- | -- | -- | | "foo" | 1 | 1.2 | | "foo" | 2 | 3.4 | | "bar" | 1 | 5.6 | | "bar" | 2 | 7.8 | | "baz" | 3 | 1.2 | `tblB`: | k1 | k2 | v2 | v3 | | -- | -- | -- | -- | | "foo" | 2 | 123 | "x" | | "foo" | 1 | 234 | "xx" | | "baz" | 4 | 345 | "y" | | "baz" | 3 | 456 | "z" | | "baz" | 1 | 567 | "a" | | "qux" | 1 | 678 | "b" | | "qux" | 2 | 789 | "c" | | "scooby" | 42 | 123 | "d" | The key variables or "join keys" are the variables which appear in both tables, identified by their variable names. You can reduce these tables to just "key tables" by subsetting them by variables to just the key variables. The "key tuples" are the tuples subsetted to just the key variables. Two tuples (or rows) match if their key tuples are equal. Two tuples are equal if, for every variable v (in our example case, `k1` and `k2`), their values are equal for that row, as determined by `isequal(tblA.v(i,:), tblB.v(j,:))`. For the various relational operations (joins, setdiffs, and so on), we need to compute some combination of: * The list of all pairs of matching rows between `tblA` and `tblB` * The list of rows in `tblA` that match no rows in `tblB` * The list of rows in `tblB` that match no rows in `tblA` This is tricky because if you want to Go Fast in Octave, you need to use vectorized operations, but Octave has no built-in efficient operations for the comparison of heterogenous tuple-like structures. There's no form of `ismember` or the like which takes multiple column vectors on either side. And even cell's `ismember` doesn't support non-cellstr cells. A naive implementation of the key-tuple-matching logic in a `table` join might look something like the following. Assume the two `table`s to be joined are `tblA` with columns `k1`, `k2`, `k3`, `v1`, and `v2`, and `tblB` with columns `k1`, `k2`, `k3`, and `v3`. ```octave keyVars = intersect(tblA.Properties.VariableNames, tblB.Properties.VariableNames); keyTblA = tblA(:,keyVars); keyTblB = tblB(:,keyVars); keysA = table2cell(keyTblA); keysB = table2cell(keyTblB); ixMatches = NaN(0, 2); for iA = 1:height(tblA) for iB = 1:height(tblB) tfRowsMatch = isequal(keysA(iA), keysB(iB)); if tfRowsMatch ixMatches(end+1,:) = [iA iB]; end end end ``` This is not going to be efficient: loops are slow, the conversion from table to cell will be inefficient because it breaks up any primitive variables into a bunch of cells containing scalars or single rows, and the repeated isequal() calls will have overhead. Your next idea might be to do two-way `ismember` calls on each of the individual key variables, and then combine those results. But there's no easy way (that I know of) to compose this single-variable ismember results into the correct result that considers all the variables at once. ### How proxy keys work Tablicious' approach to this problem is a trick I call "proxy keys". For an input key variable of any type, it transforms the values present in that array into a corresponding set of numeric values which have the same equality and ordering relationships as the original input values. (These numeric replacement values are the "proxy keys" for the original keys.) Then you can combine those numeric proxy key vectors into homogeneous numeric matrixes and do `ismember` and similar calls on them, which will be fast because they use the vectorized Octave built-ins defined for numerics. The trick for efficiently and generically computing the proxy keys for any input type is to use the 3-argout form of `unique`: ```octave [Y, I, J] = unique(X) ``` The output `Y` is the unique values from `X`, in sorted order. The output `I` is an index vector corresponding to `Y` indicating where in `X` they were found. The output `J` is an index vector the same size as `X` that indicates the index in `Y` that holds the unique value found in `X(i)`. The numbers in that `J` output have the properties required of proxy keys! For the proxy keys to be comparable across two inputs (i.e. for the key variable in the two input tables to a join operation), you need to compute the proxy keys for the values from both inputs together. To do this, you concatenate the two input variables and consider them together. ```octave allVals = [tblA.k1; tblB.k1]; [~, ~, pkeys] = unique(allVals); nA = height(tblA); pkeysA = pkeys(1:nA); pkeysB = pkeys(nA+1:end); ``` When you're operating on multiple key variables, compute the proxy keys for each variable, and then horzcat them together. This will produce a pair of multi-row proxy key matrixes. ```octave nKeyVars = numel(keyVars); pkeyBufA = cell(1, nKeyVars); pkeyBufB = cell(1, nKeyVars); nA = height(tblA); for iKey = 1:nKeyVars keyA = tblA.(keyVars{iKey}); keyB = tblB.(keyVars{iKey}); keysTogether = [keyA; keyB]; [~, ~, pkeys] = unique(keysTogether); pkeyA = pkeys(1:nA); pkeyB = pkeys(nA+1:end); pkeyBufA{iKey} = pkeyA; pkeyBufB{iKey} = pkeyB; end pkeysA = cat(2, pkeyBufA{:}); pkeysB = cat(2, pkeyBufB{:}); ``` Now, do row-wise matching operations on the proxy key matrixes using `ismember(..., 'rows')`, `sortrows()`, `unique(..., 'rows')` and so on. If two rows in the proxy keys are equal, then the corresponding rows in the original input keys are equal. The order in which the rows in the proxy keys sort is the order in which the rows in the inputs should sort. And it's all done in the numeric domain with efficient vectorized Octave builtin functions. You can go one step further than this, and do `unique(..., 'rows')` on the proxy key matrix itself to produce single-column proxy keys, where each distinct input tuple is represented by a distinct scalar numeric proxy key. This would make proxy key matching operations even faster, because they'd be done on scalar values instead of rows. But you'd have to pay the cost of that `unique()` to calculate them, which is probably O((nA+nB) * log (na+nB)). I don't know if that's worth it. For a multi-column variable, you can produce proxy keys either by using the `[~,~,pkeys] = unique(x, 'rows')` calling form, or by reshaping the input variable to a column vector, using regular `[~,~,pkeys] = unique(x)` on that, and reshaping the resulting pkeys to match the original input. I haven't decided yet which approach is better. The `'rows'` approach results in scalar proxy keys instead of multi-column proxy keys, which is nicer, but it adds the requirement that the variable types support the `'rows'` option to `unique`, which not all types do. You could add some logic to do it either way, depending on whether the input supports the `unique(..., 'rows')` option. But there's no easy way to programmatically detect whether an input does or not. ### Proxy key type Proxy keys are `double`s. It may be more efficient to represent them as `single` or an int type instead, possibly even choosing which type dynamically based on the type and cardinality of the input key values. TBD whether to do that. This would purely be about efficiency. ### Optimized proxy key calculation For some types, you may be able to directly compute proxy key values in an efficient manner without doing the `unique` trick. Possibly much more efficiently. For most numeric types, including `double`, `single`, and all integer values except `int64` and `uint64`, their values are _already_ valid proxy keys. So just cast them to `double` and you're done. `int64` and `uint64` may contain values that cannot be exactly or uniquely represented as `double`, so you need to do the `unique` trick on them. Any other type that has an exact or distinct `double` representation can also be converted directly over. For example, Tablicious' `datetime` type is internally equivalent to a `double` datenum, so you can just use that. And `categorical` arrays already have a numeric form. It would be possible to define an interface that allowed user-defined classdef types to provide an optimized proxy key conversion. To do this, we would need to specify the name and signature of a method that did that conversion; something like `proxykeys(x)`. ## What Matlab does I have no idea what Matlab does in its `table` implementation to support heterogeneous-variable matching, or whether it's anything like my proxy keys trick. I have intentionally avoided learning about the internals of Matlab's `table` to avoid becoming tainted with MathWorks intellectual property in this area. If you know how Matlab's `table` works internally, please don't tell me. apjanke-octave-tablicious-8a1b10f/docs/README.txt000066400000000000000000000031721515050116100215660ustar00rootroot00000000000000# README - Tablicious docs/ This `docs/` dir is the project documentation, for online and local consumption. This is the whole project web site, not just the doco for one particular release. This is (currently) a GitHub Pages site. So this folder needs to be named exactly `docs`, and when you push changes to the `main` branch, they get published on GH Pages automatically. This file is internal developer documentation, not really meant for presentation on the website, but it doesn't matter if users or the public see it anyway. ## Dir structure * `/` - The main web site! * `*.md` - Articles and pages that apply across multiple versions, or are about the project as a whole. * `release/` - Package doco for released versions (frozen). * `v/` - Package doco for a specific version (frozen). * `devel/` - Package doco for the in-development version on `main` ("living"/unstable). * `index.md` - The front page that gets displayed when a user goes to the site. * `CNAME` - GH Pages config file for using a custom domain name (may not exist, auto-managed). * `Gemfile`, `_config.yml` - GH Pages configuration for both deployment and local use. Under each package doco dir, either a `release/v/` or `devel/`, we have: * `/` * `user-guide/` * `html/` - Multi-page version of the User Guide. * `tablicious.html` - Single-page version of the User Guide. * `tablicious.pdf` - PDF version of the User Guide. Yep, that's it for now. TODO: Add an index.md or index.html for those, at the top level, with a brief info, links to the various formats of the user guide, and a link to the main project site. apjanke-octave-tablicious-8a1b10f/docs/Release-Checklist.md000066400000000000000000000127111515050116100237000ustar00rootroot00000000000000# Tablicious Release Checklist Here's the process for doing a Tablicious release. 1. Make a new `release/v` branch for the prep work and switch to it. 1. Run all the tests. 1. `tblish_inst = fullfile(getenv('HOME'), 'repos', 'octave-repos', 'tablicious', 'inst')` 1. `addpath(tblish_inst)` 1. `__run_test_suite__ ({tblish_inst}, {})` 1. `tblish.internal.runtests` 1. Run the MP-Test tests 1. `addpath(fullfile(getenv('HOME'), 'repos', 'octave-repos', 'mptest', 'lib'))` 1. `tblish_mptest_tablicious` 1. Wouldn't hurt to do `make clean && git status && make test` and manual-cleanup, just to be sure. 1. Update the version info in the repo. 1. Update the version number and date in `DESCRIPTION`. 1. Remove the `-SNAPSHOT` suffix from the version number. 1. Update the `CHANGES.txt` file with the release date. 1. Use the current calendar date in UTC time, not your local time. 1. And check that it has a complete change list for the release. This _ought_ to be done as changes are committed, but we're not good about that at all, so assume it didn't happen. 1. Update the installation instructions in `README.md` to use the upcoming release tarball URL. 1. Format is: `https://github.com/apjanke/octave-tablicious/releases/download/v/tablicious-.tar.gz` 1. As of 2024-10, this is just in the "Quick start" section. 1. Commit the changes so far with commit message `[release] v - prep`. 1. Use form: `git add -A; git commit -a -m '[release] v - prep'` 1. Update the generated doco. 1. Regenerate the doco: `make doc; make gh-pages` 1. Make a versioned copy for this release: * `ver=$(grep ^Version DESCRIPTION | cut -d ' ' -f 2); mkdir -p docs/release/v${ver}/user-guide; cd doc; cp -R html tablicious.html tablicious.pdf ../docs/release/v${ver}/user-guide` 1. What is wrong with you, Andrew? Make this a `make` target instead of a 200-character-long bash cut-and-paste command. 1. Update `docs/index.md` with the new release. 1. Add a section for the new release's doco under Releases. 1. Update the links in the first paragraph in User Documentation to point to the new release, and the release number in the second paragraph. 1. Commit the regenerated doco with commit message `[release] v - doco`. 1. Use form: `git add -A; git commit -a -m '[release] v - doco'` 1. Make sure your repo is clean: `git status` should show no local changes. 1. Run `make dist` first to make sure it works. 1. This has to be done _after_ the commit, because it extracts from git history. 1. Test the dist tarball. 1. Install it with `pkg install /target/tablicious-x.y.z.tar.gz`. 1. Run tests on the installed package. 1. `pkg test tablicious` will do the BISTs. (But only the oruntest-discoverable ones?) Prob have to run the MP-Test suite manually for now. 1. Preferably on both Mac and Linux, and maybe Windows. 1. Merge the `release/v` branch to `main` and switch to `main`. 1. Create a git tag and push it and the above changes to GitHub. 1. `git tag v; git push; git push --tags` 1. Create a new GitHub release from the tag. 1. Just use `` as the name for the release, not `v` or `v `. 1. Copy and paste the changes for this release from the `CHANGES.txt` file into the GitHub Release description field. 1. Upload the dist tarball as a file for the release. 1. Test installing the release using `pkg install` against the new release URL. 1. Do this by copy-and-pasting the `pkg install` example from the [live README page](https://github.com/apjanke/octave-tablicious/blob/master/README.md) on the GitHub repo. This makes sure the current install instructions are correct. 1. Don't short-circuit this by just editing an entry from your Octave command history! Open GitHub in a browser and actually copy-and-paste it. 1. Run tests on the installed package, like above. 1. Open development for next version. 1. Update version number and date in `DESCRIPTION` to next patch or minor version, as appropriate. 1. Include a `-SNAPSHOT` suffix to indicate this is a work in progress. 1. Add a section to `CHANGES.txt` for the new upcoming release, at the top. Use `(unreleased)` for its release date. 1. `git add -A; git commit -a -m '[release] open development for next version ()'; git push` 1. Close the GitHub Issues [Milestone](https://github.com/apjanke/octave-tablicious/milestones) for this release. 1. Create a new Milestone for the next release, if one doesn't already exist. 1. Announce the release. 1. Update the OF packages index info in the gnu-octave/packages repo. 1. Put in a PR to the [`gnu-octave/packages` repo](https://github.com/gnu-octave/packages) to update `packages/tablicious.yaml`. 1. Add a new item to its `versions` section for this new release. 1. See example: [Tablicious index update for 0.3.7 - 0.4.2](https://github.com/gnu-octave/packages/pull/401). 1. Add a comment to the [Tablicious News issue](https://github.com/apjanke/octave-tablicious/issues/131) with a link to the new release. 1. Post an announcement on the [Savannah bug for datetime support](https://savannah.gnu.org/bugs/index.php?47032) if this is a significant release with respect to the date/time classes. If there were any problems following these instructions exactly as written, please report it as a bug. apjanke-octave-tablicious-8a1b10f/docs/TODO.md000066400000000000000000000122771515050116100211650ustar00rootroot00000000000000# Tablicious TODO ## For this document * This TODO document is way out of date as of 2024. Review it and remove stuff that got done or WONTFIXed. ## Overall * Convert internal dnums representation to UTC. * Math goes bad around DST and other zone transitions if you don't do that. * Documentation * Add more BISTs * Consider using GNU FDL for the documentation license. * Propagate NaN-filling fix for planargen back to Janklab. * Report crash: giving duration a days method while it has a days property results in a crash. ## Unit tests * Automatic MP-test detection is broken. * A single M-code script to run both BISTs and MP-Test suite. * Doing what the `runtests.sh` shell wrapper does now, moving it in to M-code. ## Build and packaging * Make the "versioned copy of doco" step of the release a script or `make target` instead of a big copy-pasted command. * Rework the Release Checklist so that the generated doco building and capture is a separate commit from the "config" changes and headline doco changes for a release. * Have Release Checklist test the local package from dist tarball before pushing the release to GitHub. ## Doco stuff * Document my new class/namespace-supporting stuff. * Stick a README in the doc/ directory describing how my new stuff works, and detailing the texinfo conventions needed to work with it. * Add support for namespaces/packages. * Recursively find files in `+blah` dirs under main source path dirs. * Establish node naming convention for things in namespaces. ## Areas * `table` * summary * Description, Units, and CustomProperties for variables in table * `timetable` * More documentation for individual methods * File I/O * readtable, writetable * tableread * table.textscan, table.csvread, table.dlmread * Maybe pull in Apache FOP for this. Will need to write custom Java layer for acceptable performance. * But most Octave users don't want to use Java. Better to take a C/C++ dependency. Or even a Python one, these days, I think. * viewtable() * Takes single table or struct with tabular field contents * Doing this in Java would be easier; in Qt would be nicer * But doing it in Qt would require users installing the package to have Qt dev tools installed so the build can work. Yuck. * `datetime` * Convert internal dnums representation to UTC * Math goes bad around DST and other zone transitions if you don't do that * I think this is actually done already? * Time zone support * Normalization of "nonexistent" times like between 02:00 and 03:00 on DST leap ahead days * Leap second conversion * `Format` support * Needs LDML format support, not datestr() format placeholders * between() - calendarDuration diffs between datetimes * caldiff * dateshift * week() - ISO calendar week-of-year calculation * isdst/isweekend * Additional `ConvertFrom` types * POSIX zone rule support for dates outside range of Olson database * This affects dates before around 1880 and after around 2038 * It also affects current dates for time zones that don't use DST! * TODO: First, implement simplified no-DST POSIX time zones. That'll at least get us Tokyo time support. Sheesh. * `TzDb` * timezones() function: add UTCOffset/DSTOffset * `calendarDuration` and its associated functions * split() * Can different fields be mixed positive/negative, in addition to the overall Sign? Current arithmetic implementation can result in this. Should that be normalized? Maybe. Not sure it can be fully normalized. * proxykeys: pull isnan up to front of precedence? Maybe invert so NaNs sort to end? * Fix expansion filling? * e.g. `d = datetime; d(5) = d` produced bad `d(2:4)` before patch; this probably does similar now * It's in the expansion of numerics: their default value is 0, not NaN. * Refactor out promote() * Plotting support * Maybe with just shims and conversion to datenums * `duration` * `InputFmt` support * `Format` support * Documentation * Keyword index, including all function and class names * Fix Travis CI doco build * Correct asciibetical ordering in Functions Alphabetically * Fix this: `warning: doc_cache_create: unusable help text found in file 'datetime'` * Get `help datetime` to recognize my datetime * Currently, `which datetime` gives `'datetime' is a built-in function` and `help datetime` gives `error: help: 'datetime' is not documented` * Get `mkdoc.pl` to ignore files in `+internal` namespaces. * categorical stuff * summary * countcats * Other "Missing Data" stuff * fillmissing() * ## Examples stuff * Lowess smoothing (several datasets) * ANOVA display (`chickwts`) * Linear model regression results plotting (`cars`, `chickwts`) * The thing that does a 2-by-2 grid with residuals, Q-Q, and Scale-Location plots * I think this comes from R’s `lm()` linear model function * Equivalent of R’s `coplot()` (`DNase`) * Equivalent of R’s “mosaic plot” (`esoph`) * Good luck on this one ## Wishlist and maybes * MAT-file representation compatibility with Matlab? * Documentation * A new Texinfo `@defmfcn` macro for Matlab's idiosyncratic function signatures apjanke-octave-tablicious-8a1b10f/docs/_config.yml000066400000000000000000000014551515050116100222210ustar00rootroot00000000000000title: Tablicious email: andrew@apjanke.net description: Table and related classes for Octave baseurl: "/" url: "" theme: jekyll-theme-slate github_username: apjanke # Fixed GitHub Pages configuration markdown: kramdown lsi: false safe: true incremental: false highlighter: rouge gist: noscript: false kramdown: math_engine: mathjax syntax_highlighter: rouge exclude: - Gemfile* keep_files: - images plugins: # Forced by GitHub Pages - jekyll-asciidoc - jekyll-coffeescript - jekyll-default-layout - jekyll-gist - jekyll-github-metadata - jekyll-last-modified-at - jekyll-optional-front-matter - jekyll-paginate - jekyll-readme-index - jekyll-relative-links - jekyll-titles-from-headings # Supported by GitHub Pages - jekyll-feed - jekyll-seo-tag - jekyll-sitemap apjanke-octave-tablicious-8a1b10f/docs/_sass/000077500000000000000000000000001515050116100211755ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/_sass/jekyll-theme-slate.scss000066400000000000000000000176471515050116100256110ustar00rootroot00000000000000/* * This is a customized version of jekyll-theme-slate.scss that turns off the * background coloring on inline code spans, which I think improves readability. * -apjanke */ @import "rouge-github"; 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} } @media screen and (max-width: 480px) { body { font-size:14px; } #downloads { display: none; } .inner { min-width: 320px; max-width: 480px; } #project_title { font-size: 32px; } h1 { font-size: 28px; } h2 { font-size: 24px; } h3 { font-size: 21px; } h4 { font-size: 18px; } h5 { font-size: 14px; } h6 { font-size: 12px; } code, pre { font-size: 11px; } } @media screen and (max-width: 320px) { body { font-size:14px; } #downloads { display: none; } .inner { min-width: 240px; max-width: 320px; } #project_title { font-size: 28px; } h1 { font-size: 24px; } h2 { font-size: 21px; } h3 { font-size: 18px; } h4 { font-size: 16px; } h5 { font-size: 14px; } h6 { font-size: 12px; } code, pre { min-width: 240px; max-width: 320px; font-size: 11px; } } apjanke-octave-tablicious-8a1b10f/docs/devel/000077500000000000000000000000001515050116100211645ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/000077500000000000000000000000001515050116100232355ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/000077500000000000000000000000001515050116100242015ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/API-Alphabetically.html000066400000000000000000000135611515050116100304220ustar00rootroot00000000000000 API Alphabetically (Tablicious v0.4.7)     apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/API-Reference.html000066400000000000000000000047401515050116100274010ustar00rootroot00000000000000 API Reference (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/API-by-Category.html000066400000000000000000000360501515050116100276670ustar00rootroot00000000000000 API by Category (Tablicious v0.4.7)

    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like.

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Copying.html000066400000000000000000000110511515050116100264750ustar00rootroot00000000000000 Copying (Tablicious v0.4.7)

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    9 Copying

    Previous: API Reference, Up: Tablicious for GNU Octave   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056771515050116100300330ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.7)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Date-and-Time-Representation.html000066400000000000000000000060621515050116100324040ustar00rootroot00000000000000 Date and Time Representation (Tablicious v0.4.7)

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074701515050116100313030ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.7)

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060161515050116100304200ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.7)

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Durations.html000066400000000000000000000046521515050116100270460ustar00rootroot00000000000000 Durations (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Example-Data-Sets.html000066400000000000000000000102261515050116100302460ustar00rootroot00000000000000 Example Data Sets (Tablicious v0.4.7)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Getting-Started.html000066400000000000000000000064201515050116100300760ustar00rootroot00000000000000 Getting Started (Tablicious v0.4.7)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Introduction.html000066400000000000000000000072461515050116100275610ustar00rootroot00000000000000 Introduction (Tablicious v0.4.7)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.7 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Missing-Functionality.html000066400000000000000000000117541515050116100313360ustar00rootroot00000000000000 Missing Functionality (Tablicious v0.4.7)

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support
    • Things omitted for compatibility:
      • ismissing function

    A global ismissing function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. ismissing methods for various classes in Tablicious are defined. Tablicious also supplies an isnanny function with behavior similar to ismissing for clients which which wish to have ismissing-like behavior using a dependency only on Tablicious.

    It is the author’s hope that many of these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/NaC.html000066400000000000000000000076401515050116100255370ustar00rootroot00000000000000 NaC (Tablicious v0.4.7)

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/NaS.html000066400000000000000000000075251515050116100255610ustar00rootroot00000000000000 NaS (Tablicious v0.4.7)

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/NaT.html000066400000000000000000000075711515050116100255630ustar00rootroot00000000000000 NaT (Tablicious v0.4.7)

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Table-Construction.html000066400000000000000000000070421515050116100306110ustar00rootroot00000000000000 Table Construction (Tablicious v0.4.7)

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Table-Representation.html000066400000000000000000000100321515050116100311120ustar00rootroot00000000000000 Table Representation (Tablicious v0.4.7)

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Tables-vs-SQL.html000066400000000000000000000113661515050116100273730ustar00rootroot00000000000000 Tables vs SQL (Tablicious v0.4.7)

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Time-Zones.html000066400000000000000000000141321515050116100270620ustar00rootroot00000000000000 Time Zones (Tablicious v0.4.7)

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/Validation-Functions.html000066400000000000000000000117531515050116100311360ustar00rootroot00000000000000 Validation Functions (Tablicious v0.4.7)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/array2table.html000066400000000000000000000103041515050116100272750ustar00rootroot00000000000000 array2table (Tablicious v0.4.7)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/caldays.html000066400000000000000000000065041515050116100265140ustar00rootroot00000000000000 caldays (Tablicious v0.4.7)

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration-Class.html000066400000000000000000000063161515050116100314170ustar00rootroot00000000000000 calendarDuration Class (Tablicious v0.4.7)

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration.html000066400000000000000000000107151515050116100303520ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.7)

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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

    calendarDuration_002ecalendarDuration.html000066400000000000000000000077751515050116100342350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html calendarDuration.calendarDuration (Tablicious v0.4.7)

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002edatevec.html000066400000000000000000000077011515050116100324350ustar00rootroot00000000000000 calendarDuration.datevec (Tablicious v0.4.7)

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    8.2.3.2 calendarDuration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert a calendarDuration to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this calendarDuration.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002edispstrs.html000066400000000000000000000062011515050116100326670ustar00rootroot00000000000000 calendarDuration.dispstrs (Tablicious v0.4.7)

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    8.2.3.3 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002eismissing.html000066400000000000000000000062451515050116100330310ustar00rootroot00000000000000 calendarDuration.ismissing (Tablicious v0.4.7)

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    8.2.3.4 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002eisnan.html000066400000000000000000000063111515050116100321260ustar00rootroot00000000000000 calendarDuration.isnan (Tablicious v0.4.7)

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    8.2.3.5 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002eminus.html000066400000000000000000000061611515050116100321540ustar00rootroot00000000000000 calendarDuration.minus (Tablicious v0.4.7)

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    8.2.3.6 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002emtimes.html000066400000000000000000000063521515050116100323210ustar00rootroot00000000000000 calendarDuration.mtimes (Tablicious v0.4.7)

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    8.2.3.7 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002eplus.html000066400000000000000000000070301515050116100320000ustar00rootroot00000000000000 calendarDuration.plus (Tablicious v0.4.7)

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    8.2.3.8 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002etimes.html000066400000000000000000000062251515050116100321430ustar00rootroot00000000000000 calendarDuration.times (Tablicious v0.4.7)

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    8.2.3.9 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calendarDuration_002euminus.html000066400000000000000000000056051515050116100323430ustar00rootroot00000000000000 calendarDuration.uminus (Tablicious v0.4.7)

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    8.2.3.10 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calmonths.html000066400000000000000000000065161515050116100270670ustar00rootroot00000000000000 calmonths (Tablicious v0.4.7)

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/calyears.html000066400000000000000000000060241515050116100266740ustar00rootroot00000000000000 calyears (Tablicious v0.4.7)

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical.html000066400000000000000000000177441515050116100273610ustar00rootroot00000000000000 categorical (Tablicious v0.4.7)

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

    Next: cell2table, Previous: calyears, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eaddcats.html000066400000000000000000000060111515050116100314140ustar00rootroot00000000000000 categorical.addcats (Tablicious v0.4.7)

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002ecategorical.html000066400000000000000000000137231515050116100322760ustar00rootroot00000000000000 categorical.categorical (Tablicious v0.4.7)

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002ecategories.html000066400000000000000000000062041515050116100321420ustar00rootroot00000000000000 categorical.categories (Tablicious v0.4.7)

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062631515050116100314720ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.7)

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062721515050116100316750ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.7)

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002edouble.html000066400000000000000000000067041515050116100312740ustar00rootroot00000000000000 categorical.double (Tablicious v0.4.7)

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eiscategory.html000066400000000000000000000063231515050116100321700ustar00rootroot00000000000000 categorical.iscategory (Tablicious v0.4.7)

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eismissing.html000066400000000000000000000061561515050116100320300ustar00rootroot00000000000000 categorical.ismissing (Tablicious v0.4.7)

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eisnanny.html000066400000000000000000000062611515050116100314770ustar00rootroot00000000000000 categorical.isnanny (Tablicious v0.4.7)

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eisordinal.html000066400000000000000000000061651515050116100320070ustar00rootroot00000000000000 categorical.isordinal (Tablicious v0.4.7)

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eisundefined.html000066400000000000000000000064771515050116100323260ustar00rootroot00000000000000 categorical.isundefined (Tablicious v0.4.7)

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002emergecats.html000066400000000000000000000076321515050116100317750ustar00rootroot00000000000000 categorical.mergecats (Tablicious v0.4.7)

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002emissing.html000066400000000000000000000075071515050116100314750ustar00rootroot00000000000000 categorical.missing (Tablicious v0.4.7)

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eremovecats.html000066400000000000000000000075211515050116100321700ustar00rootroot00000000000000 categorical.removecats (Tablicious v0.4.7)

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002erenamecats.html000066400000000000000000000070311515050116100321360ustar00rootroot00000000000000 categorical.renamecats (Tablicious v0.4.7)

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002ereordercats.html000066400000000000000000000072631515050116100323400ustar00rootroot00000000000000 categorical.reordercats (Tablicious v0.4.7)

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002esetcats.html000066400000000000000000000062751515050116100314730ustar00rootroot00000000000000 categorical.setcats (Tablicious v0.4.7)

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002esqueezecats.html000066400000000000000000000061351515050116100323540ustar00rootroot00000000000000 categorical.squeezecats (Tablicious v0.4.7)

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002estring.html000066400000000000000000000062731515050116100313310ustar00rootroot00000000000000 categorical.string (Tablicious v0.4.7)

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002esummary.html000066400000000000000000000060651515050116100315170ustar00rootroot00000000000000 categorical.summary (Tablicious v0.4.7)

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/categorical_002eundefined.html000066400000000000000000000067011515050116100317600ustar00rootroot00000000000000 categorical.undefined (Tablicious v0.4.7)

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/cell2table.html000066400000000000000000000102361515050116100271020ustar00rootroot00000000000000 cell2table (Tablicious v0.4.7)

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/colvecfun.html000066400000000000000000000065701515050116100270630ustar00rootroot00000000000000 colvecfun (Tablicious v0.4.7)

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/contains.html000066400000000000000000000074631515050116100267170ustar00rootroot00000000000000 contains (Tablicious v0.4.7)

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datasets_002edescription.html000066400000000000000000000065321515050116100316770ustar00rootroot00000000000000 datasets.description (Tablicious v0.4.7)

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datasets_002elist.html000066400000000000000000000065501515050116100303270ustar00rootroot00000000000000 datasets.list (Tablicious v0.4.7)

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datasets_002eload.html000066400000000000000000000062751515050116100302770ustar00rootroot00000000000000 datasets.load (Tablicious v0.4.7)

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime-Class.html000066400000000000000000000063731515050116100277370ustar00rootroot00000000000000 datetime Class (Tablicious v0.4.7)

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime.html000066400000000000000000000202661515050116100266710ustar00rootroot00000000000000 datetime (Tablicious v0.4.7)

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

    Next: days, Previous: contains, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eNaT.html000066400000000000000000000075611515050116100300250ustar00rootroot00000000000000 datetime.NaT (Tablicious v0.4.7)

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    8.2.10.26 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ecolon.html000066400000000000000000000117221515050116100304470ustar00rootroot00000000000000 datetime.colon (Tablicious v0.4.7)

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

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    datetime_002econvertDatenumTimeZone.html000066400000000000000000000070101515050116100337220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html datetime.convertDatenumTimeZone (Tablicious v0.4.7)

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatenum.html000066400000000000000000000060321515050116100307700ustar00rootroot00000000000000 datetime.datenum (Tablicious v0.4.7)

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatenum2posix.html000066400000000000000000000061221515050116100321350ustar00rootroot00000000000000 datetime.datenum2posix (Tablicious v0.4.7)

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatestr.html000066400000000000000000000066421515050116100310100ustar00rootroot00000000000000 datetime.datestr (Tablicious v0.4.7)

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatestrs.html000066400000000000000000000067141515050116100311730ustar00rootroot00000000000000 datetime.datestrs (Tablicious v0.4.7)

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatestruct.html000066400000000000000000000066771515050116100315340ustar00rootroot00000000000000 datetime.datestruct (Tablicious v0.4.7)

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatetime.html000066400000000000000000000136431515050116100311350ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.7)

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edatevec.html000066400000000000000000000071271515050116100307540ustar00rootroot00000000000000 datetime.datevec (Tablicious v0.4.7)

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ediff.html000066400000000000000000000061161515050116100302460ustar00rootroot00000000000000 datetime.diff (Tablicious v0.4.7)

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002edispstrs.html000066400000000000000000000057171515050116100312170ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.7)

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eeq.html000066400000000000000000000062461515050116100277470ustar00rootroot00000000000000 datetime.eq (Tablicious v0.4.7)

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ege.html000066400000000000000000000062611515050116100277320ustar00rootroot00000000000000 datetime.ge (Tablicious v0.4.7)

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002egmtime.html000066400000000000000000000060751515050116100306240ustar00rootroot00000000000000 datetime.gmtime (Tablicious v0.4.7)

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002egt.html000066400000000000000000000062501515050116100277470ustar00rootroot00000000000000 datetime.gt (Tablicious v0.4.7)

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ehms.html000066400000000000000000000063471515050116100301330ustar00rootroot00000000000000 datetime.hms (Tablicious v0.4.7)

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a datetime. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eisbetween.html000066400000000000000000000063651515050116100313310ustar00rootroot00000000000000 datetime.isbetween (Tablicious v0.4.7)

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eismissing.html000066400000000000000000000061621515050116100313440ustar00rootroot00000000000000 datetime.ismissing (Tablicious v0.4.7)

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    8.2.10.18 datetime.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eisnan.html000066400000000000000000000060441515050116100304460ustar00rootroot00000000000000 datetime.isnan (Tablicious v0.4.7)

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    8.2.10.19 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eisnat.html000066400000000000000000000056241515050116100304570ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.7)

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    8.2.10.20 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ele.html000066400000000000000000000063011515050116100277320ustar00rootroot00000000000000 datetime.le (Tablicious v0.4.7)

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    8.2.10.21 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002elinspace.html000066400000000000000000000067351515050116100311430ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.7)

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    8.2.10.22 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002elocaltime.html000066400000000000000000000067331515050116100313140ustar00rootroot00000000000000 datetime.localtime (Tablicious v0.4.7)

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    8.2.10.23 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002elt.html000066400000000000000000000062711515050116100277570ustar00rootroot00000000000000 datetime.lt (Tablicious v0.4.7)

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    8.2.10.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eminus.html000066400000000000000000000070211515050116100304650ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.7)

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    8.2.10.25 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002ene.html000066400000000000000000000062621515050116100277420ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.7)

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    8.2.10.27 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eofDatenum.html000066400000000000000000000060461515050116100312620ustar00rootroot00000000000000 datetime.ofDatenum (Tablicious v0.4.7)

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    8.2.10.28 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eofDatestruct.html000066400000000000000000000063031515050116100320030ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.7)

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    8.2.10.29 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eplus.html000066400000000000000000000071111515050116100303150ustar00rootroot00000000000000 datetime.plus (Tablicious v0.4.7)

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    8.2.10.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eposix2datenum.html000066400000000000000000000062171515050116100321420ustar00rootroot00000000000000 datetime.posix2datenum (Tablicious v0.4.7)

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    8.2.10.31 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eposixtime.html000066400000000000000000000062761515050116100313660ustar00rootroot00000000000000 datetime.posixtime (Tablicious v0.4.7)

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    8.2.10.32 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eproxyKeys.html000066400000000000000000000065131515050116100313540ustar00rootroot00000000000000 datetime.proxyKeys (Tablicious v0.4.7)

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    8.2.10.33 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002etimeofday.html000066400000000000000000000061611515050116100313170ustar00rootroot00000000000000 datetime.timeofday (Tablicious v0.4.7)

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    8.2.10.34 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eweek.html000066400000000000000000000055641515050116100302770ustar00rootroot00000000000000 datetime.week (Tablicious v0.4.7)

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    8.2.10.35 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eymd.html000066400000000000000000000061321515050116100301250ustar00rootroot00000000000000 datetime.ymd (Tablicious v0.4.7)

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    8.2.10.36 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/datetime_002eymdhms.html000066400000000000000000000060671515050116100306440ustar00rootroot00000000000000 datetime.ymdhms (Tablicious v0.4.7)

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    8.2.10.37 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/days.html000066400000000000000000000062471515050116100260400ustar00rootroot00000000000000 days (Tablicious v0.4.7)

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/discretize.html000066400000000000000000000122201515050116100272310ustar00rootroot00000000000000 discretize (Tablicious v0.4.7)

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/dispstrs.html000066400000000000000000000066111515050116100267460ustar00rootroot00000000000000 dispstrs (Tablicious v0.4.7)

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration-Class.html000066400000000000000000000057651515050116100277740ustar00rootroot00000000000000 duration Class (Tablicious v0.4.7)

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration.html000066400000000000000000000125211515050116100267150ustar00rootroot00000000000000 duration (Tablicious v0.4.7)

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002echar.html000066400000000000000000000056721515050116100303120ustar00rootroot00000000000000 duration.char (Tablicious v0.4.7)

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002edatevec.html000066400000000000000000000101361515050116100307770ustar00rootroot00000000000000 duration.datevec (Tablicious v0.4.7)

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    8.2.14.2 duration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert obj to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002edispstrs.html000066400000000000000000000057321515050116100312450ustar00rootroot00000000000000 duration.dispstrs (Tablicious v0.4.7)

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    8.2.14.3 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002ehms.html000066400000000000000000000061511515050116100301550ustar00rootroot00000000000000 duration.hms (Tablicious v0.4.7)

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    8.2.14.4 duration.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second values of a duration. These are the hour, minute, and second values that when combined are equal to the same duration value.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002ehours.html000066400000000000000000000057731515050116100305370ustar00rootroot00000000000000 duration.hours (Tablicious v0.4.7)

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    8.2.14.5 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002elinspace.html000066400000000000000000000067741515050116100311770ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.7)

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    8.2.14.6 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002emilliseconds.html000066400000000000000000000061011515050116100320460ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.7)

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    8.2.14.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002eminutes.html000066400000000000000000000060551515050116100310550ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.7)

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    8.2.14.8 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002eofDays.html000066400000000000000000000062411515050116100306130ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.7)

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    8.2.14.9 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002eseconds.html000066400000000000000000000057731515050116100310350ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.7)

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    8.2.14.10 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/duration_002eyears.html000066400000000000000000000055461515050116100305200ustar00rootroot00000000000000 duration.years (Tablicious v0.4.7)

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    8.2.14.11 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/eqn.html000066400000000000000000000074611515050116100256620ustar00rootroot00000000000000 eqn (Tablicious v0.4.7)

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/head.html000066400000000000000000000075101515050116100257730ustar00rootroot00000000000000 head (Tablicious v0.4.7) apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/hours.html000066400000000000000000000063121515050116100262310ustar00rootroot00000000000000 hours (Tablicious v0.4.7)

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/index.html000066400000000000000000001327631515050116100262120ustar00rootroot00000000000000 Top (Tablicious v0.4.7)

    Next:   [Contents]

    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.7.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/iscalendarduration.html000066400000000000000000000062521515050116100307470ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.7)

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/iscategorical.html000066400000000000000000000062261515050116100277060ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.7)

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/isdatetime.html000066400000000000000000000061361515050116100272250ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.7)

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/isduration.html000066400000000000000000000061021515050116100272470ustar00rootroot00000000000000 isduration (Tablicious v0.4.7)

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/isfile.html000066400000000000000000000044601515050116100263460ustar00rootroot00000000000000 isfile (Tablicious v0.4.7)

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    8.2.22 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/isfolder.html000066400000000000000000000044501515050116100267010ustar00rootroot00000000000000 isfolder (Tablicious v0.4.7)

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    8.2.23 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/isnanny.html000066400000000000000000000100301515050116100265400ustar00rootroot00000000000000 isnanny (Tablicious v0.4.7)

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like.

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.
    3. Octave does not define an ismissing function, but the Octave Forge statistics package does, and adding an ismissing function to Tablicious would conflict with that in hard-to-resolve ways.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/istable.html000066400000000000000000000065071515050116100265220ustar00rootroot00000000000000 istable (Tablicious v0.4.7)

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/istabular.html000066400000000000000000000062271515050116100270640ustar00rootroot00000000000000 istabular (Tablicious v0.4.7)

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/istimetable.html000066400000000000000000000066031515050116100273760ustar00rootroot00000000000000 istimetable (Tablicious v0.4.7)

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate.html000066400000000000000000000123071515050116100270220ustar00rootroot00000000000000 localdate (Tablicious v0.4.7)

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002eNaT.html000066400000000000000000000101531515050116100301500ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.7)

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002edatenum.html000066400000000000000000000055131515050116100311270ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.7)

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002edatestr.html000066400000000000000000000066401515050116100311420ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.7)

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002edatestrs.html000066400000000000000000000067421515050116100313300ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.7)

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002edatestruct.html000066400000000000000000000071341515050116100316550ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.7)

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002edispstrs.html000066400000000000000000000060041515050116100313410ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.7)

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002eisnan.html000066400000000000000000000060611515050116100306010ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.7)

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002eisnat.html000066400000000000000000000057051515050116100306130ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.7)

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002elocaldate.html000066400000000000000000000111501515050116100314140ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.7)

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002eposixtime.html000066400000000000000000000063341515050116100315150ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.7)

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/localdate_002eymd.html000066400000000000000000000055611515050116100302660ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.7)

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/milliseconds.html000066400000000000000000000064151515050116100275620ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.7)

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/minutes.html000066400000000000000000000056261515050116100265640ustar00rootroot00000000000000 minutes (Tablicious v0.4.7)

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing.html000066400000000000000000000075361515050116100265530ustar00rootroot00000000000000 missing (Tablicious v0.4.7)

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing_002edispstrs.html000066400000000000000000000057241515050116100310720ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.7)

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing_002eismissing.html000066400000000000000000000060661515050116100312240ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.7)

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing_002eisnan.html000066400000000000000000000060171515050116100303230ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.7)

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing_002eisnanny.html000066400000000000000000000060301515050116100306650ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.7)

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/missing_002emissing.html000066400000000000000000000054711515050116100306670ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.7)

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeA.html000066400000000000000000000044761515050116100264420ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.7)

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    8.2.32 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeCellstr.html000066400000000000000000000045421515050116100276640ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.7)

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    8.2.33 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeCharvec.html000066400000000000000000000045661515050116100276350ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.7)

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    8.2.34 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeFinite.html000066400000000000000000000045641515050116100274760ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.7)

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    8.2.35 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeInteger.html000066400000000000000000000045621515050116100276530ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.7)

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    8.2.36 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeMember.html000066400000000000000000000045701515050116100274640ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.7)

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    8.2.37 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeNonempty.html000066400000000000000000000045741515050116100300720ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.7)

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    8.2.38 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeNumeric.html000066400000000000000000000045621515050116100276600ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.7)

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    8.2.39 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeReal.html000066400000000000000000000045541515050116100271420ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.7)

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    8.2.40 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeSameSize.html000066400000000000000000000045601515050116100277740ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.7)

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    8.2.41 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeScalar.html000066400000000000000000000046201515050116100274560ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.7)

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    8.2.42 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeScalarLogical.html000066400000000000000000000046261515050116100307570ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.7)

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    8.2.43 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/mustBeVector.html000066400000000000000000000045441515050116100275200ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.7)

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    8.2.44 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/pp.html000066400000000000000000000077771515050116100255300ustar00rootroot00000000000000 pp (Tablicious v0.4.7)

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/scalarexpand.html000066400000000000000000000072671515050116100275500ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.7)

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/seconds.html000066400000000000000000000063341515050116100265330ustar00rootroot00000000000000 seconds (Tablicious v0.4.7)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/size2str.html000066400000000000000000000065321515050116100266620ustar00rootroot00000000000000 size2str (Tablicious v0.4.7)

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/splitapply.html000066400000000000000000000114251515050116100272730ustar00rootroot00000000000000 splitapply (Tablicious v0.4.7)

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string.html000066400000000000000000000163061515050116100264030ustar00rootroot00000000000000 string (Tablicious v0.4.7)

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

    Next: struct2table, Previous: splitapply, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002ecell.html000066400000000000000000000057431515050116100277740ustar00rootroot00000000000000 string.cell (Tablicious v0.4.7)

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002ecellstr.html000066400000000000000000000057721515050116100305270ustar00rootroot00000000000000 string.cellstr (Tablicious v0.4.7)

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002echar.html000066400000000000000000000062351515050116100277670ustar00rootroot00000000000000 string.char (Tablicious v0.4.7)

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002ecmp.html000066400000000000000000000075371515050116100276370ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.7)

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002edecode.html000066400000000000000000000062221515050116100302710ustar00rootroot00000000000000 string.decode (Tablicious v0.4.7)

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002edispstrs.html000066400000000000000000000064731515050116100307310ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.7)

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eempty.html000066400000000000000000000061601515050116100302050ustar00rootroot00000000000000 string.empty (Tablicious v0.4.7)

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eencode.html000066400000000000000000000063471515050116100303130ustar00rootroot00000000000000 string.encode (Tablicious v0.4.7)

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eendsWith.html000066400000000000000000000066321515050116100306400ustar00rootroot00000000000000 string.endsWith (Tablicious v0.4.7)

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    8.2.53.9 string.endsWith

    Method: out = endsWith (obj, pattern)
    Method: out = endsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string ends with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eerase.html000066400000000000000000000060441515050116100301470ustar00rootroot00000000000000 string.erase (Tablicious v0.4.7)

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    8.2.53.10 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eismissing.html000066400000000000000000000061651515050116100310610ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.7)

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    8.2.53.11 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eisnanny.html000066400000000000000000000060131515050116100305230ustar00rootroot00000000000000 string.isnanny (Tablicious v0.4.7)

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    8.2.53.12 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eisstring.html000066400000000000000000000057361515050116100307210ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.7)

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    8.2.53.13 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002elower.html000066400000000000000000000062321515050116100301770ustar00rootroot00000000000000 string.lower (Tablicious v0.4.7)

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    8.2.53.14 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002emissing.html000066400000000000000000000061511515050116100305200ustar00rootroot00000000000000 string.missing (Tablicious v0.4.7)

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    8.2.53.15 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eplus.html000066400000000000000000000062761515050116100300420ustar00rootroot00000000000000 string.plus (Tablicious v0.4.7)

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    8.2.53.16 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eregexprep.html000066400000000000000000000070151515050116100310500ustar00rootroot00000000000000 string.regexprep (Tablicious v0.4.7)

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    8.2.53.17 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002ereverse.html000066400000000000000000000062201515050116100305170ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.7)

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    8.2.53.18 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002ereverse_005fbytes.html000066400000000000000000000067541515050116100323340ustar00rootroot00000000000000 string.reverse_bytes (Tablicious v0.4.7)

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    8.2.53.19 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estartsWith.html000066400000000000000000000067351515050116100312330ustar00rootroot00000000000000 string.startsWith (Tablicious v0.4.7)

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    8.2.53.20 string.startsWith

    Method: out = startsWith (obj, pattern)
    Method: out = startsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string starts with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrcat.html000066400000000000000000000062461515050116100303540ustar00rootroot00000000000000 string.strcat (Tablicious v0.4.7)

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    8.2.53.21 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrcmp.html000066400000000000000000000065601515050116100303630ustar00rootroot00000000000000 string.strcmp (Tablicious v0.4.7)

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    8.2.53.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrfind.html000066400000000000000000000070041515050116100305160ustar00rootroot00000000000000 string.strfind (Tablicious v0.4.7)

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    8.2.53.23 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estring.html000066400000000000000000000071311515050116100303540ustar00rootroot00000000000000 string.string (Tablicious v0.4.7)

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    8.2.53.24 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrlength.html000066400000000000000000000072421515050116100310630ustar00rootroot00000000000000 string.strlength (Tablicious v0.4.7)

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    8.2.53.25 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrlength_005fbytes.html000066400000000000000000000066771515050116100326770ustar00rootroot00000000000000 string.strlength_bytes (Tablicious v0.4.7)

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    8.2.53.26 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002estrrep.html000066400000000000000000000072611515050116100303710ustar00rootroot00000000000000 string.strrep (Tablicious v0.4.7)

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    8.2.53.27 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/string_002eupper.html000066400000000000000000000057651515050116100302140ustar00rootroot00000000000000 string.upper (Tablicious v0.4.7)

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    8.2.53.28 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/struct2table.html000066400000000000000000000067701515050116100275170ustar00rootroot00000000000000 struct2table (Tablicious v0.4.7)

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table.html000066400000000000000000000262031515050116100261610ustar00rootroot00000000000000 table (Tablicious v0.4.7)

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tableOuterFillValue.html000066400000000000000000000045321515050116100310050ustar00rootroot00000000000000 tableOuterFillValue (Tablicious v0.4.7)

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    8.2.56 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eaddvars.html000066400000000000000000000077661515050116100302710ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.7)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eantijoin.html000066400000000000000000000070521515050116100304440ustar00rootroot00000000000000 table.antijoin (Tablicious v0.4.7)

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002ecartesian.html000066400000000000000000000077021515050116100306040ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.7)

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002econvertvars.html000066400000000000000000000073451515050116100312120ustar00rootroot00000000000000 table.convertvars (Tablicious v0.4.7)

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002efindgroups.html000066400000000000000000000064171515050116100310150ustar00rootroot00000000000000 table.findgroups (Tablicious v0.4.7)

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002egetvar.html000066400000000000000000000063001515050116100301140ustar00rootroot00000000000000 table.getvar (Tablicious v0.4.7)

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002egetvars.html000066400000000000000000000061731515050116100303070ustar00rootroot00000000000000 table.getvars (Tablicious v0.4.7)

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002egroupby.html000066400000000000000000000075131515050116100303220ustar00rootroot00000000000000 table.groupby (Tablicious v0.4.7)

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eheight.html000066400000000000000000000057631515050116100301100ustar00rootroot00000000000000 table.height (Tablicious v0.4.7)

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002ehorzcat.html000066400000000000000000000063241515050116100303040ustar00rootroot00000000000000 table.horzcat (Tablicious v0.4.7)

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002einnerjoin.html000066400000000000000000000073421515050116100306260ustar00rootroot00000000000000 table.innerjoin (Tablicious v0.4.7)

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eintersect.html000066400000000000000000000065211515050116100306310ustar00rootroot00000000000000 table.intersect (Tablicious v0.4.7)

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eisempty.html000066400000000000000000000057041515050116100303250ustar00rootroot00000000000000 table.isempty (Tablicious v0.4.7)

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eismember.html000066400000000000000000000061361515050116100304360ustar00rootroot00000000000000 table.ismember (Tablicious v0.4.7)

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eismissing.html000066400000000000000000000072721515050116100306420ustar00rootroot00000000000000 table.ismissing (Tablicious v0.4.7)

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eistable.html000066400000000000000000000054721515050116100302600ustar00rootroot00000000000000 table.istable (Tablicious v0.4.7)

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002ejoin.html000066400000000000000000000074661515050116100276010ustar00rootroot00000000000000 table.join (Tablicious v0.4.7)

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002emergevars.html000066400000000000000000000073301515050116100306230ustar00rootroot00000000000000 table.mergevars (Tablicious v0.4.7)

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002emovevars.html000066400000000000000000000070471515050116100304770ustar00rootroot00000000000000 table.movevars (Tablicious v0.4.7)

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002endims.html000066400000000000000000000056351515050116100277500ustar00rootroot00000000000000 table.ndims (Tablicious v0.4.7)

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002enumel.html000066400000000000000000000061211515050116100277450ustar00rootroot00000000000000 table.numel (Tablicious v0.4.7)

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eouterfillvals.html000066400000000000000000000060461515050116100315260ustar00rootroot00000000000000 table.outerfillvals (Tablicious v0.4.7)

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eouterjoin.html000066400000000000000000000076021515050116100306500ustar00rootroot00000000000000 table.outerjoin (Tablicious v0.4.7)

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eprettyprint.html000066400000000000000000000060461515050116100312370ustar00rootroot00000000000000 table.prettyprint (Tablicious v0.4.7)

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erealjoin.html000066400000000000000000000100301515050116100304220ustar00rootroot00000000000000 table.realjoin (Tablicious v0.4.7)

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eremovevars.html000066400000000000000000000061311515050116100310170ustar00rootroot00000000000000 table.removevars (Tablicious v0.4.7)

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erenamevars.html000066400000000000000000000067151515050116100310010ustar00rootroot00000000000000 table.renamevars (Tablicious v0.4.7)

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erepelem.html000066400000000000000000000067011515050116100302620ustar00rootroot00000000000000 table.repelem (Tablicious v0.4.7)

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erepmat.html000066400000000000000000000065131515050116100301220ustar00rootroot00000000000000 table.repmat (Tablicious v0.4.7)

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erestrict.html000066400000000000000000000106041515050116100304650ustar00rootroot00000000000000 table.restrict (Tablicious v0.4.7)

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erowfun.html000066400000000000000000000157061515050116100301560ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.7)

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.rows2vars, Previous: table.restrict, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002erows2vars.html000066400000000000000000000107341515050116100306020ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.7)

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esemijoin.html000066400000000000000000000072311515050116100304450ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.7)

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetDimensionNames.html000066400000000000000000000105551515050116100322600ustar00rootroot00000000000000 table.setDimensionNames (Tablicious v0.4.7)

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetRowNames.html000066400000000000000000000061611515050116100311000ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.7)

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetVariableNames.html000066400000000000000000000076371515050116100320670ustar00rootroot00000000000000 table.setVariableNames (Tablicious v0.4.7)

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetdiff.html000066400000000000000000000063661515050116100302640ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.7)

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetvar.html000066400000000000000000000072021515050116100301320ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.7)

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esetxor.html000066400000000000000000000065501515050116100301570ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.7)

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esize.html000066400000000000000000000072721515050116100276070ustar00rootroot00000000000000 table.size (Tablicious v0.4.7)

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esplitapply.html000066400000000000000000000071331515050116100310320ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.7)

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esplitvars.html000066400000000000000000000076141515050116100306640ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.7)

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esqueeze.html000066400000000000000000000057471515050116100303230ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.7)

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002estack.html000066400000000000000000000072771515050116100277470ustar00rootroot00000000000000 table.stack (Tablicious v0.4.7)

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002esummary.html000066400000000000000000000067701515050116100303340ustar00rootroot00000000000000 table.summary (Tablicious v0.4.7)

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002etable.html000066400000000000000000000126421515050116100277210ustar00rootroot00000000000000 table.table (Tablicious v0.4.7)

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002etable2array.html000066400000000000000000000056061515050116100310440ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.7)

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002etable2cell.html000066400000000000000000000062551515050116100306460ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.7)

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002etable2struct.html000066400000000000000000000072201515050116100312440ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.7)

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002eunion.html000066400000000000000000000064341515050116100277640ustar00rootroot00000000000000 table.union (Tablicious v0.4.7)

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002evarfun.html000066400000000000000000000103541515050116100301310ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.7)

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002evarnames.html000066400000000000000000000064661515050116100304550ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.7)

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002evertcat.html000066400000000000000000000064321515050116100303020ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.7)

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/table_002ewidth.html000066400000000000000000000054111515050116100277450ustar00rootroot00000000000000 table.width (Tablicious v0.4.7)

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tail.html000066400000000000000000000076611515050116100260320ustar00rootroot00000000000000 tail (Tablicious v0.4.7)

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset.html000066400000000000000000000244331515050116100304560ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.7)

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    tblish_002edataset_002eAirPassengers.html000066400000000000000000000076151515050116100336170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.AirPassengers (Tablicious v0.4.7)

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eBJsales.html000066400000000000000000000110771515050116100324500ustar00rootroot00000000000000 tblish.dataset.BJsales (Tablicious v0.4.7)

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eBOD.html000066400000000000000000000103211515050116100315200ustar00rootroot00000000000000 tblish.dataset.BOD (Tablicious v0.4.7)

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eChickWeight.html000066400000000000000000000113441515050116100333130ustar00rootroot00000000000000 tblish.dataset.ChickWeight (Tablicious v0.4.7)

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eDNase.html000066400000000000000000000112311515050116100321070ustar00rootroot00000000000000 tblish.dataset.DNase (Tablicious v0.4.7)

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111721515050116100337460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.EuStockMarkets (Tablicious v0.4.7)

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114561515050116100334520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.Formaldehyde (Tablicious v0.4.7)

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151431515050116100333710ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.HairEyeColor (Tablicious v0.4.7)

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eHarman23cor.html000066400000000000000000000103311515050116100331740ustar00rootroot00000000000000 tblish.dataset.Harman23cor (Tablicious v0.4.7)

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eHarman74cor.html000066400000000000000000000104061515050116100332050ustar00rootroot00000000000000 tblish.dataset.Harman74cor (Tablicious v0.4.7)

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eIndometh.html000066400000000000000000000110441515050116100326660ustar00rootroot00000000000000 tblish.dataset.Indometh (Tablicious v0.4.7)

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    tblish_002edataset_002eInsectSprays.html000066400000000000000000000105031515050116100334660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.InsectSprays (Tablicious v0.4.7)

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000103151515050116100340150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.7)

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eLakeHuron.html000066400000000000000000000105331515050116100330110ustar00rootroot00000000000000 tblish.dataset.LakeHuron (Tablicious v0.4.7)

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126651515050116100342440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.7)

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eLoblolly.html000066400000000000000000000111411515050116100327050ustar00rootroot00000000000000 tblish.dataset.Loblolly (Tablicious v0.4.7)

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eNile.html000066400000000000000000000112541515050116100320110ustar00rootroot00000000000000 tblish.dataset.Nile (Tablicious v0.4.7)

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eOrange.html000066400000000000000000000115611515050116100323360ustar00rootroot00000000000000 tblish.dataset.Orange (Tablicious v0.4.7)

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    tblish_002edataset_002eOrchardSprays.html000066400000000000000000000133131515050116100336250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.OrchardSprays (Tablicious v0.4.7)

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

    Next: tblish.dataset.PlantGrowth, Previous: tblish.dataset.Orange, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102551515050116100333730ustar00rootroot00000000000000 tblish.dataset.PlantGrowth (Tablicious v0.4.7)

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ePuromycin.html000066400000000000000000000125301515050116100331050ustar00rootroot00000000000000 tblish.dataset.Puromycin (Tablicious v0.4.7)

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eTheoph.html000066400000000000000000000143171515050116100323540ustar00rootroot00000000000000 tblish.dataset.Theoph (Tablicious v0.4.7)

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eTitanic.html000066400000000000000000000146421515050116100325210ustar00rootroot00000000000000 tblish.dataset.Titanic (Tablicious v0.4.7)

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eToothGrowth.html000066400000000000000000000115701515050116100334130ustar00rootroot00000000000000 tblish.dataset.ToothGrowth (Tablicious v0.4.7)

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000141361515050116100335100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.UCBAdmissions (Tablicious v0.4.7)

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000150021515050116100336620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.7)

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000111351515050116100333370ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.UKLungDeaths (Tablicious v0.4.7)

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eUKgas.html000066400000000000000000000104261515050116100321340ustar00rootroot00000000000000 tblish.dataset.UKgas (Tablicious v0.4.7)

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000101121515050116100332010ustar00rootroot00000000000000 tblish.dataset.USAccDeaths (Tablicious v0.4.7)

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eUSArrests.html000066400000000000000000000140141515050116100330120ustar00rootroot00000000000000 tblish.dataset.USArrests (Tablicious v0.4.7)

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116621515050116100337040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.USJudgeRatings (Tablicious v0.4.7)

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111451515050116100353120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.7)

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eVADeaths.html000066400000000000000000000113641515050116100325630ustar00rootroot00000000000000 tblish.dataset.VADeaths (Tablicious v0.4.7)

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eWWWusage.html000066400000000000000000000104121515050116100326260ustar00rootroot00000000000000 tblish.dataset.WWWusage (Tablicious v0.4.7)

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eWorldPhones.html000066400000000000000000000105531515050116100333670ustar00rootroot00000000000000 tblish.dataset.WorldPhones (Tablicious v0.4.7)

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eairmiles.html000066400000000000000000000072671515050116100327400ustar00rootroot00000000000000 tblish.dataset.airmiles (Tablicious v0.4.7)

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eairquality.html000066400000000000000000000134011515050116100333020ustar00rootroot00000000000000 tblish.dataset.airquality (Tablicious v0.4.7)

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

    Next: tblish.dataset.anscombe, Previous: tblish.dataset.AirPassengers, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eanscombe.html000066400000000000000000000122041515050116100327050ustar00rootroot00000000000000 tblish.dataset.anscombe (Tablicious v0.4.7)

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eattenu.html000066400000000000000000000115071515050116100324230ustar00rootroot00000000000000 tblish.dataset.attenu (Tablicious v0.4.7)

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eattitude.html000066400000000000000000000111411515050116100327400ustar00rootroot00000000000000 tblish.dataset.attitude (Tablicious v0.4.7)

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eaustres.html000066400000000000000000000102601515050116100326040ustar00rootroot00000000000000 tblish.dataset.austres (Tablicious v0.4.7)

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ebeavers.html000066400000000000000000000107021515050116100325460ustar00rootroot00000000000000 tblish.dataset.beavers (Tablicious v0.4.7)

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ecars.html000066400000000000000000000117301515050116100320510ustar00rootroot00000000000000 tblish.dataset.cars (Tablicious v0.4.7)

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002echickwts.html000066400000000000000000000114611515050116100327410ustar00rootroot00000000000000 tblish.dataset.chickwts (Tablicious v0.4.7)

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eco2.html000066400000000000000000000117241515050116100316070ustar00rootroot00000000000000 tblish.dataset.co2 (Tablicious v0.4.7)

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ecrimtab.html000066400000000000000000000164371515050116100325530ustar00rootroot00000000000000 tblish.dataset.crimtab (Tablicious v0.4.7)

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ecupcake.html000066400000000000000000000105321515050116100325330ustar00rootroot00000000000000 tblish.dataset.cupcake (Tablicious v0.4.7)

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ediscoveries.html000066400000000000000000000106601515050116100334410ustar00rootroot00000000000000 tblish.dataset.discoveries (Tablicious v0.4.7)

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eesoph.html000066400000000000000000000107221515050116100322370ustar00rootroot00000000000000 tblish.dataset.esoph (Tablicious v0.4.7)

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eeuro.html000066400000000000000000000117151515050116100320760ustar00rootroot00000000000000 tblish.dataset.euro (Tablicious v0.4.7)

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eeurodist.html000066400000000000000000000101521515050116100327540ustar00rootroot00000000000000 tblish.dataset.eurodist (Tablicious v0.4.7)

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002efaithful.html000066400000000000000000000121511515050116100327210ustar00rootroot00000000000000 tblish.dataset.faithful (Tablicious v0.4.7)

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002efreeny.html000066400000000000000000000121441515050116100324110ustar00rootroot00000000000000 tblish.dataset.freeny (Tablicious v0.4.7)

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002einfert.html000066400000000000000000000116411515050116100324110ustar00rootroot00000000000000 tblish.dataset.infert (Tablicious v0.4.7)

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eiris.html000066400000000000000000000122141515050116100320650ustar00rootroot00000000000000 tblish.dataset.iris (Tablicious v0.4.7)

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eislands.html000066400000000000000000000104371515050116100325610ustar00rootroot00000000000000 tblish.dataset.islands (Tablicious v0.4.7)

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002elh.html000066400000000000000000000102321515050116100315200ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.7)

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002elongley.html000066400000000000000000000116001515050116100325660ustar00rootroot00000000000000 tblish.dataset.longley (Tablicious v0.4.7)

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002elynx.html000066400000000000000000000113741515050116100321170ustar00rootroot00000000000000 tblish.dataset.lynx (Tablicious v0.4.7)

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002emorley.html000066400000000000000000000121731515050116100324320ustar00rootroot00000000000000 tblish.dataset.morley (Tablicious v0.4.7)

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002emtcars.html000066400000000000000000000124651515050116100324200ustar00rootroot00000000000000 tblish.dataset.mtcars (Tablicious v0.4.7)

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002enhtemp.html000066400000000000000000000105361515050116100324170ustar00rootroot00000000000000 tblish.dataset.nhtemp (Tablicious v0.4.7)

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002enottem.html000066400000000000000000000102001515050116100324160ustar00rootroot00000000000000 tblish.dataset.nottem (Tablicious v0.4.7)

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002enpk.html000066400000000000000000000113431515050116100317110ustar00rootroot00000000000000 tblish.dataset.npk (Tablicious v0.4.7)

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000104221515050116100347240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.occupationalStatus (Tablicious v0.4.7)

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eprecip.html000066400000000000000000000104241515050116100324020ustar00rootroot00000000000000 tblish.dataset.precip (Tablicious v0.4.7)

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002epresidents.html000066400000000000000000000112301515050116100332740ustar00rootroot00000000000000 tblish.dataset.presidents (Tablicious v0.4.7)

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002epressure.html000066400000000000000000000112671515050116100327760ustar00rootroot00000000000000 tblish.dataset.pressure (Tablicious v0.4.7)

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002equakes.html000066400000000000000000000121101515050116100324030ustar00rootroot00000000000000 tblish.dataset.quakes (Tablicious v0.4.7)

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002erandu.html000066400000000000000000000111701515050116100322300ustar00rootroot00000000000000 tblish.dataset.randu (Tablicious v0.4.7)

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002erivers.html000066400000000000000000000103221515050116100324270ustar00rootroot00000000000000 tblish.dataset.rivers (Tablicious v0.4.7)

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002erock.html000066400000000000000000000111031515050116100320510ustar00rootroot00000000000000 tblish.dataset.rock (Tablicious v0.4.7)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002esleep.html000066400000000000000000000113441515050116100322320ustar00rootroot00000000000000 tblish.dataset.sleep (Tablicious v0.4.7)

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002estackloss.html000066400000000000000000000142331515050116100331300ustar00rootroot00000000000000 tblish.dataset.stackloss (Tablicious v0.4.7)

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002estate.html000066400000000000000000000130401515050116100322350ustar00rootroot00000000000000 tblish.dataset.state (Tablicious v0.4.7)

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000105111515050116100343510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.sunspot_month (Tablicious v0.4.7)

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102511515050116100341650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.dataset.sunspot_year (Tablicious v0.4.7)

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002esunspots.html000066400000000000000000000105501515050116100330160ustar00rootroot00000000000000 tblish.dataset.sunspots (Tablicious v0.4.7)

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002eswiss.html000066400000000000000000000141301515050116100322660ustar00rootroot00000000000000 tblish.dataset.swiss (Tablicious v0.4.7)

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002etreering.html000066400000000000000000000112321515050116100327350ustar00rootroot00000000000000 tblish.dataset.treering (Tablicious v0.4.7)

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002etrees.html000066400000000000000000000114021515050116100322370ustar00rootroot00000000000000 tblish.dataset.trees (Tablicious v0.4.7)

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002euspop.html000066400000000000000000000103031515050116100322620ustar00rootroot00000000000000 tblish.dataset.uspop (Tablicious v0.4.7)

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002evolcano.html000066400000000000000000000112521515050116100325610ustar00rootroot00000000000000 tblish.dataset.volcano (Tablicious v0.4.7)

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ewarpbreaks.html000066400000000000000000000112341515050116100332610ustar00rootroot00000000000000 tblish.dataset.warpbreaks (Tablicious v0.4.7)

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ewomen.html000066400000000000000000000114041515050116100322440ustar00rootroot00000000000000 tblish.dataset.women (Tablicious v0.4.7)

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edataset_002ezCO2.html000066400000000000000000000105101515050116100316710ustar00rootroot00000000000000 tblish.dataset.zCO2 (Tablicious v0.4.7)

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002edatasets.html000066400000000000000000000064671515050116100306500ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.7)

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002eevalWithTableVars.html000066400000000000000000000076241515050116100324230ustar00rootroot00000000000000 tblish.evalWithTableVars (Tablicious v0.4.7)

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002eexamples_002eSpDb.html000066400000000000000000000100221515050116100321330ustar00rootroot00000000000000 tblish.examples.SpDb (Tablicious v0.4.7)

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002eexamples_002ecoplot.html000066400000000000000000000151211515050116100326100ustar00rootroot00000000000000 tblish.examples.coplot (Tablicious v0.4.7)

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000120361515050116100340020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html tblish.examples.plot_pairs (Tablicious v0.4.7)

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002esizeof2.html000066400000000000000000000124661515050116100304150ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.7)

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/tblish_002etable_002egrpstats.html000066400000000000000000000105451515050116100324350ustar00rootroot00000000000000 tblish.table.grpstats (Tablicious v0.4.7)

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/timezones.html000066400000000000000000000100401515050116100270770ustar00rootroot00000000000000 timezones (Tablicious v0.4.7)

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/todatetime.html000066400000000000000000000070451515050116100272340ustar00rootroot00000000000000 todatetime (Tablicious v0.4.7)

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/vartype.html000066400000000000000000000065261515050116100265720ustar00rootroot00000000000000 vartype (Tablicious v0.4.7)

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/vecfun.html000066400000000000000000000066651515050116100263720ustar00rootroot00000000000000 vecfun (Tablicious v0.4.7)

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/html/years.html000066400000000000000000000064661515050116100262260ustar00rootroot00000000000000 years (Tablicious v0.4.7)

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    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/devel/user-guide/tablicious.html000066400000000000000000027523651515050116100263050ustar00rootroot00000000000000 Tablicious v0.4.7

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.7.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.7 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.7/tablicious-0.4.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support
    • Things omitted for compatibility:
      • ismissing function

    A global ismissing function is intentionally omitted from Tablicious to avoid conflict with the Octave Forge statistics package. ismissing methods for various classes in Tablicious are defined. Tablicious also supplies an isnanny function with behavior similar to ismissing for clients which which wish to have ismissing-like behavior using a dependency only on Tablicious.

    It is the author’s hope that many of these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like.

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.3.2 calendarDuration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert a calendarDuration to a datevec whose elements represent its components of. The components are the year, month, day, hour, minute, and second quantities of this calendarDuration.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.3.3 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.3.4 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3.5 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3.6 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.3.7 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

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    8.2.3.8 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

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    8.2.3.9 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

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    8.2.3.10 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a datetime. These are the time of day components. The Year, Month, and Day components are ignored, and not incorporated in to the result.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.10.18 datetime.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, the special NaT value is considered missing. This is equivalent to a NaN used as a datenum.

    Returns logical array the same size as obj.

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    8.2.10.19 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.10.20 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.10.21 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.22 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.10.23 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.10.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.25 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.10.26 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.10.27 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.28 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.10.29 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.10.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

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    8.2.10.31 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.10.32 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.10.33 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.10.34 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.10.35 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.10.36 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.37 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.14.2 duration.datevec

    Method: [dvec] = datevec (obj)
    Method: [Y, M, D, H, MN, S] = datevec (obj)

    Convert obj to a datevec whose elements represent the components of this duration. The components are the year, month, days, hour, minute, and second quantities of this duration. The years are in terms of fixed size years that are 365.2425 days long, and days are fixed size 24-hour days. The fixed size year size can be found in the FixedSizeYearDays constant class property on the duration class. The month component is always zero, since months are variable lengths and there is no equivalent fixed size month. Thus, the days component may be more than 31.

    When nargout is 0 or 1, returns a double array of size n-by-6, where n is numel(obj). When nargout is 2 or more, returns double arrays the same size as obj.

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    8.2.14.3 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.14.4 duration.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second values of a duration. These are the hour, minute, and second values that when combined are equal to the same duration value.

    Returns double arrays the same size as obj.

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    8.2.14.5 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.6 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.8 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.9 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.14.10 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.11 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.22 isfile

    Not documented

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    8.2.23 isfolder

    Not documented

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like.

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.
    3. Octave does not define an ismissing function, but the Octave Forge statistics package does, and adding an ismissing function to Tablicious would conflict with that in hard-to-resolve ways.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.32 mustBeA

    Not documented

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    8.2.33 mustBeCellstr

    Not documented

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    8.2.34 mustBeCharvec

    Not documented

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    8.2.35 mustBeFinite

    Not documented

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    8.2.36 mustBeInteger

    Not documented

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    8.2.37 mustBeMember

    Not documented

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    8.2.38 mustBeNonempty

    Not documented

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    8.2.39 mustBeNumeric

    Not documented

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    8.2.40 mustBeReal

    Not documented

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    8.2.41 mustBeSameSize

    Not documented

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    8.2.42 mustBeScalar

    Not documented

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    8.2.43 mustBeScalarLogical

    Not documented

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    8.2.44 mustBeVector

    Not documented

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.53.9 string.endsWith

    Method: out = endsWith (obj, pattern)
    Method: out = endsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string ends with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

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    8.2.53.10 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.53.11 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.53.12 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.53.13 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.53.14 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53.15 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.53.16 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.53.17 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.53.18 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.53.19 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.53.20 string.startsWith

    Method: out = startsWith (obj, pattern)
    Method: out = startsWith (obj, pattern, 'IgnoreCase', true/false)

    Test if string starts with pattern.

    Returns a logical array the size of the scalar expansion of obj and pattern.

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    8.2.53.21 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.53.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.53.23 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.53.24 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.53.25 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.53.26 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.53.27 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.53.28 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.56 tableOuterFillValue

    Not documented

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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B|drbr ΎV0LuU;Ճ1y,lƲM粚o}U;_zi_TI&͔[ NśNE'r3>7>̫i$ԣ 3 L1|H|YR:$|t3!pi-2$efV&8W_F^֝P#Wt>MG*} egh(&(:ĘvԚMD;'kFH+;'Vi]5u曣"kv d2UKf v>Rڜ{Ƙ/|!Y '42"WV*&&$1ּYN[muWSTDUSuUenËGiSٱ.|a0oTC>j!Q܍GTV`qEp%\E>T_q^>i/2rCec3@pdeQ d(q4Zfv˵`k2pf[E¨,LJp 'Q]N4[RʹE]fe-gfڶUuUZ/ .AcK:43 B6S09FgvcjbN &c!_s7jW}y<q,JtzsvJW.02ja7f"ߞZ&mj\FP_5Ydwx{ O[S{os]ȐbFC>l/|g/G΍irOMBd#PE2:W|=u]7)[b::DjpzQPuoj4 5jV7/@r,XPeTW {T~mx)CNܐ/K͵@ZUH6A6>Lܾ{jo8z3XP"BGe> stream xXK6"U[EsشM(=peBW:C)K ,!.\E.bw Yd啫zwﮨAHY^}6 uR7pjqO~ܰeO-;Br{OU-ۋi/^ =_'%OKEIP?@sxYvydyɝMJ;7' (q.65؟ՃV:izRZGܓ͔=y5) [ IyF;`í K>;m礚TSg%N74x2︀p"6] ^Lnʂ#Zk[,7 z7;.ҳiC1ٷ|ҲR柌p;w"c\g#€8]|Dwy '{;_BcdpĀu'&hB`ވ w.[T8od>*y$a1d:\l?%E5Vz'OOA `HFfzEV lct]Klqxvb_s,E{\BzBU IIgGI t% niThNg{$W"L-{˪6{E{f:3~3`3v68醳'9!2oۼZj pȈ^OOzLO&dB=Ҋfr2̺4ґ 55!Ws˖haE%6_AտpBb(~y\O11-3ZS1xdbUYNV+#  LF/N *Y7jF_\2X |;S`"(@;إ#RNb*|gEt,^ @W ֚X7>۾!Y3SqDI2ʶ} &toSeZdU8}}>|8gϝx 2 o;Oֳ PI, GAQep}ٲzdh„}٩XTzY:z=ħQQ|. *>(qg?3J&sOaX9OXLuƧ&3@Ǚ%jǑŦ=w>*~USv|dFʚM4]@tډ9ǯ-<ۭ`iŠ endstream endobj 2508 0 obj << /Length 1629 /Filter /FlateDecode >> stream xXKs6W͘ 7`'iǝ&mmRӡ%b"U Eʠi::ŷo`s2$Aea,6GTG7>bf ѫahƜj(jt.u\ HzaDN~?|| (~ gC < 8 =]>Ybým̾AzY~1?r.?pA,6f^E>.r*׭4K!"?CL`"3mKƺ9.PK*+8lS/u\8i;ZT<坫4]j:"-ot?+<D-pR/:Fv!A@P뿯3?!|!kjдȕ\[^f4^OjP :̅,sl1npcxXyeyS+hv|Er=sOɆ`H=|UGM9y&iHramY, 0BA 0nA-70jh`@=V vOݓX b䳕7ŎMUޖҭb9y5epB#`_FniM 8)"/vA=LP_y6GNn?v.agb^3'̘Ssu V8'J[9 PTG#Oo&(o5Ku*%hDJ8w%;[!N!ri@< FkRDn+)Vj}qb-KƮ5ow,?A0;1XKh:Oǔ&🗷Vߌ!.{nZor,ycح^t*ӟz17|\**bpVqjWhIbovs.ܯձ?hS0jP>18vBd/ 8vD,"@hrk[BMl<)gCΛns."By7*CnmM,Id)1/ep?{1sRɓ?/Ms]vt Oj=@u:E4P B̭;yKͮHtIVҟ\ݼphrVm[UԔ,;en%4[!ÓSP,O&iڢ *"yŻC4HQpD $+텕zA0Ec L-cy-hU_qٜhXW ,ftaM,c!uUCPjGE C TZ&XQmk΅ovhnў} X<;H&#h;x y#y{Tp؎L<Sgu1ƈջya&W;uY}a:9/:5 endstream endobj 2512 0 obj << /Length 1615 /Filter /FlateDecode >> stream xYo6_"Mߖ M. aHHB<}Hݑ'Y%i( G&cMLgMBs2u#tI0%XMEtXwV[\M,К,]VxrǮ|[BaMw]Q["Lڧ,Zm W &#t~*ta5mJTFsM,@8gtzD=wmcū$R ug XIqeg#aay] 䱷qB]T4ٽ:4byw{H+wSځ{0q;ܗ@IQ%=e7v(h쫅7ӯJPB^  #Rt:j N#ef6ȗ]r޽6-a2k9^an6#n#fNT1n~ [/֊bqoZn ?Ǚo̓ +@ZGlʆ )YҋdM"l*ᓚrʁ23UA NՔjaHeV&t^5jz >W4eSTovaWZx+fiJ"ΦWwV e(u?@yI ^Nb؅WP՛ݸ-ڇwYe '}+SU߅)fֆ|{Uۊخ$5m=EH((ányJٽ'c>B %^G֙Ib:؛wDl c/Wr+5.3tC)p%V=Xk&QzOڳDb)Ҙ~/.Q>E֗Czot)- ~Pz]Ɇu궼 u}DYp(B .bA燯c6˓]u;o_J*qRV=+MzNCࠁi#R+F%(1a-.G6BEx>@.ƾ Pv! p!b9^~b4p46y7S]P6$ѤZL7hEؿGиSײkm޽ ;6,п^V>fn7o@f۟ҋ8>f1"2pq_ S?FiZ7/U@8h=h+}z:H~umcч kO endstream endobj 2516 0 obj << /Length 1509 /Filter /FlateDecode >> stream xXێ6}߯[)RE],Eri8@Mh+:_)˲v7n&@ș39̅?>[eϒU5^^p=φ`ŏ/p]g.lu34JgY&6l,}dA'hJFVl/|n}Xu = t7it܄FjsH67Ȱ$ YJuw1) $IW$!=R498LنxR6$C0ll-}(jg6y" 㩂uRzbM4)hynBj}qj vN R4x'"Pe0ϱU )Em:z'^`Mji`IUv0G lͱyS1iRJ re u)rɹ4[y A otz)Q%tx"U?<̩KCU1S`& k8ַJ0 [澻/\罀LBD1DAo\mkUIqw q_\Kɸ#;hᯒ{38y)i7)o%vAJЀEu^)tȒM}n+5Unb{$@W\L&tkC첹J$'pS/Ӫ?gM`4ד߂=_*0 endstream endobj 2461 0 obj << /Type /ObjStm /N 100 /First 926 /Length 1422 /Filter /FlateDecode >> stream xYMo7W^(r3$#@@AQC*H}PNH[sN 04KPT]pD]TvQmAőᡚ3g'L.D\4TX.W2!JB&iO9QPԬGfBH 8iw)H.J jP sNURs ^,l*q*X@I4;xМ7y IWsJ ixH9<@2Ij>RK[+}F8FSQ 8fPS# f9 CPMPaM{u.gkdބ8|,^!EJRhx&U@%c[IL؁$bqK :nL-iJA؋HBpdb^jsI,,/o"kQXZu#I.! -sU|ZD,`聀b!ż")a9QCjkiL'6 L"ggsw̨җnǛ?Y {^ܼ]Q8]2'>'F;G7yH½|^obf? %ϡ#]{zbl{{yuUM™WH|ai#[yqO~\zxFJ[_=gHR!TdvZ=R4σ})7$E%] 0'KH uWN2o9M)>χ)@Nc"0%qO+܅]6tYwBI}?xvq{ fݮ>nfx\cXygCr`& ٪5,~mo/wcmumv RAv[M`NFb)I&Q\AzOw" |2zqRh*C|_3:8x=&G/SҀ\s(!y99V?%pH(N@ݑ@Q 2nqvF>0f Y~rlnWng#:}`E!;v8$7tcƒ㜄2o}DPxc&oW.'1M0Yhhx8Wz!&hD8pq($QDhHT\~ endstream endobj 2522 0 obj << /Length 1729 /Filter /FlateDecode >> stream xYK62R=M@"Ch^+d_!g(Q]'E A,I y|{ yY"-\/pGt6O7WE u[lMV7y-ovVliad+\x÷Uʗ{k淫7?Q8Iy"]/qAa2]WmYwS`N¡k' (8P]UFN j; 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s?\{钗/xb(FFQuRC|z v}:З-FR69חi{Z^sN?&>RCZ36F+e!qw9W0ٕqvJ^y$;WXH` '߸UP@;fL!Gݞ+\6uELY'-*zf {1U8-(%:G/B0_]ξG` `/7,<ĉ-?8+PpY6$Y'Gj71Jsg&*^Dy/~>|﷿)5lߍAy~EJ1_4REbTQ9 }>?hzvl3?]{e@1OW-TCckz{5NҩYC}yg^[찺 ZGBdǼvnJ?Bxq`M'n?l<^?ah8@G0ߚ{VT_.Nme)Bqlк`U 4 endstream endobj 2915 0 obj << /Length 1979 /Filter /FlateDecode >> stream xڥn8_a`*SEgEg2%/I%`>~M*z!y7ƋxQE2M*[/fW[ͫ7|GaUb4NUtʲg*+ `IK$$]sLRۀdqfu<,ϕ1%-<. ?(*5L T,O"NAq53\cEh OHH@zNVQ5n6WP x5± L.zBd("55T[xEm?y4WV/5}.q6-L.whqTӞ,&fsW(^)^t}J1( GO,OäLiR)?WR{4|<%Jypoq|b؆Rz[B1jE7ofgT*~ɨrE9IC QpKVZ( >F_*giV$LÍK}k8UO^@ӍԠ*$< Z*E3@JLJJYMi_e{Ȑa2]6B>~jãKHr̘H]!y*-)19AG,S M+$Ys<]`ccEԤ2{n%>҂qТ-ӝθ3GsE+'Dz4ɻYK>Bc\V{OqSx^W!_ehʓr"PetϲC KCoHk[%=j~_qA._?'nòW1OCGP,I t^?2Eh;=C F/yw)kؙW2 #H l+JF"Fj7?Z endstream endobj 2920 0 obj << /Length 1356 /Filter /FlateDecode >> stream xWo6_at/a-ڴhCFfldtlt#wJgl{:~GއL?9+,c^&Ŭڜ~[hr!#狓/t&/E)grvku 8Y,$eg_};T:,K2,^\,F(:J^d"2N^#<+0"kc|EPƬnkݹ5BxSO H~DJnܔ \D5v;i2^!G\덦 쮿 '!K^6鹁ft9=cC>=dBP?Ɨ]W#R͜WL<*bX2|CI I7a}P3!>7}f~%<赝Uj7Tw BzWc{ե6);?1<֯kY*٫:LFN٥#_k>#w|2- z_^6[6(E}#I<09~[HPe7&#ż S*"WX$}BcP_ G.dUpi^|b_1tz8F)I)HEr# ֓),߱ Է C{+x,<./M^ۺ;̂OdS}g|3o;M{1#niVEKʩ[=Z]fl4}ۧ ~6 ɧ0B@)sdL@6SXwfv_'lip[0X?j"l6HS:0̂%dwf-e$<:Nf lq ;jYA#KL̟ssOqb endstream endobj 2926 0 obj << /Length 1416 /Filter /FlateDecode >> stream xڭWo6_@M(K-M"݂bm""KE5:A"x<`X/ˣ(id$rR zPliMNBXD(gbd޻#\[e8I?HAN{Z(x0?O><*8?;νXL#4YBYxJ]6xelD89K+qCgFj3Ws?ڣו,z٥Nc, ÅW^z3ʋΌnl UkXaD%1eyy<E8OCGe mT) /Da,6C ,c*xnLi@Wp$ȭ2Z5~*.3bxa9ᓏO/'LKBgNPʾ>nBa04>!  > zWk!Ouz/#,HehS@u,ɭvag:m|mTWթ{]kRODJÒ`#em`§KT %ߨ%^Ɓ]N~ F8c)<`}_/0> ֣ϙM&Oibz u ~5Zj# Lޮ' endstream endobj 2935 0 obj << /Length 1812 /Filter /FlateDecode >> stream xڍWY6 ~ϯhb al E}AckgZ. &EQ"eQ$8qՌ_HA?\8I Dr?TfwZm~E^~>8.~yC^+4 <)/^-v&aLP楇8IQ xyL6׫q_HDLӯ?͉)H+^׃]$Ͷ0#V?6joo#NOҤH0Ž?ԲF`hM .4t#¼oE>>] )9atLr[] x׺BMQzoy|7cs$`/OW<+|ɸUucźv )c[bTPm9'88K M5v` "n7~`=rw%T!Iv9ȌS&1B8'dHlHa ) Y0đ\W܌gHIJVs j< Bq3 0@~(L I⊨}Ō<;KkroX$߹ 4*s҇ڮD$FFO~KBQE# $; PxPVGi{B.)^ 'ch7 ;7.GC,])mhݷ88at9ޡKNseWA7ݹx#4\efzb ZhcSPImYD\vE5S%.2auk b!lRPG%JvO|L4'<|(!<@xqq"M_mYc> stream xXo6~_al/4P1~Y0 tK%ݽ}e"YI ȣە]4b#>YoE4^Q1׫=l||ƽ]u89yo<^j<=BT "]8NQΤB0MFx_7g/N0<^xD8hK(FﾊzUImB4v4aFy QfRzA- 4|{9KfF, ?YBwWwؼoVhQ^U5 ~ #$ۍ#H4Ȣ3IFCf7@oTԏ8J h#_ I Ĕ=0i/ 4N$ v yE+vql3F>?ޅ< #pS<z-*SBK!6ߪ <.晣&jp%uʕw yx"܍!~~LYKli\=/[,AZ!5fQ=ƈhf-*̥Eb+K<&eUI0OHlzB܈%Zz*\ɢi1ddԫuT(9k}Nz LRee4-"^e`R^Bz4^GCD/RvvMۥJnQpZځkmJ5J^󘸅B Ppi8fgMU5  IB v8-چ;!)վgoa,#4W+tKj&P10ȭW r6QjmmB$! -}i Q=wL|,O휕s~AڑP-N=I=> !nWk?WΒ˜fZVxESO'Mxނ[5 endstream endobj 2946 0 obj << /Length 1236 /Filter /FlateDecode >> stream xVKo8Wً zf4iM6==2c ,;P(E/ ֈ87d ?6H $~<Ȫ=jWl=p#:O/A0`$4a}Wx:Nt^|4~_}Åy/gr8aPq69><^pA y10A(gc,& }„mhɻ,";UC{r5wP&NLSNjR}Aʒ8'1oK&P&d|T4@E,k)gESn{~R.xd] ]Pu:5^6˴ԅlL^h%J^NW(`VL/UZ.,B{SYy3ₛt{79꥚Vm&+n5"3*LQY!5nI+̒Bf~JqY]G`$BsZ-uݣxL$qejNOJBSUr|^.|*{^ ~d%ؽiؐQ<'46Z o+Z/H/duu8} U +~km >q)ӀeYF4td{?l?t2B/_v9VsMssa Nl۵G$A[܌s^Miod|͑9>G~pJ" " x,y;@u,LcFFnj4Qvers7d%FV)B{x'>& 5 Oj endstream endobj 2951 0 obj << /Length 954 /Filter /FlateDecode >> stream xڽV[oH~ϯ@~ >8jtbJGOz>9ŔFu[shXw⭐gNODP9mjT`qoQl<?: @tͦ~P*i"iL7UDFmMڱ LX<q7ܝ;fdJ3P]U0P:}L:06vpltOCfMPH=UŒ.% CrR E5I*u+,Scruc lf?3S& gV37(_@:KР7>wk]jXw6BIr\  endstream endobj 2956 0 obj << /Length 1514 /Filter /FlateDecode >> stream xڭWmo6_al*c1CVvpvhAbm&"(Kx#kP<yo=$Eße7"ţluĝZhpۑzP4_]M,]ߵnKY֪( FA' ԝT1SA D?_?zs1C=1yQD{#dĆiaPOsml.kiUn&RnQ2dqaĢ&I-k=U4(RpF,IBaliYPVE츔Ky'.26*dM^J0d,I@ 0VEV]RxH&ΔU)|m1diSv6yeQP  *u,&B1 tAMJ(0 8?g;r~UXWsUcV&e1xgiPU]t *9C;K;ү `clXKA2 #dPqn  m66"fj 0t($Ô\깲.4^eHA~Zrg>-m##IwD!WR@>sW={ Wm.k*nPcVmdO Ҕ{.ZpA3:bCOJ{J 0z:A7Tta6JpA⿅;Lm-IW g^2YY,aH=nC&K.jj* Y3)3K hD (sq抾a-. 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҉s)}ߠN~BWɄ/O6%j25T^1d·Ryx=47DI]ɇL4h RGLJ/H@]rU/ uMg|v쐿 ץ1hubL?C];S.'12Yߗ-::GS;>>}\?1~%ct~;?ocLbOa*Gc?131%,c^NT1&P{Ӻ֌^Rg>TY77")n:IѦ6e9J?;fjjrj.L_<;t?55b4r{W)JSR_o1}PMojh;6Uu9򳱃͝ՏϞ(g$ţ9x4y81xMMSpIT4+*ؼ|ij9෦c̿3ѴnMm^O?ث͠᪩תIoti$M6'c9TTw0*"6_?1nF㎚ѽ.UV܍9D+*[Vl2/xjTu4 ؛*c'܆نơT?;_ȆGƨܿbr7<6Ʈq&U;<4ƨܿ7c&*߉1 }Qs1v`Torwixl^K/F-l4(l`6CTz6:C4pFS~垍VM]k6z?X<m*8ӝzt.+iNP 0U< `!,Ű2X+`%հ:X`#lͰ6;`'ݰ> p18'$p98"\p&*\pn- w.܃i_bWK/y endstream endobj startxref 468691 %%EOF apjanke-octave-tablicious-8a1b10f/docs/index.md000066400000000000000000000077501515050116100215270ustar00rootroot00000000000000# Tablicious Online Documentation This is the documentation site for Tablicious. Tablicious is a package for GNU Octave that provides relational data structures for Octave. It includes implementations of `table` arrays, `datetime`, `string`, `categorical`, and some other related stuff. You can think of it as "pandas for Octave". The repo for this project is at , where you can get the source code, report issues, and find more information. ## User Documentation See the online [User Guide](release/v0.4.7/user-guide/html/index.html) for info and API Reference. There's also a [single-page version of the User Guide](release/v0.4.7/user-guide/tablicious.html), and a downloadable [PDF version](release/v0.4.7/user-guide/tablicious.pdf). Those are for the latest release (currently 0.4.7). You can also see the docs for older specific releases, and the current in-progress development version (for the `main` branch of the Git repo). ### Releases * Development branch (unreleased) * [User Guide, multiple pages](devel/user-guide/html/index.html) * [User Guide, single page](devel/user-guide/tablicious.html) * [User Guide, PDF](devel/user-guide/tablicious.pdf) * v0.4.7 (2026-02-28) * [User Guide, multiple pages](release/v0.4.7/user-guide/html/index.html) * [User Guide, single page](release/v0.4.7/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.7/user-guide/tablicious.pdf) * v0.4.6 (2026-02-16) * [User Guide, multiple pages](release/v0.4.6/user-guide/html/index.html) * [User Guide, single page](release/v0.4.6/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.6/user-guide/tablicious.pdf) * v0.4.5 (2025-07-03) * [User Guide, multiple pages](release/v0.4.5/user-guide/html/index.html) * [User Guide, single page](release/v0.4.5/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.5/user-guide/tablicious.pdf) * v0.4.4 (2024-10-23) * [User Guide, multiple pages](release/v0.4.4/user-guide/html/index.html) * [User Guide, single page](release/v0.4.4/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.4/user-guide/tablicious.pdf) * v0.4.3 (2024-07-06) * [User Guide, multiple pages](release/v0.4.3/user-guide/html/index.html) * [User Guide, single page](release/v0.4.3/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.3/user-guide/tablicious.pdf) * v0.4.2 (2024-02-07) * [User Guide, multiple pages](release/v0.4.2/user-guide/html/index.html) * [User Guide, single page](release/v0.4.2/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.2/user-guide/tablicious.pdf) * v0.4.1 (2024-02-07) * [User Guide, multiple pages](release/v0.4.1/user-guide/html/index.html) * [User Guide, single page](release/v0.4.1/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.1/user-guide/tablicious.pdf) * v0.4.0 (2024-02-07) * [User Guide, multiple pages](release/v0.4.0/user-guide/html/index.html) * [User Guide, single page](release/v0.4.0/user-guide/tablicious.html) * [User Guide, PDF](release/v0.4.0/user-guide/tablicious.pdf) * v0.3.7 (2023-01-05) * [User Guide, multiple pages](release/v0.3.7/user-guide/html/index.html) * [User Guide, single page](release/v0.3.7/user-guide/tablicious.html) * [User Guide, PDF](release/v0.3.7/user-guide/tablicious.pdf) * v0.3.6 (2021-07-05) * [User Guide, multiple pages](release/v0.3.6/user-guide/html/index.html) * [User Guide, single page](release/v0.3.6/user-guide/tablicious.html) * [User Guide, PDF](release/v0.3.6/user-guide/tablicious.pdf) There are older releases, too. Let me know if you actually want to see the doco for those online. ## Developer Documentation * [Developer Notes](Developer-Notes.html) – documentation for developers of Tablicious itself * [Join Behavior](Join-Behavior.html) – Notes on "proxy keys" and other join-related stuff * [Design and Justification](Design-and-Justification.html) * [Release Checklist](Release-Checklist.html) * [TODO](TODO.html) * [FAQ](FAQ.html) – Frequently Asked Questions apjanke-octave-tablicious-8a1b10f/docs/release/000077500000000000000000000000001515050116100215055ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/000077500000000000000000000000001515050116100223375ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/000077500000000000000000000000001515050116100244105ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/000077500000000000000000000000001515050116100253545ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/API-Alphabetically.html000066400000000000000000000131431515050116100315710ustar00rootroot00000000000000 API Alphabetically (Octave Tablicious v0.3.6) apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/API-Reference.html000066400000000000000000000047261515050116100305600ustar00rootroot00000000000000 API Reference (Octave Tablicious v0.3.6) apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/API-by-Category.html000066400000000000000000000313061515050116100310410ustar00rootroot00000000000000 API by Category (Octave Tablicious v0.3.6)

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    array2table

    Convert an array to a table.

    cell2table

    Convert a cell array to a table.

    struct2table

    Convert struct to a table.

    tableOuterFillValue

    Outer fill value for variable within a table.

    vartype

    Filter by variable type for use in suscripting.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    startsWith

    Test if strings start with a pattern.

    endsWith

    Test if strings end with a pattern.

    contains

    Test if strings contain a pattern.

    categorical

    Categorical variable array.

    discretize

    Group data into discrete bins or categories.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    localdate

    Represents a complete day using the Gregorian calendar.

    isdatetime

    True if input is a ’datetime’ array, false otherwise.

    NaT

    “Not-a-Time”.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    calmonths

    Create a ’calendarDuration’ that is a given number of calendar months long.

    calyears

    Construct a ’calendarDuration’ a given number of years long.

    days

    Duration in days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    hours

    Create a ’duration’ X hours long, or get the hours in a ’duration’ X.

    isduration

    True if input is a ’duration’ array, false otherwise.

    milliseconds

    Create a ’duration’ X milliseconds long, or get the milliseconds in a ’duration’ X.

    minutes

    Create a ’duration’ X hours long, or get the hours in a ’duration’ X.

    seconds

    Create a ’duration’ X seconds long, or get the seconds in a ’duration’ X.

    timezones

    List all the time zones defined on this system.

    years

    Create a ’duration’ X years long, or get the years in a ’duration’ X.

    8.1.4 Missing Data

    fillmissing

    Fill missing values.

    ismissing

    Find missing values.

    rmmissing

    Remove missing values.

    standardizeMissing

    Insert standard missing values.

    missing

    Generic auto-converting missing value.

    isnanny

    Test if elements are NaN or NaN-like

    eqn

    Determine element-wise equality, treating NaNs as equal

    8.1.5 Validation Functions

    mustBeA

    Requires that input is of a given type.

    mustBeCellstr

    Requires that input is a cellstr.

    mustBeCharvec

    Requires that input is a char row vector.

    mustBeFinite
    mustBeInteger
    mustBeMember
    mustBeNonempty
    mustBeNumeric
    mustBeReal
    mustBeSameSize

    Requires that the inputs are the same size.

    mustBeScalar

    Requires that input is scalar.

    mustBeScalarLogical

    Requires that input is a scalar logical.

    mustBeVector

    Requires that input is a vector or empty.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    dispstrs

    Display strings for array.

    isfile
    isfolder
    pp

    Alias for prettyprint, for interactive use.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    size2str

    Format an array size for display.

    splitapply

    Split data into groups and apply function.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    8.1.7 Example Datasets

    octave.datasets

    Example dataset collection.

    octave.dataset

    The ’dataset’ class provides convenient access to the various datasets included with Tablicious.

    8.1.8 Example Code

    octave.examples.plot_pairs

    Plot pairs of variables against each other.

    8.1.9 Uncategorized

    iscategorical

    True if input is a ’categorical’ array, false otherwise.

    octave.chrono.dummy_function

    A dummy function just for testing the doco tools.

    octave.chrono.DummyClass

    A do-nothing class just for testing the doco tools.

    octave.examples.coplot

    Conditioning plot.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Copying.html000066400000000000000000000067701515050116100276640ustar00rootroot00000000000000 Copying (Octave Tablicious v0.3.6)

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056651515050116100312030ustar00rootroot00000000000000 Data Sets from R (Octave Tablicious v0.3.6)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

    Date-and-Time-Representation.html000066400000000000000000000060501515050116100334750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html Date and Time Representation (Octave Tablicious v0.3.6)

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074561515050116100324620ustar00rootroot00000000000000 Datenum Compatibility (Octave Tablicious v0.3.6)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060051515050116100315710ustar00rootroot00000000000000 Defined Time Zones (Octave Tablicious v0.3.6)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Durations.html000066400000000000000000000046401515050116100302160ustar00rootroot00000000000000 Durations (Octave Tablicious v0.3.6) apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Example-Data-Sets.html000066400000000000000000000102141515050116100314160ustar00rootroot00000000000000 Example Data Sets (Octave Tablicious v0.3.6)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the octave.datasets and octave.dataset classes.

    To see a list of the available data sets, run octave.datasets.list(). Then to load one of the example data sets, run octave.datasets.load('examplename'). For example: 

    octave.datasets.list
t = octave.datasets.load('cupcake')

    You can also load it by calling octave.dataset.<name>. This does the same thing. For example: 

    t = octave.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on octave.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc octave.dataset.cupcake

    (The command help octave.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Getting-Started.html000066400000000000000000000064101515050116100312500ustar00rootroot00000000000000 Getting Started (Octave Tablicious v0.3.6)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.3.6/tablicious-0.3.6.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Introduction.html000066400000000000000000000072241515050116100307300ustar00rootroot00000000000000 Introduction (Octave Tablicious v0.3.6)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.3.6 for GNU Octave.

    Tablicious provides Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports most of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that all these will be implemented some day.

    These areas of missing functionality are all tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/1.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/NaT.html000066400000000000000000000077521515050116100267370ustar00rootroot00000000000000 NaT (Octave Tablicious v0.3.6)

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    8.2.42 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Table-Representation.html000066400000000000000000000100201515050116100322620ustar00rootroot00000000000000 Table Representation (Octave Tablicious v0.3.6)

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Tables-vs-SQL.html000066400000000000000000000113551515050116100305440ustar00rootroot00000000000000 Tables vs SQL (Octave Tablicious v0.3.6)

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Time-Zones.html000066400000000000000000000141241515050116100302360ustar00rootroot00000000000000 Time Zones (Octave Tablicious v0.3.6)

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/Validation-Functions.html000066400000000000000000000117411515050116100323060ustar00rootroot00000000000000 Validation Functions (Octave Tablicious v0.3.6)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

    Next: Example Data Sets, Previous: Date and Time Representation, Up: Tablicious for GNU Octave   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/array2table.html000066400000000000000000000102711515050116100304530ustar00rootroot00000000000000 array2table (Octave Tablicious v0.3.6)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/calendarDuration-Class.html000066400000000000000000000063061515050116100325710ustar00rootroot00000000000000 calendarDuration Class (Octave Tablicious v0.3.6)

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/calendarDuration.html000066400000000000000000000206471515050116100315320ustar00rootroot00000000000000 calendarDuration (Octave Tablicious v0.3.6)

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

    Next: calmonths, Previous: array2table, Up: API Alphabetically   [Contents]

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.8 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    calendarDuration_002eisnan.html000066400000000000000000000057341515050116100332320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html calendarDuration.isnan (Octave Tablicious v0.3.6)

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    8.2.2.9 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is just an alias for isnat, provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    calendarDuration_002eisnat.html000066400000000000000000000060311515050116100332270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html calendarDuration.isnat (Octave Tablicious v0.3.6)

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    8.2.2.3 calendarDuration.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    calendarDuration_002eminus.html000066400000000000000000000061401515050116100332450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html calendarDuration.minus (Octave Tablicious v0.3.6)

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    8.2.2.7 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/calendarDuration_002eplus.html000066400000000000000000000066101515050116100331560ustar00rootroot00000000000000 calendarDuration.plus (Octave Tablicious v0.3.6)

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    8.2.2.5 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

    calendarDuration_002esizeof.html000066400000000000000000000057631515050116100334230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html calendarDuration.sizeof (Octave Tablicious v0.3.6)

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    8.2.2.2 calendarDuration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.2.6 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

    calendarDuration_002euminus.html000066400000000000000000000057501515050116100334400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html calendarDuration.uminus (Octave Tablicious v0.3.6)

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    8.2.2.4 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/calmonths.html000066400000000000000000000064611515050116100302410ustar00rootroot00000000000000 calmonths (Octave Tablicious v0.3.6)

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/calyears.html000066400000000000000000000057671515050116100300640ustar00rootroot00000000000000 calyears (Octave Tablicious v0.3.6)

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical.html000066400000000000000000000173171515050116100305300ustar00rootroot00000000000000 categorical (Octave Tablicious v0.3.6)

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff: - gt, ge, lt, le - Ordinal support in general - countcats - summary

    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

    Next: cell2table, Previous: calyears, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002eaddcats.html000066400000000000000000000062361515050116100326000ustar00rootroot00000000000000 categorical.addcats (Octave Tablicious v0.3.6)

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    categorical_002ecategorical.html000066400000000000000000000135671515050116100334000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.categorical (Octave Tablicious v0.3.6)

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

    categorical_002ecategories.html000066400000000000000000000061371515050116100332430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.categories (Octave Tablicious v0.3.6)

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062061515050116100326420ustar00rootroot00000000000000 categorical.cellstr (Octave Tablicious v0.3.6)

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062411515050116100330440ustar00rootroot00000000000000 categorical.dispstrs (Octave Tablicious v0.3.6)

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    categorical_002eiscategory.html000066400000000000000000000063061515050116100332650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.iscategory (Octave Tablicious v0.3.6)

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002eismissing.html000066400000000000000000000061221515050116100331740ustar00rootroot00000000000000 categorical.ismissing (Octave Tablicious v0.3.6)

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002eisnanny.html000066400000000000000000000062351515050116100326530ustar00rootroot00000000000000 categorical.isnanny (Octave Tablicious v0.3.6)

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002eisordinal.html000066400000000000000000000061171515050116100331570ustar00rootroot00000000000000 categorical.isordinal (Octave Tablicious v0.3.6)

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

    categorical_002eisundefined.html000066400000000000000000000064321515050116100334110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.isundefined (Octave Tablicious v0.3.6)

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002emergecats.html000066400000000000000000000075741515050116100331550ustar00rootroot00000000000000 categorical.mergecats (Octave Tablicious v0.3.6)

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

    categorical_002eremovecats.html000066400000000000000000000074351515050116100332700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.removecats (Octave Tablicious v0.3.6)

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

    categorical_002erenamecats.html000066400000000000000000000067571515050116100332500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.renamecats (Octave Tablicious v0.3.6)

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

    categorical_002ereordercats.html000066400000000000000000000072151515050116100334310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html categorical.reordercats (Octave Tablicious v0.3.6)

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002esetcats.html000066400000000000000000000062401515050116100326360ustar00rootroot00000000000000 categorical.setcats (Octave Tablicious v0.3.6)

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002esizeof.html000066400000000000000000000056351515050116100324760ustar00rootroot00000000000000 categorical.sizeof (Octave Tablicious v0.3.6)

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    8.2.5.3 categorical.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002estring.html000066400000000000000000000062251515050116100325010ustar00rootroot00000000000000 categorical.string (Octave Tablicious v0.3.6)

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/categorical_002esummary.html000066400000000000000000000060301515050116100326620ustar00rootroot00000000000000 categorical.summary (Octave Tablicious v0.3.6)

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/colvecfun.html000066400000000000000000000065331515050116100302350ustar00rootroot00000000000000 colvecfun (Octave Tablicious v0.3.6)

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime.html000066400000000000000000000177311515050116100300470ustar00rootroot00000000000000 datetime (Octave Tablicious v0.3.6)

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

    Next: days, Previous: contains, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002eNaT.html000066400000000000000000000076211515050116100311750ustar00rootroot00000000000000 datetime.NaT (Octave Tablicious v0.3.6)

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.35 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.19 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002edatestr.html000066400000000000000000000065501515050116100321610ustar00rootroot00000000000000 datetime.datestr (Octave Tablicious v0.3.6)

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    8.2.9.15 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002edatestrs.html000066400000000000000000000066461515050116100323520ustar00rootroot00000000000000 datetime.datestrs (Octave Tablicious v0.3.6)

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    8.2.9.16 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.17 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002edatetime.html000066400000000000000000000132301515050116100323000ustar00rootroot00000000000000 datetime.datetime (Octave Tablicious v0.3.6)

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.32 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002edispstrs.html000066400000000000000000000057051515050116100323670ustar00rootroot00000000000000 datetime.dispstrs (Octave Tablicious v0.3.6)

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    8.2.9.14 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.27 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002ege.html000066400000000000000000000062031515050116100311010ustar00rootroot00000000000000 datetime.ge (Octave Tablicious v0.3.6)

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    8.2.9.28 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002egmtime.html000066400000000000000000000061171515050116100317740ustar00rootroot00000000000000 datetime.gmtime (Octave Tablicious v0.3.6)

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    8.2.9.20 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002egt.html000066400000000000000000000061761515050116100311310ustar00rootroot00000000000000 datetime.gt (Octave Tablicious v0.3.6)

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    8.2.9.29 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002ehms.html000066400000000000000000000060761515050116100313050ustar00rootroot00000000000000 datetime.hms (Octave Tablicious v0.3.6)

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    8.2.9.10 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002eisbetween.html000066400000000000000000000063271515050116100325020ustar00rootroot00000000000000 datetime.isbetween (Octave Tablicious v0.3.6)

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    8.2.9.33 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.23 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002eisnat.html000066400000000000000000000056261515050116100316340ustar00rootroot00000000000000 datetime.isnat (Octave Tablicious v0.3.6)

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    8.2.9.22 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002elinspace.html000066400000000000000000000070171515050116100323100ustar00rootroot00000000000000 datetime.linspace (Octave Tablicious v0.3.6)

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    8.2.9.34 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002elocaltime.html000066400000000000000000000067011515050116100324620ustar00rootroot00000000000000 datetime.localtime (Octave Tablicious v0.3.6)

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    8.2.9.21 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
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    8.2.9.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.31 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002eofDatenum.html000066400000000000000000000060371515050116100324350ustar00rootroot00000000000000 datetime.ofDatenum (Octave Tablicious v0.3.6)

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/datetime_002eofDatestruct.html000066400000000000000000000062401515050116100331560ustar00rootroot00000000000000 datetime.ofDatestruct (Octave Tablicious v0.3.6)

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.18 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.8 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.7 datetime.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.9.12 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.13 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.9 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.16 duration.char

    8.2.13.17 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.14 duration.dispstrs

    8.2.13.15 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.6 duration.hours

    8.2.13.7 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.18 duration.linspace

    8.2.13.19 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.13.12 duration.milliseconds

    8.2.13.13 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.8 duration.minutes

    8.2.13.9 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.1 duration.ofDays

    8.2.13.2 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.10 duration.seconds

    8.2.13.11 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.3 duration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.13.4 duration.years

    8.2.13.5 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14 endsWith

    Function: out = endsWith (str, pattern)
    Function: out = endsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings end with a pattern.

    Tests whether the given strings end with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 fillmissing

    Function: [out, tfFilled] = fillmissing (X, method)
    Function: [out, tfFilled] = fillmissing (X, 'constant', fill_val)
    Function: [out, tfFilled] = fillmissing (X, movmethod, window)

    Fill missing values.

    Fills missing values in X according to the method specified by method.

    This method is only partially implemented.

    method may be: 'constant' 'previous' 'next' 'nearest' 'linear' 'spline' 'pchip' movmethod may be: 'movmean' 'movmedian'

    Returns out, which is X but with missing values filled in, and tfFilled, a logical array the same size as X which indicates which elements were filled.

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/index.html000066400000000000000000001413611515050116100273570ustar00rootroot00000000000000 Top (Octave Tablicious v0.3.6)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.3.6.

    Table of Contents

    Next:   [Contents]

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/ismissing.html000066400000000000000000000131331515050116100302500ustar00rootroot00000000000000 ismissing (Octave Tablicious v0.3.6)

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    8.2.23 ismissing

    Function: out = ismissing (X)
    Function: out = ismissing (X, indicator)

    Find missing values.

    Determines which elements of X contain missing values. If an indicator input is not provided, standard missing values depending on the input type of X are used.

    Standard missing values depend on the data type:

    • NaN for double, single, duration, and calendarDuration
    • NaT for datetime
    • ' ' for char
    • {''} for cellstrs
    • Integer numeric types have no standard missing value; they are never considered missing.
    • Structs are never considered missing.
    • Logicals are never considered missing.
    • Other types have no standard missing value; it is currently an error to call ismissing on them without providing an indicator.
      • This includes cells which are not cellstrs; calling ismissing on them results in an error.
      • TODO: Determine whether this should really be an error, or if it should default to never considering those types as missing.
      • TODO: Decide whether, for classdef objects, ismissing should polymorphically detect isnan()/isnat()/isnanny() methods and use those, or whether we should require classes to override ismissing() itself.

    If indicator is supplied, it is an array containing multiple values, all of which are considered to be missing values. Only indicator values that are type-compatible with the input are considered; other indicator value types are silently ignored. This is by design, so you can pass an indicator that holds sentinel values for disparate types in to ismissing() used for any type, or for compound types like table.

    Indicators are currently not supported for struct or logical inputs. This is probably a bug.

    Table defines its own ismissing() method which respects individual variables’ data types; see table.ismissing.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/isnanny.html000066400000000000000000000074511515050116100277300ustar00rootroot00000000000000 isnanny (Octave Tablicious v0.3.6)

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: isnan, isnat, ismissing, eqn, isequaln

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    8.2.25 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.25.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.25.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.25.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.25.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.25.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.25.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.25.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.25.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.25.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/localdate_002eposixtime.html000066400000000000000000000063521515050116100326700ustar00rootroot00000000000000 localdate.posixtime (Octave Tablicious v0.3.6)

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    8.2.25.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/localdate_002eymd.html000066400000000000000000000057501515050116100314410ustar00rootroot00000000000000 localdate.ymd (Octave Tablicious v0.3.6)

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    8.2.25.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/milliseconds.html000066400000000000000000000063601515050116100307340ustar00rootroot00000000000000 milliseconds (Octave Tablicious v0.3.6)

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    8.2.26 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/minutes.html000066400000000000000000000055711515050116100277360ustar00rootroot00000000000000 minutes (Octave Tablicious v0.3.6)

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    8.2.27 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing.html000066400000000000000000000075011515050116100277160ustar00rootroot00000000000000 missing (Octave Tablicious v0.3.6)

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    8.2.28 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing_002edispstrs.html000066400000000000000000000061341515050116100322410ustar00rootroot00000000000000 missing.dispstrs (Octave Tablicious v0.3.6)

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    8.2.28.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing_002eismissing.html000066400000000000000000000060311515050116100323670ustar00rootroot00000000000000 missing.ismissing (Octave Tablicious v0.3.6)

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    8.2.28.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing_002eisnan.html000066400000000000000000000057621515050116100315040ustar00rootroot00000000000000 missing.isnan (Octave Tablicious v0.3.6)

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    8.2.28.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing_002eisnanny.html000066400000000000000000000055321515050116100320460ustar00rootroot00000000000000 missing.isnanny (Octave Tablicious v0.3.6)

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    8.2.28.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/missing_002emissing.html000066400000000000000000000054341515050116100320410ustar00rootroot00000000000000 missing.missing (Octave Tablicious v0.3.6)

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    8.2.28.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeA.html000066400000000000000000000071321515050116100276050ustar00rootroot00000000000000 mustBeA (Octave Tablicious v0.3.6)

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    8.2.29 mustBeA

    Function File: x = mustBeA (x, type)
    Function File: x = mustBeA (x, type, label)

    Requires that input is of a given type.

    Raises an error if the input x is not of type type, as determined by isa (x, type).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeCellstr.html000066400000000000000000000064471515050116100310450ustar00rootroot00000000000000 mustBeCellstr (Octave Tablicious v0.3.6)

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    8.2.30 mustBeCellstr

    Function File: x = mustBeCellstr (x, label)

    Requires that input is a cellstr.

    Raises an error if the input x is not a cellstr (a cell array of char arrays).

    TODO: Decide whether to require the contained char arrays be rowvec/empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeCharvec.html000066400000000000000000000071621515050116100310030ustar00rootroot00000000000000 mustBeCharvec (Octave Tablicious v0.3.6)

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    8.2.31 mustBeCharvec

    Function File: x = mustBeCharvec (x, label)

    Requires that input is a char row vector.

    Raises an error if the input x is not a row vector of chars. char row vectors are Octave’s normal representation of single strings. (They are what are produced by '...' string literals.) As a special case, 0-by-0 empty chars (what is produced by the string literal '') are also considered charvecs.

    This does not differentiate between single-quoted and double-quoted strings.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeFinite.html000066400000000000000000000045521515050116100306460ustar00rootroot00000000000000 mustBeFinite (Octave Tablicious v0.3.6)

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    8.2.32 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeInteger.html000066400000000000000000000045501515050116100310230ustar00rootroot00000000000000 mustBeInteger (Octave Tablicious v0.3.6)

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    8.2.33 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeMember.html000066400000000000000000000045561515050116100306430ustar00rootroot00000000000000 mustBeMember (Octave Tablicious v0.3.6)

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    8.2.34 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeNonempty.html000066400000000000000000000045621515050116100312420ustar00rootroot00000000000000 mustBeNonempty (Octave Tablicious v0.3.6)

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    8.2.35 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeNumeric.html000066400000000000000000000045501515050116100310300ustar00rootroot00000000000000 mustBeNumeric (Octave Tablicious v0.3.6)

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    8.2.36 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeReal.html000066400000000000000000000045421515050116100303120ustar00rootroot00000000000000 mustBeReal (Octave Tablicious v0.3.6)

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    8.2.37 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeSameSize.html000066400000000000000000000066631515050116100311550ustar00rootroot00000000000000 mustBeSameSize (Octave Tablicious v0.3.6)

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    8.2.38 mustBeSameSize

    Function File: [a, b] = mustBeSameSize (a, b, labelA, labelB)

    Requires that the inputs are the same size.

    Raises an error if the inputs a and b are not the same size, as determined by isequal (size (a), size (b)).

    labelA and labelB are optional inputs that determine how the input will be described in error messages. If not supplied, inputname (...) is used, and if that is empty, it falls back to "input 1" and "input 2".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeScalar.html000066400000000000000000000063671515050116100306430ustar00rootroot00000000000000 mustBeScalar (Octave Tablicious v0.3.6)

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    8.2.39 mustBeScalar

    Function File: x = mustBeScalar (x, label)

    Requires that input is scalar.

    Raises an error if the input x is not scalar, as determined by isscalar (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeScalarLogical.html000066400000000000000000000064651515050116100321350ustar00rootroot00000000000000 mustBeScalarLogical (Octave Tablicious v0.3.6)

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    8.2.40 mustBeScalarLogical

    Function File: x = mustBeScalarLogical (x, label)

    Requires that input is a scalar logical.

    Raises an error if the input x is not scalar, as determined by isscalar (x) && islogical (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/mustBeVector.html000066400000000000000000000062731515050116100306740ustar00rootroot00000000000000 mustBeVector (Octave Tablicious v0.3.6)

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    8.2.41 mustBeVector

    Function File: x = mustBeVector (x, label)

    Requires that input is a vector or empty.

    Raises an error if the input x is not a row vector and is not 0-by-0 empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    octave_002echrono_002eDummyClass.html000066400000000000000000000106001515050116100341300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.chrono.DummyClass (Octave Tablicious v0.3.6)

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    8.2.44 octave.chrono.DummyClass

    Class: DummyClass

    A do-nothing class just for testing the doco tools.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    Instance Variable of DummyClass: double x

    An x. Has no semantics.

    Instance Variable of DummyClass: double y

    A y. Has no semantics.

    octave_002echrono_002eDummyClass_002eDummyClass.html000066400000000000000000000071361515050116100366720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.chrono.DummyClass.DummyClass (Octave Tablicious v0.3.6)

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    8.2.44.1 octave.chrono.DummyClass.DummyClass

    Constructor: obj = octave.chrono.DummyClass ()

    Constructs a new scalar DummyClass with default values.

    Constructor: obj = octave.chrono.DummyClass (x, y)

    Constructs a new DummyClass with the specified values.

    octave_002echrono_002eDummyClass_002ebar.html000066400000000000000000000061461515050116100353550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.chrono.DummyClass.bar (Octave Tablicious v0.3.6)

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    8.2.44.3 octave.chrono.DummyClass.bar

    Method: out = bar (obj)

    Computes a bar value.

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    octave_002echrono_002eDummyClass_002efoo.html000066400000000000000000000065471515050116100354010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.chrono.DummyClass.foo (Octave Tablicious v0.3.6)

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    8.2.44.2 octave.chrono.DummyClass.foo

    Method: out = foo (obj)

    Computes a foo value.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    octave_002echrono_002edummy_005ffunction.html000066400000000000000000000063141515050116100355110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.chrono.dummy_function (Octave Tablicious v0.3.6)

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    8.2.43 octave.chrono.dummy_function

    Function: out = dummy_function (x)

    A dummy function just for testing the doco tools.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/octave_002edataset.html000066400000000000000000000244121515050116100316220ustar00rootroot00000000000000 octave.dataset (Octave Tablicious v0.3.6)

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    8.2.45 octave.dataset

    Class: dataset

    The dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It’s provided so you can use tab completion on the dataset list.

    octave_002edataset_002eAirPassengers.html000066400000000000000000000075571515050116100347730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.AirPassengers (Octave Tablicious v0.3.6)

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    8.2.45.2 octave.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    octave_002edataset_002eBJsales.html000066400000000000000000000110411515050116100335270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.BJsales (Octave Tablicious v0.3.6)

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    8.2.45.9 octave.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eBOD.html000066400000000000000000000102631515050116100326150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.BOD (Octave Tablicious v0.3.6)

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    8.2.45.10 octave.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002eChickWeight.html000066400000000000000000000113071515050116100344020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.ChickWeight (Octave Tablicious v0.3.6)

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    8.2.45.12 octave.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.ChickWeight

octave.examples.coplot (t, "Time", "weight", "Chick");
    octave_002edataset_002eDNase.html000066400000000000000000000111741515050116100332050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.DNase (Octave Tablicious v0.3.6)

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    8.2.45.18 octave.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.DNase;

# TODO: Port this from R

octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    octave_002edataset_002eEuStockMarkets.html000066400000000000000000000111341515050116100351130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.EuStockMarkets (Octave Tablicious v0.3.6)

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    8.2.45.22 octave.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = octave.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price,1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    octave_002edataset_002eFormaldehyde.html000066400000000000000000000114201515050116100346100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Formaldehyde (Octave Tablicious v0.3.6)

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    8.2.45.24 octave.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
% TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

% TODO: Add linear model summary output
% TOD: Add linear model summary plot
    octave_002edataset_002eHairEyeColor.html000066400000000000000000000151061515050116100345370ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.HairEyeColor (Octave Tablicious v0.3.6)

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    8.2.45.26 octave.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    octave.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: octave.dataset.Harman23cor, Previous: octave.dataset.freeny, Up: octave.dataset   [Contents]

    octave_002edataset_002eHarman23cor.html000066400000000000000000000102741515050116100342720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Harman23cor (Octave Tablicious v0.3.6)

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    8.2.45.27 octave.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    octave.dataset.Harman23cor;

# TODO: Port factanal to Octave
    octave_002edataset_002eHarman74cor.html000066400000000000000000000103511515050116100342740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Harman74cor (Octave Tablicious v0.3.6)

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    8.2.45.28 octave.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    octave.dataset.Harman74cor;

# TODO: Port factanal to Octave
    octave_002edataset_002eIndometh.html000066400000000000000000000110071515050116100337550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Indometh (Octave Tablicious v0.3.6)

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    8.2.45.29 octave.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    octave_002edataset_002eInsectSprays.html000066400000000000000000000104461515050116100346430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.InsectSprays (Octave Tablicious v0.3.6)

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    8.2.45.31 octave.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    octave_002edataset_002eJohnsonJohnson.html000066400000000000000000000102601515050116100351630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.JohnsonJohnson (Octave Tablicious v0.3.6)

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    8.2.45.34 octave.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = octave.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    octave_002edataset_002eLakeHuron.html000066400000000000000000000104761515050116100341070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.LakeHuron (Octave Tablicious v0.3.6)

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    8.2.45.35 octave.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = octave.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    octave_002edataset_002eLifeCycleSavings.html000066400000000000000000000126301515050116100354030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.LifeCycleSavings (Octave Tablicious v0.3.6)

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    8.2.45.37 octave.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = octave.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    octave_002edataset_002eLoblolly.html000066400000000000000000000111041515050116100337740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Loblolly (Octave Tablicious v0.3.6)

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    8.2.45.38 octave.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    octave_002edataset_002eNile.html000066400000000000000000000112161515050116100330770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Nile (Octave Tablicious v0.3.6)

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    8.2.45.44 octave.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = octave.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    octave_002edataset_002eOrange.html000066400000000000000000000115241515050116100334250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Orange (Octave Tablicious v0.3.6)

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    8.2.45.48 octave.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    octave_002edataset_002eOrchardSprays.html000066400000000000000000000124761515050116100350050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.OrchardSprays (Octave Tablicious v0.3.6)

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    8.2.45.49 octave.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.OrchardSprays;

octave.examples.plot_pairs (t);
    octave_002edataset_002ePlantGrowth.html000066400000000000000000000102201515050116100344530ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.PlantGrowth (Octave Tablicious v0.3.6)

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    8.2.45.50 octave.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = octave.dataset.PlantGrowth;

# TODO: Port anova to Octave
    octave_002edataset_002ePuromycin.html000066400000000000000000000124731515050116100342030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Puromycin (Octave Tablicious v0.3.6)

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    8.2.45.54 octave.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = octave.dataset.Puromycin;

# TODO: Port example to Octave
    octave_002edataset_002eTheoph.html000066400000000000000000000142621515050116100334430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Theoph (Octave Tablicious v0.3.6)

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    8.2.45.66 octave.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = octave.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: octave.dataset.Titanic, Previous: octave.dataset.swiss, Up: octave.dataset   [Contents]

    octave_002edataset_002eTitanic.html000066400000000000000000000146051515050116100336100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.Titanic (Octave Tablicious v0.3.6)

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    8.2.45.67 octave.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    octave.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: octave.dataset.ToothGrowth, Previous: octave.dataset.Theoph, Up: octave.dataset   [Contents]

    octave_002edataset_002eToothGrowth.html000066400000000000000000000115331515050116100345020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.ToothGrowth (Octave Tablicious v0.3.6)

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    8.2.45.68 octave.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = octave.dataset.ToothGrowth;

octave.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    octave_002edataset_002eUCBAdmissions.html000066400000000000000000000141011515050116100346470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.UCBAdmissions (Octave Tablicious v0.3.6)

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    8.2.45.71 octave.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    octave.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: octave.dataset.UKDriverDeaths, Previous: octave.dataset.trees, Up: octave.dataset   [Contents]

    octave_002edataset_002eUKDriverDeaths.html000066400000000000000000000147451515050116100350460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.UKDriverDeaths (Octave Tablicious v0.3.6)

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    8.2.45.72 octave.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    octave.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: octave.dataset.UKgas, Previous: octave.dataset.UCBAdmissions, Up: octave.dataset   [Contents]

    octave_002edataset_002eUKLungDeaths.html000066400000000000000000000111001515050116100344760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.UKLungDeaths (Octave Tablicious v0.3.6)

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    8.2.45.74 octave.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = octave.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    octave_002edataset_002eUKgas.html000066400000000000000000000103711515050116100332230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.UKgas (Octave Tablicious v0.3.6)

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    8.2.45.73 octave.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = octave.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    octave_002edataset_002eUSAccDeaths.html000066400000000000000000000100541515050116100342760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.USAccDeaths (Octave Tablicious v0.3.6)

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    8.2.45.75 octave.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = octave.dataset.USAccDeaths;
    octave_002edataset_002eUSArrests.html000066400000000000000000000137571515050116100341170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.USArrests (Octave Tablicious v0.3.6)

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    8.2.45.76 octave.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.USArrests;

summary (t);

octave.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: octave.dataset.USJudgeRatings, Previous: octave.dataset.USAccDeaths, Up: octave.dataset   [Contents]

    octave_002edataset_002eUSJudgeRatings.html000066400000000000000000000116241515050116100350510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.USJudgeRatings (Octave Tablicious v0.3.6)

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    8.2.45.77 octave.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = octave.dataset.USJudgeRatings;

figure
octave.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    octave_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111101515050116100364510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.USPersonalExpenditure (Octave Tablicious v0.3.6)

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    8.2.45.78 octave.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    octave_002edataset_002eVADeaths.html000066400000000000000000000113271515050116100336520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.VADeaths (Octave Tablicious v0.3.6)

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    8.2.45.80 octave.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.VADeaths;

# TODO: Port to Octave
    octave_002edataset_002eWWWusage.html000066400000000000000000000103541515050116100337230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.WWWusage (Octave Tablicious v0.3.6)

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    8.2.45.85 octave.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eWorldPhones.html000066400000000000000000000105161515050116100344560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.WorldPhones (Octave Tablicious v0.3.6)

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    8.2.45.84 octave.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    octave_002edataset_002eairmiles.html000066400000000000000000000072311515050116100340170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.airmiles (Octave Tablicious v0.3.6)

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    8.2.45.1 octave.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = octave.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    octave_002edataset_002eairquality.html000066400000000000000000000125501515050116100343760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.airquality (Octave Tablicious v0.3.6)

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    8.2.45.3 octave.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = octave.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if i == j
      continue
    endif
    ix_subplot = (n_vars*(j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    octave_002edataset_002eanscombe.html000066400000000000000000000121441515050116100340000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.anscombe (Octave Tablicious v0.3.6)

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    8.2.45.4 octave.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = octave.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim(haxs(1), x_limits);
ylim(haxs(1), y_limits);
    octave_002edataset_002eattenu.html000066400000000000000000000114521515050116100335120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.attenu (Octave Tablicious v0.3.6)

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    8.2.45.5 octave.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    octave_002edataset_002eattitude.html000066400000000000000000000111041515050116100340270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.attitude (Octave Tablicious v0.3.6)

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    8.2.45.6 octave.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = octave.dataset.attitude

octave.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    octave_002edataset_002eaustres.html000066400000000000000000000102221515050116100336720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.austres (Octave Tablicious v0.3.6)

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    8.2.45.7 octave.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = octave.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    octave_002edataset_002ebeavers.html000066400000000000000000000106451515050116100336440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.beavers (Octave Tablicious v0.3.6)

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    8.2.45.8 octave.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    octave_002edataset_002ecars.html000066400000000000000000000116701515050116100331440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.cars (Octave Tablicious v0.3.6)

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    8.2.45.11 octave.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = octave.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    octave_002edataset_002echickwts.html000066400000000000000000000114241515050116100340300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.chickwts (Octave Tablicious v0.3.6)

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    8.2.45.13 octave.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = octave.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    octave_002edataset_002eco2.html000066400000000000000000000116671515050116100327050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.co2 (Octave Tablicious v0.3.6)

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    8.2.45.14 octave.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = octave.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    octave_002edataset_002ecrimtab.html000066400000000000000000000164011515050116100336320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.crimtab (Octave Tablicious v0.3.6)

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    8.2.45.15 octave.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: octave.dataset.cupcake, Previous: octave.dataset.co2, Up: octave.dataset   [Contents]

    octave_002edataset_002ecupcake.html000066400000000000000000000104721515050116100336260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.cupcake (Octave Tablicious v0.3.6)

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    8.2.45.16 octave.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = octave.dataset.cupcake
plot(datenum(t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric")
    octave_002edataset_002ediscoveries.html000066400000000000000000000106231515050116100345300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.discoveries (Octave Tablicious v0.3.6)

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    8.2.45.17 octave.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    octave_002edataset_002eesoph.html000066400000000000000000000106651515050116100333350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.esoph (Octave Tablicious v0.3.6)

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    8.2.45.19 octave.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    octave_002edataset_002eeuro.html000066400000000000000000000116601515050116100331650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.euro (Octave Tablicious v0.3.6)

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    8.2.45.20 octave.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    octave_002edataset_002eeurodist.html000066400000000000000000000101151515050116100340430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.eurodist (Octave Tablicious v0.3.6)

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    8.2.45.21 octave.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    octave_002edataset_002efaithful.html000066400000000000000000000121131515050116100340070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.faithful (Octave Tablicious v0.3.6)

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    8.2.45.23 octave.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = octave.dataset.faithful;

% Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

% Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

% Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    octave_002edataset_002efreeny.html000066400000000000000000000121061515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.freeny (Octave Tablicious v0.3.6)

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    8.2.45.25 octave.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.freeny;

summary(t)

octave.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    octave_002edataset_002einfert.html000066400000000000000000000116041515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.infert (Octave Tablicious v0.3.6)

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    8.2.45.30 octave.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = octave.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    octave_002edataset_002eiris.html000066400000000000000000000121561515050116100331620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.iris (Octave Tablicious v0.3.6)

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    8.2.45.32 octave.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002eislands.html000066400000000000000000000104021515050116100336410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.islands (Octave Tablicious v0.3.6)

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    8.2.45.33 octave.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/octave_002edataset_002elh.html000066400000000000000000000101741515050116100326740ustar00rootroot00000000000000 octave.dataset.lh (Octave Tablicious v0.3.6)

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    8.2.45.36 octave.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = octave.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    octave_002edataset_002elongley.html000066400000000000000000000115431515050116100336640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.longley (Octave Tablicious v0.3.6)

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    8.2.45.39 octave.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    octave_002edataset_002elynx.html000066400000000000000000000113371515050116100332060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.lynx (Octave Tablicious v0.3.6)

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    8.2.45.40 octave.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = octave.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    octave_002edataset_002emorley.html000066400000000000000000000121361515050116100335210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.morley (Octave Tablicious v0.3.6)

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    8.2.45.41 octave.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = octave.dataset.morley;

# TODO: Port to Octave
    octave_002edataset_002emtcars.html000066400000000000000000000124301515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.mtcars (Octave Tablicious v0.3.6)

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    8.2.45.42 octave.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002enhtemp.html000066400000000000000000000105011515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.nhtemp (Octave Tablicious v0.3.6)

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    8.2.45.43 octave.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    octave_002edataset_002enottem.html000066400000000000000000000101421515050116100335130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.nottem (Octave Tablicious v0.3.6)

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    8.2.45.45 octave.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002enpk.html000066400000000000000000000113061515050116100330000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.npk (Octave Tablicious v0.3.6)

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    8.2.45.46 octave.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = octave.dataset.npk;

# TODO: Port aov() and LM to Octave
    octave_002edataset_002eoccupationalStatus.html000066400000000000000000000103641515050116100361000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.occupationalStatus (Octave Tablicious v0.3.6)

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    8.2.45.47 octave.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eprecip.html000066400000000000000000000103671515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.precip (Octave Tablicious v0.3.6)

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    8.2.45.51 octave.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.precip;

# TODO: Port dot plot to Octave
    octave_002edataset_002epresidents.html000066400000000000000000000111731515050116100343720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.presidents (Octave Tablicious v0.3.6)

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    8.2.45.52 octave.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    octave_002edataset_002epressure.html000066400000000000000000000112321515050116100340560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.pressure (Octave Tablicious v0.3.6)

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    8.2.45.53 octave.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    octave_002edataset_002equakes.html000066400000000000000000000120521515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.quakes (Octave Tablicious v0.3.6)

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    8.2.45.55 octave.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002erandu.html000066400000000000000000000111331515050116100333170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.randu (Octave Tablicious v0.3.6)

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    8.2.45.56 octave.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = octave.dataset.randu;
    octave_002edataset_002erivers.html000066400000000000000000000102651515050116100335250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.rivers (Octave Tablicious v0.3.6)

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    8.2.45.57 octave.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    octave_002edataset_002erock.html000066400000000000000000000110461515050116100331470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.rock (Octave Tablicious v0.3.6)

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    8.2.45.58 octave.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = octave.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    octave_002edataset_002esleep.html000066400000000000000000000113071515050116100333210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.sleep (Octave Tablicious v0.3.6)

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    8.2.45.59 octave.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = octave.dataset.sleep;

# TODO: Port to Octave
    octave_002edataset_002estackloss.html000066400000000000000000000141761515050116100342260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.stackloss (Octave Tablicious v0.3.6)

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    8.2.45.60 octave.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = octave.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: octave.dataset.state, Previous: octave.dataset.sleep, Up: octave.dataset   [Contents]

    octave_002edataset_002estate.html000066400000000000000000000130021515050116100333230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.state (Octave Tablicious v0.3.6)

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    8.2.45.61 octave.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.state;
    octave_002edataset_002esunspot_005fmonth.html000066400000000000000000000104541515050116100355260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.sunspot_month (Octave Tablicious v0.3.6)

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    8.2.45.62 octave.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = octave.dataset.sunspot_month;
    octave_002edataset_002esunspot_005fyear.html000066400000000000000000000102131515050116100353320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.sunspot_year (Octave Tablicious v0.3.6)

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    8.2.45.63 octave.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = octave.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    octave_002edataset_002esunspots.html000066400000000000000000000105131515050116100341050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.sunspots (Octave Tablicious v0.3.6)

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    8.2.45.64 octave.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = octave.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    octave_002edataset_002eswiss.html000066400000000000000000000140731515050116100333640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.swiss (Octave Tablicious v0.3.6)

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    8.2.45.65 octave.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.swiss;

# TODO: Port linear model to Octave

    Next: octave.dataset.Theoph, Previous: octave.dataset.sunspots, Up: octave.dataset   [Contents]

    octave_002edataset_002etreering.html000066400000000000000000000111751515050116100340330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.treering (Octave Tablicious v0.3.6)

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    8.2.45.69 octave.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = octave.dataset.treering;
    octave_002edataset_002etrees.html000066400000000000000000000113451515050116100333350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.trees (Octave Tablicious v0.3.6)

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    8.2.45.70 octave.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = octave.dataset.trees;

figure
octave.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    octave_002edataset_002euspop.html000066400000000000000000000102461515050116100333600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.uspop (Octave Tablicious v0.3.6)

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    8.2.45.79 octave.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    octave_002edataset_002evolcano.html000066400000000000000000000112151515050116100336500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.volcano (Octave Tablicious v0.3.6)

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    8.2.45.81 octave.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    octave.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    octave_002edataset_002ewarpbreaks.html000066400000000000000000000111771515050116100343570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.warpbreaks (Octave Tablicious v0.3.6)

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    8.2.45.82 octave.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    octave_002edataset_002ewomen.html000066400000000000000000000113471515050116100333420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.women (Octave Tablicious v0.3.6)

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    8.2.45.83 octave.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    octave_002edataset_002ezCO2.html000066400000000000000000000104531515050116100327670ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.dataset.zCO2 (Octave Tablicious v0.3.6)

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    8.2.45.86 octave.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/octave_002edatasets.html000066400000000000000000000064451515050116100320130ustar00rootroot00000000000000 octave.datasets (Octave Tablicious v0.3.6)

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    8.2.46 octave.datasets

    Class: datasets

    Example dataset collection.

    datasets is a collection of example datasets to go with the Tablicious package.

    The datasets class provides methods for listing and loading the example datasets.

    octave_002edatasets_002edescription.html000066400000000000000000000066261515050116100347270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.datasets.description (Octave Tablicious v0.3.6)

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    8.2.46.3 octave.datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.46.1 octave.datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

    octave_002edatasets_002eload.html000066400000000000000000000067061515050116100333220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.datasets.load (Octave Tablicious v0.3.6)

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    8.2.46.2 octave.datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

    octave_002eexamples_002ecoplot.html000066400000000000000000000144341515050116100337060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html octave.examples.coplot (Octave Tablicious v0.3.6)

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    8.2.47 octave.examples.coplot

    Function: [fig, hax] = coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: octave.examples.plot_pairs, Previous: octave.datasets, Up: API Alphabetically   [Contents]

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    8.2.48 octave.examples.plot_pairs

    Function: out = plot_pairs (data)
    Function: out = plot_pairs (data, plot_type)
    Function: out = plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/pp.html000066400000000000000000000100571515050116100266640ustar00rootroot00000000000000 pp (Octave Tablicious v0.3.6)

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    8.2.49 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/rmmissing.html000066400000000000000000000106451515050116100302600ustar00rootroot00000000000000 rmmissing (Octave Tablicious v0.3.6)

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    8.2.50 rmmissing

    Function: [out, tf] = rmmissing (X)
    Function: [out, tf] = rmmissing (X, dim)
    Function: [out, tf] = rmmissing (…, 'MinNumMissing', MinNumMissing)

    Remove missing values.

    If x is a vector, removes elements with missing values. If x is a matrix, removes rows or columns with missing data elements.

    dim is the dimension to operate along. Specifying a dimension forces rmmissing to operate in matrix instead of vector mode.

    MinNumMissing indicates how many missing element values there must be in a row or column for it to be considered missing and this removed. This option is only used in matrix mode; it is silently ignored in vector mode.

    Returns: out - the input, with missing elements or rows or columns removed tf - a logical index vector indicating which elements, rows, or columns were removed

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    8.2.51 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/seconds.html000066400000000000000000000062771515050116100277140ustar00rootroot00000000000000 seconds (Octave Tablicious v0.3.6)

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    8.2.52 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.53 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/splitapply.html000066400000000000000000000114261515050116100304470ustar00rootroot00000000000000 splitapply (Octave Tablicious v0.3.6)

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    8.2.54 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/standardizeMissing.html000066400000000000000000000071131515050116100321060ustar00rootroot00000000000000 standardizeMissing (Octave Tablicious v0.3.6)

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    8.2.55 standardizeMissing

    Function: out = standardizeMissing (X, indicator)

    Insert standard missing values.

    Standardizes missing values in X by replacing the values listed in indicator with the standard missing values for the type of X.

    Standard missing values depend on the data type: * NaN for double, single, duration, and calendarDuration * NaT for datetime * ' ' for char * {''} for cellstrs * Integer numeric types have no standard missing value; they are never considered missing. * Structs are never considered missing. * Logicals are never considered missing.

    See also: table.standardizeMissing

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    8.2.56 startsWith

    Function: out = startsWith (str, pattern)
    Function: out = startsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings start with a pattern.

    Tests whether the given strings start with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/string.html000066400000000000000000000154511515050116100275560ustar00rootroot00000000000000 string (Octave Tablicious v0.3.6)

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    8.2.57 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where: - Each element of a string array is a single string - A single string is a 1-dimensional row vector of Unicode characters - Those characters are encoded in UTF-8 - This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles. - It is a type-safe object wrapper around Octave’s base primitive character types. - It adds ismissing() semantics. - And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

    Next: struct2table, Previous: startsWith, Up: API Alphabetically   [Contents]

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    8.2.57.8 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.57.7 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/string_002echar.html000066400000000000000000000062001515050116100311320ustar00rootroot00000000000000 string.char (Octave Tablicious v0.3.6)

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    8.2.57.9 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.57.23 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/string_002edecode.html000066400000000000000000000057451515050116100314550ustar00rootroot00000000000000 string.decode (Octave Tablicious v0.3.6)

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    8.2.57.25 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.57.3 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.57.10 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.57.18 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.57.5 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.57.6 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.57.2 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.57.16 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.24 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz.

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    8.2.57.21 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.57.14 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

    string_002ereverse_005fbytes.html000066400000000000000000000067131515050116100334230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html string.reverse_bytes (Octave Tablicious v0.3.6)

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    8.2.57.13 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.57.4 string.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/string_002estrcat.html000066400000000000000000000061711515050116100315240ustar00rootroot00000000000000 string.strcat (Octave Tablicious v0.3.6)

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    8.2.57.15 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.57.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.57.20 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.57.1 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs. - chars and cellstrs are converted via cellstr() - numerics are converted via num2str() - datetimes are converted via datestr()

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    8.2.57.12 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.57.11 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.57.19 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.57.17 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.58 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table.html000066400000000000000000000246061515050116100273410ustar00rootroot00000000000000 table (Octave Tablicious v0.3.6)

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    8.2.59 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/tableOuterFillValue.html000066400000000000000000000074701515050116100321640ustar00rootroot00000000000000 tableOuterFillValue (Octave Tablicious v0.3.6)

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    8.2.60 tableOuterFillValue

    Function: out = tableOuterFillValue (x)

    Outer fill value for variable within a table.

    Determines the fill value to usse for a given variable value x when that value is used as a variable in a table that is involved in an outer join.

    The default implementation for tableOuterFillValue has support for all Octave primitive types, plus cellstrs, datetime & friends, strings, and table-valued variables.

    This function may become private to table before version 1.0. It is currently global to make debugging more convenient. It (or an equivalent) will remain global if we want to allow user-defined classes to customize their fill value. It also has default logic that will determine the fill value for an arbitrary type by detecting the value used to fill elements during array expansion operations. This will be appropriate for most data types.

    Returns a 1-by-ncols value of the same type as x, which may be any type, where ncols is the number of columns in the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002eaddvars.html000066400000000000000000000101431515050116100314230ustar00rootroot00000000000000 table.addvars (Octave Tablicious v0.3.6)

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    8.2.59.38 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002eantijoin.html000066400000000000000000000066701515050116100316240ustar00rootroot00000000000000 table.antijoin (Octave Tablicious v0.3.6)

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    8.2.59.50 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002ecartesian.html000066400000000000000000000074741515050116100317650ustar00rootroot00000000000000 table.cartesian (Octave Tablicious v0.3.6)

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    8.2.59.51 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

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    8.2.59.17 table.columns

    Method: out = columns (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002econgruentize.html000066400000000000000000000067151515050116100325250ustar00rootroot00000000000000 table.congruentize (Octave Tablicious v0.3.6)

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    8.2.59.56 table.congruentize

    Method: [outA, outB] = congruentize (A, B)

    Make tables congruent.

    Makes tables congruent by ensuring they have the same variables of the same types in the same order. Congruent tables may be safely unioned, intersected, vertcatted, or have other set operations done to them.

    Variable names present in one input but not in the other produces an error. Variables with the same name but different types in the inputs produces an error. Inputs must either both have row names or both not have row names; it is an error if one has row names and the other doesn’t. Variables in different orders are reordered to be in the same order as A.

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    8.2.59.39 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.59.68 table.evalWithVars

    Method: out = evalWithVars (obj, expr)

    Evaluate an expression against table’s variables.

    Evaluates the M-code expression expr in a workspace where all of obj’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = table_examples.SpDb
tmp = join (sp, p);
shipment_weight = evalWithVars (tmp, "Qty .* Weight")
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    8.2.59.67 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.59.35 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.59.36 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.59.52 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

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    8.2.59.53 table.grpstats

    Method: [out] = grpstats (obj, groupvar)
    Method: [out] = grpstats (…, 'DataVars', DataVars)

    Statistics by group.

    See also: table.groupby.

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    8.2.59.25 table.hasrownames

    Method: out = hasrownames (obj)

    True if this table has row names defined.

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    8.2.59.43 table.head

    Method: out = head (obj)
    Method: out = head (obj, k)

    Get first K rows of table.

    Returns the first k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.14 table.height

    Method: out = height (obj)

    Number of rows in table.

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    8.2.59.27 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.59.46 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.59.58 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.22 table.iscol

    Method: out = iscol (obj)

    Test whether array is a column vector.

    For tables, iscol is true if the input has a single variable. The number of columns within that variable does not matter.

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    8.2.59.19 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.59.20 table.ismatrix

    Method: out = ismatrix (obj)

    Test whether array is a matrix.

    For tables, ismatrix is always true, by definition.

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    8.2.59.61 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.59.62 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.59.21 table.isrow

    Method: out = isrow (obj)

    Test whether array is a row vector.

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    8.2.59.24 table.isscalar

    Method: out = isscalar (obj)

    Test whether array is scalar.

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    8.2.59.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.59.23 table.isvector

    Method: out = isvector (obj)

    Test whether array is a vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002ejoin.html000066400000000000000000000074041515050116100307440ustar00rootroot00000000000000 table.join (Octave Tablicious v0.3.6)

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    8.2.59.45 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.59.10 table.length

    Method: out = length (obj)

    Length along longest dimension

    Use of length is not recommended. Use numel or size instead.

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    8.2.59.40 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.59.34 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.59.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2.

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    8.2.59.18 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.59.48 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.59.47 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.59.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.59.33 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.59.70 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.59.29 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables.

    Only two dimensions are supported for repelem on tables.

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    8.2.59.28 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.59.71 table.resolveVarRef

    Method: [ixVar, varNames] = resolveVarRef (obj, varRef)
    Method: [ixVar, varNames] = resolveVarRef (obj, varRef, strictness)

    Resolve a variable reference against this table.

    A varRef is a numeric or char/cellstr indicator of which variables within obj are being referenced.

    strictness controls what to do when the given variable references could not be resolved. It may be ’strict’ (the default) or ’lenient’.

    Returns: ixVar - the indexes of the variables in obj varNames - a cellstr of the names of the variables in obj

    Raises an error if any of the specified variables could not be resolved, unless strictness is ’lenient’, in which case it will return 0 for the index and ” for the name for each variable which could not be resolved.

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    8.2.59.69 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with evalWithVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, evalWithVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = table_examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))
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    8.2.59.63 table.rmmissing

    Method: [out, tf] = rmmissing (obj)
    Method: [out, tf] = rmmissing (obj, indicator)
    Method: [out, tf] = rmmissing (…, 'DataVariables', vars)
    Method: [out, tf] = rmmissing (…, 'MinNumMissing', minNumMissing)

    Remove rows with missing values.

    Removes the rows from obj that have missing values.

    If the ’DataVariables’ option is given, only the data in the specified variables is considered.

    Returns: out - A table the same as obj, but with rows with missing values removed. tf - A logical index vector indicating which rows were removed.

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    8.2.59.66 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002erows.html000066400000000000000000000053661515050116100310040ustar00rootroot00000000000000 table.rows (Octave Tablicious v0.3.6)

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    8.2.59.15 table.rows

    Method: out = rows (obj)

    Number of rows in table.

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    8.2.59.55 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.59.49 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

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    8.2.59.31 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.32 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.59.30 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.60 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.59.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.59.59 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.59.13 table.sizeof

    Method: out = sizeof (obj)

    Approximate size of array in bytes. For tables, this returns the sume of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently unimplemented.

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    8.2.59.54 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby

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    8.2.59.41 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.59.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002estack.html000066400000000000000000000072141515050116100311110ustar00rootroot00000000000000 table.stack (Octave Tablicious v0.3.6)

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    8.2.59.42 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    table_002estandardizeMissing.html000066400000000000000000000100471515050116100335650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html table.standardizeMissing (Octave Tablicious v0.3.6)

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    8.2.59.64 table.standardizeMissing

    Method: out = standardizeMissing (obj, indicator)
    Method: out = standardizeMissing (…, 'DataVariables', vars)

    Insert standard missing values.

    Standardizes missing values in variable data.

    If the DataVariables option is supplied, only the indicated variables are standardized.

    indicator is passed along to standardizeMissing when it is called on each of the data variables in turn. The same indicator is used for all variables. You can mix and match indicator types by just passing in mixed indicator types in a cell array; indicators that don’t match the type of the column they are operating on are just ignored.

    Returns a table with same variable names and types as obj, but with variable values standardized.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002esubsetrows.html000066400000000000000000000060211515050116100322170ustar00rootroot00000000000000 table.subsetrows (Octave Tablicious v0.3.6)

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    8.2.59.72 table.subsetrows

    Method: out = subsetrows (obj, ixRows)

    Subset table by rows.

    Subsets this table by rows.

    ixRows may be a numeric or logical index into the rows of obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002esubsetvars.html000066400000000000000000000057701515050116100322120ustar00rootroot00000000000000 table.subsetvars (Octave Tablicious v0.3.6)

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    8.2.59.73 table.subsetvars

    Method: out = subsetvars (obj, ixVars)

    Subset table by variables.

    Subsets table obj by subsetting it along its variables.

    ixVars may be: - a numeric index vector - a logical index vector - ":" - a cellstr vector of variable names

    The resulting table will have its variables reordered to match ixVars.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002esummary.html000066400000000000000000000055611515050116100315040ustar00rootroot00000000000000 table.summary (Octave Tablicious v0.3.6)

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    8.2.59.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002etable.html000066400000000000000000000115751515050116100311000ustar00rootroot00000000000000 table.table (Octave Tablicious v0.3.6)

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    8.2.59.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002etable2array.html000066400000000000000000000056001515050116100322110ustar00rootroot00000000000000 table.table2array (Octave Tablicious v0.3.6)

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    8.2.59.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002etable2cell.html000066400000000000000000000062171515050116100320170ustar00rootroot00000000000000 table.table2cell (Octave Tablicious v0.3.6)

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    8.2.59.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002etable2struct.html000066400000000000000000000071751515050116100324300ustar00rootroot00000000000000 table.table2struct (Octave Tablicious v0.3.6)

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    8.2.59.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002etail.html000066400000000000000000000065341515050116100307410ustar00rootroot00000000000000 table.tail (Octave Tablicious v0.3.6)

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    8.2.59.44 table.tail

    Method: out = tail (obj)
    Method: out = tail (obj, k)

    Get last K rows of table.

    Returns the last k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002eunion.html000066400000000000000000000064131515050116100311340ustar00rootroot00000000000000 table.union (Octave Tablicious v0.3.6)

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    8.2.59.57 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002evarfun.html000066400000000000000000000103341515050116100313020ustar00rootroot00000000000000 table.varfun (Octave Tablicious v0.3.6)

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    8.2.59.65 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002evarnames.html000066400000000000000000000064431515050116100316230ustar00rootroot00000000000000 table.varnames (Octave Tablicious v0.3.6)

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    8.2.59.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002evertcat.html000066400000000000000000000061261515050116100314550ustar00rootroot00000000000000 table.vertcat (Octave Tablicious v0.3.6)

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    8.2.59.26 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/table_002ewidth.html000066400000000000000000000055711515050116100311270ustar00rootroot00000000000000 table.width (Octave Tablicious v0.3.6)

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    8.2.59.16 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/timezones.html000066400000000000000000000077261515050116100302730ustar00rootroot00000000000000 timezones (Octave Tablicious v0.3.6)

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    8.2.61 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/vartype.html000066400000000000000000000064651515050116100277470ustar00rootroot00000000000000 vartype (Octave Tablicious v0.3.6)

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    8.2.62 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/vecfun.html000066400000000000000000000066301515050116100275350ustar00rootroot00000000000000 vecfun (Octave Tablicious v0.3.6)

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    8.2.63 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/html/years.html000066400000000000000000000064311515050116100273710ustar00rootroot00000000000000 years (Octave Tablicious v0.3.6)

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    8.2.64 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.6/user-guide/tablicious.html000066400000000000000000023552141515050116100274500ustar00rootroot00000000000000 Octave Tablicious v0.3.6

    Octave Tablicious v0.3.6

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.3.6.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.3.6 for GNU Octave.

    Tablicious provides Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <floss@apjanke.net>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.3.6/tablicious-0.3.6.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the octave.datasets and octave.dataset classes.

    To see a list of the available data sets, run octave.datasets.list(). Then to load one of the example data sets, run octave.datasets.load('examplename'). For example: 

    octave.datasets.list
t = octave.datasets.load('cupcake')

    You can also load it by calling octave.dataset.<name>. This does the same thing. For example: 

    t = octave.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on octave.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc octave.dataset.cupcake

    (The command help octave.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports most of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that all these will be implemented some day.

    These areas of missing functionality are all tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/1.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    array2table

    Convert an array to a table.

    cell2table

    Convert a cell array to a table.

    struct2table

    Convert struct to a table.

    tableOuterFillValue

    Outer fill value for variable within a table.

    vartype

    Filter by variable type for use in suscripting.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    startsWith

    Test if strings start with a pattern.

    endsWith

    Test if strings end with a pattern.

    contains

    Test if strings contain a pattern.

    categorical

    Categorical variable array.

    discretize

    Group data into discrete bins or categories.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    localdate

    Represents a complete day using the Gregorian calendar.

    isdatetime

    True if input is a ’datetime’ array, false otherwise.

    NaT

    “Not-a-Time”.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    calmonths

    Create a ’calendarDuration’ that is a given number of calendar months long.

    calyears

    Construct a ’calendarDuration’ a given number of years long.

    days

    Duration in days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    hours

    Create a ’duration’ X hours long, or get the hours in a ’duration’ X.

    isduration

    True if input is a ’duration’ array, false otherwise.

    milliseconds

    Create a ’duration’ X milliseconds long, or get the milliseconds in a ’duration’ X.

    minutes

    Create a ’duration’ X hours long, or get the hours in a ’duration’ X.

    seconds

    Create a ’duration’ X seconds long, or get the seconds in a ’duration’ X.

    timezones

    List all the time zones defined on this system.

    years

    Create a ’duration’ X years long, or get the years in a ’duration’ X.

    8.1.4 Missing Data

    fillmissing

    Fill missing values.

    ismissing

    Find missing values.

    rmmissing

    Remove missing values.

    standardizeMissing

    Insert standard missing values.

    missing

    Generic auto-converting missing value.

    isnanny

    Test if elements are NaN or NaN-like

    eqn

    Determine element-wise equality, treating NaNs as equal

    8.1.5 Validation Functions

    mustBeA

    Requires that input is of a given type.

    mustBeCellstr

    Requires that input is a cellstr.

    mustBeCharvec

    Requires that input is a char row vector.

    mustBeFinite
    mustBeInteger
    mustBeMember
    mustBeNonempty
    mustBeNumeric
    mustBeReal
    mustBeSameSize

    Requires that the inputs are the same size.

    mustBeScalar

    Requires that input is scalar.

    mustBeScalarLogical

    Requires that input is a scalar logical.

    mustBeVector

    Requires that input is a vector or empty.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    dispstrs

    Display strings for array.

    isfile
    isfolder
    pp

    Alias for prettyprint, for interactive use.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    size2str

    Format an array size for display.

    splitapply

    Split data into groups and apply function.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    8.1.7 Example Datasets

    octave.datasets

    Example dataset collection.

    octave.dataset

    The ’dataset’ class provides convenient access to the various datasets included with Tablicious.

    8.1.8 Example Code

    octave.examples.plot_pairs

    Plot pairs of variables against each other.

    8.1.9 Uncategorized

    iscategorical

    True if input is a ’categorical’ array, false otherwise.

    octave.chrono.dummy_function

    A dummy function just for testing the doco tools.

    octave.chrono.DummyClass

    A do-nothing class just for testing the doco tools.

    octave.examples.coplot

    Conditioning plot.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.2.3 calendarDuration.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.2.4 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.2.5 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.8 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.9 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is just an alias for isnat, provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff: - gt, ge, lt, le - Ordinal support in general - countcats - summary

    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.3 categorical.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.7 datetime.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.9.8 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.9 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.10 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.12 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.13 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.14 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.15 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.16 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.17 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.18 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.19 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.20 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.21 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.22 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.23 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.27 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.28 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.29 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.31 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.32 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.33 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.34 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.35 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    Up: duration   [Contents]

    8.2.13.1 duration.ofDays

    8.2.13.2 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.3 duration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.13.4 duration.years

    8.2.13.5 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    Up: duration   [Contents]

    8.2.13.6 duration.hours

    8.2.13.7 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    Up: duration   [Contents]

    8.2.13.8 duration.minutes

    8.2.13.9 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    Up: duration   [Contents]

    8.2.13.10 duration.seconds

    8.2.13.11 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    Up: duration   [Contents]

    8.2.13.12 duration.milliseconds

    8.2.13.13 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    Up: duration   [Contents]

    8.2.13.14 duration.dispstrs

    8.2.13.15 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    Up: duration   [Contents]

    8.2.13.16 duration.char

    8.2.13.17 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    Up: duration   [Contents]

    8.2.13.18 duration.linspace

    8.2.13.19 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14 endsWith

    Function: out = endsWith (str, pattern)
    Function: out = endsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings end with a pattern.

    Tests whether the given strings end with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 fillmissing

    Function: [out, tfFilled] = fillmissing (X, method)
    Function: [out, tfFilled] = fillmissing (X, 'constant', fill_val)
    Function: [out, tfFilled] = fillmissing (X, movmethod, window)

    Fill missing values.

    Fills missing values in X according to the method specified by method.

    This method is only partially implemented.

    method may be: 'constant' 'previous' 'next' 'nearest' 'linear' 'spline' 'pchip' movmethod may be: 'movmean' 'movmedian'

    Returns out, which is X but with missing values filled in, and tfFilled, a logical array the same size as X which indicates which elements were filled.

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 ismissing

    Function: out = ismissing (X)
    Function: out = ismissing (X, indicator)

    Find missing values.

    Determines which elements of X contain missing values. If an indicator input is not provided, standard missing values depending on the input type of X are used.

    Standard missing values depend on the data type:

    • NaN for double, single, duration, and calendarDuration
    • NaT for datetime
    • ' ' for char
    • {''} for cellstrs
    • Integer numeric types have no standard missing value; they are never considered missing.
    • Structs are never considered missing.
    • Logicals are never considered missing.
    • Other types have no standard missing value; it is currently an error to call ismissing on them without providing an indicator.
      • - This includes cells which are not cellstrs; calling ismissing on them results in an error.
      • - TODO: Determine whether this should really be an error, or if it should default to never considering those types as missing.
      • - TODO: Decide whether, for classdef objects, ismissing should polymorphically detect isnan()/isnat()/isnanny() methods and use those, or whether we should require classes to override ismissing() itself.

    If indicator is supplied, it is an array containing multiple values, all of which are considered to be missing values. Only indicator values that are type-compatible with the input are considered; other indicator value types are silently ignored. This is by design, so you can pass an indicator that holds sentinel values for disparate types in to ismissing() used for any type, or for compound types like table.

    Indicators are currently not supported for struct or logical inputs. This is probably a bug.

    Table defines its own ismissing() method which respects individual variables’ data types; see table.ismissing.

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: isnan, isnat, ismissing, eqn, isequaln

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    8.2.25 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.25.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.25.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.25.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.25.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.25.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.25.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.25.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.25.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.25.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.25.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.25.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.26 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.27 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.28 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.28.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.28.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.28.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.28.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.28.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.29 mustBeA

    Function File: x = mustBeA (x, type)
    Function File: x = mustBeA (x, type, label)

    Requires that input is of a given type.

    Raises an error if the input x is not of type type, as determined by isa (x, type).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.30 mustBeCellstr

    Function File: x = mustBeCellstr (x, label)

    Requires that input is a cellstr.

    Raises an error if the input x is not a cellstr (a cell array of char arrays).

    TODO: Decide whether to require the contained char arrays be rowvec/empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.31 mustBeCharvec

    Function File: x = mustBeCharvec (x, label)

    Requires that input is a char row vector.

    Raises an error if the input x is not a row vector of chars. char row vectors are Octave’s normal representation of single strings. (They are what are produced by '...' string literals.) As a special case, 0-by-0 empty chars (what is produced by the string literal '') are also considered charvecs.

    This does not differentiate between single-quoted and double-quoted strings.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.32 mustBeFinite

    Not documented

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    8.2.33 mustBeInteger

    Not documented

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    8.2.34 mustBeMember

    Not documented

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    8.2.35 mustBeNonempty

    Not documented

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    8.2.36 mustBeNumeric

    Not documented

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    8.2.37 mustBeReal

    Not documented

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    8.2.38 mustBeSameSize

    Function File: [a, b] = mustBeSameSize (a, b, labelA, labelB)

    Requires that the inputs are the same size.

    Raises an error if the inputs a and b are not the same size, as determined by isequal (size (a), size (b)).

    labelA and labelB are optional inputs that determine how the input will be described in error messages. If not supplied, inputname (...) is used, and if that is empty, it falls back to "input 1" and "input 2".

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    8.2.39 mustBeScalar

    Function File: x = mustBeScalar (x, label)

    Requires that input is scalar.

    Raises an error if the input x is not scalar, as determined by isscalar (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.40 mustBeScalarLogical

    Function File: x = mustBeScalarLogical (x, label)

    Requires that input is a scalar logical.

    Raises an error if the input x is not scalar, as determined by isscalar (x) && islogical (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.41 mustBeVector

    Function File: x = mustBeVector (x, label)

    Requires that input is a vector or empty.

    Raises an error if the input x is not a row vector and is not 0-by-0 empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.42 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.43 octave.chrono.dummy_function

    Function: out = dummy_function (x)

    A dummy function just for testing the doco tools.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

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    8.2.44 octave.chrono.DummyClass

    Class: DummyClass

    A do-nothing class just for testing the doco tools.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    Instance Variable of DummyClass: double x

    An x. Has no semantics.

    Instance Variable of DummyClass: double y

    A y. Has no semantics.

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    8.2.44.1 octave.chrono.DummyClass.DummyClass

    Constructor: obj = octave.chrono.DummyClass ()

    Constructs a new scalar DummyClass with default values.

    Constructor: obj = octave.chrono.DummyClass (x, y)

    Constructs a new DummyClass with the specified values.

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    8.2.44.2 octave.chrono.DummyClass.foo

    Method: out = foo (obj)

    Computes a foo value.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

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    8.2.44.3 octave.chrono.DummyClass.bar

    Method: out = bar (obj)

    Computes a bar value.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

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    8.2.45 octave.dataset

    Class: dataset

    The dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It’s provided so you can use tab completion on the dataset list.

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    8.2.45.1 octave.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = octave.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.45.2 octave.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.45.3 octave.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = octave.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if i == j
      continue
    endif
    ix_subplot = (n_vars*(j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.45.4 octave.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = octave.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim(haxs(1), x_limits);
ylim(haxs(1), y_limits);

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    8.2.45.5 octave.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.45.6 octave.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = octave.dataset.attitude

octave.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.45.7 octave.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = octave.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.45.8 octave.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.45.9 octave.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.45.10 octave.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.45.11 octave.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = octave.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.45.12 octave.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.ChickWeight

octave.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.45.13 octave.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = octave.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.45.14 octave.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = octave.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.45.15 octave.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.45.16 octave.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = octave.dataset.cupcake
plot(datenum(t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric")

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    8.2.45.17 octave.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.45.18 octave.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.DNase;

# TODO: Port this from R

octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.45.19 octave.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.45.20 octave.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.45.21 octave.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.45.22 octave.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = octave.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price,1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.45.23 octave.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = octave.dataset.faithful;

% Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

% Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

% Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.45.24 octave.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
% TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

% TODO: Add linear model summary output
% TOD: Add linear model summary plot

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    8.2.45.25 octave.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.freeny;

summary(t)

octave.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.45.26 octave.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    octave.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.45.27 octave.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    octave.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.45.28 octave.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    octave.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.45.29 octave.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.45.30 octave.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = octave.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.45.31 octave.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.45.32 octave.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.45.33 octave.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.45.34 octave.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = octave.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.45.35 octave.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = octave.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.45.36 octave.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = octave.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.45.37 octave.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = octave.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.45.38 octave.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.45.39 octave.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.45.40 octave.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = octave.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.45.41 octave.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = octave.dataset.morley;

# TODO: Port to Octave

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    8.2.45.42 octave.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.45.43 octave.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.45.44 octave.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = octave.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.45.45 octave.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.45.46 octave.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = octave.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.45.47 octave.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.45.48 octave.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.45.49 octave.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.OrchardSprays;

octave.examples.plot_pairs (t);

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    8.2.45.50 octave.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = octave.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.45.51 octave.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.45.52 octave.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.45.53 octave.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.45.54 octave.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = octave.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.45.55 octave.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.45.56 octave.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = octave.dataset.randu;

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    8.2.45.57 octave.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.45.58 octave.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = octave.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.45.59 octave.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = octave.dataset.sleep;

# TODO: Port to Octave

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    8.2.45.60 octave.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = octave.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.45.61 octave.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.state;

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    8.2.45.62 octave.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = octave.dataset.sunspot_month;

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    8.2.45.63 octave.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = octave.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.45.64 octave.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = octave.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.45.65 octave.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.45.66 octave.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = octave.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.45.67 octave.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    octave.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.45.68 octave.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = octave.dataset.ToothGrowth;

octave.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.45.69 octave.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = octave.dataset.treering;

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    8.2.45.70 octave.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = octave.dataset.trees;

figure
octave.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.45.71 octave.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    octave.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.45.72 octave.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    octave.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.45.73 octave.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = octave.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.45.74 octave.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = octave.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.45.75 octave.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = octave.dataset.USAccDeaths;

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    8.2.45.76 octave.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.USArrests;

summary (t);

octave.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.45.77 octave.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = octave.dataset.USJudgeRatings;

figure
octave.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.45.78 octave.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.45.79 octave.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.45.80 octave.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.45.81 octave.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    octave.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.45.82 octave.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.45.83 octave.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.45.84 octave.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.45.85 octave.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.45.86 octave.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.46 octave.datasets

    Class: datasets

    Example dataset collection.

    datasets is a collection of example datasets to go with the Tablicious package.

    The datasets class provides methods for listing and loading the example datasets.

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    8.2.46.1 octave.datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.46.2 octave.datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.46.3 octave.datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.47 octave.examples.coplot

    Function: [fig, hax] = coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.48 octave.examples.plot_pairs

    Function: out = plot_pairs (data)
    Function: out = plot_pairs (data, plot_type)
    Function: out = plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.49 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.50 rmmissing

    Function: [out, tf] = rmmissing (X)
    Function: [out, tf] = rmmissing (X, dim)
    Function: [out, tf] = rmmissing (…, 'MinNumMissing', MinNumMissing)

    Remove missing values.

    If x is a vector, removes elements with missing values. If x is a matrix, removes rows or columns with missing data elements.

    dim is the dimension to operate along. Specifying a dimension forces rmmissing to operate in matrix instead of vector mode.

    MinNumMissing indicates how many missing element values there must be in a row or column for it to be considered missing and this removed. This option is only used in matrix mode; it is silently ignored in vector mode.

    Returns: out - the input, with missing elements or rows or columns removed tf - a logical index vector indicating which elements, rows, or columns were removed

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    8.2.51 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.52 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.53 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.54 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.55 standardizeMissing

    Function: out = standardizeMissing (X, indicator)

    Insert standard missing values.

    Standardizes missing values in X by replacing the values listed in indicator with the standard missing values for the type of X.

    Standard missing values depend on the data type: * NaN for double, single, duration, and calendarDuration * NaT for datetime * ' ' for char * {''} for cellstrs * Integer numeric types have no standard missing value; they are never considered missing. * Structs are never considered missing. * Logicals are never considered missing.

    See also: table.standardizeMissing

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    8.2.56 startsWith

    Function: out = startsWith (str, pattern)
    Function: out = startsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings start with a pattern.

    Tests whether the given strings start with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.57 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where: - Each element of a string array is a single string - A single string is a 1-dimensional row vector of Unicode characters - Those characters are encoded in UTF-8 - This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles. - It is a type-safe object wrapper around Octave’s base primitive character types. - It adds ismissing() semantics. - And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.57.1 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs. - chars and cellstrs are converted via cellstr() - numerics are converted via num2str() - datetimes are converted via datestr()

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    8.2.57.2 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.57.3 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.57.4 string.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.57.5 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.57.6 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.57.7 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.57.8 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.57.9 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.57.10 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.57.11 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.57.12 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.57.13 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.57.14 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.57.15 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.57.16 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.17 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.18 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.57.19 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.57.20 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.57.21 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.57.22 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.57.23 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.57.24 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz.

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    8.2.57.25 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.58 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.59 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.59.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.59.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.59.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.59.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.59.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.59.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.59.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.59.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.59.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.59.10 table.length

    Method: out = length (obj)

    Length along longest dimension

    Use of length is not recommended. Use numel or size instead.

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    8.2.59.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2.

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    8.2.59.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions.

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    8.2.59.13 table.sizeof

    Method: out = sizeof (obj)

    Approximate size of array in bytes. For tables, this returns the sume of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently unimplemented.

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    8.2.59.14 table.height

    Method: out = height (obj)

    Number of rows in table.

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    8.2.59.15 table.rows

    Method: out = rows (obj)

    Number of rows in table.

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    8.2.59.16 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.59.17 table.columns

    Method: out = columns (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.59.18 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.59.19 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.59.20 table.ismatrix

    Method: out = ismatrix (obj)

    Test whether array is a matrix.

    For tables, ismatrix is always true, by definition.

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    8.2.59.21 table.isrow

    Method: out = isrow (obj)

    Test whether array is a row vector.

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    8.2.59.22 table.iscol

    Method: out = iscol (obj)

    Test whether array is a column vector.

    For tables, iscol is true if the input has a single variable. The number of columns within that variable does not matter.

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    8.2.59.23 table.isvector

    Method: out = isvector (obj)

    Test whether array is a vector.

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    8.2.59.24 table.isscalar

    Method: out = isscalar (obj)

    Test whether array is scalar.

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    8.2.59.25 table.hasrownames

    Method: out = hasrownames (obj)

    True if this table has row names defined.

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    8.2.59.26 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible.

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    8.2.59.27 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.59.28 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.59.29 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables.

    Only two dimensions are supported for repelem on tables.

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    8.2.59.30 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.31 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.32 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.59.33 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.59.34 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.59.35 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.59.36 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.59.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.59.38 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table

    Adds the specified variables to a table.

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    8.2.59.39 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.59.40 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.59.41 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.59.42 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.59.43 table.head

    Method: out = head (obj)
    Method: out = head (obj, k)

    Get first K rows of table.

    Returns the first k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.44 table.tail

    Method: out = tail (obj)
    Method: out = tail (obj, k)

    Get last K rows of table.

    Returns the last k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.45 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.59.46 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.59.47 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.59.48 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.59.49 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

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    8.2.59.50 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

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    8.2.59.51 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

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    8.2.59.52 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

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    8.2.59.53 table.grpstats

    Method: [out] = grpstats (obj, groupvar)
    Method: [out] = grpstats (…, 'DataVars', DataVars)

    Statistics by group.

    See also: table.groupby.

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    8.2.59.54 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby

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    8.2.59.55 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.59.56 table.congruentize

    Method: [outA, outB] = congruentize (A, B)

    Make tables congruent.

    Makes tables congruent by ensuring they have the same variables of the same types in the same order. Congruent tables may be safely unioned, intersected, vertcatted, or have other set operations done to them.

    Variable names present in one input but not in the other produces an error. Variables with the same name but different types in the inputs produces an error. Inputs must either both have row names or both not have row names; it is an error if one has row names and the other doesn’t. Variables in different orders are reordered to be in the same order as A.

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    8.2.59.57 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.58 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.59 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.60 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.59.61 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.59.62 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.59.63 table.rmmissing

    Method: [out, tf] = rmmissing (obj)
    Method: [out, tf] = rmmissing (obj, indicator)
    Method: [out, tf] = rmmissing (…, 'DataVariables', vars)
    Method: [out, tf] = rmmissing (…, 'MinNumMissing', minNumMissing)

    Remove rows with missing values.

    Removes the rows from obj that have missing values.

    If the ’DataVariables’ option is given, only the data in the specified variables is considered.

    Returns: out - A table the same as obj, but with rows with missing values removed. tf - A logical index vector indicating which rows were removed.

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    8.2.59.64 table.standardizeMissing

    Method: out = standardizeMissing (obj, indicator)
    Method: out = standardizeMissing (…, 'DataVariables', vars)

    Insert standard missing values.

    Standardizes missing values in variable data.

    If the DataVariables option is supplied, only the indicated variables are standardized.

    indicator is passed along to standardizeMissing when it is called on each of the data variables in turn. The same indicator is used for all variables. You can mix and match indicator types by just passing in mixed indicator types in a cell array; indicators that don’t match the type of the column they are operating on are just ignored.

    Returns a table with same variable names and types as obj, but with variable values standardized.

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    8.2.59.65 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.59.66 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.59.67 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.59.68 table.evalWithVars

    Method: out = evalWithVars (obj, expr)

    Evaluate an expression against table’s variables.

    Evaluates the M-code expression expr in a workspace where all of obj’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = table_examples.SpDb
tmp = join (sp, p);
shipment_weight = evalWithVars (tmp, "Qty .* Weight")

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    8.2.59.69 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with evalWithVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, evalWithVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = table_examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

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    8.2.59.70 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.59.71 table.resolveVarRef

    Method: [ixVar, varNames] = resolveVarRef (obj, varRef)
    Method: [ixVar, varNames] = resolveVarRef (obj, varRef, strictness)

    Resolve a variable reference against this table.

    A varRef is a numeric or char/cellstr indicator of which variables within obj are being referenced.

    strictness controls what to do when the given variable references could not be resolved. It may be ’strict’ (the default) or ’lenient’.

    Returns: ixVar - the indexes of the variables in obj varNames - a cellstr of the names of the variables in obj

    Raises an error if any of the specified variables could not be resolved, unless strictness is ’lenient’, in which case it will return 0 for the index and ” for the name for each variable which could not be resolved.

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    8.2.59.72 table.subsetrows

    Method: out = subsetrows (obj, ixRows)

    Subset table by rows.

    Subsets this table by rows.

    ixRows may be a numeric or logical index into the rows of obj.

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    8.2.59.73 table.subsetvars

    Method: out = subsetvars (obj, ixVars)

    Subset table by variables.

    Subsets table obj by subsetting it along its variables.

    ixVars may be: - a numeric index vector - a logical index vector - ":" - a cellstr vector of variable names

    The resulting table will have its variables reordered to match ixVars.

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    8.2.60 tableOuterFillValue

    Function: out = tableOuterFillValue (x)

    Outer fill value for variable within a table.

    Determines the fill value to usse for a given variable value x when that value is used as a variable in a table that is involved in an outer join.

    The default implementation for tableOuterFillValue has support for all Octave primitive types, plus cellstrs, datetime & friends, strings, and table-valued variables.

    This function may become private to table before version 1.0. It is currently global to make debugging more convenient. It (or an equivalent) will remain global if we want to allow user-defined classes to customize their fill value. It also has default logic that will determine the fill value for an arbitrary type by detecting the value used to fill elements during array expansion operations. This will be appropriate for most data types.

    Returns a 1-by-ncols value of the same type as x, which may be any type, where ncols is the number of columns in the input.

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    8.2.61 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.62 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.63 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.64 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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A/M⹳5gWPoXܦ_j%*uFuR7o.:z%a$RL:$óN?Q+9pN5޼VC?z(:*O7ͳҨ/yg7MҢ!>i h%a쨑:k6tbMX v걐ZI= E,NBz,PG4=RM?WjR樮X3M$(X By,` I6vv#X/١nACmQ\4C$ĤuZҠ8XXP5KՃKsdG44gVg+Jijvf6t2wF%7ߜЮhWL9c\֘*`>JE{WnŪ~So18V+{eT'ձV1R 3i@[-3L˨ɈFo&S1`ә:s-J.cxnD`4UK@ oRW<s3&Mȥ"]2fL;Q}32}22fL25QΈy,Zs1`z賁z#1,1 Zydcn\0E ύw0ʧqlWR36kԻ0fG]s.0юcRj.#IY_m1`D_b[dtLQ1^DU/SQ1`:#zߐJ.cxnDk#׿t=g/{JcxnDs3(˖1`D߽#玜1`8r-sr#] 1B-A0JxtDvհ!)R%1<7bR|`1`L}qW7c)ɌF?31e ͘w06_Xcvs1fL;qCd+͒/0JXf_'W͘w0e=1<1`TݛɌFp2:J.cxnD=s61*rs3&蹭3#!1`dE=m[{%1+1<7cX1r4yfdZ-1,7b;jBk}oh ?3M QĘ߷7\pt%}O?/*VO?i˼r+-p7w( _|Ew??_2wPk)>=|~xS.xÏ{{(?<"oܷ\}ŷTj|IT#FFoH%i7Bo z3fЛ7Co zzzzzzzzzzz+V[Bo z+E)G"H{#RtFj^ z-ZkLoGn{.iRkD)ѓS{"gwzߋ{: % Mgwb<}6jCSn1`Y}?';CaF[>1w}3}]a}Ϟ endstream endobj 550 0 obj << /Length 1455 /Filter /FlateDecode >> stream xKsF:J[Ƶ٤bWKȢ}h3i9) UF04=nLF~#2\f ܌O_ iˇwBF2zXa?|S2a&A)kƑft$5GQ9bxOTgU4OͲȳ]W UU%zJvp#Ws&S"v#~mk~mNFSF vƈ@~dʸrܞμWmL%5m5gavF=y}P$K !C׃%RC#֓S\Qk c'K`B=@x,u&U=q“f=SXfCY9˛ƱBX-0[a[UqVݞ"Iix{0I{l#d0Ő{hC +uY@B}e~lŝ]@H4mR>m/ZF lH Tk':bJйY QI,8 NlW[R+ZAAh2iDϿM4Ӳɝ)&H˽Qm# _&VU*-KQo۫8Nlpy9Ȑs*HQ"Y5!z6ޞSSu_ض0^lF G)"/ #',ߍ+KO0q'μZ.ch7y-#Di CeUAp)"I"HQEfm ^.tH,CNIWO+6'mNQԻYW)*a[PW:*n˜IjX?2:(0@֙XE&d٢E9w#V .([s HQRl< _>̰RT{) 샞Q6HayDpj(9ŇDBljkZFc2>JE\T>5: )ud^112U/EsS32,ϰ`o±A<@E%eeҗ!B0CqEO]&i`k7ݩ*@-m5i_} > stream xڽA+xL.VE;|!"/#ƮZο+NM LЂi"î\Z l(>Q",czUlj %jL>B ,~I =_tu YKdVqZ͍ Ӏz>cYm^Y .uYxAO7k7h?MtQtN-7H[^yaՊtFEE۩/F;he':: }DžF\}LfQv1FE$1 n~Z]thF$`K>Qx\VG0 0hNnFFQ BVl,+#2]R 6eA͹/Ik74;]g9xiJ̉\k7:{b@:+]~$KqZ.Ǩ#"v6ȥz?6E|,񿅠L e_ ($]yw yLO>{O4m/N鮜x:|y:cawww` 1΢.5xQo뇷?/8Ct։M+ƐC<ȸ6R,ċ&L\՗ Z.DHxtL1ZsTg#1,7bAHj Ly1`l'rU{WB7B ˍgw/li2L)ez*_:^ٕΐ.+jjR~K4׿q+~ݵΫ-JKWPÛ;L9~T';Eu:LJOW}~iG{z>ՏaKiQ^n؏]ʨQO^K9Z%G٣ z#Fgggggggggg7Co z3fЛ7Co z+V[Bo u֢(9DQ(-eQQQQQQQQQQqqqqqqEaKzz=zC/T#N5T#N^LJГлDO߳@bebHRW>(gw,̌DHu&̳;P %()4EFatoSPg2bXnDc,OK1`cн! 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J7,]2ᄳC1J2&SaeէE|#X8.)gE~OǥQUZ4mMrzŨ䣑q:P';) \EJ"NU0{v!CͿ]vq3AQFwY +LI=V7igmK - q YUe;\+J\l`/[bIY9x\M8#1-\ybKK@uui v7aׁ3UUG9V5f ` frӬP-;bڭYJqMDVu=LۊEr8[^WyW {-E ݵjכqѽ !Ѱ{5ERevd0M5Ay~&N"Nw`bnn:?{qL2>@ʕi8G}QG{U-`Z烬:'_+ijKRT~_`]⇖Ie8@4S7&-?խG> stream xYIFϯ[@*ƒ(/G#Yv_tSc4aqj`=X#5PWSUtu<Ȓ}+jף:Ow=]-<WV 1'gMN{:'%oxwALy!b&hB&++2:E(W!(qy!ڬ"'} (F1D>|%_uVLGF_%'?s|4CQcxʦ G("DE˃lbdw#uB"zqINym (_iyEyuR}UQK[:DBSk~ϦbD!4-vnS N5ݿ] 4F!+ztpxH66gfY;/_bߵdݡ|ozuy-/xIv({UꤕRSF0q;OFQZ?Vua`gqѝ"ՇΡQW``DuPi&)˵~EKڼ*,7RBD!Jim}jDtkUK~sR,iuˋwZVi]ͫa|jJ׈eƆ35҇||FKe*J!R-URv}M{s]W Z ڞgr$.G\ʓ` S$-Ȅ۪f׻>W L @R̬rWȓ|}:: d mZ4AaUyEkORw6K'v)x8$V%sǴňwbaX ) A$dJ.)Su0fuj]ByKO]!7cXk+njqI7"r=>r#j8-82)d>NXfEؼsFgJ0&y㋀:;moJ؋ OFKI$@5`tqOOuҪb[_LIcKbE >;'s"\_p<̾i3&mVu 5n8'<j$wqj8F1aw @y}oj}-M6$Jܦe./Ph?c?'(fDw]+bx]:]6ny7w)#( .o} K.%ooжmȾr+Mz[ utd:6~]oDzD -UiX7R,3M>k13wSԱ_v/g(K{;B\ l9?>_oaQ> stream xYێ6}߯0X&QҦ@ qi27Ԗ\3Hm%A"@LQ!yP }?fD><]İ|XH76v`Wο=k("icAԜygUHjdh )wmM ?{i7s]3,%gzN<+ϕcg!gȯN0*qƊoX05`p̸1csJΤ6e}OFFM-9ˌB߈9K&t[$ rѫŅ;ɴlѾ j)Q^MK?{oaH|/WDLQdמ7#aYWy$E:"~KD|^m[T~Zu@Hx )οQw#X۬&2tn]#Ebod52(D.T&d- X4iԺ(6hĠ!i8 qPCks4[6,KCa^]d4d9L1/+ZhP 60jaXèj3eWZ%5ˠ$SRڕҩM6&aYoI< M'XKf` SMδYu@ )J^-g2s6;u ܇ƇW#[dRLp )8[\a7_Cμ@6p7>*cB9-N7UY5;rRPX0 =3veBb2Ӡ)ց3TUn֝zPlтDDpXz$ Ϗ1 )Ñ'56dxdpkB0sqdTuA4A);;-"f="簉f'tr|kAqMQW]u}̺߀}#$> stream xXێF}߯G  J@2=!קlO2%hu]>]}Typ8`]]fvpvvF3Gw?r%qt;wn}vvQJCu1Ç??6oz""1C"VogWOqMNȅ-t9tB1)"(A$^E\py_T9U+C=hNHB ""I7 nڗQ9/E7޹s+`"pB%8'^ <|Ĉ/>D ʥqkL5WS?T*}#0aqpp(:dq$b'L3}hI ngDؖ#Ļ--yiֶ+ p oVH'4 ӽ{ݱaog:3{z7.n}0ĀyA9`~6LGaV>:ܳ'^ iu*,dq+.HsCnm3{'" &ESpE\8 ޥFXZZx:Ot4Yy/e@K.Sr7bjܑrXлSND!PW%Y\WRL%1 D+RYDavH:!5S4Q*v* 䳷cQӮJc{ol k'GN%z:awy"̮#JEEӻ9fs}7w8knq.k7e9)mfSVծ1*:4wG1 M.#I#Epm & (L՗\C6\ڕ@/\-P|zJO̪Cߟl$t Mok+!Cm{ +lDGKɗ恇}홃p[]6.,5},J49)EWgׯ3ZtUUXP@g5= 8˼2])n> stream xMo6MFW,EM[E*l+_ߡH}9t9D3g(DHq24Wl}r_ψK0X^NϾE`&zj:?-mmIs_ \kSbf& M1M';8"H#*&4gA+c؟S 4J(E2^n {?IH%Wovw-VT ektPYH<#Y~rD9m.B%i՛B&\FN *Q£ց⌧rg kC=@Jg1 *[rVjx\/m]77+4ߛ8lR%y<]>|gS/Mp@ζљ)5\|KdV֡\@; 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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    array2table

    Convert an array to a table.

    cell2table

    Convert a cell array to a table.

    struct2table

    Convert struct to a table.

    tableOuterFillValue

    Outer fill value for variable within a table.

    vartype

    Filter by variable type for use in suscripting.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    startsWith

    Test if strings start with a pattern.

    endsWith

    Test if strings end with a pattern.

    contains

    Test if strings contain a pattern.

    categorical

    Categorical variable array.

    discretize

    Group data into discrete bins or categories.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    localdate

    Represents a complete day using the Gregorian calendar.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    NaT

    “Not-a-Time”.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    days

    Duration in days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    timezones

    List all the time zones defined on this system.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    8.1.4 Missing Data

    fillmissing

    Fill missing values.

    ismissing

    Find missing values.

    rmmissing

    Remove missing values.

    standardizeMissing

    Insert standard missing values.

    missing

    Generic auto-converting missing value.

    isnanny

    Test if elements are NaN or NaN-like

    eqn

    Determine element-wise equality, treating NaNs as equal

    8.1.5 Validation Functions

    mustBeA

    Requires that input is of a given type.

    mustBeCellstr

    Requires that input is a cellstr.

    mustBeCharvec

    Requires that input is a char row vector.

    mustBeFinite
    mustBeInteger
    mustBeMember
    mustBeNonempty
    mustBeNumeric
    mustBeReal
    mustBeSameSize

    Requires that the inputs are the same size.

    mustBeScalar

    Requires that input is scalar.

    mustBeScalarLogical

    Requires that input is a scalar logical.

    mustBeVector

    Requires that input is a vector or empty.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    dispstrs

    Display strings for array.

    isfile
    isfolder
    pp

    Alias for prettyprint, for interactive use.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    size2str

    Format an array size for display.

    splitapply

    Split data into groups and apply function.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    8.1.7 Example Datasets

    octave.datasets

    Example dataset collection.

    octave.dataset

    The ‘dataset’ class provides convenient access to the various datasets included with Tablicious.

    8.1.8 Example Code

    octave.examples.plot_pairs

    Plot pairs of variables against each other.

    8.1.9 Uncategorized

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    octave.chrono.dummy_function

    A dummy function just for testing the doco tools.

    octave.chrono.DummyClass

    A do-nothing class just for testing the doco tools.

    octave.examples.coplot

    Conditioning plot.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Copying.html000066400000000000000000000067701515050116100276650ustar00rootroot00000000000000 Copying (Octave Tablicious v0.3.7)

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056651515050116100312040ustar00rootroot00000000000000 Data Sets from R (Octave Tablicious v0.3.7)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074561515050116100324630ustar00rootroot00000000000000 Datenum Compatibility (Octave Tablicious v0.3.7)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060051515050116100315720ustar00rootroot00000000000000 Defined Time Zones (Octave Tablicious v0.3.7)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Durations.html000066400000000000000000000046401515050116100302170ustar00rootroot00000000000000 Durations (Octave Tablicious v0.3.7) apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Example-Data-Sets.html000066400000000000000000000102141515050116100314170ustar00rootroot00000000000000 Example Data Sets (Octave Tablicious v0.3.7)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the octave.datasets and octave.dataset classes.

    To see a list of the available data sets, run octave.datasets.list(). Then to load one of the example data sets, run octave.datasets.load('examplename'). For example: 

    octave.datasets.list
t = octave.datasets.load('cupcake')

    You can also load it by calling octave.dataset.<name>. This does the same thing. For example: 

    t = octave.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on octave.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc octave.dataset.cupcake

    (The command help octave.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.3.7/tablicious-0.3.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/Introduction.html000066400000000000000000000072241515050116100307310ustar00rootroot00000000000000 Introduction (Octave Tablicious v0.3.7)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.3.7 for GNU Octave.

    Tablicious provides Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports most of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that all these will be implemented some day.

    These areas of missing functionality are all tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/1.

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    8.2.42 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.8 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    calendarDuration_002eisnan.html000066400000000000000000000057341515050116100332330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html calendarDuration.isnan (Octave Tablicious v0.3.7)

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    8.2.2.9 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is just an alias for isnat, provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    calendarDuration_002eisnat.html000066400000000000000000000060311515050116100332300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html calendarDuration.isnat (Octave Tablicious v0.3.7)

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    8.2.2.3 calendarDuration.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.2.7 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.5 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.2 calendarDuration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.2.6 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.4 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff: - gt, ge, lt, le - Ordinal support in general - countcats - summary

    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062061515050116100326430ustar00rootroot00000000000000 categorical.cellstr (Octave Tablicious v0.3.7)

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062411515050116100330450ustar00rootroot00000000000000 categorical.dispstrs (Octave Tablicious v0.3.7)

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    categorical_002eiscategory.html000066400000000000000000000063061515050116100332660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html categorical.iscategory (Octave Tablicious v0.3.7)

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.3 categorical.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.35 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.19 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.15 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.16 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.17 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.32 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.14 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.27 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.28 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.20 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.29 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/datetime_002ehms.html000066400000000000000000000060761515050116100313060ustar00rootroot00000000000000 datetime.hms (Octave Tablicious v0.3.7)

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    8.2.9.10 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.33 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.23 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/datetime_002eisnat.html000066400000000000000000000056261515050116100316350ustar00rootroot00000000000000 datetime.isnat (Octave Tablicious v0.3.7)

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    8.2.9.22 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.34 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.21 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
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    8.2.9.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.31 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/datetime_002eofDatenum.html000066400000000000000000000060371515050116100324360ustar00rootroot00000000000000 datetime.ofDatenum (Octave Tablicious v0.3.7)

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.18 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.8 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.7 datetime.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.9.12 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/datetime_002eweek.html000066400000000000000000000055521515050116100314500ustar00rootroot00000000000000 datetime.week (Octave Tablicious v0.3.7)

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    8.2.9.13 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.9 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.9 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.8 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.4 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.10 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.13.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.5 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.6 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.2 duration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.13.3 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14 endsWith

    Function: out = endsWith (str, pattern)
    Function: out = endsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings end with a pattern.

    Tests whether the given strings end with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/fillmissing.html000066400000000000000000000110471515050116100305660ustar00rootroot00000000000000 fillmissing (Octave Tablicious v0.3.7)

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    8.2.16 fillmissing

    Function: [out, tfFilled] = fillmissing (X, method)
    Function: [out, tfFilled] = fillmissing (X, 'constant', fill_val)
    Function: [out, tfFilled] = fillmissing (X, movmethod, window)

    Fill missing values.

    Fills missing values in X according to the method specified by method.

    This method is only partially implemented.

    method may be: 'constant' 'previous' 'next' 'nearest' 'linear' 'spline' 'pchip' movmethod may be: 'movmean' 'movmedian'

    Returns out, which is X but with missing values filled in, and tfFilled, a logical array the same size as X which indicates which elements were filled.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/hours.html000066400000000000000000000062651515050116100274140ustar00rootroot00000000000000 hours (Octave Tablicious v0.3.7)

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/index.html000066400000000000000000001373661515050116100273720ustar00rootroot00000000000000 Top (Octave Tablicious v0.3.7)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.3.7.

    Table of Contents

    Next:   [Contents]

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/ismissing.html000066400000000000000000000131331515050116100302510ustar00rootroot00000000000000 ismissing (Octave Tablicious v0.3.7)

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    8.2.23 ismissing

    Function: out = ismissing (X)
    Function: out = ismissing (X, indicator)

    Find missing values.

    Determines which elements of X contain missing values. If an indicator input is not provided, standard missing values depending on the input type of X are used.

    Standard missing values depend on the data type:

    • NaN for double, single, duration, and calendarDuration
    • NaT for datetime
    • ' ' for char
    • {''} for cellstrs
    • Integer numeric types have no standard missing value; they are never considered missing.
    • Structs are never considered missing.
    • Logicals are never considered missing.
    • Other types have no standard missing value; it is currently an error to call ismissing on them without providing an indicator.
      • This includes cells which are not cellstrs; calling ismissing on them results in an error.
      • TODO: Determine whether this should really be an error, or if it should default to never considering those types as missing.
      • TODO: Decide whether, for classdef objects, ismissing should polymorphically detect isnan()/isnat()/isnanny() methods and use those, or whether we should require classes to override ismissing() itself.

    If indicator is supplied, it is an array containing multiple values, all of which are considered to be missing values. Only indicator values that are type-compatible with the input are considered; other indicator value types are silently ignored. This is by design, so you can pass an indicator that holds sentinel values for disparate types in to ismissing() used for any type, or for compound types like table.

    Indicators are currently not supported for struct or logical inputs. This is probably a bug.

    Table defines its own ismissing() method which respects individual variables’ data types; see table.ismissing.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/isnanny.html000066400000000000000000000074511515050116100277310ustar00rootroot00000000000000 isnanny (Octave Tablicious v0.3.7)

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: isnan, isnat, ismissing, eqn, isequaln

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    8.2.25 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002eNaT.html000066400000000000000000000100551515050116100313250ustar00rootroot00000000000000 localdate.NaT (Octave Tablicious v0.3.7)

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    8.2.25.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002edatenum.html000066400000000000000000000057331515050116100323070ustar00rootroot00000000000000 localdate.datenum (Octave Tablicious v0.3.7)

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    8.2.25.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.25.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.25.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002edatestruct.html000066400000000000000000000071031515050116100330250ustar00rootroot00000000000000 localdate.datestruct (Octave Tablicious v0.3.7)

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    8.2.25.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002edispstrs.html000066400000000000000000000057231515050116100325240ustar00rootroot00000000000000 localdate.dispstrs (Octave Tablicious v0.3.7)

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    8.2.25.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002eisnan.html000066400000000000000000000055501515050116100317570ustar00rootroot00000000000000 localdate.isnan (Octave Tablicious v0.3.7)

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    8.2.25.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002eisnat.html000066400000000000000000000056411515050116100317660ustar00rootroot00000000000000 localdate.isnat (Octave Tablicious v0.3.7)

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    8.2.25.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002elocaldate.html000066400000000000000000000105701515050116100325750ustar00rootroot00000000000000 localdate.localdate (Octave Tablicious v0.3.7)

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    8.2.25.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/localdate_002eposixtime.html000066400000000000000000000063521515050116100326710ustar00rootroot00000000000000 localdate.posixtime (Octave Tablicious v0.3.7)

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    8.2.25.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.25.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/milliseconds.html000066400000000000000000000063601515050116100307350ustar00rootroot00000000000000 milliseconds (Octave Tablicious v0.3.7)

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    8.2.26 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/minutes.html000066400000000000000000000055711515050116100277370ustar00rootroot00000000000000 minutes (Octave Tablicious v0.3.7)

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    8.2.27 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/missing.html000066400000000000000000000075011515050116100277170ustar00rootroot00000000000000 missing (Octave Tablicious v0.3.7)

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    8.2.28 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.28.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.28.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/missing_002eisnan.html000066400000000000000000000057621515050116100315050ustar00rootroot00000000000000 missing.isnan (Octave Tablicious v0.3.7)

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    8.2.28.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.28.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/missing_002emissing.html000066400000000000000000000054341515050116100320420ustar00rootroot00000000000000 missing.missing (Octave Tablicious v0.3.7)

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    8.2.28.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeA.html000066400000000000000000000071321515050116100276060ustar00rootroot00000000000000 mustBeA (Octave Tablicious v0.3.7)

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    8.2.29 mustBeA

    Function File: x = mustBeA (x, type)
    Function File: x = mustBeA (x, type, label)

    Requires that input is of a given type.

    Raises an error if the input x is not of type type, as determined by isa (x, type).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.30 mustBeCellstr

    Function File: x = mustBeCellstr (x, label)

    Requires that input is a cellstr.

    Raises an error if the input x is not a cellstr (a cell array of char arrays).

    TODO: Decide whether to require the contained char arrays be rowvec/empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.31 mustBeCharvec

    Function File: x = mustBeCharvec (x, label)

    Requires that input is a char row vector.

    Raises an error if the input x is not a row vector of chars. char row vectors are Octave’s normal representation of single strings. (They are what are produced by '...' string literals.) As a special case, 0-by-0 empty chars (what is produced by the string literal '') are also considered charvecs.

    This does not differentiate between single-quoted and double-quoted strings.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.32 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeInteger.html000066400000000000000000000045501515050116100310240ustar00rootroot00000000000000 mustBeInteger (Octave Tablicious v0.3.7)

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    8.2.33 mustBeInteger

    Not documented

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    8.2.34 mustBeMember

    Not documented

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    8.2.35 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeNumeric.html000066400000000000000000000045501515050116100310310ustar00rootroot00000000000000 mustBeNumeric (Octave Tablicious v0.3.7)

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    8.2.36 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeReal.html000066400000000000000000000045421515050116100303130ustar00rootroot00000000000000 mustBeReal (Octave Tablicious v0.3.7)

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    8.2.37 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeSameSize.html000066400000000000000000000066631515050116100311560ustar00rootroot00000000000000 mustBeSameSize (Octave Tablicious v0.3.7)

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    8.2.38 mustBeSameSize

    Function File: [a, b] = mustBeSameSize (a, b, labelA, labelB)

    Requires that the inputs are the same size.

    Raises an error if the inputs a and b are not the same size, as determined by isequal (size (a), size (b)).

    labelA and labelB are optional inputs that determine how the input will be described in error messages. If not supplied, inputname (...) is used, and if that is empty, it falls back to "input 1" and "input 2".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeScalar.html000066400000000000000000000063671515050116100306440ustar00rootroot00000000000000 mustBeScalar (Octave Tablicious v0.3.7)

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    8.2.39 mustBeScalar

    Function File: x = mustBeScalar (x, label)

    Requires that input is scalar.

    Raises an error if the input x is not scalar, as determined by isscalar (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeScalarLogical.html000066400000000000000000000064651515050116100321360ustar00rootroot00000000000000 mustBeScalarLogical (Octave Tablicious v0.3.7)

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    8.2.40 mustBeScalarLogical

    Function File: x = mustBeScalarLogical (x, label)

    Requires that input is a scalar logical.

    Raises an error if the input x is not scalar, as determined by isscalar (x) && islogical (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/mustBeVector.html000066400000000000000000000062731515050116100306750ustar00rootroot00000000000000 mustBeVector (Octave Tablicious v0.3.7)

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    8.2.41 mustBeVector

    Function File: x = mustBeVector (x, label)

    Requires that input is a vector or empty.

    Raises an error if the input x is not a row vector and is not 0-by-0 empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

    octave_002echrono_002eDummyClass.html000066400000000000000000000106001515050116100341310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.chrono.DummyClass (Octave Tablicious v0.3.7)

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    8.2.44 octave.chrono.DummyClass

    Class: DummyClass

    A do-nothing class just for testing the doco tools.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    Instance Variable of DummyClass: double x

    An x. Has no semantics.

    Instance Variable of DummyClass: double y

    A y. Has no semantics.

    octave_002echrono_002eDummyClass_002eDummyClass.html000066400000000000000000000071361515050116100366730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.chrono.DummyClass.DummyClass (Octave Tablicious v0.3.7)

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    8.2.44.1 octave.chrono.DummyClass.DummyClass

    Constructor: obj = octave.chrono.DummyClass ()

    Constructs a new scalar DummyClass with default values.

    Constructor: obj = octave.chrono.DummyClass (x, y)

    Constructs a new DummyClass with the specified values.

    octave_002echrono_002eDummyClass_002ebar.html000066400000000000000000000061461515050116100353560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.chrono.DummyClass.bar (Octave Tablicious v0.3.7)

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    8.2.44.3 octave.chrono.DummyClass.bar

    Method: out = bar (obj)

    Computes a bar value.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    octave_002echrono_002eDummyClass_002efoo.html000066400000000000000000000065471515050116100354020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.chrono.DummyClass.foo (Octave Tablicious v0.3.7)

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    8.2.44.2 octave.chrono.DummyClass.foo

    Method: out = foo (obj)

    Computes a foo value.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    octave_002echrono_002edummy_005ffunction.html000066400000000000000000000063141515050116100355120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.chrono.dummy_function (Octave Tablicious v0.3.7)

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    8.2.43 octave.chrono.dummy_function

    Function: out = dummy_function (x)

    A dummy function just for testing the doco tools.

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur ullamcorper pulvinar ligula, sit amet accumsan turpis dapibus at. Ut sit amet quam orci. Donec vel mauris elementum massa pretium tincidunt.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/octave_002edataset.html000066400000000000000000000244121515050116100316230ustar00rootroot00000000000000 octave.dataset (Octave Tablicious v0.3.7)

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    8.2.45 octave.dataset

    Class: dataset

    The dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It’s provided so you can use tab completion on the dataset list.

    octave_002edataset_002eAirPassengers.html000066400000000000000000000075571515050116100347740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.AirPassengers (Octave Tablicious v0.3.7)

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    8.2.45.2 octave.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    octave_002edataset_002eBJsales.html000066400000000000000000000110411515050116100335300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.BJsales (Octave Tablicious v0.3.7)

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    8.2.45.9 octave.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eBOD.html000066400000000000000000000102631515050116100326160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.BOD (Octave Tablicious v0.3.7)

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    8.2.45.10 octave.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002eChickWeight.html000066400000000000000000000113071515050116100344030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.ChickWeight (Octave Tablicious v0.3.7)

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    8.2.45.12 octave.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.ChickWeight

octave.examples.coplot (t, "Time", "weight", "Chick");
    octave_002edataset_002eDNase.html000066400000000000000000000111741515050116100332060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.DNase (Octave Tablicious v0.3.7)

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    8.2.45.18 octave.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.DNase;

# TODO: Port this from R

octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    octave_002edataset_002eEuStockMarkets.html000066400000000000000000000111341515050116100351140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.EuStockMarkets (Octave Tablicious v0.3.7)

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    8.2.45.22 octave.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = octave.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price,1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    octave_002edataset_002eFormaldehyde.html000066400000000000000000000114201515050116100346110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Formaldehyde (Octave Tablicious v0.3.7)

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    8.2.45.24 octave.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
% TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

% TODO: Add linear model summary output
% TOD: Add linear model summary plot
    octave_002edataset_002eHairEyeColor.html000066400000000000000000000151061515050116100345400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.HairEyeColor (Octave Tablicious v0.3.7)

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    8.2.45.26 octave.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    octave.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: octave.dataset.Harman23cor, Previous: octave.dataset.freeny, Up: octave.dataset   [Contents]

    octave_002edataset_002eHarman23cor.html000066400000000000000000000102741515050116100342730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Harman23cor (Octave Tablicious v0.3.7)

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    8.2.45.27 octave.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    octave.dataset.Harman23cor;

# TODO: Port factanal to Octave
    octave_002edataset_002eHarman74cor.html000066400000000000000000000103511515050116100342750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Harman74cor (Octave Tablicious v0.3.7)

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    8.2.45.28 octave.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    octave.dataset.Harman74cor;

# TODO: Port factanal to Octave
    octave_002edataset_002eIndometh.html000066400000000000000000000110071515050116100337560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Indometh (Octave Tablicious v0.3.7)

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    8.2.45.29 octave.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    octave_002edataset_002eInsectSprays.html000066400000000000000000000104461515050116100346440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.InsectSprays (Octave Tablicious v0.3.7)

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    8.2.45.31 octave.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    octave_002edataset_002eJohnsonJohnson.html000066400000000000000000000102601515050116100351640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.JohnsonJohnson (Octave Tablicious v0.3.7)

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    8.2.45.34 octave.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = octave.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    octave_002edataset_002eLakeHuron.html000066400000000000000000000104761515050116100341100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.LakeHuron (Octave Tablicious v0.3.7)

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    8.2.45.35 octave.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = octave.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    octave_002edataset_002eLifeCycleSavings.html000066400000000000000000000126301515050116100354040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.LifeCycleSavings (Octave Tablicious v0.3.7)

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    8.2.45.37 octave.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = octave.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    octave_002edataset_002eLoblolly.html000066400000000000000000000111041515050116100337750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Loblolly (Octave Tablicious v0.3.7)

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    8.2.45.38 octave.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    octave_002edataset_002eNile.html000066400000000000000000000112161515050116100331000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Nile (Octave Tablicious v0.3.7)

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    8.2.45.44 octave.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = octave.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    octave_002edataset_002eOrange.html000066400000000000000000000115241515050116100334260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Orange (Octave Tablicious v0.3.7)

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    8.2.45.48 octave.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    octave_002edataset_002eOrchardSprays.html000066400000000000000000000124761515050116100350060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.OrchardSprays (Octave Tablicious v0.3.7)

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    8.2.45.49 octave.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.OrchardSprays;

octave.examples.plot_pairs (t);
    octave_002edataset_002ePlantGrowth.html000066400000000000000000000102201515050116100344540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.PlantGrowth (Octave Tablicious v0.3.7)

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    8.2.45.50 octave.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = octave.dataset.PlantGrowth;

# TODO: Port anova to Octave
    octave_002edataset_002ePuromycin.html000066400000000000000000000124731515050116100342040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Puromycin (Octave Tablicious v0.3.7)

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    8.2.45.54 octave.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = octave.dataset.Puromycin;

# TODO: Port example to Octave
    octave_002edataset_002eTheoph.html000066400000000000000000000142621515050116100334440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Theoph (Octave Tablicious v0.3.7)

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    8.2.45.66 octave.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = octave.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: octave.dataset.Titanic, Previous: octave.dataset.swiss, Up: octave.dataset   [Contents]

    octave_002edataset_002eTitanic.html000066400000000000000000000146051515050116100336110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.Titanic (Octave Tablicious v0.3.7)

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    8.2.45.67 octave.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    octave.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: octave.dataset.ToothGrowth, Previous: octave.dataset.Theoph, Up: octave.dataset   [Contents]

    octave_002edataset_002eToothGrowth.html000066400000000000000000000115331515050116100345030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.ToothGrowth (Octave Tablicious v0.3.7)

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    8.2.45.68 octave.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = octave.dataset.ToothGrowth;

octave.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    octave_002edataset_002eUCBAdmissions.html000066400000000000000000000141011515050116100346500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.UCBAdmissions (Octave Tablicious v0.3.7)

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    8.2.45.71 octave.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    octave.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: octave.dataset.UKDriverDeaths, Previous: octave.dataset.trees, Up: octave.dataset   [Contents]

    octave_002edataset_002eUKDriverDeaths.html000066400000000000000000000147451515050116100350470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.UKDriverDeaths (Octave Tablicious v0.3.7)

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    8.2.45.72 octave.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    octave.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: octave.dataset.UKgas, Previous: octave.dataset.UCBAdmissions, Up: octave.dataset   [Contents]

    octave_002edataset_002eUKLungDeaths.html000066400000000000000000000111001515050116100344770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.UKLungDeaths (Octave Tablicious v0.3.7)

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    8.2.45.74 octave.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = octave.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    octave_002edataset_002eUKgas.html000066400000000000000000000103711515050116100332240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.UKgas (Octave Tablicious v0.3.7)

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    8.2.45.73 octave.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = octave.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    octave_002edataset_002eUSAccDeaths.html000066400000000000000000000100541515050116100342770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.USAccDeaths (Octave Tablicious v0.3.7)

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    8.2.45.75 octave.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = octave.dataset.USAccDeaths;
    octave_002edataset_002eUSArrests.html000066400000000000000000000137571515050116100341200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.USArrests (Octave Tablicious v0.3.7)

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    8.2.45.76 octave.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.USArrests;

summary (t);

octave.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: octave.dataset.USJudgeRatings, Previous: octave.dataset.USAccDeaths, Up: octave.dataset   [Contents]

    octave_002edataset_002eUSJudgeRatings.html000066400000000000000000000116241515050116100350520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.USJudgeRatings (Octave Tablicious v0.3.7)

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    8.2.45.77 octave.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = octave.dataset.USJudgeRatings;

figure
octave.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    octave_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111101515050116100364520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.USPersonalExpenditure (Octave Tablicious v0.3.7)

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    8.2.45.78 octave.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    octave_002edataset_002eVADeaths.html000066400000000000000000000113271515050116100336530ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.VADeaths (Octave Tablicious v0.3.7)

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    8.2.45.80 octave.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.VADeaths;

# TODO: Port to Octave
    octave_002edataset_002eWWWusage.html000066400000000000000000000103541515050116100337240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.WWWusage (Octave Tablicious v0.3.7)

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    8.2.45.85 octave.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eWorldPhones.html000066400000000000000000000105161515050116100344570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.WorldPhones (Octave Tablicious v0.3.7)

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    8.2.45.84 octave.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    octave_002edataset_002eairmiles.html000066400000000000000000000072311515050116100340200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.airmiles (Octave Tablicious v0.3.7)

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    8.2.45.1 octave.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = octave.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    octave_002edataset_002eairquality.html000066400000000000000000000125501515050116100343770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.airquality (Octave Tablicious v0.3.7)

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    8.2.45.3 octave.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = octave.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if i == j
      continue
    endif
    ix_subplot = (n_vars*(j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    octave_002edataset_002eanscombe.html000066400000000000000000000121441515050116100340010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.anscombe (Octave Tablicious v0.3.7)

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    8.2.45.4 octave.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = octave.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim(haxs(1), x_limits);
ylim(haxs(1), y_limits);
    octave_002edataset_002eattenu.html000066400000000000000000000114521515050116100335130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.attenu (Octave Tablicious v0.3.7)

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    8.2.45.5 octave.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    octave_002edataset_002eattitude.html000066400000000000000000000111041515050116100340300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.attitude (Octave Tablicious v0.3.7)

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    8.2.45.6 octave.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = octave.dataset.attitude

octave.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    octave_002edataset_002eaustres.html000066400000000000000000000102221515050116100336730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.austres (Octave Tablicious v0.3.7)

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    8.2.45.7 octave.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = octave.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    octave_002edataset_002ebeavers.html000066400000000000000000000106451515050116100336450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.beavers (Octave Tablicious v0.3.7)

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    8.2.45.8 octave.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    octave_002edataset_002ecars.html000066400000000000000000000116701515050116100331450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.cars (Octave Tablicious v0.3.7)

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    8.2.45.11 octave.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = octave.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    octave_002edataset_002echickwts.html000066400000000000000000000114241515050116100340310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.chickwts (Octave Tablicious v0.3.7)

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    8.2.45.13 octave.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = octave.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    octave_002edataset_002eco2.html000066400000000000000000000116671515050116100327060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.co2 (Octave Tablicious v0.3.7)

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    8.2.45.14 octave.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = octave.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    octave_002edataset_002ecrimtab.html000066400000000000000000000164011515050116100336330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.crimtab (Octave Tablicious v0.3.7)

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    8.2.45.15 octave.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: octave.dataset.cupcake, Previous: octave.dataset.co2, Up: octave.dataset   [Contents]

    octave_002edataset_002ecupcake.html000066400000000000000000000104721515050116100336270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.cupcake (Octave Tablicious v0.3.7)

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    8.2.45.16 octave.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = octave.dataset.cupcake
plot(datenum(t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric")
    octave_002edataset_002ediscoveries.html000066400000000000000000000106231515050116100345310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.discoveries (Octave Tablicious v0.3.7)

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    8.2.45.17 octave.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    octave_002edataset_002eesoph.html000066400000000000000000000106651515050116100333360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.esoph (Octave Tablicious v0.3.7)

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    8.2.45.19 octave.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    octave_002edataset_002eeuro.html000066400000000000000000000116601515050116100331660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.euro (Octave Tablicious v0.3.7)

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    8.2.45.20 octave.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    octave_002edataset_002eeurodist.html000066400000000000000000000101151515050116100340440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.eurodist (Octave Tablicious v0.3.7)

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    8.2.45.21 octave.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    octave_002edataset_002efaithful.html000066400000000000000000000121131515050116100340100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.faithful (Octave Tablicious v0.3.7)

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    8.2.45.23 octave.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = octave.dataset.faithful;

% Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

% Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

% Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    octave_002edataset_002efreeny.html000066400000000000000000000121061515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.freeny (Octave Tablicious v0.3.7)

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    8.2.45.25 octave.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.freeny;

summary(t)

octave.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    octave_002edataset_002einfert.html000066400000000000000000000116041515050116100335010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.infert (Octave Tablicious v0.3.7)

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    8.2.45.30 octave.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = octave.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    octave_002edataset_002eiris.html000066400000000000000000000121561515050116100331630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.iris (Octave Tablicious v0.3.7)

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    8.2.45.32 octave.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002eislands.html000066400000000000000000000104021515050116100336420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.islands (Octave Tablicious v0.3.7)

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    8.2.45.33 octave.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/octave_002edataset_002elh.html000066400000000000000000000101741515050116100326750ustar00rootroot00000000000000 octave.dataset.lh (Octave Tablicious v0.3.7)

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    8.2.45.36 octave.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = octave.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    octave_002edataset_002elongley.html000066400000000000000000000115431515050116100336650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.longley (Octave Tablicious v0.3.7)

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    8.2.45.39 octave.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    octave_002edataset_002elynx.html000066400000000000000000000113371515050116100332070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.lynx (Octave Tablicious v0.3.7)

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    8.2.45.40 octave.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = octave.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    octave_002edataset_002emorley.html000066400000000000000000000121361515050116100335220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.morley (Octave Tablicious v0.3.7)

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    8.2.45.41 octave.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = octave.dataset.morley;

# TODO: Port to Octave
    octave_002edataset_002emtcars.html000066400000000000000000000124301515050116100335010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.mtcars (Octave Tablicious v0.3.7)

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    8.2.45.42 octave.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    octave_002edataset_002enhtemp.html000066400000000000000000000105011515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.nhtemp (Octave Tablicious v0.3.7)

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    8.2.45.43 octave.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    octave_002edataset_002enottem.html000066400000000000000000000101421515050116100335140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.nottem (Octave Tablicious v0.3.7)

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    8.2.45.45 octave.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002enpk.html000066400000000000000000000113061515050116100330010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.npk (Octave Tablicious v0.3.7)

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    8.2.45.46 octave.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = octave.dataset.npk;

# TODO: Port aov() and LM to Octave
    octave_002edataset_002eoccupationalStatus.html000066400000000000000000000103641515050116100361010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.occupationalStatus (Octave Tablicious v0.3.7)

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    8.2.45.47 octave.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002eprecip.html000066400000000000000000000103671515050116100335010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.precip (Octave Tablicious v0.3.7)

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    8.2.45.51 octave.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.precip;

# TODO: Port dot plot to Octave
    octave_002edataset_002epresidents.html000066400000000000000000000111731515050116100343730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.presidents (Octave Tablicious v0.3.7)

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    8.2.45.52 octave.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    octave_002edataset_002epressure.html000066400000000000000000000112321515050116100340570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.pressure (Octave Tablicious v0.3.7)

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    8.2.45.53 octave.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    octave_002edataset_002equakes.html000066400000000000000000000120521515050116100335010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.quakes (Octave Tablicious v0.3.7)

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    8.2.45.55 octave.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    octave_002edataset_002erandu.html000066400000000000000000000111331515050116100333200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.randu (Octave Tablicious v0.3.7)

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    8.2.45.56 octave.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = octave.dataset.randu;
    octave_002edataset_002erivers.html000066400000000000000000000102651515050116100335260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.rivers (Octave Tablicious v0.3.7)

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    8.2.45.57 octave.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    octave_002edataset_002erock.html000066400000000000000000000110461515050116100331500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.rock (Octave Tablicious v0.3.7)

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    8.2.45.58 octave.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = octave.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    octave_002edataset_002esleep.html000066400000000000000000000113071515050116100333220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.sleep (Octave Tablicious v0.3.7)

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    8.2.45.59 octave.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = octave.dataset.sleep;

# TODO: Port to Octave
    octave_002edataset_002estackloss.html000066400000000000000000000141761515050116100342270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.stackloss (Octave Tablicious v0.3.7)

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    8.2.45.60 octave.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = octave.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: octave.dataset.state, Previous: octave.dataset.sleep, Up: octave.dataset   [Contents]

    octave_002edataset_002estate.html000066400000000000000000000130021515050116100333240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.state (Octave Tablicious v0.3.7)

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    8.2.45.61 octave.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.state;
    octave_002edataset_002esunspot_005fmonth.html000066400000000000000000000104541515050116100355270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.sunspot_month (Octave Tablicious v0.3.7)

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    8.2.45.62 octave.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = octave.dataset.sunspot_month;
    octave_002edataset_002esunspot_005fyear.html000066400000000000000000000102131515050116100353330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.sunspot_year (Octave Tablicious v0.3.7)

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    8.2.45.63 octave.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = octave.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    octave_002edataset_002esunspots.html000066400000000000000000000105131515050116100341060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.sunspots (Octave Tablicious v0.3.7)

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    8.2.45.64 octave.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = octave.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    octave_002edataset_002eswiss.html000066400000000000000000000140731515050116100333650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.swiss (Octave Tablicious v0.3.7)

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    8.2.45.65 octave.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.swiss;

# TODO: Port linear model to Octave

    Next: octave.dataset.Theoph, Previous: octave.dataset.sunspots, Up: octave.dataset   [Contents]

    octave_002edataset_002etreering.html000066400000000000000000000111751515050116100340340ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.treering (Octave Tablicious v0.3.7)

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    8.2.45.69 octave.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = octave.dataset.treering;
    octave_002edataset_002etrees.html000066400000000000000000000113451515050116100333360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.trees (Octave Tablicious v0.3.7)

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    8.2.45.70 octave.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = octave.dataset.trees;

figure
octave.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    octave_002edataset_002euspop.html000066400000000000000000000102461515050116100333610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.uspop (Octave Tablicious v0.3.7)

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    8.2.45.79 octave.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    octave_002edataset_002evolcano.html000066400000000000000000000112151515050116100336510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.volcano (Octave Tablicious v0.3.7)

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    8.2.45.81 octave.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    octave.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    octave_002edataset_002ewarpbreaks.html000066400000000000000000000111771515050116100343600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.warpbreaks (Octave Tablicious v0.3.7)

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    8.2.45.82 octave.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    octave_002edataset_002ewomen.html000066400000000000000000000113471515050116100333430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.women (Octave Tablicious v0.3.7)

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    8.2.45.83 octave.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    octave_002edataset_002ezCO2.html000066400000000000000000000104531515050116100327700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.dataset.zCO2 (Octave Tablicious v0.3.7)

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    8.2.45.86 octave.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/octave_002edatasets.html000066400000000000000000000064451515050116100320140ustar00rootroot00000000000000 octave.datasets (Octave Tablicious v0.3.7)

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    8.2.46 octave.datasets

    Class: datasets

    Example dataset collection.

    datasets is a collection of example datasets to go with the Tablicious package.

    The datasets class provides methods for listing and loading the example datasets.

    octave_002edatasets_002edescription.html000066400000000000000000000066261515050116100347300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.datasets.description (Octave Tablicious v0.3.7)

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    8.2.46.3 octave.datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

    octave_002edatasets_002elist.html000066400000000000000000000063431515050116100333540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.datasets.list (Octave Tablicious v0.3.7)

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    8.2.46.1 octave.datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

    octave_002edatasets_002eload.html000066400000000000000000000067061515050116100333230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.datasets.load (Octave Tablicious v0.3.7)

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    8.2.46.2 octave.datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

    octave_002eexamples_002ecoplot.html000066400000000000000000000144341515050116100337070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.examples.coplot (Octave Tablicious v0.3.7)

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    8.2.47 octave.examples.coplot

    Function: [fig, hax] = coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: octave.examples.plot_pairs, Previous: octave.datasets, Up: API Alphabetically   [Contents]

    octave_002eexamples_002eplot_005fpairs.html000066400000000000000000000113271515050116100351540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html octave.examples.plot_pairs (Octave Tablicious v0.3.7)

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    8.2.48 octave.examples.plot_pairs

    Function: out = plot_pairs (data)
    Function: out = plot_pairs (data, plot_type)
    Function: out = plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/pp.html000066400000000000000000000100571515050116100266650ustar00rootroot00000000000000 pp (Octave Tablicious v0.3.7)

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    8.2.49 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/rmmissing.html000066400000000000000000000106451515050116100302610ustar00rootroot00000000000000 rmmissing (Octave Tablicious v0.3.7)

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    8.2.50 rmmissing

    Function: [out, tf] = rmmissing (X)
    Function: [out, tf] = rmmissing (X, dim)
    Function: [out, tf] = rmmissing (…, 'MinNumMissing', MinNumMissing)

    Remove missing values.

    If x is a vector, removes elements with missing values. If x is a matrix, removes rows or columns with missing data elements.

    dim is the dimension to operate along. Specifying a dimension forces rmmissing to operate in matrix instead of vector mode.

    MinNumMissing indicates how many missing element values there must be in a row or column for it to be considered missing and this removed. This option is only used in matrix mode; it is silently ignored in vector mode.

    Returns: out - the input, with missing elements or rows or columns removed tf - a logical index vector indicating which elements, rows, or columns were removed

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/scalarexpand.html000066400000000000000000000072661515050116100307230ustar00rootroot00000000000000 scalarexpand (Octave Tablicious v0.3.7)

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    8.2.51 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/seconds.html000066400000000000000000000062771515050116100277150ustar00rootroot00000000000000 seconds (Octave Tablicious v0.3.7)

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    8.2.52 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.53 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
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    8.2.54 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.55 standardizeMissing

    Function: out = standardizeMissing (X, indicator)

    Insert standard missing values.

    Standardizes missing values in X by replacing the values listed in indicator with the standard missing values for the type of X.

    Standard missing values depend on the data type: * NaN for double, single, duration, and calendarDuration * NaT for datetime * ' ' for char * {''} for cellstrs * Integer numeric types have no standard missing value; they are never considered missing. * Structs are never considered missing. * Logicals are never considered missing.

    See also: table.standardizeMissing

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    8.2.56 startsWith

    Function: out = startsWith (str, pattern)
    Function: out = startsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings start with a pattern.

    Tests whether the given strings start with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.57 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where: - Each element of a string array is a single string - A single string is a 1-dimensional row vector of Unicode characters - Those characters are encoded in UTF-8 - This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles. - It is a type-safe object wrapper around Octave’s base primitive character types. - It adds ismissing() semantics. - And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.57.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.57.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.57.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.57.24 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.57.26 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.57.4 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.57.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.57.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.57.19 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.57.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.57.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.57.3 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.57.17 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.25 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz.

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    8.2.57.22 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.57.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.57.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.57.5 string.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.57.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.57.23 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.57.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.57.2 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs. - chars and cellstrs are converted via cellstr() - numerics are converted via num2str() - datetimes are converted via datestr()

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    8.2.57.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.57.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.57.20 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.57.18 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.58 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.59 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.60 tableOuterFillValue

    Function: out = tableOuterFillValue (x)

    Outer fill value for variable within a table.

    Determines the fill value to usse for a given variable value x when that value is used as a variable in a table that is involved in an outer join.

    The default implementation for tableOuterFillValue has support for all Octave primitive types, plus cellstrs, datetime & friends, strings, and table-valued variables.

    This function may become private to table before version 1.0. It is currently global to make debugging more convenient. It (or an equivalent) will remain global if we want to allow user-defined classes to customize their fill value. It also has default logic that will determine the fill value for an arbitrary type by detecting the value used to fill elements during array expansion operations. This will be appropriate for most data types.

    Returns a 1-by-ncols value of the same type as x, which may be any type, where ncols is the number of columns in the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eaddvars.html000066400000000000000000000101431515050116100314240ustar00rootroot00000000000000 table.addvars (Octave Tablicious v0.3.7)

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    8.2.59.39 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eantijoin.html000066400000000000000000000066701515050116100316250ustar00rootroot00000000000000 table.antijoin (Octave Tablicious v0.3.7)

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    8.2.59.51 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002ecartesian.html000066400000000000000000000074741515050116100317660ustar00rootroot00000000000000 table.cartesian (Octave Tablicious v0.3.7)

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    8.2.59.52 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

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    8.2.59.18 table.columns

    Method: out = columns (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002econgruentize.html000066400000000000000000000067151515050116100325260ustar00rootroot00000000000000 table.congruentize (Octave Tablicious v0.3.7)

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    8.2.59.57 table.congruentize

    Method: [outA, outB] = congruentize (A, B)

    Make tables congruent.

    Makes tables congruent by ensuring they have the same variables of the same types in the same order. Congruent tables may be safely unioned, intersected, vertcatted, or have other set operations done to them.

    Variable names present in one input but not in the other produces an error. Variables with the same name but different types in the inputs produces an error. Inputs must either both have row names or both not have row names; it is an error if one has row names and the other doesn’t. Variables in different orders are reordered to be in the same order as A.

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    8.2.59.40 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eevalWithVars.html000066400000000000000000000071561515050116100324310ustar00rootroot00000000000000 table.evalWithVars (Octave Tablicious v0.3.7)

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    8.2.59.69 table.evalWithVars

    Method: out = evalWithVars (obj, expr)

    Evaluate an expression against table’s variables.

    Evaluates the M-code expression expr in a workspace where all of obj’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = table_examples.SpDb
tmp = join (sp, p);
shipment_weight = evalWithVars (tmp, "Qty .* Weight")
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    8.2.59.68 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.59.36 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002egetvars.html000066400000000000000000000061331515050116100314570ustar00rootroot00000000000000 table.getvars (Octave Tablicious v0.3.7)

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    8.2.59.37 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002egroupby.html000066400000000000000000000073451515050116100315010ustar00rootroot00000000000000 table.groupby (Octave Tablicious v0.3.7)

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    8.2.59.53 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002egrpstats.html000066400000000000000000000064751515050116100316640ustar00rootroot00000000000000 table.grpstats (Octave Tablicious v0.3.7)

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    8.2.59.54 table.grpstats

    Method: [out] = grpstats (obj, groupvar)
    Method: [out] = grpstats (…, 'DataVars', DataVars)

    Statistics by group.

    See also: table.groupby.

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    8.2.59.26 table.hasrownames

    Method: out = hasrownames (obj)

    True if this table has row names defined.

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    8.2.59.44 table.head

    Method: out = head (obj)
    Method: out = head (obj, k)

    Get first K rows of table.

    Returns the first k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.15 table.height

    Method: out = height (obj)

    Number of rows in table.

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    8.2.59.28 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.59.47 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.59.59 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eiscol.html000066400000000000000000000056611515050116100311220ustar00rootroot00000000000000 table.iscol (Octave Tablicious v0.3.7)

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    8.2.59.23 table.iscol

    Method: out = iscol (obj)

    Test whether array is a column vector.

    For tables, iscol is true if the input has a single variable. The number of columns within that variable does not matter.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eisempty.html000066400000000000000000000056271515050116100315050ustar00rootroot00000000000000 table.isempty (Octave Tablicious v0.3.7)

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    8.2.59.20 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eismatrix.html000066400000000000000000000055731515050116100316530ustar00rootroot00000000000000 table.ismatrix (Octave Tablicious v0.3.7)

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    8.2.59.21 table.ismatrix

    Method: out = ismatrix (obj)

    Test whether array is a matrix.

    For tables, ismatrix is always true, by definition.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eismember.html000066400000000000000000000061011515050116100316020ustar00rootroot00000000000000 table.ismember (Octave Tablicious v0.3.7)

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    8.2.59.62 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.59.63 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eisrow.html000066400000000000000000000054221515050116100311470ustar00rootroot00000000000000 table.isrow (Octave Tablicious v0.3.7)

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    8.2.59.22 table.isrow

    Method: out = isrow (obj)

    Test whether array is a row vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eisscalar.html000066400000000000000000000054771515050116100316170ustar00rootroot00000000000000 table.isscalar (Octave Tablicious v0.3.7)

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    8.2.59.25 table.isscalar

    Method: out = isscalar (obj)

    Test whether array is scalar.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002eistable.html000066400000000000000000000054241515050116100314310ustar00rootroot00000000000000 table.istable (Octave Tablicious v0.3.7)

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    8.2.59.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.59.24 table.isvector

    Method: out = isvector (obj)

    Test whether array is a vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002ejoin.html000066400000000000000000000074041515050116100307450ustar00rootroot00000000000000 table.join (Octave Tablicious v0.3.7)

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    8.2.59.46 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.59.10 table.length

    Method: out = length (obj)

    Length along longest dimension

    Use of length is not recommended. Use numel or size instead.

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    8.2.59.41 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.59.35 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.59.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2.

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    8.2.59.19 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.59.49 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.59.48 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.59.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.59.34 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.59.71 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.59.30 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables.

    Only two dimensions are supported for repelem on tables.

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    8.2.59.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.59.72 table.resolveVarRef

    Method: [ixVar, varNames] = resolveVarRef (obj, varRef)
    Method: [ixVar, varNames] = resolveVarRef (obj, varRef, strictness)

    Resolve a variable reference against this table.

    A varRef is a numeric or char/cellstr indicator of which variables within obj are being referenced.

    strictness controls what to do when the given variable references could not be resolved. It may be ’strict’ (the default) or ’lenient’.

    Returns: ixVar - the indexes of the variables in obj varNames - a cellstr of the names of the variables in obj

    Raises an error if any of the specified variables could not be resolved, unless strictness is ’lenient’, in which case it will return 0 for the index and ” for the name for each variable which could not be resolved.

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    8.2.59.70 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with evalWithVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, evalWithVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = table_examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))
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    8.2.59.64 table.rmmissing

    Method: [out, tf] = rmmissing (obj)
    Method: [out, tf] = rmmissing (obj, indicator)
    Method: [out, tf] = rmmissing (…, 'DataVariables', vars)
    Method: [out, tf] = rmmissing (…, 'MinNumMissing', minNumMissing)

    Remove rows with missing values.

    Removes the rows from obj that have missing values.

    If the ’DataVariables’ option is given, only the data in the specified variables is considered.

    Returns: out - A table the same as obj, but with rows with missing values removed. tf - A logical index vector indicating which rows were removed.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002erowfun.html000066400000000000000000000156361515050116100313340ustar00rootroot00000000000000 table.rowfun (Octave Tablicious v0.3.7)

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    8.2.59.67 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.findgroups, Previous: table.varfun, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002erows.html000066400000000000000000000053661515050116100310050ustar00rootroot00000000000000 table.rows (Octave Tablicious v0.3.7)

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    8.2.59.16 table.rows

    Method: out = rows (obj)

    Number of rows in table.

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    8.2.59.56 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002esemijoin.html000066400000000000000000000070661515050116100316270ustar00rootroot00000000000000 table.semijoin (Octave Tablicious v0.3.7)

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    8.2.59.50 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

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    8.2.59.32 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002esetRowNames.html000066400000000000000000000061441515050116100322550ustar00rootroot00000000000000 table.setRowNames (Octave Tablicious v0.3.7)

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    8.2.59.33 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.59.31 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.61 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.59.38 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.59.60 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.59.13 table.size_equal

    Method: out = size_equal (varargin)

    True if the dimensions of all arguments agree.

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    8.2.59.14 table.sizeof

    Method: out = sizeof (obj)

    Approximate size of array in bytes. For tables, this returns the sume of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently unimplemented.

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    8.2.59.55 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002esplitvars.html000066400000000000000000000075211515050116100320350ustar00rootroot00000000000000 table.splitvars (Octave Tablicious v0.3.7)

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    8.2.59.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.59.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002estack.html000066400000000000000000000072141515050116100311120ustar00rootroot00000000000000 table.stack (Octave Tablicious v0.3.7)

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    8.2.59.43 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.59.65 table.standardizeMissing

    Method: out = standardizeMissing (obj, indicator)
    Method: out = standardizeMissing (…, 'DataVariables', vars)

    Insert standard missing values.

    Standardizes missing values in variable data.

    If the DataVariables option is supplied, only the indicated variables are standardized.

    indicator is passed along to standardizeMissing when it is called on each of the data variables in turn. The same indicator is used for all variables. You can mix and match indicator types by just passing in mixed indicator types in a cell array; indicators that don’t match the type of the column they are operating on are just ignored.

    Returns a table with same variable names and types as obj, but with variable values standardized.

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    8.2.59.73 table.subsetrows

    Method: out = subsetrows (obj, ixRows)

    Subset table by rows.

    Subsets this table by rows.

    ixRows may be a numeric or logical index into the rows of obj.

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    8.2.59.74 table.subsetvars

    Method: out = subsetvars (obj, ixVars)

    Subset table by variables.

    Subsets table obj by subsetting it along its variables.

    ixVars may be: - a numeric index vector - a logical index vector - ":" - a cellstr vector of variable names

    The resulting table will have its variables reordered to match ixVars.

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    8.2.59.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.59.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.59.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.59.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.59.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.59.45 table.tail

    Method: out = tail (obj)
    Method: out = tail (obj, k)

    Get last K rows of table.

    Returns the last k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.58 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.66 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.59.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.59.27 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/table_002ewidth.html000066400000000000000000000055711515050116100311300ustar00rootroot00000000000000 table.width (Octave Tablicious v0.3.7)

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    8.2.59.17 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/timezones.html000066400000000000000000000077261515050116100302740ustar00rootroot00000000000000 timezones (Octave Tablicious v0.3.7)

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    8.2.61 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/vartype.html000066400000000000000000000064651515050116100277500ustar00rootroot00000000000000 vartype (Octave Tablicious v0.3.7)

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    8.2.62 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/vecfun.html000066400000000000000000000066301515050116100275360ustar00rootroot00000000000000 vecfun (Octave Tablicious v0.3.7)

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    8.2.63 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/html/years.html000066400000000000000000000064311515050116100273720ustar00rootroot00000000000000 years (Octave Tablicious v0.3.7)

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    8.2.64 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.3.7/user-guide/tablicious.html000066400000000000000000027636071515050116100274620ustar00rootroot00000000000000 Octave Tablicious v0.3.7

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.3.7.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.3.7 for GNU Octave.

    Tablicious provides Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.3.7/tablicious-0.3.7.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the octave.datasets and octave.dataset classes.

    To see a list of the available data sets, run octave.datasets.list(). Then to load one of the example data sets, run octave.datasets.load('examplename'). For example: 

    octave.datasets.list
t = octave.datasets.load('cupcake')

    You can also load it by calling octave.dataset.<name>. This does the same thing. For example: 

    t = octave.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on octave.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc octave.dataset.cupcake

    (The command help octave.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports most of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that all these will be implemented some day.

    These areas of missing functionality are all tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/1.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    array2table

    Convert an array to a table.

    cell2table

    Convert a cell array to a table.

    struct2table

    Convert struct to a table.

    tableOuterFillValue

    Outer fill value for variable within a table.

    vartype

    Filter by variable type for use in suscripting.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    startsWith

    Test if strings start with a pattern.

    endsWith

    Test if strings end with a pattern.

    contains

    Test if strings contain a pattern.

    categorical

    Categorical variable array.

    discretize

    Group data into discrete bins or categories.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    localdate

    Represents a complete day using the Gregorian calendar.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    NaT

    “Not-a-Time”.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    days

    Duration in days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    timezones

    List all the time zones defined on this system.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    8.1.4 Missing Data

    fillmissing

    Fill missing values.

    ismissing

    Find missing values.

    rmmissing

    Remove missing values.

    standardizeMissing

    Insert standard missing values.

    missing

    Generic auto-converting missing value.

    isnanny

    Test if elements are NaN or NaN-like

    eqn

    Determine element-wise equality, treating NaNs as equal

    8.1.5 Validation Functions

    mustBeA

    Requires that input is of a given type.

    mustBeCellstr

    Requires that input is a cellstr.

    mustBeCharvec

    Requires that input is a char row vector.

    mustBeFinite
    mustBeInteger
    mustBeMember
    mustBeNonempty
    mustBeNumeric
    mustBeReal
    mustBeSameSize

    Requires that the inputs are the same size.

    mustBeScalar

    Requires that input is scalar.

    mustBeScalarLogical

    Requires that input is a scalar logical.

    mustBeVector

    Requires that input is a vector or empty.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    dispstrs

    Display strings for array.

    isfile
    isfolder
    pp

    Alias for prettyprint, for interactive use.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    size2str

    Format an array size for display.

    splitapply

    Split data into groups and apply function.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    8.1.7 Example Datasets

    octave.datasets

    Example dataset collection.

    octave.dataset

    The ‘dataset’ class provides convenient access to the various datasets included with Tablicious.

    8.1.8 Example Code

    octave.examples.plot_pairs

    Plot pairs of variables against each other.

    8.1.9 Uncategorized

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    octave.chrono.dummy_function

    A dummy function just for testing the doco tools.

    octave.chrono.DummyClass

    A do-nothing class just for testing the doco tools.

    octave.examples.coplot

    Conditioning plot.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.2.3 calendarDuration.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.2.4 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.2.5 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.8 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.9 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is just an alias for isnat, provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff: - gt, ge, lt, le - Ordinal support in general - countcats - summary

    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.3 categorical.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.7 datetime.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.9.8 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.9 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.10 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.12 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.13 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.14 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.15 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.16 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.17 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.18 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.19 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.20 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.21 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.22 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.23 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.24 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.27 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.28 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.29 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.30 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.31 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.32 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.33 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.34 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.35 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.2 duration.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.13.3 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.5 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.8 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.9 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.10 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14 endsWith

    Function: out = endsWith (str, pattern)
    Function: out = endsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings end with a pattern.

    Tests whether the given strings end with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 fillmissing

    Function: [out, tfFilled] = fillmissing (X, method)
    Function: [out, tfFilled] = fillmissing (X, 'constant', fill_val)
    Function: [out, tfFilled] = fillmissing (X, movmethod, window)

    Fill missing values.

    Fills missing values in X according to the method specified by method.

    This method is only partially implemented.

    method may be: 'constant' 'previous' 'next' 'nearest' 'linear' 'spline' 'pchip' movmethod may be: 'movmean' 'movmedian'

    Returns out, which is X but with missing values filled in, and tfFilled, a logical array the same size as X which indicates which elements were filled.

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 ismissing

    Function: out = ismissing (X)
    Function: out = ismissing (X, indicator)

    Find missing values.

    Determines which elements of X contain missing values. If an indicator input is not provided, standard missing values depending on the input type of X are used.

    Standard missing values depend on the data type:

    • NaN for double, single, duration, and calendarDuration
    • NaT for datetime
    • ' ' for char
    • {''} for cellstrs
    • Integer numeric types have no standard missing value; they are never considered missing.
    • Structs are never considered missing.
    • Logicals are never considered missing.
    • Other types have no standard missing value; it is currently an error to call ismissing on them without providing an indicator.
      • This includes cells which are not cellstrs; calling ismissing on them results in an error.
      • TODO: Determine whether this should really be an error, or if it should default to never considering those types as missing.
      • TODO: Decide whether, for classdef objects, ismissing should polymorphically detect isnan()/isnat()/isnanny() methods and use those, or whether we should require classes to override ismissing() itself.

    If indicator is supplied, it is an array containing multiple values, all of which are considered to be missing values. Only indicator values that are type-compatible with the input are considered; other indicator value types are silently ignored. This is by design, so you can pass an indicator that holds sentinel values for disparate types in to ismissing() used for any type, or for compound types like table.

    Indicators are currently not supported for struct or logical inputs. This is probably a bug.

    Table defines its own ismissing() method which respects individual variables’ data types; see table.ismissing.

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: isnan, isnat, ismissing, eqn, isequaln

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    8.2.25 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.25.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.25.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.25.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.25.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.25.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.25.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.25.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.25.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.25.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.25.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.25.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.26 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.27 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.28 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.28.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.28.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.28.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.28.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.28.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.29 mustBeA

    Function File: x = mustBeA (x, type)
    Function File: x = mustBeA (x, type, label)

    Requires that input is of a given type.

    Raises an error if the input x is not of type type, as determined by isa (x, type).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.30 mustBeCellstr

    Function File: x = mustBeCellstr (x, label)

    Requires that input is a cellstr.

    Raises an error if the input x is not a cellstr (a cell array of char arrays).

    TODO: Decide whether to require the contained char arrays be rowvec/empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.31 mustBeCharvec

    Function File: x = mustBeCharvec (x, label)

    Requires that input is a char row vector.

    Raises an error if the input x is not a row vector of chars. char row vectors are Octave’s normal representation of single strings. (They are what are produced by '...' string literals.) As a special case, 0-by-0 empty chars (what is produced by the string literal '') are also considered charvecs.

    This does not differentiate between single-quoted and double-quoted strings.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.32 mustBeFinite

    Not documented

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    8.2.33 mustBeInteger

    Not documented

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    8.2.34 mustBeMember

    Not documented

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    8.2.35 mustBeNonempty

    Not documented

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    8.2.36 mustBeNumeric

    Not documented

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    8.2.37 mustBeReal

    Not documented

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    8.2.38 mustBeSameSize

    Function File: [a, b] = mustBeSameSize (a, b, labelA, labelB)

    Requires that the inputs are the same size.

    Raises an error if the inputs a and b are not the same size, as determined by isequal (size (a), size (b)).

    labelA and labelB are optional inputs that determine how the input will be described in error messages. If not supplied, inputname (...) is used, and if that is empty, it falls back to "input 1" and "input 2".

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    8.2.39 mustBeScalar

    Function File: x = mustBeScalar (x, label)

    Requires that input is scalar.

    Raises an error if the input x is not scalar, as determined by isscalar (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.40 mustBeScalarLogical

    Function File: x = mustBeScalarLogical (x, label)

    Requires that input is a scalar logical.

    Raises an error if the input x is not scalar, as determined by isscalar (x) && islogical (x).

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.41 mustBeVector

    Function File: x = mustBeVector (x, label)

    Requires that input is a vector or empty.

    Raises an error if the input x is not a row vector and is not 0-by-0 empty.

    label is an optional input that determines how the input will be described in error messages. If not supplied, inputname (1) is used, and if that is empty, it falls back to "input".

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    8.2.42 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.43 octave.chrono.dummy_function

    Function: out = dummy_function (x)

    A dummy function just for testing the doco tools.

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    8.2.44 octave.chrono.DummyClass

    Class: DummyClass

    A do-nothing class just for testing the doco tools.

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    Instance Variable of DummyClass: double x

    An x. Has no semantics.

    Instance Variable of DummyClass: double y

    A y. Has no semantics.

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    8.2.44.1 octave.chrono.DummyClass.DummyClass

    Constructor: obj = octave.chrono.DummyClass ()

    Constructs a new scalar DummyClass with default values.

    Constructor: obj = octave.chrono.DummyClass (x, y)

    Constructs a new DummyClass with the specified values.

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    8.2.44.2 octave.chrono.DummyClass.foo

    Method: out = foo (obj)

    Computes a foo value.

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    8.2.44.3 octave.chrono.DummyClass.bar

    Method: out = bar (obj)

    Computes a bar value.

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    8.2.45 octave.dataset

    Class: dataset

    The dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It’s provided so you can use tab completion on the dataset list.

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    8.2.45.1 octave.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = octave.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.45.2 octave.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.45.3 octave.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = octave.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if i == j
      continue
    endif
    ix_subplot = (n_vars*(j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.45.4 octave.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = octave.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim(haxs(1), x_limits);
ylim(haxs(1), y_limits);

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    8.2.45.5 octave.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.45.6 octave.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = octave.dataset.attitude

octave.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.45.7 octave.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = octave.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.45.8 octave.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.45.9 octave.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.45.10 octave.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.45.11 octave.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = octave.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.45.12 octave.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.ChickWeight

octave.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.45.13 octave.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = octave.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.45.14 octave.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = octave.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.45.15 octave.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.45.16 octave.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = octave.dataset.cupcake
plot(datenum(t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric")

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    8.2.45.17 octave.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.45.18 octave.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.DNase;

# TODO: Port this from R

octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.45.19 octave.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.45.20 octave.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.45.21 octave.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.45.22 octave.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = octave.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price,1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.45.23 octave.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = octave.dataset.faithful;

% Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

% Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

% Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.45.24 octave.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
% TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

% TODO: Add linear model summary output
% TOD: Add linear model summary plot

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    8.2.45.25 octave.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.freeny;

summary(t)

octave.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.45.26 octave.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    octave.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.45.27 octave.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    octave.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.45.28 octave.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    octave.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.45.29 octave.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.45.30 octave.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = octave.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.45.31 octave.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.45.32 octave.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.45.33 octave.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.45.34 octave.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = octave.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.45.35 octave.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = octave.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.45.36 octave.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = octave.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.45.37 octave.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = octave.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.45.38 octave.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.45.39 octave.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.45.40 octave.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = octave.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.45.41 octave.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = octave.dataset.morley;

# TODO: Port to Octave

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    8.2.45.42 octave.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.45.43 octave.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.45.44 octave.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = octave.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.45.45 octave.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.45.46 octave.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = octave.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.45.47 octave.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.45.48 octave.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.45.49 octave.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.OrchardSprays;

octave.examples.plot_pairs (t);

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    8.2.45.50 octave.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = octave.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.45.51 octave.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.45.52 octave.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.45.53 octave.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.45.54 octave.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = octave.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.45.55 octave.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.45.56 octave.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = octave.dataset.randu;

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    8.2.45.57 octave.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.45.58 octave.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = octave.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.45.59 octave.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = octave.dataset.sleep;

# TODO: Port to Octave

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    8.2.45.60 octave.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = octave.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.45.61 octave.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.state;

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    8.2.45.62 octave.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = octave.dataset.sunspot_month;

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    8.2.45.63 octave.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = octave.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.45.64 octave.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = octave.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.45.65 octave.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = octave.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.45.66 octave.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = octave.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.45.67 octave.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    octave.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.45.68 octave.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = octave.dataset.ToothGrowth;

octave.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.45.69 octave.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = octave.dataset.treering;

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    8.2.45.70 octave.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = octave.dataset.trees;

figure
octave.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.45.71 octave.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    octave.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.45.72 octave.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    octave.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.45.73 octave.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = octave.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.45.74 octave.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = octave.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.45.75 octave.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = octave.dataset.USAccDeaths;

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    8.2.45.76 octave.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.USArrests;

summary (t);

octave.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.45.77 octave.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = octave.dataset.USJudgeRatings;

figure
octave.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.45.78 octave.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.45.79 octave.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.45.80 octave.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.45.81 octave.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    octave.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.45.82 octave.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.45.83 octave.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = octave.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.45.84 octave.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    octave.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.45.85 octave.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.45.86 octave.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = octave.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.46 octave.datasets

    Class: datasets

    Example dataset collection.

    datasets is a collection of example datasets to go with the Tablicious package.

    The datasets class provides methods for listing and loading the example datasets.

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    8.2.46.1 octave.datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.46.2 octave.datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.46.3 octave.datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.47 octave.examples.coplot

    Function: [fig, hax] = coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.48 octave.examples.plot_pairs

    Function: out = plot_pairs (data)
    Function: out = plot_pairs (data, plot_type)
    Function: out = plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.49 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.50 rmmissing

    Function: [out, tf] = rmmissing (X)
    Function: [out, tf] = rmmissing (X, dim)
    Function: [out, tf] = rmmissing (…, 'MinNumMissing', MinNumMissing)

    Remove missing values.

    If x is a vector, removes elements with missing values. If x is a matrix, removes rows or columns with missing data elements.

    dim is the dimension to operate along. Specifying a dimension forces rmmissing to operate in matrix instead of vector mode.

    MinNumMissing indicates how many missing element values there must be in a row or column for it to be considered missing and this removed. This option is only used in matrix mode; it is silently ignored in vector mode.

    Returns: out - the input, with missing elements or rows or columns removed tf - a logical index vector indicating which elements, rows, or columns were removed

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    8.2.51 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.52 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.53 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.54 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.55 standardizeMissing

    Function: out = standardizeMissing (X, indicator)

    Insert standard missing values.

    Standardizes missing values in X by replacing the values listed in indicator with the standard missing values for the type of X.

    Standard missing values depend on the data type: * NaN for double, single, duration, and calendarDuration * NaT for datetime * ' ' for char * {''} for cellstrs * Integer numeric types have no standard missing value; they are never considered missing. * Structs are never considered missing. * Logicals are never considered missing.

    See also: table.standardizeMissing

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    8.2.56 startsWith

    Function: out = startsWith (str, pattern)
    Function: out = startsWith (…, 'IgnoreCase', IgnoreCase)

    Test if strings start with a pattern.

    Tests whether the given strings start with the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

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    8.2.57 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where: - Each element of a string array is a single string - A single string is a 1-dimensional row vector of Unicode characters - Those characters are encoded in UTF-8 - This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles. - It is a type-safe object wrapper around Octave’s base primitive character types. - It adds ismissing() semantics. - And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.57.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.57.2 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs. - chars and cellstrs are converted via cellstr() - numerics are converted via num2str() - datetimes are converted via datestr()

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    8.2.57.3 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.57.4 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.57.5 string.sizeof

    Method: out = sizeof (obj)

    Size of array in bytes.

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    8.2.57.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.57.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.57.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.57.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.57.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.57.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.57.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.57.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.57.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.57.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.57.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.57.17 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.18 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.57.19 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.57.20 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.57.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.57.22 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.57.23 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.57.24 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.57.25 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz.

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    8.2.57.26 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.58 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.59 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.59.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.59.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.59.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.59.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.59.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.59.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.59.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.59.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.59.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.59.10 table.length

    Method: out = length (obj)

    Length along longest dimension

    Use of length is not recommended. Use numel or size instead.

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    8.2.59.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2.

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    8.2.59.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions.

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    8.2.59.13 table.size_equal

    Method: out = size_equal (varargin)

    True if the dimensions of all arguments agree.

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    8.2.59.14 table.sizeof

    Method: out = sizeof (obj)

    Approximate size of array in bytes. For tables, this returns the sume of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently unimplemented.

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    8.2.59.15 table.height

    Method: out = height (obj)

    Number of rows in table.

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    8.2.59.16 table.rows

    Method: out = rows (obj)

    Number of rows in table.

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    8.2.59.17 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.59.18 table.columns

    Method: out = columns (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.59.19 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.59.20 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.59.21 table.ismatrix

    Method: out = ismatrix (obj)

    Test whether array is a matrix.

    For tables, ismatrix is always true, by definition.

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    8.2.59.22 table.isrow

    Method: out = isrow (obj)

    Test whether array is a row vector.

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    8.2.59.23 table.iscol

    Method: out = iscol (obj)

    Test whether array is a column vector.

    For tables, iscol is true if the input has a single variable. The number of columns within that variable does not matter.

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    8.2.59.24 table.isvector

    Method: out = isvector (obj)

    Test whether array is a vector.

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    8.2.59.25 table.isscalar

    Method: out = isscalar (obj)

    Test whether array is scalar.

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    8.2.59.26 table.hasrownames

    Method: out = hasrownames (obj)

    True if this table has row names defined.

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    8.2.59.27 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible.

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    8.2.59.28 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.59.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.59.30 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables.

    Only two dimensions are supported for repelem on tables.

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    8.2.59.31 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.32 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.59.33 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.59.34 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.59.35 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.59.36 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.59.37 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.59.38 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.59.39 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table

    Adds the specified variables to a table.

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    8.2.59.40 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.59.41 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.59.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.59.43 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.59.44 table.head

    Method: out = head (obj)
    Method: out = head (obj, k)

    Get first K rows of table.

    Returns the first k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.45 table.tail

    Method: out = tail (obj)
    Method: out = tail (obj, k)

    Get last K rows of table.

    Returns the last k rows of obj, as a table.

    k defaults to 8.

    If there are less than k rows in obj, returns all rows.

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    8.2.59.46 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.59.47 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.59.48 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.59.49 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.59.50 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

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    8.2.59.51 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

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    8.2.59.52 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

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    8.2.59.53 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

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    8.2.59.54 table.grpstats

    Method: [out] = grpstats (obj, groupvar)
    Method: [out] = grpstats (…, 'DataVars', DataVars)

    Statistics by group.

    See also: table.groupby.

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    8.2.59.55 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby

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    8.2.59.56 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.59.57 table.congruentize

    Method: [outA, outB] = congruentize (A, B)

    Make tables congruent.

    Makes tables congruent by ensuring they have the same variables of the same types in the same order. Congruent tables may be safely unioned, intersected, vertcatted, or have other set operations done to them.

    Variable names present in one input but not in the other produces an error. Variables with the same name but different types in the inputs produces an error. Inputs must either both have row names or both not have row names; it is an error if one has row names and the other doesn’t. Variables in different orders are reordered to be in the same order as A.

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    8.2.59.58 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.59 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.60 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.59.61 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.59.62 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.59.63 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.59.64 table.rmmissing

    Method: [out, tf] = rmmissing (obj)
    Method: [out, tf] = rmmissing (obj, indicator)
    Method: [out, tf] = rmmissing (…, 'DataVariables', vars)
    Method: [out, tf] = rmmissing (…, 'MinNumMissing', minNumMissing)

    Remove rows with missing values.

    Removes the rows from obj that have missing values.

    If the ’DataVariables’ option is given, only the data in the specified variables is considered.

    Returns: out - A table the same as obj, but with rows with missing values removed. tf - A logical index vector indicating which rows were removed.

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    8.2.59.65 table.standardizeMissing

    Method: out = standardizeMissing (obj, indicator)
    Method: out = standardizeMissing (…, 'DataVariables', vars)

    Insert standard missing values.

    Standardizes missing values in variable data.

    If the DataVariables option is supplied, only the indicated variables are standardized.

    indicator is passed along to standardizeMissing when it is called on each of the data variables in turn. The same indicator is used for all variables. You can mix and match indicator types by just passing in mixed indicator types in a cell array; indicators that don’t match the type of the column they are operating on are just ignored.

    Returns a table with same variable names and types as obj, but with variable values standardized.

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    8.2.59.66 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.59.67 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.59.68 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.59.69 table.evalWithVars

    Method: out = evalWithVars (obj, expr)

    Evaluate an expression against table’s variables.

    Evaluates the M-code expression expr in a workspace where all of obj’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = table_examples.SpDb
tmp = join (sp, p);
shipment_weight = evalWithVars (tmp, "Qty .* Weight")

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    8.2.59.70 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with evalWithVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, evalWithVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = table_examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

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    8.2.59.71 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.59.72 table.resolveVarRef

    Method: [ixVar, varNames] = resolveVarRef (obj, varRef)
    Method: [ixVar, varNames] = resolveVarRef (obj, varRef, strictness)

    Resolve a variable reference against this table.

    A varRef is a numeric or char/cellstr indicator of which variables within obj are being referenced.

    strictness controls what to do when the given variable references could not be resolved. It may be ’strict’ (the default) or ’lenient’.

    Returns: ixVar - the indexes of the variables in obj varNames - a cellstr of the names of the variables in obj

    Raises an error if any of the specified variables could not be resolved, unless strictness is ’lenient’, in which case it will return 0 for the index and ” for the name for each variable which could not be resolved.

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    8.2.59.73 table.subsetrows

    Method: out = subsetrows (obj, ixRows)

    Subset table by rows.

    Subsets this table by rows.

    ixRows may be a numeric or logical index into the rows of obj.

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    8.2.59.74 table.subsetvars

    Method: out = subsetvars (obj, ixVars)

    Subset table by variables.

    Subsets table obj by subsetting it along its variables.

    ixVars may be: - a numeric index vector - a logical index vector - ":" - a cellstr vector of variable names

    The resulting table will have its variables reordered to match ixVars.

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    8.2.60 tableOuterFillValue

    Function: out = tableOuterFillValue (x)

    Outer fill value for variable within a table.

    Determines the fill value to usse for a given variable value x when that value is used as a variable in a table that is involved in an outer join.

    The default implementation for tableOuterFillValue has support for all Octave primitive types, plus cellstrs, datetime & friends, strings, and table-valued variables.

    This function may become private to table before version 1.0. It is currently global to make debugging more convenient. It (or an equivalent) will remain global if we want to allow user-defined classes to customize their fill value. It also has default logic that will determine the fill value for an arbitrary type by detecting the value used to fill elements during array expansion operations. This will be appropriate for most data types.

    Returns a 1-by-ncols value of the same type as x, which may be any type, where ncols is the number of columns in the input.

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    8.2.61 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.62 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.63 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.64 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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J7,]2ᄳC1J2&SaeէE|#X8.)gE~OǥQUZ4mMrzŨ䣑q:P';) \EJ"NU0{v!CͿ]vq3AQFwY +LI=V7igmK - q YUe;\+J\l`/[bIY9x\M8#1-\ybKK@uui v7aׁ3UUG9V5f ` frӬP-;bڭYJqMDVu=LۊEr8[^WyW {-E ݵjכqѽ !Ѱ{5ERevd0M5Ay~&N"Nw`bnn:?{qL2>@ʕi8G}QG{U-`Z烬:'_+ijKRT~_`]⇖Ie8@4S7&-?խG> stream xYIFϯ[@*ƒ(/G#Yv_tSc4aqj`=X#5PWSUtu<Ȓ}+jף:Ow=]-<WV 1'gMN{:'%oxwALy!b&hB&++2:E(W!(qy!ڬ"'} (F1D>|%_uVLGF_%'?s|4CQcxʦ G("DE˃lbdw#uB"zqINym (_iyEyuR}UQK[:DBSk~ϦbD!4-vnS N5ݿ] 4F!+ztpxH66gfY;/_bߵdݡ|ozuy-/xIv({UꤕRSF0q;OFQZ?Vua`gqѝ"ՇΡQW``DuPi&)˵~EKڼ*,7RBD!Jim}jDtkUK~sR,iuˋwZVi]ͫa|jJ׈eƆ35҇||FKe*J!R-URv}M{s]W Z ڞgr$.G\ʓ` S$-Ȅ۪f׻>W L @R̬rWȓ|}:: d mZ4AaUyEkORw6K'v)x8$V%sǴňwbaX ) A$dJ.)Su0fuj]ByKO]!7cXk+njqI7"r=>r#j8-82)d>NXfEؼsFgJ0&y㋀:;moJ؋ OFKI$@5`tqOOuҪb[_LIcKbE >;'s"\_p<̾i3&mVu 5n8'<j$wqj8F1aw @y}oj}-M6$Jܦe./Ph?c?'(fDw]+bx]:]6ny7w)#( .o} K.%ooжmȾr+Mz[ utd:6~]oDzD -UiX7R,3M>k13wSԱ_v/g(K{;B\ l9?>_oaQ> stream xYێ6}߯0X&QҦ@ qi27Ԗ\3Hm%A"@LQ!yP }?fD><]İ|XH76v`Wο=k("icAԜygUHjdh )wmM ?{i7s]3,%gzN<+ϕcg!gȯN0*qƊoX05`p̸1csJΤ6e}OFFM-9ˌB߈9K&t[$ rѫŅ;ɴlѾ j)Q^MK?{oaH|/WDLQdמ7#aYWy$E:"~KD|^m[T~Zu@Hx )οQw#X۬&2tn]#Ebod52(D.T&d- X4iԺ(6hĠ!i8 qPCks4[6,KCa^]d4d9L1/+ZhP 60jaXèj3eWZ%5ˠ$SRڕҩM6&aYoI< M'XKf` SMδYu@ )J^-g2s6;u ܇ƇW#[dRLp )8[\a7_Cμ@6p7>*cB9-N7UY5;rRPX0 =3veBb2Ӡ)ց3TUn֝zPlтDDpXz$ Ϗ1 )Ñ'56dxdpkB0sqdTuA4A);;-"f="簉f'tr|kAqMQW]u}̺߀}#$> stream xY[k[G~ׯe; 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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Copying.html000066400000000000000000000077421515050116100256060ustar00rootroot00000000000000 Copying (Tablicious v0.4.0)

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Data-Sets-from-R.html000066400000000000000000000056631515050116100271230ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.0)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Date-and-Time-Representation.html000066400000000000000000000060461515050116100315030ustar00rootroot00000000000000 Date and Time Representation (Tablicious v0.4.0)

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Datenum-Compatibility.html000066400000000000000000000074541515050116100304020ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.0)

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Defined-Time-Zones.html000066400000000000000000000060021515050116100275100ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.0)

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Durations.html000066400000000000000000000046361515050116100261450ustar00rootroot00000000000000 Durations (Tablicious v0.4.0) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Example-Data-Sets.html000066400000000000000000000102121515050116100273360ustar00rootroot00000000000000 Example Data Sets (Tablicious v0.4.0)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Getting-Started.html000066400000000000000000000064041515050116100271750ustar00rootroot00000000000000 Getting Started (Tablicious v0.4.0)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.0/tablicious-0.4.0.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Introduction.html000066400000000000000000000072321515050116100266510ustar00rootroot00000000000000 Introduction (Tablicious v0.4.0)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.0 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Missing-Functionality.html000066400000000000000000000105251515050116100304260ustar00rootroot00000000000000 Missing Functionality (Tablicious v0.4.0)

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/NaC.html000066400000000000000000000075701515050116100246360ustar00rootroot00000000000000 NaC (Tablicious v0.4.0)

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/NaS.html000066400000000000000000000074551515050116100246600ustar00rootroot00000000000000 NaS (Tablicious v0.4.0)

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/NaT.html000066400000000000000000000075211515050116100246530ustar00rootroot00000000000000 NaT (Tablicious v0.4.0)

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Table-Construction.html000066400000000000000000000070261515050116100277100ustar00rootroot00000000000000 Table Construction (Tablicious v0.4.0)

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Table-Representation.html000066400000000000000000000100161515050116100302110ustar00rootroot00000000000000 Table Representation (Tablicious v0.4.0)

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Tables-vs-SQL.html000066400000000000000000000113521515050116100264630ustar00rootroot00000000000000 Tables vs SQL (Tablicious v0.4.0)

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Time-Zones.html000066400000000000000000000141161515050116100261610ustar00rootroot00000000000000 Time Zones (Tablicious v0.4.0)

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/Validation-Functions.html000066400000000000000000000117371515050116100302350ustar00rootroot00000000000000 Validation Functions (Tablicious v0.4.0)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/array2table.html000066400000000000000000000102671515050116100264020ustar00rootroot00000000000000 array2table (Tablicious v0.4.0)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration-Class.html000066400000000000000000000063021515050116100305070ustar00rootroot00000000000000 calendarDuration Class (Tablicious v0.4.0)

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration.html000066400000000000000000000205171515050116100274500ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.0)

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

    Next: calmonths, Previous: array2table, Up: API Alphabetically   [Contents]

    calendarDuration_002ecalendarDuration.html000066400000000000000000000076761515050116100333320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html calendarDuration.calendarDuration (Tablicious v0.4.0)

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002edispstrs.html000066400000000000000000000061121515050116100317650ustar00rootroot00000000000000 calendarDuration.dispstrs (Tablicious v0.4.0)

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    8.2.2.6 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002eismissing.html000066400000000000000000000056751515050116100321340ustar00rootroot00000000000000 calendarDuration.ismissing (Tablicious v0.4.0)

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    8.2.2.8 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002eisnan.html000066400000000000000000000062661515050116100312340ustar00rootroot00000000000000 calendarDuration.isnan (Tablicious v0.4.0)

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    8.2.2.7 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002eminus.html000066400000000000000000000061361515050116100312530ustar00rootroot00000000000000 calendarDuration.minus (Tablicious v0.4.0)

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002eplus.html000066400000000000000000000066061515050116100311050ustar00rootroot00000000000000 calendarDuration.plus (Tablicious v0.4.0)

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    8.2.2.3 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002etimes.html000066400000000000000000000061251515050116100312370ustar00rootroot00000000000000 calendarDuration.times (Tablicious v0.4.0)

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    8.2.2.4 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calendarDuration_002euminus.html000066400000000000000000000060221515050116100314320ustar00rootroot00000000000000 calendarDuration.uminus (Tablicious v0.4.0)

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    8.2.2.2 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calmonths.html000066400000000000000000000064571515050116100261700ustar00rootroot00000000000000 calmonths (Tablicious v0.4.0)

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/calyears.html000066400000000000000000000057651515050116100260040ustar00rootroot00000000000000 calyears (Tablicious v0.4.0)

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical.html000066400000000000000000000174631515050116100264540ustar00rootroot00000000000000 categorical (Tablicious v0.4.0)

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

    Next: cell2table, Previous: calyears, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eaddcats.html000066400000000000000000000062341515050116100305200ustar00rootroot00000000000000 categorical.addcats (Tablicious v0.4.0)

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002ecategorical.html000066400000000000000000000135751515050116100314000ustar00rootroot00000000000000 categorical.categorical (Tablicious v0.4.0)

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    8.2.5.3 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002ecategories.html000066400000000000000000000061611515050116100312410ustar00rootroot00000000000000 categorical.categories (Tablicious v0.4.0)

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002ecellstr.html000066400000000000000000000062041515050116100305620ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.0)

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002edispstrs.html000066400000000000000000000062371515050116100307730ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.0)

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eiscategory.html000066400000000000000000000063041515050116100312640ustar00rootroot00000000000000 categorical.iscategory (Tablicious v0.4.0)

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eismissing.html000066400000000000000000000061241515050116100311200ustar00rootroot00000000000000 categorical.ismissing (Tablicious v0.4.0)

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eisnanny.html000066400000000000000000000062331515050116100305730ustar00rootroot00000000000000 categorical.isnanny (Tablicious v0.4.0)

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eisordinal.html000066400000000000000000000061151515050116100310770ustar00rootroot00000000000000 categorical.isordinal (Tablicious v0.4.0)

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eisundefined.html000066400000000000000000000064301515050116100314100ustar00rootroot00000000000000 categorical.isundefined (Tablicious v0.4.0)

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002emergecats.html000066400000000000000000000075721515050116100310750ustar00rootroot00000000000000 categorical.mergecats (Tablicious v0.4.0)

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002emissing.html000066400000000000000000000074421515050116100305700ustar00rootroot00000000000000 categorical.missing (Tablicious v0.4.0)

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    8.2.5.2 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002erenamecats.html000066400000000000000000000067551515050116100312470ustar00rootroot00000000000000 categorical.renamecats (Tablicious v0.4.0)

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002ereordercats.html000066400000000000000000000072131515050116100314300ustar00rootroot00000000000000 categorical.reordercats (Tablicious v0.4.0)

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002esetcats.html000066400000000000000000000062361515050116100305650ustar00rootroot00000000000000 categorical.setcats (Tablicious v0.4.0)

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002esqueezecats.html000066400000000000000000000056211515050116100314500ustar00rootroot00000000000000 categorical.squeezecats (Tablicious v0.4.0)

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002estring.html000066400000000000000000000062231515050116100304210ustar00rootroot00000000000000 categorical.string (Tablicious v0.4.0)

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002esummary.html000066400000000000000000000060261515050116100306110ustar00rootroot00000000000000 categorical.summary (Tablicious v0.4.0)

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/categorical_002eundefined.html000066400000000000000000000066241515050116100310610ustar00rootroot00000000000000 categorical.undefined (Tablicious v0.4.0)

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/cell2table.html000066400000000000000000000101551515050116100261770ustar00rootroot00000000000000 cell2table (Tablicious v0.4.0)

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/colvecfun.html000066400000000000000000000065311515050116100261550ustar00rootroot00000000000000 colvecfun (Tablicious v0.4.0)

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/contains.html000066400000000000000000000074131515050116100260070ustar00rootroot00000000000000 contains (Tablicious v0.4.0)

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datasets_002edescription.html000066400000000000000000000064721515050116100307770ustar00rootroot00000000000000 datasets.description (Tablicious v0.4.0)

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    8.2.58.3 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datasets_002elist.html000066400000000000000000000062071515050116100274230ustar00rootroot00000000000000 datasets.list (Tablicious v0.4.0)

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    8.2.58.1 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datasets_002eload.html000066400000000000000000000065061515050116100273710ustar00rootroot00000000000000 datasets.load (Tablicious v0.4.0)

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    8.2.58.2 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime-Class.html000066400000000000000000000063571515050116100270360ustar00rootroot00000000000000 datetime Class (Tablicious v0.4.0)

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime.html000066400000000000000000000176271515050116100257750ustar00rootroot00000000000000 datetime (Tablicious v0.4.0)

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

    Next: days, Previous: contains, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eNaT.html000066400000000000000000000076171515050116100271240ustar00rootroot00000000000000 datetime.NaT (Tablicious v0.4.0)

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002econvertDatenumTimeZone.html000066400000000000000000000065201515050116100331030ustar00rootroot00000000000000 datetime.convertDatenumTimeZone (Tablicious v0.4.0)

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    8.2.9.34 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatenum.html000066400000000000000000000056521515050116100300740ustar00rootroot00000000000000 datetime.datenum (Tablicious v0.4.0)

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    8.2.9.18 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatenum2posix.html000066400000000000000000000061221515050116100312320ustar00rootroot00000000000000 datetime.datenum2posix (Tablicious v0.4.0)

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatestr.html000066400000000000000000000065461515050116100301100ustar00rootroot00000000000000 datetime.datestr (Tablicious v0.4.0)

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    8.2.9.14 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatestrs.html000066400000000000000000000066441515050116100302720ustar00rootroot00000000000000 datetime.datestrs (Tablicious v0.4.0)

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    8.2.9.15 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatestruct.html000066400000000000000000000066441515050116100306230ustar00rootroot00000000000000 datetime.datestruct (Tablicious v0.4.0)

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    8.2.9.16 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edatetime.html000066400000000000000000000132261515050116100302270ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.0)

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002ediff.html000066400000000000000000000060521515050116100273420ustar00rootroot00000000000000 datetime.diff (Tablicious v0.4.0)

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    8.2.9.31 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002edispstrs.html000066400000000000000000000057031515050116100303070ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.0)

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    8.2.9.13 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eeq.html000066400000000000000000000061561515050116100270440ustar00rootroot00000000000000 datetime.eq (Tablicious v0.4.0)

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    8.2.9.26 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002ege.html000066400000000000000000000062011515050116100270210ustar00rootroot00000000000000 datetime.ge (Tablicious v0.4.0)

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    8.2.9.27 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002egmtime.html000066400000000000000000000061151515050116100277140ustar00rootroot00000000000000 datetime.gmtime (Tablicious v0.4.0)

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    8.2.9.19 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002egt.html000066400000000000000000000061741515050116100270510ustar00rootroot00000000000000 datetime.gt (Tablicious v0.4.0)

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    8.2.9.28 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002ehms.html000066400000000000000000000060731515050116100272240ustar00rootroot00000000000000 datetime.hms (Tablicious v0.4.0)

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    8.2.9.9 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eisbetween.html000066400000000000000000000063251515050116100304220ustar00rootroot00000000000000 datetime.isbetween (Tablicious v0.4.0)

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    8.2.9.32 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eisnan.html000066400000000000000000000057501515050116100275460ustar00rootroot00000000000000 datetime.isnan (Tablicious v0.4.0)

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    8.2.9.22 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eisnat.html000066400000000000000000000056201515050116100275500ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.0)

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    8.2.9.21 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002ele.html000066400000000000000000000061751515050116100270400ustar00rootroot00000000000000 datetime.le (Tablicious v0.4.0)

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    8.2.9.24 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002elinspace.html000066400000000000000000000070151515050116100302300ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.0)

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    8.2.9.33 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002elocaltime.html000066400000000000000000000066771515050116100304200ustar00rootroot00000000000000 datetime.localtime (Tablicious v0.4.0)

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002elt.html000066400000000000000000000061751515050116100270570ustar00rootroot00000000000000 datetime.lt (Tablicious v0.4.0)

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    8.2.9.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eminus.html000066400000000000000000000067751515050116100276010ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.0)

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    8.2.9.30 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002ene.html000066400000000000000000000061621515050116100270360ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.0)

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    8.2.9.25 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eofDatenum.html000066400000000000000000000060351515050116100303550ustar00rootroot00000000000000 datetime.ofDatenum (Tablicious v0.4.0)

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eofDatestruct.html000066400000000000000000000062361515050116100311050ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.0)

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eplus.html000066400000000000000000000065351515050116100274230ustar00rootroot00000000000000 datetime.plus (Tablicious v0.4.0)

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    8.2.9.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eposix2datenum.html000066400000000000000000000061721515050116100312370ustar00rootroot00000000000000 datetime.posix2datenum (Tablicious v0.4.0)

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eposixtime.html000066400000000000000000000062121515050116100304510ustar00rootroot00000000000000 datetime.posixtime (Tablicious v0.4.0)

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    8.2.9.17 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eproxyKeys.html000066400000000000000000000064421515050116100304520ustar00rootroot00000000000000 datetime.proxyKeys (Tablicious v0.4.0)

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    8.2.9.7 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002etimeofday.html000066400000000000000000000061051515050116100304120ustar00rootroot00000000000000 datetime.timeofday (Tablicious v0.4.0)

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    8.2.9.11 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eweek.html000066400000000000000000000055501515050116100273670ustar00rootroot00000000000000 datetime.week (Tablicious v0.4.0)

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    8.2.9.12 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eymd.html000066400000000000000000000061011515050116100272160ustar00rootroot00000000000000 datetime.ymd (Tablicious v0.4.0)

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    8.2.9.8 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/datetime_002eymdhms.html000066400000000000000000000063041515050116100277330ustar00rootroot00000000000000 datetime.ymdhms (Tablicious v0.4.0)

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    8.2.9.10 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/days.html000066400000000000000000000062101515050116100251230ustar00rootroot00000000000000 days (Tablicious v0.4.0)

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/discretize.html000066400000000000000000000121151515050116100263310ustar00rootroot00000000000000 discretize (Tablicious v0.4.0)

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/dispstrs.html000066400000000000000000000065521515050116100260470ustar00rootroot00000000000000 dispstrs (Tablicious v0.4.0)

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration-Class.html000066400000000000000000000057511515050116100270640ustar00rootroot00000000000000 duration Class (Tablicious v0.4.0)

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration.html000066400000000000000000000122201515050116100260060ustar00rootroot00000000000000 duration (Tablicious v0.4.0)

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002echar.html000066400000000000000000000061141515050116100273770ustar00rootroot00000000000000 duration.char (Tablicious v0.4.0)

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    8.2.13.8 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002edispstrs.html000066400000000000000000000057231515050116100303420ustar00rootroot00000000000000 duration.dispstrs (Tablicious v0.4.0)

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    8.2.13.7 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002ehours.html000066400000000000000000000057401515050116100276260ustar00rootroot00000000000000 duration.hours (Tablicious v0.4.0)

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002elinspace.html000066400000000000000000000064401515050116100302620ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.0)

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    8.2.13.9 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002emilliseconds.html000066400000000000000000000060421515050116100311470ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.0)

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    8.2.13.6 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002eminutes.html000066400000000000000000000057661515050116100301620ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.0)

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    8.2.13.4 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002eofDays.html000066400000000000000000000057211515050116100277120ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.0)

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002eseconds.html000066400000000000000000000057731515050116100301320ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.0)

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    8.2.13.5 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/duration_002eyears.html000066400000000000000000000057371515050116100276170ustar00rootroot00000000000000 duration.years (Tablicious v0.4.0)

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    8.2.13.2 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/eqn.html000066400000000000000000000074221515050116100247540ustar00rootroot00000000000000 eqn (Tablicious v0.4.0)

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/head.html000066400000000000000000000074401515050116100250720ustar00rootroot00000000000000 head (Tablicious v0.4.0) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/hours.html000066400000000000000000000062531515050116100253320ustar00rootroot00000000000000 hours (Tablicious v0.4.0)

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/index.html000066400000000000000000001315141515050116100253000ustar00rootroot00000000000000 Top (Tablicious v0.4.0)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.0.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/iscalendarduration.html000066400000000000000000000062131515050116100300410ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.0)

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/iscategorical.html000066400000000000000000000061671515050116100270070ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.0)

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/isdatetime.html000066400000000000000000000060771515050116100263260ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.0)

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/isduration.html000066400000000000000000000060431515050116100263500ustar00rootroot00000000000000 isduration (Tablicious v0.4.0)

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/isfile.html000066400000000000000000000044441515050116100254450ustar00rootroot00000000000000 isfile (Tablicious v0.4.0)

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    8.2.21 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/isfolder.html000066400000000000000000000044341515050116100260000ustar00rootroot00000000000000 isfolder (Tablicious v0.4.0)

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/isnanny.html000066400000000000000000000073751515050116100256570ustar00rootroot00000000000000 isnanny (Tablicious v0.4.0)

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/istable.html000066400000000000000000000064501515050116100256140ustar00rootroot00000000000000 istable (Tablicious v0.4.0)

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/istabular.html000066400000000000000000000061701515050116100261560ustar00rootroot00000000000000 istabular (Tablicious v0.4.0)

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/istimetable.html000066400000000000000000000065441515050116100264770ustar00rootroot00000000000000 istimetable (Tablicious v0.4.0)

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate.html000066400000000000000000000122021515050116100261110ustar00rootroot00000000000000 localdate (Tablicious v0.4.0)

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002eNaT.html000066400000000000000000000100531515050116100272440ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.0)

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    8.2.27.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002edatenum.html000066400000000000000000000057311515050116100302260ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.0)

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    8.2.27.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002edatestr.html000066400000000000000000000065741515050116100302450ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.0)

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    8.2.27.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002edatestrs.html000066400000000000000000000066721515050116100304270ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.0)

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    8.2.27.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002edatestruct.html000066400000000000000000000071011515050116100307440ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.0)

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    8.2.27.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002edispstrs.html000066400000000000000000000057211515050116100304430ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.0)

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    8.2.27.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002eisnan.html000066400000000000000000000055461515050116100277050ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.0)

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    8.2.27.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002eisnat.html000066400000000000000000000056371515050116100277140ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.0)

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    8.2.27.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002elocaldate.html000066400000000000000000000105661515050116100305230ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.0)

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    8.2.27.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002eposixtime.html000066400000000000000000000063501515050116100306100ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.0)

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    8.2.27.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/localdate_002eymd.html000066400000000000000000000057461515050116100273700ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.0)

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    8.2.27.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/milliseconds.html000066400000000000000000000063561515050116100266630ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.0)

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/minutes.html000066400000000000000000000055671515050116100256650ustar00rootroot00000000000000 minutes (Tablicious v0.4.0)

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing.html000066400000000000000000000074771515050116100256540ustar00rootroot00000000000000 missing (Tablicious v0.4.0)

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing_002edispstrs.html000066400000000000000000000061321515050116100301610ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.0)

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    8.2.30.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing_002eismissing.html000066400000000000000000000060271515050116100303160ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.0)

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    8.2.30.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing_002eisnan.html000066400000000000000000000057601515050116100274240ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.0)

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    8.2.30.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing_002eisnanny.html000066400000000000000000000055301515050116100277660ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.0)

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    8.2.30.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/missing_002emissing.html000066400000000000000000000054321515050116100277610ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.0)

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    8.2.30.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeA.html000066400000000000000000000044621515050116100255320ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.0)

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    8.2.31 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeCellstr.html000066400000000000000000000045261515050116100267630ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.0)

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    8.2.32 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeCharvec.html000066400000000000000000000045521515050116100267250ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.0)

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    8.2.33 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeFinite.html000066400000000000000000000045501515050116100265660ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.0)

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    8.2.34 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeInteger.html000066400000000000000000000045461515050116100267520ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.0)

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    8.2.35 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeMember.html000066400000000000000000000045541515050116100265630ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.0)

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    8.2.36 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeNonempty.html000066400000000000000000000045601515050116100271620ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.0)

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    8.2.37 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeNumeric.html000066400000000000000000000045461515050116100267570ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.0)

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    8.2.38 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeReal.html000066400000000000000000000045401515050116100262320ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.0)

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    8.2.39 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeSameSize.html000066400000000000000000000045441515050116100270730ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.0)

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    8.2.40 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeScalar.html000066400000000000000000000046041515050116100265550ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.0)

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    8.2.41 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeScalarLogical.html000066400000000000000000000046121515050116100300470ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.0)

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    8.2.42 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/mustBeVector.html000066400000000000000000000045301515050116100266100ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.0)

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    8.2.43 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/pp.html000066400000000000000000000077051515050116100246140ustar00rootroot00000000000000 pp (Tablicious v0.4.0)

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/scalarexpand.html000066400000000000000000000072301515050116100266330ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.0)

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/seconds.html000066400000000000000000000062751515050116100256340ustar00rootroot00000000000000 seconds (Tablicious v0.4.0)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/size2str.html000066400000000000000000000064731515050116100257630ustar00rootroot00000000000000 size2str (Tablicious v0.4.0)

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/splitapply.html000066400000000000000000000113441515050116100263700ustar00rootroot00000000000000 splitapply (Tablicious v0.4.0)

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string.html000066400000000000000000000157531515050116100255050ustar00rootroot00000000000000 string (Tablicious v0.4.0)

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

    Next: struct2table, Previous: splitapply, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002ecell.html000066400000000000000000000061311515050116100270610ustar00rootroot00000000000000 string.cell (Tablicious v0.4.0)

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    8.2.52.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002ecellstr.html000066400000000000000000000057471515050116100276260ustar00rootroot00000000000000 string.cellstr (Tablicious v0.4.0)

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    8.2.52.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002echar.html000066400000000000000000000061771515050116100270710ustar00rootroot00000000000000 string.char (Tablicious v0.4.0)

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    8.2.52.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002ecmp.html000066400000000000000000000075111515050116100267240ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.0)

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    8.2.52.24 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002edecode.html000066400000000000000000000057231515050116100273730ustar00rootroot00000000000000 string.decode (Tablicious v0.4.0)

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    8.2.52.25 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002edispstrs.html000066400000000000000000000064641515050116100300260ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.0)

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    8.2.52.5 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eempty.html000066400000000000000000000056601515050116100273060ustar00rootroot00000000000000 string.empty (Tablicious v0.4.0)

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    8.2.52.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eencode.html000066400000000000000000000063511515050116100274030ustar00rootroot00000000000000 string.encode (Tablicious v0.4.0)

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    8.2.52.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eerase.html000066400000000000000000000057551515050116100272540ustar00rootroot00000000000000 string.erase (Tablicious v0.4.0)

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    8.2.52.19 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eismissing.html000066400000000000000000000061411515050116100301500ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.0)

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    8.2.52.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eisnanny.html000066400000000000000000000057471515050116100276350ustar00rootroot00000000000000 string.isnanny (Tablicious v0.4.0)

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    8.2.52.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eisstring.html000066400000000000000000000057061515050116100300130ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.0)

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    8.2.52.4 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002elower.html000066400000000000000000000061531515050116100272760ustar00rootroot00000000000000 string.lower (Tablicious v0.4.0)

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    8.2.52.17 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002emissing.html000066400000000000000000000061211515050116100276120ustar00rootroot00000000000000 string.missing (Tablicious v0.4.0)

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    8.2.52.2 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eregexprep.html000066400000000000000000000067551515050116100301570ustar00rootroot00000000000000 string.regexprep (Tablicious v0.4.0)

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    8.2.52.22 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002ereverse.html000066400000000000000000000061451515050116100276220ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.0)

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    8.2.52.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002ereverse_005fbytes.html000066400000000000000000000067111515050116100314220ustar00rootroot00000000000000 string.reverse_bytes (Tablicious v0.4.0)

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    8.2.52.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrcat.html000066400000000000000000000061671515050116100274530ustar00rootroot00000000000000 string.strcat (Tablicious v0.4.0)

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    8.2.52.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrcmp.html000066400000000000000000000065151515050116100274600ustar00rootroot00000000000000 string.strcmp (Tablicious v0.4.0)

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    8.2.52.23 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrfind.html000066400000000000000000000067501515050116100276220ustar00rootroot00000000000000 string.strfind (Tablicious v0.4.0)

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estring.html000066400000000000000000000070541515050116100274550ustar00rootroot00000000000000 string.string (Tablicious v0.4.0)

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    8.2.52.3 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrlength.html000066400000000000000000000072471515050116100301650ustar00rootroot00000000000000 string.strlength (Tablicious v0.4.0)

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    8.2.52.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrlength_005fbytes.html000066400000000000000000000066401515050116100317620ustar00rootroot00000000000000 string.strlength_bytes (Tablicious v0.4.0)

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    8.2.52.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002estrrep.html000066400000000000000000000071411515050116100274630ustar00rootroot00000000000000 string.strrep (Tablicious v0.4.0)

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    8.2.52.20 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/string_002eupper.html000066400000000000000000000061471515050116100273040ustar00rootroot00000000000000 string.upper (Tablicious v0.4.0)

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    8.2.52.18 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/struct2table.html000066400000000000000000000067201515050116100266070ustar00rootroot00000000000000 struct2table (Tablicious v0.4.0)

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table.html000066400000000000000000000225461515050116100252640ustar00rootroot00000000000000 table (Tablicious v0.4.0)

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tableOuterFillValue.html000066400000000000000000000045161515050116100301040ustar00rootroot00000000000000 tableOuterFillValue (Tablicious v0.4.0)

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    8.2.55 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eaddvars.html000066400000000000000000000101421515050116100273440ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.0)

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    8.2.54.29 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eantijoin.html000066400000000000000000000070201515050116100275340ustar00rootroot00000000000000 table.antijoin (Tablicious v0.4.0)

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    8.2.54.40 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002ecartesian.html000066400000000000000000000076241515050116100277040ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.0)

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    8.2.54.41 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002econvertvars.html000066400000000000000000000072731515050116100303070ustar00rootroot00000000000000 table.convertvars (Tablicious v0.4.0)

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    8.2.54.30 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eend.html000066400000000000000000000054511515050116100264750ustar00rootroot00000000000000 table.end (Tablicious v0.4.0)

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    8.2.54.10 table.end

    Method: out = end (obj, k, n)

    Last index for given dimension of a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002efindgroups.html000066400000000000000000000063451515050116100301120ustar00rootroot00000000000000 table.findgroups (Tablicious v0.4.0)

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    8.2.54.53 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002egetvar.html000066400000000000000000000062321515050116100272150ustar00rootroot00000000000000 table.getvar (Tablicious v0.4.0)

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    8.2.54.26 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002egetvars.html000066400000000000000000000061311515050116100273760ustar00rootroot00000000000000 table.getvars (Tablicious v0.4.0)

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    8.2.54.27 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002egroupby.html000066400000000000000000000075051515050116100274200ustar00rootroot00000000000000 table.groupby (Tablicious v0.4.0)

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    8.2.54.42 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eheight.html000066400000000000000000000057151515050116100272020ustar00rootroot00000000000000 table.height (Tablicious v0.4.0)

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    8.2.54.13 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002ehorzcat.html000066400000000000000000000062551515050116100274040ustar00rootroot00000000000000 table.horzcat (Tablicious v0.4.0)

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    8.2.54.18 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002einnerjoin.html000066400000000000000000000072521515050116100277230ustar00rootroot00000000000000 table.innerjoin (Tablicious v0.4.0)

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    8.2.54.35 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eintersect.html000066400000000000000000000064361515050116100277330ustar00rootroot00000000000000 table.intersect (Tablicious v0.4.0)

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    8.2.54.46 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eisempty.html000066400000000000000000000056211515050116100274200ustar00rootroot00000000000000 table.isempty (Tablicious v0.4.0)

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    8.2.54.16 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eismember.html000066400000000000000000000060771515050116100275370ustar00rootroot00000000000000 table.ismember (Tablicious v0.4.0)

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    8.2.54.49 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eismissing.html000066400000000000000000000072161515050116100277350ustar00rootroot00000000000000 table.ismissing (Tablicious v0.4.0)

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    8.2.54.50 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eistable.html000066400000000000000000000054261515050116100273540ustar00rootroot00000000000000 table.istable (Tablicious v0.4.0)

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    8.2.54.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002ejoin.html000066400000000000000000000074061515050116100266700ustar00rootroot00000000000000 table.join (Tablicious v0.4.0)

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    8.2.54.34 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002emergevars.html000066400000000000000000000073071515050116100277240ustar00rootroot00000000000000 table.mergevars (Tablicious v0.4.0)

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    8.2.54.31 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002emovevars.html000066400000000000000000000070201515050116100275630ustar00rootroot00000000000000 table.movevars (Tablicious v0.4.0)

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    8.2.54.25 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002endims.html000066400000000000000000000055621515050116100270440ustar00rootroot00000000000000 table.ndims (Tablicious v0.4.0)

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    8.2.54.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002enumel.html000066400000000000000000000060321515050116100270430ustar00rootroot00000000000000 table.numel (Tablicious v0.4.0)

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    8.2.54.15 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eouterfillvals.html000066400000000000000000000060231515050116100306160ustar00rootroot00000000000000 table.outerfillvals (Tablicious v0.4.0)

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    8.2.54.38 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eouterjoin.html000066400000000000000000000075161515050116100277510ustar00rootroot00000000000000 table.outerjoin (Tablicious v0.4.0)

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    8.2.54.37 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eprettyprint.html000066400000000000000000000060061515050116100303300ustar00rootroot00000000000000 table.prettyprint (Tablicious v0.4.0)

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    8.2.54.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erealjoin.html000066400000000000000000000077441515050116100275410ustar00rootroot00000000000000 table.realjoin (Tablicious v0.4.0)

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    8.2.54.36 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eremovevars.html000066400000000000000000000060761515050116100301240ustar00rootroot00000000000000 table.removevars (Tablicious v0.4.0)

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    8.2.54.24 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erenamevars.html000066400000000000000000000064151515050116100300730ustar00rootroot00000000000000 table.renamevars (Tablicious v0.4.0)

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    8.2.54.55 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erepelem.html000066400000000000000000000066611515050116100273640ustar00rootroot00000000000000 table.repelem (Tablicious v0.4.0)

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    8.2.54.20 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erepmat.html000066400000000000000000000064501515050116100272170ustar00rootroot00000000000000 table.repmat (Tablicious v0.4.0)

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    8.2.54.19 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erestrict.html000066400000000000000000000105741515050116100275700ustar00rootroot00000000000000 table.restrict (Tablicious v0.4.0)

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    8.2.54.54 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erowfun.html000066400000000000000000000156341515050116100272530ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.0)

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    8.2.54.52 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.findgroups, Previous: table.varfun, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002erows2vars.html000066400000000000000000000106571515050116100277030ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.0)

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    8.2.54.44 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esemijoin.html000066400000000000000000000072161515050116100275450ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.0)

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    8.2.54.39 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetDimensionNames.html000066400000000000000000000100151515050116100313440ustar00rootroot00000000000000 table.setDimensionNames (Tablicious v0.4.0)

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    8.2.54.22 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetRowNames.html000066400000000000000000000061421515050116100301740ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.0)

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    8.2.54.23 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetVariableNames.html000066400000000000000000000076471515050116100311650ustar00rootroot00000000000000 table.setVariableNames (Tablicious v0.4.0)

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    8.2.54.21 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetdiff.html000066400000000000000000000062531515050116100273540ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.0)

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    8.2.54.48 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetvar.html000066400000000000000000000070571515050116100272370ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.0)

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    8.2.54.28 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esetxor.html000066400000000000000000000064711515050116100272560ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.0)

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    8.2.54.47 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esize.html000066400000000000000000000071601515050116100267000ustar00rootroot00000000000000 table.size (Tablicious v0.4.0)

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    8.2.54.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esplitapply.html000066400000000000000000000070771515050116100301360ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.0)

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    8.2.54.43 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.54.32 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esqueeze.html000066400000000000000000000056741515050116100274170ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.0)

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    8.2.54.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002estack.html000066400000000000000000000072121515050116100270310ustar00rootroot00000000000000 table.stack (Tablicious v0.4.0)

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    8.2.54.33 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002esummary.html000066400000000000000000000055571515050116100274330ustar00rootroot00000000000000 table.summary (Tablicious v0.4.0)

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    8.2.54.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002etable.html000066400000000000000000000122231515050116100270110ustar00rootroot00000000000000 table.table (Tablicious v0.4.0)

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    8.2.54.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002etable2array.html000066400000000000000000000055761515050116100301470ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.0)

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    8.2.54.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.54.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002etable2struct.html000066400000000000000000000071731515050116100303500ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.0)

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    8.2.54.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002eunion.html000066400000000000000000000063751515050116100270650ustar00rootroot00000000000000 table.union (Tablicious v0.4.0)

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    8.2.54.45 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002evarfun.html000066400000000000000000000102661515050116100272300ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.0)

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002evarnames.html000066400000000000000000000064411515050116100275430ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.0)

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    8.2.54.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002evertcat.html000066400000000000000000000063771515050116100274070ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.0)

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    8.2.54.17 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/table_002ewidth.html000066400000000000000000000055671515050116100270560ustar00rootroot00000000000000 table.width (Tablicious v0.4.0)

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    8.2.54.14 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tail.html000066400000000000000000000076111515050116100251220ustar00rootroot00000000000000 tail (Tablicious v0.4.0)

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset.html000066400000000000000000000243741515050116100275570ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.0)

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eAirPassengers.html000066400000000000000000000075561515050116100327770ustar00rootroot00000000000000 tblish.dataset.AirPassengers (Tablicious v0.4.0)

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eBJsales.html000066400000000000000000000110401515050116100315330ustar00rootroot00000000000000 tblish.dataset.BJsales (Tablicious v0.4.0)

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eBOD.html000066400000000000000000000102621515050116100306210ustar00rootroot00000000000000 tblish.dataset.BOD (Tablicious v0.4.0)

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eChickWeight.html000066400000000000000000000113051515050116100324050ustar00rootroot00000000000000 tblish.dataset.ChickWeight (Tablicious v0.4.0)

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eDNase.html000066400000000000000000000111721515050116100312100ustar00rootroot00000000000000 tblish.dataset.DNase (Tablicious v0.4.0)

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111331515050116100330400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.EuStockMarkets (Tablicious v0.4.0)

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114171515050116100326230ustar00rootroot00000000000000 tblish.dataset.Formaldehyde (Tablicious v0.4.0)

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151041515050116100325420ustar00rootroot00000000000000 tblish.dataset.HairEyeColor (Tablicious v0.4.0)

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eHarman23cor.html000066400000000000000000000102721515050116100322750ustar00rootroot00000000000000 tblish.dataset.Harman23cor (Tablicious v0.4.0)

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eHarman74cor.html000066400000000000000000000103471515050116100323060ustar00rootroot00000000000000 tblish.dataset.Harman74cor (Tablicious v0.4.0)

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eIndometh.html000066400000000000000000000110051515050116100317600ustar00rootroot00000000000000 tblish.dataset.Indometh (Tablicious v0.4.0)

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eInsectSprays.html000066400000000000000000000104441515050116100326460ustar00rootroot00000000000000 tblish.dataset.InsectSprays (Tablicious v0.4.0)

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000102561515050116100331160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.0)

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eLakeHuron.html000066400000000000000000000104741515050116100321120ustar00rootroot00000000000000 tblish.dataset.LakeHuron (Tablicious v0.4.0)

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126261515050116100333360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.0)

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eLoblolly.html000066400000000000000000000111021515050116100317770ustar00rootroot00000000000000 tblish.dataset.Loblolly (Tablicious v0.4.0)

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eNile.html000066400000000000000000000112151515050116100311030ustar00rootroot00000000000000 tblish.dataset.Nile (Tablicious v0.4.0)

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eOrange.html000066400000000000000000000115221515050116100314300ustar00rootroot00000000000000 tblish.dataset.Orange (Tablicious v0.4.0)

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eOrchardSprays.html000066400000000000000000000124741515050116100330100ustar00rootroot00000000000000 tblish.dataset.OrchardSprays (Tablicious v0.4.0)

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102161515050116100324650ustar00rootroot00000000000000 tblish.dataset.PlantGrowth (Tablicious v0.4.0)

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ePuromycin.html000066400000000000000000000124711515050116100322060ustar00rootroot00000000000000 tblish.dataset.Puromycin (Tablicious v0.4.0)

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eTheoph.html000066400000000000000000000142601515050116100314460ustar00rootroot00000000000000 tblish.dataset.Theoph (Tablicious v0.4.0)

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eTitanic.html000066400000000000000000000146031515050116100316130ustar00rootroot00000000000000 tblish.dataset.Titanic (Tablicious v0.4.0)

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eToothGrowth.html000066400000000000000000000115311515050116100325050ustar00rootroot00000000000000 tblish.dataset.ToothGrowth (Tablicious v0.4.0)

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000140771515050116100326700ustar00rootroot00000000000000 tblish.dataset.UCBAdmissions (Tablicious v0.4.0)

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000147431515050116100327720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.0)

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000110761515050116100325170ustar00rootroot00000000000000 tblish.dataset.UKLungDeaths (Tablicious v0.4.0)

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eUKgas.html000066400000000000000000000103671515050116100312350ustar00rootroot00000000000000 tblish.dataset.UKgas (Tablicious v0.4.0)

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000100531515050116100323020ustar00rootroot00000000000000 tblish.dataset.USAccDeaths (Tablicious v0.4.0)

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eUSArrests.html000066400000000000000000000137551515050116100321220ustar00rootroot00000000000000 tblish.dataset.USArrests (Tablicious v0.4.0)

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116231515050116100327760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.USJudgeRatings (Tablicious v0.4.0)

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111061515050116100344040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.0)

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eVADeaths.html000066400000000000000000000113251515050116100316550ustar00rootroot00000000000000 tblish.dataset.VADeaths (Tablicious v0.4.0)

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eWWWusage.html000066400000000000000000000103531515050116100317270ustar00rootroot00000000000000 tblish.dataset.WWWusage (Tablicious v0.4.0)

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eWorldPhones.html000066400000000000000000000105141515050116100324610ustar00rootroot00000000000000 tblish.dataset.WorldPhones (Tablicious v0.4.0)

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eairmiles.html000066400000000000000000000072301515050116100320230ustar00rootroot00000000000000 tblish.dataset.airmiles (Tablicious v0.4.0)

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eairquality.html000066400000000000000000000125521515050116100324050ustar00rootroot00000000000000 tblish.dataset.airquality (Tablicious v0.4.0)

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eanscombe.html000066400000000000000000000121451515050116100320060ustar00rootroot00000000000000 tblish.dataset.anscombe (Tablicious v0.4.0)

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eattenu.html000066400000000000000000000114501515050116100315150ustar00rootroot00000000000000 tblish.dataset.attenu (Tablicious v0.4.0)

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eattitude.html000066400000000000000000000111021515050116100320320ustar00rootroot00000000000000 tblish.dataset.attitude (Tablicious v0.4.0)

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eaustres.html000066400000000000000000000102211515050116100316760ustar00rootroot00000000000000 tblish.dataset.austres (Tablicious v0.4.0)

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ebeavers.html000066400000000000000000000106431515050116100316470ustar00rootroot00000000000000 tblish.dataset.beavers (Tablicious v0.4.0)

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ecars.html000066400000000000000000000116711515050116100311520ustar00rootroot00000000000000 tblish.dataset.cars (Tablicious v0.4.0)

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002echickwts.html000066400000000000000000000114221515050116100320330ustar00rootroot00000000000000 tblish.dataset.chickwts (Tablicious v0.4.0)

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eco2.html000066400000000000000000000116651515050116100307100ustar00rootroot00000000000000 tblish.dataset.co2 (Tablicious v0.4.0)

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ecrimtab.html000066400000000000000000000164001515050116100316360ustar00rootroot00000000000000 tblish.dataset.crimtab (Tablicious v0.4.0)

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ecupcake.html000066400000000000000000000104731515050116100316340ustar00rootroot00000000000000 tblish.dataset.cupcake (Tablicious v0.4.0)

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ediscoveries.html000066400000000000000000000106211515050116100325330ustar00rootroot00000000000000 tblish.dataset.discoveries (Tablicious v0.4.0)

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eesoph.html000066400000000000000000000106631515050116100313400ustar00rootroot00000000000000 tblish.dataset.esoph (Tablicious v0.4.0)

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eeuro.html000066400000000000000000000116561515050116100311770ustar00rootroot00000000000000 tblish.dataset.euro (Tablicious v0.4.0)

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eeurodist.html000066400000000000000000000101131515050116100320460ustar00rootroot00000000000000 tblish.dataset.eurodist (Tablicious v0.4.0)

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002efaithful.html000066400000000000000000000121121515050116100320130ustar00rootroot00000000000000 tblish.dataset.faithful (Tablicious v0.4.0)

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002efreeny.html000066400000000000000000000121051515050116100315030ustar00rootroot00000000000000 tblish.dataset.freeny (Tablicious v0.4.0)

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002einfert.html000066400000000000000000000116021515050116100315030ustar00rootroot00000000000000 tblish.dataset.infert (Tablicious v0.4.0)

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eiris.html000066400000000000000000000121551515050116100311660ustar00rootroot00000000000000 tblish.dataset.iris (Tablicious v0.4.0)

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eislands.html000066400000000000000000000104001515050116100316440ustar00rootroot00000000000000 tblish.dataset.islands (Tablicious v0.4.0)

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002elh.html000066400000000000000000000101731515050116100306210ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.0)

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002elongley.html000066400000000000000000000115411515050116100316670ustar00rootroot00000000000000 tblish.dataset.longley (Tablicious v0.4.0)

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002elynx.html000066400000000000000000000113351515050116100312110ustar00rootroot00000000000000 tblish.dataset.lynx (Tablicious v0.4.0)

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002emorley.html000066400000000000000000000121341515050116100315240ustar00rootroot00000000000000 tblish.dataset.morley (Tablicious v0.4.0)

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002emtcars.html000066400000000000000000000124261515050116100315120ustar00rootroot00000000000000 tblish.dataset.mtcars (Tablicious v0.4.0)

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002enhtemp.html000066400000000000000000000104771515050116100315200ustar00rootroot00000000000000 tblish.dataset.nhtemp (Tablicious v0.4.0)

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002enottem.html000066400000000000000000000101411515050116100315170ustar00rootroot00000000000000 tblish.dataset.nottem (Tablicious v0.4.0)

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002enpk.html000066400000000000000000000113041515050116100310030ustar00rootroot00000000000000 tblish.dataset.npk (Tablicious v0.4.0)

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000103631515050116100340250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.occupationalStatus (Tablicious v0.4.0)

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eprecip.html000066400000000000000000000103651515050116100315030ustar00rootroot00000000000000 tblish.dataset.precip (Tablicious v0.4.0)

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002epresidents.html000066400000000000000000000111711515050116100323750ustar00rootroot00000000000000 tblish.dataset.presidents (Tablicious v0.4.0)

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002epressure.html000066400000000000000000000112301515050116100320610ustar00rootroot00000000000000 tblish.dataset.pressure (Tablicious v0.4.0)

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002equakes.html000066400000000000000000000120511515050116100315040ustar00rootroot00000000000000 tblish.dataset.quakes (Tablicious v0.4.0)

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002erandu.html000066400000000000000000000111311515050116100313220ustar00rootroot00000000000000 tblish.dataset.randu (Tablicious v0.4.0)

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002erivers.html000066400000000000000000000102631515050116100315300ustar00rootroot00000000000000 tblish.dataset.rivers (Tablicious v0.4.0)

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002erock.html000066400000000000000000000110441515050116100311520ustar00rootroot00000000000000 tblish.dataset.rock (Tablicious v0.4.0)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002esleep.html000066400000000000000000000113051515050116100313240ustar00rootroot00000000000000 tblish.dataset.sleep (Tablicious v0.4.0)

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002estackloss.html000066400000000000000000000141741515050116100322310ustar00rootroot00000000000000 tblish.dataset.stackloss (Tablicious v0.4.0)

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002estate.html000066400000000000000000000130011515050116100313270ustar00rootroot00000000000000 tblish.dataset.state (Tablicious v0.4.0)

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000104521515050116100334520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.sunspot_month (Tablicious v0.4.0)

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102121515050116100332570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.dataset.sunspot_year (Tablicious v0.4.0)

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002esunspots.html000066400000000000000000000105111515050116100321100ustar00rootroot00000000000000 tblish.dataset.sunspots (Tablicious v0.4.0)

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002eswiss.html000066400000000000000000000140711515050116100313670ustar00rootroot00000000000000 tblish.dataset.swiss (Tablicious v0.4.0)

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002etreering.html000066400000000000000000000111731515050116100320360ustar00rootroot00000000000000 tblish.dataset.treering (Tablicious v0.4.0)

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002etrees.html000066400000000000000000000113431515050116100313400ustar00rootroot00000000000000 tblish.dataset.trees (Tablicious v0.4.0)

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002euspop.html000066400000000000000000000102441515050116100313630ustar00rootroot00000000000000 tblish.dataset.uspop (Tablicious v0.4.0)

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002evolcano.html000066400000000000000000000112131515050116100316530ustar00rootroot00000000000000 tblish.dataset.volcano (Tablicious v0.4.0)

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ewarpbreaks.html000066400000000000000000000111751515050116100323620ustar00rootroot00000000000000 tblish.dataset.warpbreaks (Tablicious v0.4.0)

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ewomen.html000066400000000000000000000113451515050116100313450ustar00rootroot00000000000000 tblish.dataset.women (Tablicious v0.4.0)

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edataset_002ezCO2.html000066400000000000000000000104511515050116100307720ustar00rootroot00000000000000 tblish.dataset.zCO2 (Tablicious v0.4.0)

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002edatasets.html000066400000000000000000000064301515050116100277330ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.0)

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002eevalWithTableVars.html000066400000000000000000000075651515050116100315240ustar00rootroot00000000000000 tblish.evalWithTableVars (Tablicious v0.4.0)

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002eexamples_002eSpDb.html000066400000000000000000000077521515050116100312500ustar00rootroot00000000000000 tblish.examples.SpDb (Tablicious v0.4.0)

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002eexamples_002ecoplot.html000066400000000000000000000150401515050116100317050ustar00rootroot00000000000000 tblish.examples.coplot (Tablicious v0.4.0)

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000117551515050116100331060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html tblish.examples.plot_pairs (Tablicious v0.4.0)

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002esizeof2.html000066400000000000000000000124271515050116100275070ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.0)

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/tblish_002etable_002egrpstats.html000066400000000000000000000104751515050116100315340ustar00rootroot00000000000000 tblish.table.grpstats (Tablicious v0.4.0)

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/timezones.html000066400000000000000000000077701515050116100262140ustar00rootroot00000000000000 timezones (Tablicious v0.4.0)

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/todatetime.html000066400000000000000000000070061515050116100263260ustar00rootroot00000000000000 todatetime (Tablicious v0.4.0)

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/vartype.html000066400000000000000000000064671515050116100256730ustar00rootroot00000000000000 vartype (Tablicious v0.4.0)

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/vecfun.html000066400000000000000000000066261515050116100254640ustar00rootroot00000000000000 vecfun (Tablicious v0.4.0)

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/html/years.html000066400000000000000000000064271515050116100253200ustar00rootroot00000000000000 years (Tablicious v0.4.0)

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.0/tablicious.html000066400000000000000000026611741515050116100253770ustar00rootroot00000000000000 Tablicious v0.4.0

    Next:   [Contents]

    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.0.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.0 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.0/tablicious-0.4.0.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.2.3 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.4 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.7 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.2.8 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.5.2 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.3 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.7 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.8 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.9 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.10 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.12 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.13 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.14 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.15 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.16 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.17 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.18 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.19 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.21 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.22 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.24 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.26 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.27 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.28 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.30 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.31 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.32 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.33 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.34 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.2 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.5 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.8 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.9 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.27.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.27.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.27.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.27.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.27.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.27.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.27.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.27.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.27.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.27.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.27.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.30.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.30.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.30.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31 mustBeA

    Not documented

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    8.2.32 mustBeCellstr

    Not documented

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    8.2.33 mustBeCharvec

    Not documented

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    8.2.34 mustBeFinite

    Not documented

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    8.2.35 mustBeInteger

    Not documented

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    8.2.36 mustBeMember

    Not documented

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    8.2.37 mustBeNonempty

    Not documented

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    8.2.38 mustBeNumeric

    Not documented

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    8.2.39 mustBeReal

    Not documented

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    8.2.40 mustBeSameSize

    Not documented

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    8.2.41 mustBeScalar

    Not documented

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    8.2.42 mustBeScalarLogical

    Not documented

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    8.2.43 mustBeVector

    Not documented

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.52.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.52.2 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.52.3 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.52.4 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.52.5 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.52.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.52.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.52.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.52.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.52.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.52.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.17 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.18 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.19 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.52.20 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.22 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.52.23 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.52.24 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.52.25 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.54.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.54.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.54.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.54.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.54.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.54.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.54.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.54.10 table.end

    Method: out = end (obj, k, n)

    Last index for given dimension of a table.

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    8.2.54.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.54.13 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.14 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.54.15 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.16 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.17 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.54.18 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.19 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.20 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.21 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.22 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.23 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.54.24 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.25 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.26 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.27 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.28 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.54.29 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.30 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.31 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.32 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.54.33 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.54.34 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.35 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.36 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.37 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.38 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.39 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.40 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.41 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.42 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.43 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.54.44 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.54.45 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.46 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.47 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.48 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.54.49 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.50 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.54.52 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.54.53 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.54 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.54.55 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55 tableOuterFillValue

    Not documented

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.58.1 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.2 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.58.3 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    Previous: , Up: API Alphabetically   [Contents]

    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    Previous: , Up: Tablicious for GNU Octave   [Contents]

    9 Copying

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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    Next: API Alphabetically, Up: API Reference   [Contents]

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056631515050116100311750ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.1)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074541515050116100324540ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.1)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060021515050116100315620ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.1)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.1/tablicious-0.4.1.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.1 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/array2table.html000066400000000000000000000102671515050116100304540ustar00rootroot00000000000000 array2table (Tablicious v0.4.1)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/calendarDuration.html000066400000000000000000000205171515050116100315220ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.1)

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.6 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.8 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.2.7 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.3 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.4 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.2 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/calyears.html000066400000000000000000000057651515050116100300560ustar00rootroot00000000000000 calyears (Tablicious v0.4.1)

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/categorical.html000066400000000000000000000174631515050116100305260ustar00rootroot00000000000000 categorical (Tablicious v0.4.1)

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    categorical_002ecategorical.html000066400000000000000000000135751515050116100333730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html categorical.categorical (Tablicious v0.4.1)

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    8.2.5.3 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062041515050116100326340ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.1)

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062371515050116100330450ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.1)

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    categorical_002eiscategory.html000066400000000000000000000063041515050116100332570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html categorical.iscategory (Tablicious v0.4.1)

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.2 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.58.3 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.58.1 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.2 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.34 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.18 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002edatestr.html000066400000000000000000000065461515050116100321620ustar00rootroot00000000000000 datetime.datestr (Tablicious v0.4.1)

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    8.2.9.14 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.15 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.16 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002edatetime.html000066400000000000000000000132261515050116100323010ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.1)

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.31 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002edispstrs.html000066400000000000000000000057031515050116100323610ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.1)

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    8.2.9.13 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.26 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.27 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.19 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.28 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.9 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.32 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.22 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002eisnat.html000066400000000000000000000056201515050116100316220ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.1)

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    8.2.9.21 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.24 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002elinspace.html000066400000000000000000000070151515050116100323020ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.1)

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    8.2.9.33 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
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    8.2.9.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002eminus.html000066400000000000000000000067751515050116100316530ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.1)

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    8.2.9.30 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002ene.html000066400000000000000000000061621515050116100311100ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.1)

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    8.2.9.25 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002eofDatestruct.html000066400000000000000000000062361515050116100331570ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.1)

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.17 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/datetime_002eproxyKeys.html000066400000000000000000000064421515050116100325240ustar00rootroot00000000000000 datetime.proxyKeys (Tablicious v0.4.1)

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    8.2.9.7 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.11 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.12 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.8 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.10 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.8 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.7 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002elinspace.html000066400000000000000000000064401515050116100323340ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.1)

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    8.2.13.9 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002emilliseconds.html000066400000000000000000000060421515050116100332210ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.1)

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    8.2.13.6 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002eminutes.html000066400000000000000000000057661515050116100322340ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.1)

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    8.2.13.4 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002eofDays.html000066400000000000000000000057211515050116100317640ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.1)

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002eseconds.html000066400000000000000000000057731515050116100322040ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.1)

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    8.2.13.5 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/duration_002eyears.html000066400000000000000000000057371515050116100316710ustar00rootroot00000000000000 duration.years (Tablicious v0.4.1)

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    8.2.13.2 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/eqn.html000066400000000000000000000074221515050116100270260ustar00rootroot00000000000000 eqn (Tablicious v0.4.1)

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/head.html000066400000000000000000000074401515050116100271440ustar00rootroot00000000000000 head (Tablicious v0.4.1) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/hours.html000066400000000000000000000062531515050116100274040ustar00rootroot00000000000000 hours (Tablicious v0.4.1)

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/index.html000066400000000000000000001316611515050116100273550ustar00rootroot00000000000000 Top (Tablicious v0.4.1)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.1.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/iscalendarduration.html000066400000000000000000000062131515050116100321130ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.1)

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/iscategorical.html000066400000000000000000000061671515050116100310610ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.1)

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/isdatetime.html000066400000000000000000000060771515050116100304000ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.1)

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/isduration.html000066400000000000000000000060431515050116100304220ustar00rootroot00000000000000 isduration (Tablicious v0.4.1)

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/isfile.html000066400000000000000000000044441515050116100275170ustar00rootroot00000000000000 isfile (Tablicious v0.4.1)

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    8.2.21 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/isfolder.html000066400000000000000000000044341515050116100300520ustar00rootroot00000000000000 isfolder (Tablicious v0.4.1)

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/isnanny.html000066400000000000000000000073751515050116100277310ustar00rootroot00000000000000 isnanny (Tablicious v0.4.1)

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/istable.html000066400000000000000000000064501515050116100276660ustar00rootroot00000000000000 istable (Tablicious v0.4.1)

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/istabular.html000066400000000000000000000061701515050116100302300ustar00rootroot00000000000000 istabular (Tablicious v0.4.1)

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/istimetable.html000066400000000000000000000065441515050116100305510ustar00rootroot00000000000000 istimetable (Tablicious v0.4.1)

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate.html000066400000000000000000000122021515050116100301630ustar00rootroot00000000000000 localdate (Tablicious v0.4.1)

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002eNaT.html000066400000000000000000000100531515050116100313160ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.1)

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    8.2.27.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002edatenum.html000066400000000000000000000057311515050116100323000ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.1)

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    8.2.27.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002edatestr.html000066400000000000000000000065741515050116100323170ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.1)

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    8.2.27.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002edatestrs.html000066400000000000000000000066721515050116100325010ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.1)

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    8.2.27.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002edatestruct.html000066400000000000000000000071011515050116100330160ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.1)

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    8.2.27.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002edispstrs.html000066400000000000000000000057211515050116100325150ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.1)

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    8.2.27.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002eisnan.html000066400000000000000000000055461515050116100317570ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.1)

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    8.2.27.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002eisnat.html000066400000000000000000000056371515050116100317660ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.1)

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    8.2.27.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002elocaldate.html000066400000000000000000000105661515050116100325750ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.1)

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    8.2.27.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002eposixtime.html000066400000000000000000000063501515050116100326620ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.1)

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    8.2.27.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/localdate_002eymd.html000066400000000000000000000057461515050116100314420ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.1)

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    8.2.27.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/milliseconds.html000066400000000000000000000063561515050116100307350ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.1)

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/minutes.html000066400000000000000000000055671515050116100277370ustar00rootroot00000000000000 minutes (Tablicious v0.4.1)

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing.html000066400000000000000000000074771515050116100277260ustar00rootroot00000000000000 missing (Tablicious v0.4.1)

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing_002edispstrs.html000066400000000000000000000061321515050116100322330ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.1)

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    8.2.30.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing_002eismissing.html000066400000000000000000000060271515050116100323700ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.1)

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    8.2.30.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing_002eisnan.html000066400000000000000000000057601515050116100314760ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.1)

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    8.2.30.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing_002eisnanny.html000066400000000000000000000055301515050116100320400ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.1)

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    8.2.30.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/missing_002emissing.html000066400000000000000000000054321515050116100320330ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.1)

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    8.2.30.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeA.html000066400000000000000000000044621515050116100276040ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.1)

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    8.2.31 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeCellstr.html000066400000000000000000000045261515050116100310350ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.1)

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    8.2.32 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeCharvec.html000066400000000000000000000045521515050116100307770ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.1)

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    8.2.33 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeFinite.html000066400000000000000000000045501515050116100306400ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.1)

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    8.2.34 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeInteger.html000066400000000000000000000045461515050116100310240ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.1)

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    8.2.35 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeMember.html000066400000000000000000000045541515050116100306350ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.1)

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    8.2.36 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeNonempty.html000066400000000000000000000045601515050116100312340ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.1)

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    8.2.37 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeNumeric.html000066400000000000000000000045461515050116100310310ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.1)

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    8.2.38 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeReal.html000066400000000000000000000045401515050116100303040ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.1)

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    8.2.39 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeSameSize.html000066400000000000000000000045441515050116100311450ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.1)

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    8.2.40 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeScalar.html000066400000000000000000000046041515050116100306270ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.1)

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    8.2.41 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeScalarLogical.html000066400000000000000000000046121515050116100321210ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.1)

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    8.2.42 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/mustBeVector.html000066400000000000000000000045301515050116100306620ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.1)

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    8.2.43 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/pp.html000066400000000000000000000077051515050116100266660ustar00rootroot00000000000000 pp (Tablicious v0.4.1)

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/scalarexpand.html000066400000000000000000000072301515050116100307050ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.1)

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/seconds.html000066400000000000000000000062751515050116100277060ustar00rootroot00000000000000 seconds (Tablicious v0.4.1)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/size2str.html000066400000000000000000000064731515050116100300350ustar00rootroot00000000000000 size2str (Tablicious v0.4.1)

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/splitapply.html000066400000000000000000000113441515050116100304420ustar00rootroot00000000000000 splitapply (Tablicious v0.4.1)

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string.html000066400000000000000000000160431515050116100275500ustar00rootroot00000000000000 string (Tablicious v0.4.1)

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002ecell.html000066400000000000000000000061311515050116100311330ustar00rootroot00000000000000 string.cell (Tablicious v0.4.1)

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    8.2.52.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002ecellstr.html000066400000000000000000000057471515050116100317000ustar00rootroot00000000000000 string.cellstr (Tablicious v0.4.1)

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    8.2.52.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002echar.html000066400000000000000000000061771515050116100311430ustar00rootroot00000000000000 string.char (Tablicious v0.4.1)

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    8.2.52.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002ecmp.html000066400000000000000000000075111515050116100307760ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.1)

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    8.2.52.25 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002edecode.html000066400000000000000000000057231515050116100314450ustar00rootroot00000000000000 string.decode (Tablicious v0.4.1)

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    8.2.52.26 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002edispstrs.html000066400000000000000000000064641515050116100321000ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.1)

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    8.2.52.5 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eempty.html000066400000000000000000000056601515050116100313600ustar00rootroot00000000000000 string.empty (Tablicious v0.4.1)

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    8.2.52.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eencode.html000066400000000000000000000063511515050116100314550ustar00rootroot00000000000000 string.encode (Tablicious v0.4.1)

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    8.2.52.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.20 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eismissing.html000066400000000000000000000061411515050116100322220ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.1)

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    8.2.52.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eisnanny.html000066400000000000000000000057471515050116100317070ustar00rootroot00000000000000 string.isnanny (Tablicious v0.4.1)

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    8.2.52.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eisstring.html000066400000000000000000000057061515050116100320650ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.1)

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    8.2.52.4 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002elower.html000066400000000000000000000061431515050116100313470ustar00rootroot00000000000000 string.lower (Tablicious v0.4.1)

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    8.2.52.18 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002emissing.html000066400000000000000000000061211515050116100316640ustar00rootroot00000000000000 string.missing (Tablicious v0.4.1)

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    8.2.52.2 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eplus.html000066400000000000000000000062131515050116100312000ustar00rootroot00000000000000 string.plus (Tablicious v0.4.1)

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    8.2.52.17 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002eregexprep.html000066400000000000000000000067551515050116100322310ustar00rootroot00000000000000 string.regexprep (Tablicious v0.4.1)

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    8.2.52.23 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002ereverse.html000066400000000000000000000061451515050116100316740ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.1)

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    8.2.52.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

    string_002ereverse_005fbytes.html000066400000000000000000000067111515050116100334150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html string.reverse_bytes (Tablicious v0.4.1)

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    8.2.52.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estrcat.html000066400000000000000000000061631515050116100315210ustar00rootroot00000000000000 string.strcat (Tablicious v0.4.1)

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    8.2.52.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estrcmp.html000066400000000000000000000065151515050116100315320ustar00rootroot00000000000000 string.strcmp (Tablicious v0.4.1)

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    8.2.52.24 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estrfind.html000066400000000000000000000067501515050116100316740ustar00rootroot00000000000000 string.strfind (Tablicious v0.4.1)

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    8.2.52.22 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estring.html000066400000000000000000000070541515050116100315270ustar00rootroot00000000000000 string.string (Tablicious v0.4.1)

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    8.2.52.3 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estrlength.html000066400000000000000000000072471515050116100322370ustar00rootroot00000000000000 string.strlength (Tablicious v0.4.1)

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    8.2.52.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

    string_002estrlength_005fbytes.html000066400000000000000000000066401515050116100337550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html string.strlength_bytes (Tablicious v0.4.1)

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    8.2.52.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/string_002estrrep.html000066400000000000000000000071411515050116100315350ustar00rootroot00000000000000 string.strrep (Tablicious v0.4.1)

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    8.2.52.21 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.19 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/struct2table.html000066400000000000000000000067201515050116100306610ustar00rootroot00000000000000 struct2table (Tablicious v0.4.1)

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table.html000066400000000000000000000225461515050116100273360ustar00rootroot00000000000000 table (Tablicious v0.4.1)

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tableOuterFillValue.html000066400000000000000000000045161515050116100321560ustar00rootroot00000000000000 tableOuterFillValue (Tablicious v0.4.1)

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    8.2.55 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eaddvars.html000066400000000000000000000101421515050116100314160ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.1)

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    8.2.54.29 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eantijoin.html000066400000000000000000000070201515050116100316060ustar00rootroot00000000000000 table.antijoin (Tablicious v0.4.1)

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    8.2.54.40 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002ecartesian.html000066400000000000000000000076241515050116100317560ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.1)

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    8.2.54.41 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002econvertvars.html000066400000000000000000000072731515050116100323610ustar00rootroot00000000000000 table.convertvars (Tablicious v0.4.1)

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    8.2.54.30 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eend.html000066400000000000000000000054511515050116100305470ustar00rootroot00000000000000 table.end (Tablicious v0.4.1)

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    8.2.54.10 table.end

    Method: out = end (obj, k, n)

    Last index for given dimension of a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002efindgroups.html000066400000000000000000000063451515050116100321640ustar00rootroot00000000000000 table.findgroups (Tablicious v0.4.1)

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    8.2.54.53 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.26 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002egetvars.html000066400000000000000000000061311515050116100314500ustar00rootroot00000000000000 table.getvars (Tablicious v0.4.1)

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    8.2.54.27 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.42 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eheight.html000066400000000000000000000057151515050116100312540ustar00rootroot00000000000000 table.height (Tablicious v0.4.1)

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    8.2.54.13 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002ehorzcat.html000066400000000000000000000062551515050116100314560ustar00rootroot00000000000000 table.horzcat (Tablicious v0.4.1)

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    8.2.54.18 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.35 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.46 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.16 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eismember.html000066400000000000000000000060771515050116100316110ustar00rootroot00000000000000 table.ismember (Tablicious v0.4.1)

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    8.2.54.49 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.50 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eistable.html000066400000000000000000000054261515050116100314260ustar00rootroot00000000000000 table.istable (Tablicious v0.4.1)

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    8.2.54.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.34 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.31 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.25 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.15 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.38 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.37 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.36 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.24 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.55 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.54.20 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.19 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.54 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002erowfun.html000066400000000000000000000156341515050116100313250ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.1)

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    8.2.54.52 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.findgroups, Previous: table.varfun, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002erows2vars.html000066400000000000000000000106571515050116100317550ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.1)

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    8.2.54.44 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esemijoin.html000066400000000000000000000072161515050116100316170ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.1)

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    8.2.54.39 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    table_002esetDimensionNames.html000066400000000000000000000100151515050116100333370ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html table.setDimensionNames (Tablicious v0.4.1)

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    8.2.54.22 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esetRowNames.html000066400000000000000000000061421515050116100322460ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.1)

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    8.2.54.23 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    table_002esetVariableNames.html000066400000000000000000000076471515050116100331600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html table.setVariableNames (Tablicious v0.4.1)

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    8.2.54.21 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esetdiff.html000066400000000000000000000062531515050116100314260ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.1)

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    8.2.54.48 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esetvar.html000066400000000000000000000070571515050116100313110ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.1)

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    8.2.54.28 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esetxor.html000066400000000000000000000064711515050116100313300ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.1)

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    8.2.54.47 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esize.html000066400000000000000000000071601515050116100307520ustar00rootroot00000000000000 table.size (Tablicious v0.4.1)

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    8.2.54.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esplitapply.html000066400000000000000000000070771515050116100322100ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.1)

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    8.2.54.43 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esplitvars.html000066400000000000000000000075171515050116100320350ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.1)

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    8.2.54.32 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esqueeze.html000066400000000000000000000056741515050116100314710ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.1)

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    8.2.54.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002estack.html000066400000000000000000000072121515050116100311030ustar00rootroot00000000000000 table.stack (Tablicious v0.4.1)

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    8.2.54.33 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002esummary.html000066400000000000000000000055571515050116100315050ustar00rootroot00000000000000 table.summary (Tablicious v0.4.1)

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    8.2.54.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002etable.html000066400000000000000000000122231515050116100310630ustar00rootroot00000000000000 table.table (Tablicious v0.4.1)

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    8.2.54.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002etable2array.html000066400000000000000000000055761515050116100322210ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.1)

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    8.2.54.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002etable2cell.html000066400000000000000000000062151515050116100320110ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.1)

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    8.2.54.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002etable2struct.html000066400000000000000000000071731515050116100324220ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.1)

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    8.2.54.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002eunion.html000066400000000000000000000063751515050116100311370ustar00rootroot00000000000000 table.union (Tablicious v0.4.1)

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    8.2.54.45 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002evarfun.html000066400000000000000000000102661515050116100313020ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.1)

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002evarnames.html000066400000000000000000000064411515050116100316150ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.1)

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    8.2.54.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002evertcat.html000066400000000000000000000063771515050116100314610ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.1)

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    8.2.54.17 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/table_002ewidth.html000066400000000000000000000055671515050116100311300ustar00rootroot00000000000000 table.width (Tablicious v0.4.1)

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    8.2.54.14 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tail.html000066400000000000000000000076111515050116100271740ustar00rootroot00000000000000 tail (Tablicious v0.4.1)

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tblish_002edataset.html000066400000000000000000000243741515050116100316310ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.1)

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    tblish_002edataset_002eAirPassengers.html000066400000000000000000000075561515050116100347720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.AirPassengers (Tablicious v0.4.1)

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    tblish_002edataset_002eBJsales.html000066400000000000000000000110401515050116100335260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.BJsales (Tablicious v0.4.1)

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eBOD.html000066400000000000000000000102621515050116100326140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.BOD (Tablicious v0.4.1)

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eChickWeight.html000066400000000000000000000113051515050116100344000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.ChickWeight (Tablicious v0.4.1)

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    tblish_002edataset_002eDNase.html000066400000000000000000000111721515050116100332030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.DNase (Tablicious v0.4.1)

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111331515050116100351120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.EuStockMarkets (Tablicious v0.4.1)

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114171515050116100346160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Formaldehyde (Tablicious v0.4.1)

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151041515050116100345350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.HairEyeColor (Tablicious v0.4.1)

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eHarman23cor.html000066400000000000000000000102721515050116100342700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Harman23cor (Tablicious v0.4.1)

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eHarman74cor.html000066400000000000000000000103471515050116100343010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Harman74cor (Tablicious v0.4.1)

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eIndometh.html000066400000000000000000000110051515050116100337530ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Indometh (Tablicious v0.4.1)

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    tblish_002edataset_002eInsectSprays.html000066400000000000000000000104441515050116100346410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.InsectSprays (Tablicious v0.4.1)

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000102561515050116100351700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.1)

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    tblish_002edataset_002eLakeHuron.html000066400000000000000000000104741515050116100341050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.LakeHuron (Tablicious v0.4.1)

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126261515050116100354100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.1)

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    tblish_002edataset_002eLoblolly.html000066400000000000000000000111021515050116100337720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Loblolly (Tablicious v0.4.1)

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    tblish_002edataset_002eNile.html000066400000000000000000000112151515050116100330760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Nile (Tablicious v0.4.1)

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    tblish_002edataset_002eOrange.html000066400000000000000000000115221515050116100334230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Orange (Tablicious v0.4.1)

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    tblish_002edataset_002eOrchardSprays.html000066400000000000000000000124741515050116100350030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.OrchardSprays (Tablicious v0.4.1)

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);
    tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102161515050116100344600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.PlantGrowth (Tablicious v0.4.1)

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    tblish_002edataset_002ePuromycin.html000066400000000000000000000124711515050116100342010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Puromycin (Tablicious v0.4.1)

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    tblish_002edataset_002eTheoph.html000066400000000000000000000142601515050116100334410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Theoph (Tablicious v0.4.1)

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eTitanic.html000066400000000000000000000146031515050116100336060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.Titanic (Tablicious v0.4.1)

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eToothGrowth.html000066400000000000000000000115311515050116100345000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.ToothGrowth (Tablicious v0.4.1)

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000140771515050116100346630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.UCBAdmissions (Tablicious v0.4.1)

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000147431515050116100350440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.1)

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000110761515050116100345120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.UKLungDeaths (Tablicious v0.4.1)

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    tblish_002edataset_002eUKgas.html000066400000000000000000000103671515050116100332300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.UKgas (Tablicious v0.4.1)

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000100531515050116100342750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.USAccDeaths (Tablicious v0.4.1)

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    tblish_002edataset_002eUSArrests.html000066400000000000000000000137551515050116100341150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.USArrests (Tablicious v0.4.1)

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116231515050116100350500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.USJudgeRatings (Tablicious v0.4.1)

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111061515050116100364560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.1)

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    tblish_002edataset_002eVADeaths.html000066400000000000000000000113251515050116100336500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.VADeaths (Tablicious v0.4.1)

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    tblish_002edataset_002eWWWusage.html000066400000000000000000000103531515050116100337220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.WWWusage (Tablicious v0.4.1)

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eWorldPhones.html000066400000000000000000000105141515050116100344540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.WorldPhones (Tablicious v0.4.1)

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    tblish_002edataset_002eairmiles.html000066400000000000000000000072301515050116100340160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.airmiles (Tablicious v0.4.1)

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    tblish_002edataset_002eairquality.html000066400000000000000000000125521515050116100344000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.airquality (Tablicious v0.4.1)

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    tblish_002edataset_002eanscombe.html000066400000000000000000000121451515050116100340010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.anscombe (Tablicious v0.4.1)

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    tblish_002edataset_002eattenu.html000066400000000000000000000114501515050116100335100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.attenu (Tablicious v0.4.1)

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    tblish_002edataset_002eattitude.html000066400000000000000000000111021515050116100340250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.attitude (Tablicious v0.4.1)

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    tblish_002edataset_002eaustres.html000066400000000000000000000102211515050116100336710ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.austres (Tablicious v0.4.1)

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    tblish_002edataset_002ebeavers.html000066400000000000000000000106431515050116100336420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.beavers (Tablicious v0.4.1)

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    tblish_002edataset_002ecars.html000066400000000000000000000116711515050116100331450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.cars (Tablicious v0.4.1)

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    tblish_002edataset_002echickwts.html000066400000000000000000000114221515050116100340260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.chickwts (Tablicious v0.4.1)

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    tblish_002edataset_002eco2.html000066400000000000000000000116651515050116100327030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.co2 (Tablicious v0.4.1)

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    tblish_002edataset_002ecrimtab.html000066400000000000000000000164001515050116100336310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.crimtab (Tablicious v0.4.1)

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    tblish_002edataset_002ecupcake.html000066400000000000000000000104731515050116100336270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.cupcake (Tablicious v0.4.1)

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    tblish_002edataset_002ediscoveries.html000066400000000000000000000106211515050116100345260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.discoveries (Tablicious v0.4.1)

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    tblish_002edataset_002eesoph.html000066400000000000000000000106631515050116100333330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.esoph (Tablicious v0.4.1)

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    tblish_002edataset_002eeuro.html000066400000000000000000000116561515050116100331720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.euro (Tablicious v0.4.1)

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    tblish_002edataset_002eeurodist.html000066400000000000000000000101131515050116100340410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.eurodist (Tablicious v0.4.1)

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    tblish_002edataset_002efaithful.html000066400000000000000000000121121515050116100340060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.faithful (Tablicious v0.4.1)

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002efreeny.html000066400000000000000000000121051515050116100334760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.freeny (Tablicious v0.4.1)

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    tblish_002edataset_002einfert.html000066400000000000000000000116021515050116100334760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.infert (Tablicious v0.4.1)

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    tblish_002edataset_002eiris.html000066400000000000000000000121551515050116100331610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.iris (Tablicious v0.4.1)

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eislands.html000066400000000000000000000104001515050116100336370ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.islands (Tablicious v0.4.1)

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tblish_002edataset_002elh.html000066400000000000000000000101731515050116100326730ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.1)

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    tblish_002edataset_002elongley.html000066400000000000000000000115411515050116100336620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.longley (Tablicious v0.4.1)

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    tblish_002edataset_002elynx.html000066400000000000000000000113351515050116100332040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.lynx (Tablicious v0.4.1)

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    tblish_002edataset_002emorley.html000066400000000000000000000121341515050116100335170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.morley (Tablicious v0.4.1)

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    tblish_002edataset_002emtcars.html000066400000000000000000000124261515050116100335050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.mtcars (Tablicious v0.4.1)

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002enhtemp.html000066400000000000000000000104771515050116100335130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.nhtemp (Tablicious v0.4.1)

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    tblish_002edataset_002enottem.html000066400000000000000000000101411515050116100335120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.nottem (Tablicious v0.4.1)

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002enpk.html000066400000000000000000000113041515050116100327760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.npk (Tablicious v0.4.1)

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000103631515050116100360770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.occupationalStatus (Tablicious v0.4.1)

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eprecip.html000066400000000000000000000103651515050116100334760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.precip (Tablicious v0.4.1)

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    tblish_002edataset_002epresidents.html000066400000000000000000000111711515050116100343700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.presidents (Tablicious v0.4.1)

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    tblish_002edataset_002epressure.html000066400000000000000000000112301515050116100340540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.pressure (Tablicious v0.4.1)

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    tblish_002edataset_002equakes.html000066400000000000000000000120511515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.quakes (Tablicious v0.4.1)

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002erandu.html000066400000000000000000000111311515050116100333150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.randu (Tablicious v0.4.1)

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    tblish_002edataset_002erivers.html000066400000000000000000000102631515050116100335230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.rivers (Tablicious v0.4.1)

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    tblish_002edataset_002erock.html000066400000000000000000000110441515050116100331450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.rock (Tablicious v0.4.1)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    tblish_002edataset_002esleep.html000066400000000000000000000113051515050116100333170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.sleep (Tablicious v0.4.1)

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    tblish_002edataset_002estackloss.html000066400000000000000000000141741515050116100342240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.stackloss (Tablicious v0.4.1)

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    tblish_002edataset_002estate.html000066400000000000000000000130011515050116100333220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.state (Tablicious v0.4.1)

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000104521515050116100355240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.sunspot_month (Tablicious v0.4.1)

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102121515050116100353310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.sunspot_year (Tablicious v0.4.1)

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    tblish_002edataset_002esunspots.html000066400000000000000000000105111515050116100341030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.sunspots (Tablicious v0.4.1)

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    tblish_002edataset_002eswiss.html000066400000000000000000000140711515050116100333620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.swiss (Tablicious v0.4.1)

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    tblish_002edataset_002etreering.html000066400000000000000000000111731515050116100340310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.treering (Tablicious v0.4.1)

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    tblish_002edataset_002etrees.html000066400000000000000000000113431515050116100333330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.trees (Tablicious v0.4.1)

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    tblish_002edataset_002euspop.html000066400000000000000000000102441515050116100333560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.uspop (Tablicious v0.4.1)

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    tblish_002edataset_002evolcano.html000066400000000000000000000112131515050116100336460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.volcano (Tablicious v0.4.1)

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    tblish_002edataset_002ewarpbreaks.html000066400000000000000000000111751515050116100343550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.warpbreaks (Tablicious v0.4.1)

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    tblish_002edataset_002ewomen.html000066400000000000000000000113451515050116100333400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.women (Tablicious v0.4.1)

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    tblish_002edataset_002ezCO2.html000066400000000000000000000104511515050116100327650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.dataset.zCO2 (Tablicious v0.4.1)

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tblish_002edatasets.html000066400000000000000000000064301515050116100320050ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.1)

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    tblish_002eevalWithTableVars.html000066400000000000000000000075651515050116100335170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.evalWithTableVars (Tablicious v0.4.1)

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    tblish_002eexamples_002eSpDb.html000066400000000000000000000077521515050116100332430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.examples.SpDb (Tablicious v0.4.1)

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    tblish_002eexamples_002ecoplot.html000066400000000000000000000150401515050116100337000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.examples.coplot (Tablicious v0.4.1)

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000117551515050116100351600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.examples.plot_pairs (Tablicious v0.4.1)

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/tblish_002esizeof2.html000066400000000000000000000124271515050116100315610ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.1)

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    tblish_002etable_002egrpstats.html000066400000000000000000000104751515050116100335270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html tblish.table.grpstats (Tablicious v0.4.1)

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/timezones.html000066400000000000000000000077701515050116100302660ustar00rootroot00000000000000 timezones (Tablicious v0.4.1)

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/todatetime.html000066400000000000000000000070061515050116100304000ustar00rootroot00000000000000 todatetime (Tablicious v0.4.1)

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/vartype.html000066400000000000000000000064671515050116100277450ustar00rootroot00000000000000 vartype (Tablicious v0.4.1)

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/vecfun.html000066400000000000000000000066261515050116100275360ustar00rootroot00000000000000 vecfun (Tablicious v0.4.1)

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/html/years.html000066400000000000000000000064271515050116100273720ustar00rootroot00000000000000 years (Tablicious v0.4.1)

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.1/user-guide/tablicious.html000066400000000000000000026641271515050116100274520ustar00rootroot00000000000000 Tablicious v0.4.1

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.1.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.1 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.1/tablicious-0.4.1.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.2.3 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.4 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.7 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.2.8 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.5.2 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.3 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.4 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.5 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.6 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.7 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.8 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.9 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.10 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.11 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.12 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.13 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.18 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.19 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.20 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.2 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.3 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.4 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.5 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.6 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.7 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.8 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.9 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.10 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.11 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.12 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.13 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.14 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.15 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.16 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.17 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.18 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.19 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.21 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.22 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.24 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.26 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.27 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.28 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.30 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.31 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.32 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.33 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.34 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.2 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.5 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.8 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.9 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.27.1 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.27.2 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.27.3 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.27.4 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.27.5 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.27.6 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.27.7 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.27.8 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.27.9 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.27.10 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.27.11 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.30.1 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.30.2 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.30.3 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.4 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.5 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31 mustBeA

    Not documented

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    8.2.32 mustBeCellstr

    Not documented

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    8.2.33 mustBeCharvec

    Not documented

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    8.2.34 mustBeFinite

    Not documented

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    8.2.35 mustBeInteger

    Not documented

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    8.2.36 mustBeMember

    Not documented

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    8.2.37 mustBeNonempty

    Not documented

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    8.2.38 mustBeNumeric

    Not documented

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    8.2.39 mustBeReal

    Not documented

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    8.2.40 mustBeSameSize

    Not documented

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    8.2.41 mustBeScalar

    Not documented

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    8.2.42 mustBeScalarLogical

    Not documented

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    8.2.43 mustBeVector

    Not documented

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.52.1 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.52.2 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.52.3 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.52.4 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.52.5 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.52.6 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.52.7 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.52.8 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.52.9 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.52.10 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.52.11 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.12 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.13 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.14 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.15 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.16 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.17 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.52.18 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.19 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.20 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.52.21 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.22 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.23 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.52.24 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.52.25 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.52.26 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.54.1 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.54.2 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.54.3 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.4 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.54.5 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.54.6 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.54.7 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.54.8 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.9 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.54.10 table.end

    Method: out = end (obj, k, n)

    Last index for given dimension of a table.

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    8.2.54.11 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.12 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.54.13 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.14 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.54.15 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.16 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.17 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.54.18 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.19 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.20 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.21 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.22 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.23 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.54.24 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.25 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.26 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.27 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.28 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.54.29 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.30 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.31 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.32 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.54.33 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.54.34 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.35 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.36 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.37 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.38 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.39 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.40 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.41 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.42 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.43 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.54.44 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.54.45 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.46 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.47 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.48 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.54.49 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.50 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.54.52 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.54.53 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.54 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.54.55 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55 tableOuterFillValue

    Not documented

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.58.1 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.2 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.58.3 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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BgLr ΈsؕMIt>j<<[}mc d3* & dǷgRGd{3JܛL3=K;3R?n#|כCeL@VeQ0vq触{Fvx(|Ue<LJ =UыG~{7IM_V,K,)=9Bo#j((Ic|PfaZ̢q3 );:Gɟ̀;[F5욵cIgK]zܺl#-ټ a@60"ʥ)`,ס8HLDLLZ5m"j@s'sv*m(Rk<:];Kv˿o.ݠ`&M߅JJΙt]@&wt;:2) Фo%OJNHNo%H]1s?Pjr)˞YH"l$5d,q6zm;[$37XV;Nv-( Q*zF$z ;W 8αa?jޛCrܵ ̯>`6(v>G-U cl| h9=|>V=n˫z87Krx(|3[B]?vgW!v=x֍ >TڌqMSθ3X_kP̯OZwպCfb&h+d)M&,f~JTqe".}rx}s"=\v`B:j ՙ)g^,1'QZ bq]B}1ewzOvѴTuDŽ_Ky_G2zG|ޯ¸S=PWes}]A%2nh7N!~+_Yk. 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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    Next: API Alphabetically, Up: API Reference   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Copying.html000066400000000000000000000077421515050116100276610ustar00rootroot00000000000000 Copying (Tablicious v0.4.2)

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056631515050116100311760ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.2)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074541515050116100324550ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.2)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060021515050116100315630ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.2)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Durations.html000066400000000000000000000046361515050116100302200ustar00rootroot00000000000000 Durations (Tablicious v0.4.2) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Example-Data-Sets.html000066400000000000000000000102121515050116100314110ustar00rootroot00000000000000 Example Data Sets (Tablicious v0.4.2)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Getting-Started.html000066400000000000000000000064041515050116100312500ustar00rootroot00000000000000 Getting Started (Tablicious v0.4.2)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.2/tablicious-0.4.2.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Introduction.html000066400000000000000000000072321515050116100307240ustar00rootroot00000000000000 Introduction (Tablicious v0.4.2)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.2 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/NaC.html000066400000000000000000000075701515050116100267110ustar00rootroot00000000000000 NaC (Tablicious v0.4.2)

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/NaS.html000066400000000000000000000074551515050116100267330ustar00rootroot00000000000000 NaS (Tablicious v0.4.2)

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/NaT.html000066400000000000000000000075211515050116100267260ustar00rootroot00000000000000 NaT (Tablicious v0.4.2)

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Table-Representation.html000066400000000000000000000100161515050116100322640ustar00rootroot00000000000000 Table Representation (Tablicious v0.4.2)

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Tables-vs-SQL.html000066400000000000000000000113521515050116100305360ustar00rootroot00000000000000 Tables vs SQL (Tablicious v0.4.2)

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Time-Zones.html000066400000000000000000000141161515050116100302340ustar00rootroot00000000000000 Time Zones (Tablicious v0.4.2)

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/Validation-Functions.html000066400000000000000000000117371515050116100323100ustar00rootroot00000000000000 Validation Functions (Tablicious v0.4.2)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/array2table.html000066400000000000000000000102671515050116100304550ustar00rootroot00000000000000 array2table (Tablicious v0.4.2)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/calendarDuration.html000066400000000000000000000205171515050116100315230ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.2)

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    calendarDuration_002ecalendarDuration.html000066400000000000000000000077061515050116100353770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html calendarDuration.calendarDuration (Tablicious v0.4.2)

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    calendarDuration_002eismissing.html000066400000000000000000000062061515050116100341170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html calendarDuration.ismissing (Tablicious v0.4.2)

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    8.2.2.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

    calendarDuration_002eisnan.html000066400000000000000000000062521515050116100332230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html calendarDuration.isnan (Tablicious v0.4.2)

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    8.2.2.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    calendarDuration_002eminus.html000066400000000000000000000061121515050116100332410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html calendarDuration.minus (Tablicious v0.4.2)

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.8 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/categorical.html000066400000000000000000000174631515050116100305270ustar00rootroot00000000000000 categorical (Tablicious v0.4.2)

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.6 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.7 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.8 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.9 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.10 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.11 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.12 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.13 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.5.18 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.19 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.20 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.58.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.58.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.23 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.1 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.2 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.3 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.4 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.5 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002edatestruct.html000066400000000000000000000066371515050116100327000ustar00rootroot00000000000000 datetime.datestruct (Tablicious v0.4.2)

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    8.2.9.6 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002edatetime.html000066400000000000000000000134671515050116100323110ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.2)

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    8.2.9.7 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.8 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002edispstrs.html000066400000000000000000000056561515050116100323710ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.2)

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    8.2.9.9 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.10 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002ege.html000066400000000000000000000062211515050116100310760ustar00rootroot00000000000000 datetime.ge (Tablicious v0.4.2)

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    8.2.9.11 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002egmtime.html000066400000000000000000000060351515050116100317700ustar00rootroot00000000000000 datetime.gmtime (Tablicious v0.4.2)

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    8.2.9.12 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.13 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002ehms.html000066400000000000000000000061041515050116100312720ustar00rootroot00000000000000 datetime.hms (Tablicious v0.4.2)

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    8.2.9.14 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eisbetween.html000066400000000000000000000063051515050116100324730ustar00rootroot00000000000000 datetime.isbetween (Tablicious v0.4.2)

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    8.2.9.15 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.16 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eisnat.html000066400000000000000000000055641515050116100316320ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.2)

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    8.2.9.17 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.18 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002elinspace.html000066400000000000000000000066751515050116100323160ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.2)

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    8.2.9.19 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002elocaltime.html000066400000000000000000000066731515050116100324670ustar00rootroot00000000000000 datetime.localtime (Tablicious v0.4.2)

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
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    8.2.9.21 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eminus.html000066400000000000000000000067611515050116100316470ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.2)

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    8.2.9.22 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002ene.html000066400000000000000000000062221515050116100311060ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.2)

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    8.2.9.24 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eofDatenum.html000066400000000000000000000060061515050116100324260ustar00rootroot00000000000000 datetime.ofDatenum (Tablicious v0.4.2)

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    8.2.9.25 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eofDatestruct.html000066400000000000000000000062431515050116100331560ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.2)

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    8.2.9.26 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.27 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.28 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.29 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eproxyKeys.html000066400000000000000000000064531515050116100325270ustar00rootroot00000000000000 datetime.proxyKeys (Tablicious v0.4.2)

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    8.2.9.30 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002etimeofday.html000066400000000000000000000061211515050116100324630ustar00rootroot00000000000000 datetime.timeofday (Tablicious v0.4.2)

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    8.2.9.31 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eweek.html000066400000000000000000000055241515050116100314430ustar00rootroot00000000000000 datetime.week (Tablicious v0.4.2)

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    8.2.9.32 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/datetime_002eymd.html000066400000000000000000000060721515050116100313000ustar00rootroot00000000000000 datetime.ymd (Tablicious v0.4.2)

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    8.2.9.33 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.34 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.13.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/duration_002eseconds.html000066400000000000000000000057331515050116100322010ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.2)

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    8.2.13.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/duration_002eyears.html000066400000000000000000000055061515050116100316640ustar00rootroot00000000000000 duration.years (Tablicious v0.4.2)

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    8.2.13.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/eqn.html000066400000000000000000000074221515050116100270270ustar00rootroot00000000000000 eqn (Tablicious v0.4.2)

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/head.html000066400000000000000000000074401515050116100271450ustar00rootroot00000000000000 head (Tablicious v0.4.2) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/hours.html000066400000000000000000000062531515050116100274050ustar00rootroot00000000000000 hours (Tablicious v0.4.2)

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/index.html000066400000000000000000001315221515050116100273520ustar00rootroot00000000000000 Top (Tablicious v0.4.2)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.2.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/iscalendarduration.html000066400000000000000000000062131515050116100321140ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.2)

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/iscategorical.html000066400000000000000000000061671515050116100310620ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.2)

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/isdatetime.html000066400000000000000000000060771515050116100304010ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.2)

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/isduration.html000066400000000000000000000060431515050116100304230ustar00rootroot00000000000000 isduration (Tablicious v0.4.2)

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/isfile.html000066400000000000000000000044441515050116100275200ustar00rootroot00000000000000 isfile (Tablicious v0.4.2)

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    8.2.21 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/isfolder.html000066400000000000000000000044341515050116100300530ustar00rootroot00000000000000 isfolder (Tablicious v0.4.2)

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/isnanny.html000066400000000000000000000073751515050116100277320ustar00rootroot00000000000000 isnanny (Tablicious v0.4.2)

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/istable.html000066400000000000000000000064501515050116100276670ustar00rootroot00000000000000 istable (Tablicious v0.4.2)

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/istabular.html000066400000000000000000000061701515050116100302310ustar00rootroot00000000000000 istabular (Tablicious v0.4.2)

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/istimetable.html000066400000000000000000000065441515050116100305520ustar00rootroot00000000000000 istimetable (Tablicious v0.4.2)

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate.html000066400000000000000000000122021515050116100301640ustar00rootroot00000000000000 localdate (Tablicious v0.4.2)

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002eNaT.html000066400000000000000000000101031515050116100313130ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.2)

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    8.2.27.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002edatenum.html000066400000000000000000000054541515050116100323030ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.2)

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    8.2.27.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002edatestr.html000066400000000000000000000065701515050116100323140ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.2)

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    8.2.27.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002edatestrs.html000066400000000000000000000066721515050116100325020ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.2)

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    8.2.27.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002edatestruct.html000066400000000000000000000070751515050116100330310ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.2)

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    8.2.27.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002edispstrs.html000066400000000000000000000057451515050116100325240ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.2)

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    8.2.27.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002eisnan.html000066400000000000000000000060221515050116100317460ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.2)

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    8.2.27.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002eisnat.html000066400000000000000000000056461515050116100317670ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.2)

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    8.2.27.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002elocaldate.html000066400000000000000000000110331515050116100325640ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.2)

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    8.2.27.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002eposixtime.html000066400000000000000000000062751515050116100326710ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.2)

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    8.2.27.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/localdate_002eymd.html000066400000000000000000000055221515050116100314330ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.2)

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    8.2.27.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/milliseconds.html000066400000000000000000000063561515050116100307360ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.2)

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/minutes.html000066400000000000000000000055671515050116100277400ustar00rootroot00000000000000 minutes (Tablicious v0.4.2)

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing.html000066400000000000000000000074771515050116100277270ustar00rootroot00000000000000 missing (Tablicious v0.4.2)

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing_002edispstrs.html000066400000000000000000000056651515050116100322460ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.2)

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    8.2.30.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing_002eismissing.html000066400000000000000000000060271515050116100323710ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.2)

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    8.2.30.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing_002eisnan.html000066400000000000000000000057601515050116100314770ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.2)

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    8.2.30.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing_002eisnanny.html000066400000000000000000000057711515050116100320500ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.2)

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    8.2.30.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/missing_002emissing.html000066400000000000000000000054321515050116100320340ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.2)

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    8.2.30.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeA.html000066400000000000000000000044621515050116100276050ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.2)

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    8.2.31 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeCellstr.html000066400000000000000000000045261515050116100310360ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.2)

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    8.2.32 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeCharvec.html000066400000000000000000000045521515050116100310000ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.2)

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    8.2.33 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeFinite.html000066400000000000000000000045501515050116100306410ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.2)

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    8.2.34 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeInteger.html000066400000000000000000000045461515050116100310250ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.2)

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    8.2.35 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeMember.html000066400000000000000000000045541515050116100306360ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.2)

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    8.2.36 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeNonempty.html000066400000000000000000000045601515050116100312350ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.2)

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    8.2.37 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeNumeric.html000066400000000000000000000045461515050116100310320ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.2)

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    8.2.38 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeReal.html000066400000000000000000000045401515050116100303050ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.2)

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    8.2.39 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeSameSize.html000066400000000000000000000045441515050116100311460ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.2)

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    8.2.40 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeScalar.html000066400000000000000000000046041515050116100306300ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.2)

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    8.2.41 mustBeScalar

    Not documented

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    8.2.42 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/mustBeVector.html000066400000000000000000000045301515050116100306630ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.2)

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    8.2.43 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/pp.html000066400000000000000000000077051515050116100266670ustar00rootroot00000000000000 pp (Tablicious v0.4.2)

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string.html000066400000000000000000000160431515050116100275510ustar00rootroot00000000000000 string (Tablicious v0.4.2)

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002ecell.html000066400000000000000000000057041515050116100311410ustar00rootroot00000000000000 string.cell (Tablicious v0.4.2)

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    8.2.52.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.52.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002echar.html000066400000000000000000000061761515050116100311430ustar00rootroot00000000000000 string.char (Tablicious v0.4.2)

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    8.2.52.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.52.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002edecode.html000066400000000000000000000061631515050116100314450ustar00rootroot00000000000000 string.decode (Tablicious v0.4.2)

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    8.2.52.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002edispstrs.html000066400000000000000000000064341515050116100320760ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.2)

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    8.2.52.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.52.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.52.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002eismissing.html000066400000000000000000000061261515050116100322260ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.2)

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    8.2.52.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.52.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002eisstring.html000066400000000000000000000056771515050116100320750ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.2)

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    8.2.52.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002elower.html000066400000000000000000000061731515050116100313530ustar00rootroot00000000000000 string.lower (Tablicious v0.4.2)

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    8.2.52.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.52.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.52.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/string_002ereverse.html000066400000000000000000000061611515050116100316730ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.2)

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    8.2.52.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
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    8.2.52.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/struct2table.html000066400000000000000000000067201515050116100306620ustar00rootroot00000000000000 struct2table (Tablicious v0.4.2)

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table.html000066400000000000000000000224621515050116100273340ustar00rootroot00000000000000 table (Tablicious v0.4.2)

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

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    8.2.55 tableOuterFillValue

    Not documented

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    8.2.54.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.54.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.54.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.54.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esemijoin.html000066400000000000000000000071721515050116100316210ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.2)

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    8.2.54.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    table_002esetDimensionNames.html000066400000000000000000000077511515050116100333550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html table.setDimensionNames (Tablicious v0.4.2)

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    8.2.54.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esetRowNames.html000066400000000000000000000061221515050116100322450ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.2)

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    8.2.54.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    table_002esetVariableNames.html000066400000000000000000000075671515050116100331620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html table.setVariableNames (Tablicious v0.4.2)

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    8.2.54.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esetdiff.html000066400000000000000000000063271515050116100314310ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.2)

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    8.2.54.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esetvar.html000066400000000000000000000071431515050116100313060ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.2)

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    8.2.54.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esetxor.html000066400000000000000000000065111515050116100313240ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.2)

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    8.2.54.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esize.html000066400000000000000000000072111515050116100307500ustar00rootroot00000000000000 table.size (Tablicious v0.4.2)

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    8.2.54.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esplitapply.html000066400000000000000000000070631515050116100322040ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.2)

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    8.2.54.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esplitvars.html000066400000000000000000000075331515050116100320340ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.2)

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    8.2.54.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esqueeze.html000066400000000000000000000057101515050116100314610ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.2)

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    8.2.54.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002estack.html000066400000000000000000000072161515050116100311100ustar00rootroot00000000000000 table.stack (Tablicious v0.4.2)

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    8.2.54.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002esummary.html000066400000000000000000000055301515050116100314750ustar00rootroot00000000000000 table.summary (Tablicious v0.4.2)

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    8.2.54.45 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002etable.html000066400000000000000000000125011515050116100310630ustar00rootroot00000000000000 table.table (Tablicious v0.4.2)

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    8.2.54.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002etable2array.html000066400000000000000000000055471515050116100322200ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.2)

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    8.2.54.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002etable2cell.html000066400000000000000000000062161515050116100320130ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.2)

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    8.2.54.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002etable2struct.html000066400000000000000000000071501515050116100324160ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.2)

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    8.2.54.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002eunion.html000066400000000000000000000063751515050116100311400ustar00rootroot00000000000000 table.union (Tablicious v0.4.2)

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    8.2.54.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002evarfun.html000066400000000000000000000102621515050116100312770ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.2)

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002evarnames.html000066400000000000000000000064161515050116100316200ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.2)

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    8.2.54.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002evertcat.html000066400000000000000000000063731515050116100314560ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.2)

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    8.2.54.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/table_002ewidth.html000066400000000000000000000053521515050116100311210ustar00rootroot00000000000000 table.width (Tablicious v0.4.2)

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    8.2.54.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/tail.html000066400000000000000000000076111515050116100271750ustar00rootroot00000000000000 tail (Tablicious v0.4.2)

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/tblish_002edataset.html000066400000000000000000000243741515050116100316320ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.2)

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    tblish_002edataset_002eAirPassengers.html000066400000000000000000000075561515050116100347730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.AirPassengers (Tablicious v0.4.2)

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    tblish_002edataset_002eBJsales.html000066400000000000000000000110401515050116100335270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.BJsales (Tablicious v0.4.2)

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eBOD.html000066400000000000000000000102621515050116100326150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.BOD (Tablicious v0.4.2)

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eChickWeight.html000066400000000000000000000113051515050116100344010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.ChickWeight (Tablicious v0.4.2)

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    tblish_002edataset_002eDNase.html000066400000000000000000000111721515050116100332040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.DNase (Tablicious v0.4.2)

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111331515050116100351130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.EuStockMarkets (Tablicious v0.4.2)

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114171515050116100346170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Formaldehyde (Tablicious v0.4.2)

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151041515050116100345360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.HairEyeColor (Tablicious v0.4.2)

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eHarman23cor.html000066400000000000000000000102721515050116100342710ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Harman23cor (Tablicious v0.4.2)

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eHarman74cor.html000066400000000000000000000103471515050116100343020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Harman74cor (Tablicious v0.4.2)

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eIndometh.html000066400000000000000000000110051515050116100337540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Indometh (Tablicious v0.4.2)

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    tblish_002edataset_002eInsectSprays.html000066400000000000000000000104441515050116100346420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.InsectSprays (Tablicious v0.4.2)

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000102561515050116100351710ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.2)

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    tblish_002edataset_002eLakeHuron.html000066400000000000000000000104741515050116100341060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.LakeHuron (Tablicious v0.4.2)

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126261515050116100354110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.2)

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    tblish_002edataset_002eLoblolly.html000066400000000000000000000111021515050116100337730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Loblolly (Tablicious v0.4.2)

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    tblish_002edataset_002eNile.html000066400000000000000000000112151515050116100330770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Nile (Tablicious v0.4.2)

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    tblish_002edataset_002eOrange.html000066400000000000000000000115221515050116100334240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Orange (Tablicious v0.4.2)

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    tblish_002edataset_002eOrchardSprays.html000066400000000000000000000124741515050116100350040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.OrchardSprays (Tablicious v0.4.2)

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);
    tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102161515050116100344610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.PlantGrowth (Tablicious v0.4.2)

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    tblish_002edataset_002ePuromycin.html000066400000000000000000000124711515050116100342020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Puromycin (Tablicious v0.4.2)

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    tblish_002edataset_002eTheoph.html000066400000000000000000000142601515050116100334420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Theoph (Tablicious v0.4.2)

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eTitanic.html000066400000000000000000000146031515050116100336070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.Titanic (Tablicious v0.4.2)

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eToothGrowth.html000066400000000000000000000115311515050116100345010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.ToothGrowth (Tablicious v0.4.2)

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000140771515050116100346640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.UCBAdmissions (Tablicious v0.4.2)

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000147431515050116100350450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.2)

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000110761515050116100345130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.UKLungDeaths (Tablicious v0.4.2)

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    tblish_002edataset_002eUKgas.html000066400000000000000000000103671515050116100332310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.UKgas (Tablicious v0.4.2)

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000100531515050116100342760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.USAccDeaths (Tablicious v0.4.2)

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    tblish_002edataset_002eUSArrests.html000066400000000000000000000137551515050116100341160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.USArrests (Tablicious v0.4.2)

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116231515050116100350510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.USJudgeRatings (Tablicious v0.4.2)

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111061515050116100364570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.2)

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    tblish_002edataset_002eVADeaths.html000066400000000000000000000113251515050116100336510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.VADeaths (Tablicious v0.4.2)

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    tblish_002edataset_002eWWWusage.html000066400000000000000000000103531515050116100337230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.WWWusage (Tablicious v0.4.2)

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eWorldPhones.html000066400000000000000000000105141515050116100344550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.WorldPhones (Tablicious v0.4.2)

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    tblish_002edataset_002eairmiles.html000066400000000000000000000072301515050116100340170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.airmiles (Tablicious v0.4.2)

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    tblish_002edataset_002eairquality.html000066400000000000000000000125521515050116100344010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.airquality (Tablicious v0.4.2)

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    tblish_002edataset_002eanscombe.html000066400000000000000000000121451515050116100340020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.anscombe (Tablicious v0.4.2)

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    tblish_002edataset_002eattenu.html000066400000000000000000000114501515050116100335110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.attenu (Tablicious v0.4.2)

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    tblish_002edataset_002eattitude.html000066400000000000000000000111021515050116100340260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.attitude (Tablicious v0.4.2)

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    tblish_002edataset_002eaustres.html000066400000000000000000000102211515050116100336720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.austres (Tablicious v0.4.2)

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    tblish_002edataset_002ebeavers.html000066400000000000000000000106431515050116100336430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.beavers (Tablicious v0.4.2)

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    tblish_002edataset_002ecars.html000066400000000000000000000116711515050116100331460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.cars (Tablicious v0.4.2)

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    tblish_002edataset_002echickwts.html000066400000000000000000000114221515050116100340270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.chickwts (Tablicious v0.4.2)

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    tblish_002edataset_002eco2.html000066400000000000000000000116651515050116100327040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.co2 (Tablicious v0.4.2)

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    tblish_002edataset_002ecrimtab.html000066400000000000000000000164001515050116100336320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.crimtab (Tablicious v0.4.2)

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    tblish_002edataset_002ecupcake.html000066400000000000000000000104731515050116100336300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.cupcake (Tablicious v0.4.2)

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    tblish_002edataset_002ediscoveries.html000066400000000000000000000106211515050116100345270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.discoveries (Tablicious v0.4.2)

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    tblish_002edataset_002eesoph.html000066400000000000000000000106631515050116100333340ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.esoph (Tablicious v0.4.2)

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    tblish_002edataset_002eeuro.html000066400000000000000000000116561515050116100331730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.euro (Tablicious v0.4.2)

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    tblish_002edataset_002eeurodist.html000066400000000000000000000101131515050116100340420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.eurodist (Tablicious v0.4.2)

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    tblish_002edataset_002efaithful.html000066400000000000000000000121121515050116100340070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.faithful (Tablicious v0.4.2)

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002efreeny.html000066400000000000000000000121051515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.freeny (Tablicious v0.4.2)

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    tblish_002edataset_002einfert.html000066400000000000000000000116021515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.infert (Tablicious v0.4.2)

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    tblish_002edataset_002eiris.html000066400000000000000000000121551515050116100331620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.iris (Tablicious v0.4.2)

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eislands.html000066400000000000000000000104001515050116100336400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.islands (Tablicious v0.4.2)

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/tblish_002edataset_002elh.html000066400000000000000000000101731515050116100326740ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.2)

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    tblish_002edataset_002elongley.html000066400000000000000000000115411515050116100336630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.longley (Tablicious v0.4.2)

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    tblish_002edataset_002elynx.html000066400000000000000000000113351515050116100332050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.lynx (Tablicious v0.4.2)

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    tblish_002edataset_002emorley.html000066400000000000000000000121341515050116100335200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.morley (Tablicious v0.4.2)

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    tblish_002edataset_002emtcars.html000066400000000000000000000124261515050116100335060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.mtcars (Tablicious v0.4.2)

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002enhtemp.html000066400000000000000000000104771515050116100335140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.nhtemp (Tablicious v0.4.2)

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    tblish_002edataset_002enottem.html000066400000000000000000000101411515050116100335130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.nottem (Tablicious v0.4.2)

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002enpk.html000066400000000000000000000113041515050116100327770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.npk (Tablicious v0.4.2)

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000103631515050116100361000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.occupationalStatus (Tablicious v0.4.2)

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eprecip.html000066400000000000000000000103651515050116100334770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.precip (Tablicious v0.4.2)

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    tblish_002edataset_002epresidents.html000066400000000000000000000111711515050116100343710ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.presidents (Tablicious v0.4.2)

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    tblish_002edataset_002epressure.html000066400000000000000000000112301515050116100340550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.pressure (Tablicious v0.4.2)

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    tblish_002edataset_002equakes.html000066400000000000000000000120511515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.quakes (Tablicious v0.4.2)

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002erandu.html000066400000000000000000000111311515050116100333160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.randu (Tablicious v0.4.2)

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    tblish_002edataset_002erivers.html000066400000000000000000000102631515050116100335240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.rivers (Tablicious v0.4.2)

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    tblish_002edataset_002erock.html000066400000000000000000000110441515050116100331460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.rock (Tablicious v0.4.2)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    tblish_002edataset_002esleep.html000066400000000000000000000113051515050116100333200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.sleep (Tablicious v0.4.2)

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    tblish_002edataset_002estackloss.html000066400000000000000000000141741515050116100342250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.stackloss (Tablicious v0.4.2)

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    tblish_002edataset_002estate.html000066400000000000000000000130011515050116100333230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.state (Tablicious v0.4.2)

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000104521515050116100355250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.sunspot_month (Tablicious v0.4.2)

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102121515050116100353320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.sunspot_year (Tablicious v0.4.2)

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    tblish_002edataset_002esunspots.html000066400000000000000000000105111515050116100341040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.sunspots (Tablicious v0.4.2)

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    tblish_002edataset_002eswiss.html000066400000000000000000000140711515050116100333630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.swiss (Tablicious v0.4.2)

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    tblish_002edataset_002etreering.html000066400000000000000000000111731515050116100340320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.treering (Tablicious v0.4.2)

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    tblish_002edataset_002etrees.html000066400000000000000000000113431515050116100333340ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.trees (Tablicious v0.4.2)

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    tblish_002edataset_002euspop.html000066400000000000000000000102441515050116100333570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.uspop (Tablicious v0.4.2)

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    tblish_002edataset_002evolcano.html000066400000000000000000000112131515050116100336470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.volcano (Tablicious v0.4.2)

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    tblish_002edataset_002ewarpbreaks.html000066400000000000000000000111751515050116100343560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.warpbreaks (Tablicious v0.4.2)

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    tblish_002edataset_002ewomen.html000066400000000000000000000113451515050116100333410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.women (Tablicious v0.4.2)

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    tblish_002edataset_002ezCO2.html000066400000000000000000000104511515050116100327660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.dataset.zCO2 (Tablicious v0.4.2)

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/tblish_002edatasets.html000066400000000000000000000064301515050116100320060ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.2)

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    tblish_002eevalWithTableVars.html000066400000000000000000000075651515050116100335200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.evalWithTableVars (Tablicious v0.4.2)

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    tblish_002eexamples_002eSpDb.html000066400000000000000000000077521515050116100332440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.examples.SpDb (Tablicious v0.4.2)

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    tblish_002eexamples_002ecoplot.html000066400000000000000000000150401515050116100337010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.examples.coplot (Tablicious v0.4.2)

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000117551515050116100351610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.examples.plot_pairs (Tablicious v0.4.2)

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/tblish_002esizeof2.html000066400000000000000000000124271515050116100315620ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.2)

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    tblish_002etable_002egrpstats.html000066400000000000000000000104751515050116100335300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html tblish.table.grpstats (Tablicious v0.4.2)

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/timezones.html000066400000000000000000000077701515050116100302670ustar00rootroot00000000000000 timezones (Tablicious v0.4.2)

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/todatetime.html000066400000000000000000000070061515050116100304010ustar00rootroot00000000000000 todatetime (Tablicious v0.4.2)

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/vartype.html000066400000000000000000000064671515050116100277460ustar00rootroot00000000000000 vartype (Tablicious v0.4.2)

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/vecfun.html000066400000000000000000000066261515050116100275370ustar00rootroot00000000000000 vecfun (Tablicious v0.4.2)

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/html/years.html000066400000000000000000000064271515050116100273730ustar00rootroot00000000000000 years (Tablicious v0.4.2)

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.2/user-guide/tablicious.html000066400000000000000000026620061515050116100274450ustar00rootroot00000000000000 Tablicious v0.4.2

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.2.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.2 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.2/tablicious-0.4.2.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.2.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.8 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.6 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.7 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.8 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.9 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.10 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.11 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.12 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.13 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.14 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.15 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.16 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.17 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.5.18 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.19 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.20 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.2 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.3 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.4 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.5 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.6 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.7 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.8 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.9 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.10 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.11 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.12 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.13 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.14 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.15 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.16 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.17 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.18 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.19 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.21 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.22 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.23 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.24 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.26 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.27 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.28 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.29 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.30 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.31 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.32 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.33 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.34 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.13.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.27.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.27.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.27.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.27.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.27.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.27.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.27.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.27.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.27.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.27.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.27.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.30.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.30.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.31 mustBeA

    Not documented

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    8.2.32 mustBeCellstr

    Not documented

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    8.2.33 mustBeCharvec

    Not documented

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    8.2.34 mustBeFinite

    Not documented

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    8.2.35 mustBeInteger

    Not documented

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    8.2.36 mustBeMember

    Not documented

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    8.2.37 mustBeNonempty

    Not documented

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    8.2.38 mustBeNumeric

    Not documented

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    8.2.39 mustBeReal

    Not documented

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    8.2.40 mustBeSameSize

    Not documented

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    8.2.41 mustBeScalar

    Not documented

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    8.2.42 mustBeScalarLogical

    Not documented

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    8.2.43 mustBeVector

    Not documented

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.52.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.52.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.52.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.52.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.52.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.52.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.52.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.52.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.52.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.52.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.52.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.52.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.52.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.52.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.52.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.52.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

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    8.2.54.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.54.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.54.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.54.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.54.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.54.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.54.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.54.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.54.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.54.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.54.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.54.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.54.45 table.summary

    Method: summary (obj)

    Summary of table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables.

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    8.2.54.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.54.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.54.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.54.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.54.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.54.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.54.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.54.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.55 tableOuterFillValue

    Not documented

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

    Next: , Previous: , Up: tblish.dataset   [Contents]

    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.58.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.58.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    Next: API Alphabetically, Up: API Reference   [Contents]

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    9 Copying

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074541515050116100324560ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.3)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060021515050116100315640ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.3)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.3/tablicious-0.4.3.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.3 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/array2table.html000066400000000000000000000102671515050116100304560ustar00rootroot00000000000000 array2table (Tablicious v0.4.3)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/calendarDuration.html000066400000000000000000000205171515050116100315240ustar00rootroot00000000000000 calendarDuration (Tablicious v0.4.3)

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

    Next: calmonths, Previous: array2table, Up: API Alphabetically   [Contents]

    calendarDuration_002ecalendarDuration.html000066400000000000000000000077061515050116100354000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html calendarDuration.calendarDuration (Tablicious v0.4.3)

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    calendarDuration_002eismissing.html000066400000000000000000000062061515050116100341200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html calendarDuration.ismissing (Tablicious v0.4.3)

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    8.2.2.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

    calendarDuration_002eisnan.html000066400000000000000000000062521515050116100332240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html calendarDuration.isnan (Tablicious v0.4.3)

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    8.2.2.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

    calendarDuration_002eminus.html000066400000000000000000000061121515050116100332420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html calendarDuration.minus (Tablicious v0.4.3)

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/calendarDuration_002eplus.html000066400000000000000000000066021515050116100331550ustar00rootroot00000000000000 calendarDuration.plus (Tablicious v0.4.3)

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    8.2.2.6 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

    calendarDuration_002euminus.html000066400000000000000000000054551515050116100334400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html calendarDuration.uminus (Tablicious v0.4.3)

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    8.2.2.8 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/calmonths.html000066400000000000000000000064571515050116100302440ustar00rootroot00000000000000 calmonths (Tablicious v0.4.3)

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/calyears.html000066400000000000000000000057651515050116100300600ustar00rootroot00000000000000 calyears (Tablicious v0.4.3)

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical.html000066400000000000000000000175711515050116100305300ustar00rootroot00000000000000 categorical (Tablicious v0.4.3)

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

    Next: cell2table, Previous: calyears, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002eaddcats.html000066400000000000000000000057521515050116100326000ustar00rootroot00000000000000 categorical.addcats (Tablicious v0.4.3)

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    8.2.5.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

    categorical_002ecategorical.html000066400000000000000000000135751515050116100333750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.categorical (Tablicious v0.4.3)

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

    categorical_002ecategories.html000066400000000000000000000061451515050116100332400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.categories (Tablicious v0.4.3)

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    8.2.5.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062241515050116100326400ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.3)

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    8.2.5.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062331515050116100330430ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.3)

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    8.2.5.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002edouble.html000066400000000000000000000066451515050116100324510ustar00rootroot00000000000000 categorical.double (Tablicious v0.4.3)

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    8.2.5.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

    categorical_002eiscategory.html000066400000000000000000000062641515050116100332660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.iscategory (Tablicious v0.4.3)

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    8.2.5.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002eismissing.html000066400000000000000000000061171515050116100331760ustar00rootroot00000000000000 categorical.ismissing (Tablicious v0.4.3)

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    8.2.5.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002eisnanny.html000066400000000000000000000062221515050116100326450ustar00rootroot00000000000000 categorical.isnanny (Tablicious v0.4.3)

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    8.2.5.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002eisordinal.html000066400000000000000000000061261515050116100331550ustar00rootroot00000000000000 categorical.isordinal (Tablicious v0.4.3)

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    8.2.5.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

    categorical_002eisundefined.html000066400000000000000000000064401515050116100334060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.isundefined (Tablicious v0.4.3)

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    8.2.5.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002emergecats.html000066400000000000000000000075621515050116100331500ustar00rootroot00000000000000 categorical.mergecats (Tablicious v0.4.3)

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    8.2.5.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002emissing.html000066400000000000000000000074371515050116100326500ustar00rootroot00000000000000 categorical.missing (Tablicious v0.4.3)

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    8.2.5.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

    categorical_002eremovecats.html000066400000000000000000000074371515050116100332700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.removecats (Tablicious v0.4.3)

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    8.2.5.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

    categorical_002erenamecats.html000066400000000000000000000067611515050116100332410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.renamecats (Tablicious v0.4.3)

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    8.2.5.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

    categorical_002ereordercats.html000066400000000000000000000072131515050116100334250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html categorical.reordercats (Tablicious v0.4.3)

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    8.2.5.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002esetcats.html000066400000000000000000000062361515050116100326410ustar00rootroot00000000000000 categorical.setcats (Tablicious v0.4.3)

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    8.2.5.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/categorical_002estring.html000066400000000000000000000062341515050116100324770ustar00rootroot00000000000000 categorical.string (Tablicious v0.4.3)

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    8.2.5.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/colvecfun.html000066400000000000000000000065311515050116100302310ustar00rootroot00000000000000 colvecfun (Tablicious v0.4.3)

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.58.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.58.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

    Next: days, Previous: contains, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/datetime_002eNaT.html000066400000000000000000000075101515050116100311700ustar00rootroot00000000000000 datetime.NaT (Tablicious v0.4.3)

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    8.2.9.23 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.1 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.2 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.3 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.4 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.5 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.6 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.7 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.8 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.9 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.10 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.11 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.12 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.13 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.14 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.15 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.16 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/datetime_002eisnat.html000066400000000000000000000055641515050116100316330ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.3)

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    8.2.9.17 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.18 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.19 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
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    8.2.9.21 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.22 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.24 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.26 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.27 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.28 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.29 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.30 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.31 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.32 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.33 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.34 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration.html000066400000000000000000000122201515050116100300620ustar00rootroot00000000000000 duration (Tablicious v0.4.3)

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002echar.html000066400000000000000000000056371515050116100314640ustar00rootroot00000000000000 duration.char (Tablicious v0.4.3)

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    8.2.13.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002edispstrs.html000066400000000000000000000056671515050116100324250ustar00rootroot00000000000000 duration.dispstrs (Tablicious v0.4.3)

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    8.2.13.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002ehours.html000066400000000000000000000057601515050116100317040ustar00rootroot00000000000000 duration.hours (Tablicious v0.4.3)

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002elinspace.html000066400000000000000000000067351515050116100323450ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.3)

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    8.2.13.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002emilliseconds.html000066400000000000000000000060421515050116100332230ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.3)

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    8.2.13.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002eminutes.html000066400000000000000000000060161515050116100322230ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.3)

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    8.2.13.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002eofDays.html000066400000000000000000000062021515050116100317610ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.3)

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    8.2.13.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002eseconds.html000066400000000000000000000057331515050116100322020ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.3)

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    8.2.13.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/duration_002eyears.html000066400000000000000000000055061515050116100316650ustar00rootroot00000000000000 duration.years (Tablicious v0.4.3)

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    8.2.13.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/eqn.html000066400000000000000000000074221515050116100270300ustar00rootroot00000000000000 eqn (Tablicious v0.4.3)

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/head.html000066400000000000000000000074401515050116100271460ustar00rootroot00000000000000 head (Tablicious v0.4.3) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/hours.html000066400000000000000000000062531515050116100274060ustar00rootroot00000000000000 hours (Tablicious v0.4.3)

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/index.html000066400000000000000000001317131515050116100273550ustar00rootroot00000000000000 Top (Tablicious v0.4.3)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.3.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/iscalendarduration.html000066400000000000000000000062131515050116100321150ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.3)

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/iscategorical.html000066400000000000000000000061671515050116100310630ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.3)

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/isdatetime.html000066400000000000000000000060771515050116100304020ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.3)

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/isduration.html000066400000000000000000000060431515050116100304240ustar00rootroot00000000000000 isduration (Tablicious v0.4.3)

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/isfile.html000066400000000000000000000044441515050116100275210ustar00rootroot00000000000000 isfile (Tablicious v0.4.3)

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    8.2.21 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/isfolder.html000066400000000000000000000044341515050116100300540ustar00rootroot00000000000000 isfolder (Tablicious v0.4.3)

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    8.2.22 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/isnanny.html000066400000000000000000000073751515050116100277330ustar00rootroot00000000000000 isnanny (Tablicious v0.4.3)

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/istable.html000066400000000000000000000064501515050116100276700ustar00rootroot00000000000000 istable (Tablicious v0.4.3)

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/istabular.html000066400000000000000000000061701515050116100302320ustar00rootroot00000000000000 istabular (Tablicious v0.4.3)

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/istimetable.html000066400000000000000000000065441515050116100305530ustar00rootroot00000000000000 istimetable (Tablicious v0.4.3)

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate.html000066400000000000000000000122021515050116100301650ustar00rootroot00000000000000 localdate (Tablicious v0.4.3)

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002eNaT.html000066400000000000000000000101031515050116100313140ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.3)

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    8.2.27.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002edatenum.html000066400000000000000000000054541515050116100323040ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.3)

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    8.2.27.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002edatestr.html000066400000000000000000000065701515050116100323150ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.3)

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    8.2.27.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002edatestrs.html000066400000000000000000000066721515050116100325030ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.3)

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    8.2.27.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002edatestruct.html000066400000000000000000000070751515050116100330320ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.3)

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    8.2.27.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002edispstrs.html000066400000000000000000000057451515050116100325250ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.3)

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    8.2.27.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002eisnan.html000066400000000000000000000060221515050116100317470ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.3)

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    8.2.27.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002eisnat.html000066400000000000000000000056461515050116100317700ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.3)

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    8.2.27.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002elocaldate.html000066400000000000000000000110331515050116100325650ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.3)

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    8.2.27.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002eposixtime.html000066400000000000000000000062751515050116100326720ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.3)

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    8.2.27.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/localdate_002eymd.html000066400000000000000000000055221515050116100314340ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.3)

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    8.2.27.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/milliseconds.html000066400000000000000000000063561515050116100307370ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.3)

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/minutes.html000066400000000000000000000055671515050116100277410ustar00rootroot00000000000000 minutes (Tablicious v0.4.3)

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing.html000066400000000000000000000074771515050116100277300ustar00rootroot00000000000000 missing (Tablicious v0.4.3)

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing_002edispstrs.html000066400000000000000000000056651515050116100322470ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.3)

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    8.2.30.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing_002eismissing.html000066400000000000000000000060271515050116100323720ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.3)

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    8.2.30.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing_002eisnan.html000066400000000000000000000057601515050116100315000ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.3)

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    8.2.30.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing_002eisnanny.html000066400000000000000000000057711515050116100320510ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.3)

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    8.2.30.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/missing_002emissing.html000066400000000000000000000054321515050116100320350ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.3)

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    8.2.30.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeA.html000066400000000000000000000044621515050116100276060ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.3)

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    8.2.31 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeCellstr.html000066400000000000000000000045261515050116100310370ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.3)

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    8.2.32 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeCharvec.html000066400000000000000000000045521515050116100310010ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.3)

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    8.2.33 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeFinite.html000066400000000000000000000045501515050116100306420ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.3)

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    8.2.34 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeInteger.html000066400000000000000000000045461515050116100310260ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.3)

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    8.2.35 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeMember.html000066400000000000000000000045541515050116100306370ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.3)

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    8.2.36 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeNonempty.html000066400000000000000000000045601515050116100312360ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.3)

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    8.2.37 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeNumeric.html000066400000000000000000000045461515050116100310330ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.3)

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    8.2.38 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeReal.html000066400000000000000000000045401515050116100303060ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.3)

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    8.2.39 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeSameSize.html000066400000000000000000000045441515050116100311470ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.3)

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    8.2.40 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeScalar.html000066400000000000000000000046041515050116100306310ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.3)

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    8.2.41 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeScalarLogical.html000066400000000000000000000046121515050116100321230ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.3)

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    8.2.42 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/mustBeVector.html000066400000000000000000000045301515050116100306640ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.3)

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    8.2.43 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/pp.html000066400000000000000000000077051515050116100266700ustar00rootroot00000000000000 pp (Tablicious v0.4.3)

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/scalarexpand.html000066400000000000000000000072301515050116100307070ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.3)

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/seconds.html000066400000000000000000000062751515050116100277100ustar00rootroot00000000000000 seconds (Tablicious v0.4.3)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/size2str.html000066400000000000000000000064731515050116100300370ustar00rootroot00000000000000 size2str (Tablicious v0.4.3)

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/splitapply.html000066400000000000000000000113441515050116100304440ustar00rootroot00000000000000 splitapply (Tablicious v0.4.3)

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string.html000066400000000000000000000160431515050116100275520ustar00rootroot00000000000000 string (Tablicious v0.4.3)

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002ecell.html000066400000000000000000000057041515050116100311420ustar00rootroot00000000000000 string.cell (Tablicious v0.4.3)

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    8.2.52.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002ecellstr.html000066400000000000000000000057331515050116100316750ustar00rootroot00000000000000 string.cellstr (Tablicious v0.4.3)

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    8.2.52.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002echar.html000066400000000000000000000061761515050116100311440ustar00rootroot00000000000000 string.char (Tablicious v0.4.3)

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    8.2.52.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002ecmp.html000066400000000000000000000075001515050116100307760ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.3)

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    8.2.52.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002edecode.html000066400000000000000000000061631515050116100314460ustar00rootroot00000000000000 string.decode (Tablicious v0.4.3)

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    8.2.52.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002edispstrs.html000066400000000000000000000064341515050116100320770ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.3)

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    8.2.52.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eempty.html000066400000000000000000000061211515050116100313530ustar00rootroot00000000000000 string.empty (Tablicious v0.4.3)

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    8.2.52.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eencode.html000066400000000000000000000062741515050116100314630ustar00rootroot00000000000000 string.encode (Tablicious v0.4.3)

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    8.2.52.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eerase.html000066400000000000000000000057741515050116100313310ustar00rootroot00000000000000 string.erase (Tablicious v0.4.3)

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    8.2.52.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eismissing.html000066400000000000000000000061261515050116100322270ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.3)

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    8.2.52.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eisnanny.html000066400000000000000000000057541515050116100317070ustar00rootroot00000000000000 string.isnanny (Tablicious v0.4.3)

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    8.2.52.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eisstring.html000066400000000000000000000056771515050116100320760ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.3)

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    8.2.52.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002elower.html000066400000000000000000000061731515050116100313540ustar00rootroot00000000000000 string.lower (Tablicious v0.4.3)

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    8.2.52.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002emissing.html000066400000000000000000000061121515050116100316660ustar00rootroot00000000000000 string.missing (Tablicious v0.4.3)

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    8.2.52.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002eplus.html000066400000000000000000000062371515050116100312100ustar00rootroot00000000000000 string.plus (Tablicious v0.4.3)

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    8.2.52.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.52.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002ereverse.html000066400000000000000000000061611515050116100316740ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.3)

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    8.2.52.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/string_002estrfind.html000066400000000000000000000067341515050116100317000ustar00rootroot00000000000000 string.strfind (Tablicious v0.4.3)

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
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    8.2.52.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/struct2table.html000066400000000000000000000067201515050116100306630ustar00rootroot00000000000000 struct2table (Tablicious v0.4.3)

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

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    8.2.55 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002eaddvars.html000066400000000000000000000076741515050116100314400ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.3)

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    8.2.54.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002ecartesian.html000066400000000000000000000076431515050116100317610ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.3)

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    8.2.54.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002erepelem.html000066400000000000000000000066311515050116100314350ustar00rootroot00000000000000 table.repelem (Tablicious v0.4.3)

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    8.2.54.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002erepmat.html000066400000000000000000000064541515050116100312770ustar00rootroot00000000000000 table.repmat (Tablicious v0.4.3)

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    8.2.54.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002erestrict.html000066400000000000000000000105341515050116100316400ustar00rootroot00000000000000 table.restrict (Tablicious v0.4.3)

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    8.2.54.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002erowfun.html000066400000000000000000000156361515050116100313310ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.3)

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    8.2.54.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.rows2vars, Previous: table.restrict, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002erows2vars.html000066400000000000000000000106531515050116100317530ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.3)

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    8.2.54.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esemijoin.html000066400000000000000000000071721515050116100316220ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.3)

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    8.2.54.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    table_002esetDimensionNames.html000066400000000000000000000105051515050116100333450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html table.setDimensionNames (Tablicious v0.4.3)

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    8.2.54.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esetRowNames.html000066400000000000000000000061221515050116100322460ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.3)

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    8.2.54.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    table_002esetVariableNames.html000066400000000000000000000075671515050116100331630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html table.setVariableNames (Tablicious v0.4.3)

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    8.2.54.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esetdiff.html000066400000000000000000000063271515050116100314320ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.3)

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    8.2.54.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esetvar.html000066400000000000000000000071431515050116100313070ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.3)

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    8.2.54.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esetxor.html000066400000000000000000000065111515050116100313250ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.3)

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    8.2.54.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esize.html000066400000000000000000000072111515050116100307510ustar00rootroot00000000000000 table.size (Tablicious v0.4.3)

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    8.2.54.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esplitapply.html000066400000000000000000000070631515050116100322050ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.3)

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    8.2.54.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esplitvars.html000066400000000000000000000075331515050116100320350ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.3)

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    8.2.54.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esqueeze.html000066400000000000000000000057101515050116100314620ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.3)

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    8.2.54.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002estack.html000066400000000000000000000072161515050116100311110ustar00rootroot00000000000000 table.stack (Tablicious v0.4.3)

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    8.2.54.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002esummary.html000066400000000000000000000067311515050116100315020ustar00rootroot00000000000000 table.summary (Tablicious v0.4.3)

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    8.2.54.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002etable.html000066400000000000000000000125011515050116100310640ustar00rootroot00000000000000 table.table (Tablicious v0.4.3)

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    8.2.54.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002etable2array.html000066400000000000000000000055471515050116100322210ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.3)

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    8.2.54.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002etable2cell.html000066400000000000000000000062161515050116100320140ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.3)

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    8.2.54.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002etable2struct.html000066400000000000000000000071501515050116100324170ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.3)

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    8.2.54.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002eunion.html000066400000000000000000000063751515050116100311410ustar00rootroot00000000000000 table.union (Tablicious v0.4.3)

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    8.2.54.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002evarfun.html000066400000000000000000000102621515050116100313000ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.3)

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002evarnames.html000066400000000000000000000064161515050116100316210ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.3)

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    8.2.54.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002evertcat.html000066400000000000000000000063731515050116100314570ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.3)

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    8.2.54.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/table_002ewidth.html000066400000000000000000000053521515050116100311220ustar00rootroot00000000000000 table.width (Tablicious v0.4.3)

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    8.2.54.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/tail.html000066400000000000000000000076111515050116100271760ustar00rootroot00000000000000 tail (Tablicious v0.4.3)

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/tblish_002edataset.html000066400000000000000000000243741515050116100316330ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.3)

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    tblish_002edataset_002eAirPassengers.html000066400000000000000000000075561515050116100347740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.AirPassengers (Tablicious v0.4.3)

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    tblish_002edataset_002eBJsales.html000066400000000000000000000110401515050116100335300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.BJsales (Tablicious v0.4.3)

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eBOD.html000066400000000000000000000102621515050116100326160ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.BOD (Tablicious v0.4.3)

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eChickWeight.html000066400000000000000000000113051515050116100344020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.ChickWeight (Tablicious v0.4.3)

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    tblish_002edataset_002eDNase.html000066400000000000000000000111721515050116100332050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.DNase (Tablicious v0.4.3)

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111331515050116100351140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.EuStockMarkets (Tablicious v0.4.3)

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114171515050116100346200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Formaldehyde (Tablicious v0.4.3)

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151041515050116100345370ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.HairEyeColor (Tablicious v0.4.3)

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eHarman23cor.html000066400000000000000000000102721515050116100342720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Harman23cor (Tablicious v0.4.3)

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eHarman74cor.html000066400000000000000000000103471515050116100343030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Harman74cor (Tablicious v0.4.3)

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eIndometh.html000066400000000000000000000110051515050116100337550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Indometh (Tablicious v0.4.3)

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    tblish_002edataset_002eInsectSprays.html000066400000000000000000000104441515050116100346430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.InsectSprays (Tablicious v0.4.3)

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000102561515050116100351720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.3)

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    tblish_002edataset_002eLakeHuron.html000066400000000000000000000104741515050116100341070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.LakeHuron (Tablicious v0.4.3)

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126261515050116100354120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.3)

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    tblish_002edataset_002eLoblolly.html000066400000000000000000000111021515050116100337740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Loblolly (Tablicious v0.4.3)

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    tblish_002edataset_002eNile.html000066400000000000000000000112151515050116100331000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Nile (Tablicious v0.4.3)

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    tblish_002edataset_002eOrange.html000066400000000000000000000115221515050116100334250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Orange (Tablicious v0.4.3)

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    tblish_002edataset_002eOrchardSprays.html000066400000000000000000000124741515050116100350050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.OrchardSprays (Tablicious v0.4.3)

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);
    tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102161515050116100344620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.PlantGrowth (Tablicious v0.4.3)

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    tblish_002edataset_002ePuromycin.html000066400000000000000000000124711515050116100342030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Puromycin (Tablicious v0.4.3)

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    tblish_002edataset_002eTheoph.html000066400000000000000000000142601515050116100334430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Theoph (Tablicious v0.4.3)

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eTitanic.html000066400000000000000000000146031515050116100336100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.Titanic (Tablicious v0.4.3)

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eToothGrowth.html000066400000000000000000000115311515050116100345020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.ToothGrowth (Tablicious v0.4.3)

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000140771515050116100346650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.UCBAdmissions (Tablicious v0.4.3)

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000147431515050116100350460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.3)

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000110761515050116100345140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.UKLungDeaths (Tablicious v0.4.3)

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    tblish_002edataset_002eUKgas.html000066400000000000000000000103671515050116100332320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.UKgas (Tablicious v0.4.3)

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000100531515050116100342770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.USAccDeaths (Tablicious v0.4.3)

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    tblish_002edataset_002eUSArrests.html000066400000000000000000000137551515050116100341170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.USArrests (Tablicious v0.4.3)

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116231515050116100350520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.USJudgeRatings (Tablicious v0.4.3)

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111061515050116100364600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.3)

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    tblish_002edataset_002eVADeaths.html000066400000000000000000000113251515050116100336520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.VADeaths (Tablicious v0.4.3)

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    tblish_002edataset_002eWWWusage.html000066400000000000000000000103531515050116100337240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.WWWusage (Tablicious v0.4.3)

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eWorldPhones.html000066400000000000000000000105141515050116100344560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.WorldPhones (Tablicious v0.4.3)

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    tblish_002edataset_002eairmiles.html000066400000000000000000000072301515050116100340200ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.airmiles (Tablicious v0.4.3)

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    tblish_002edataset_002eairquality.html000066400000000000000000000125521515050116100344020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.airquality (Tablicious v0.4.3)

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    tblish_002edataset_002eanscombe.html000066400000000000000000000121451515050116100340030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.anscombe (Tablicious v0.4.3)

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    tblish_002edataset_002eattenu.html000066400000000000000000000114501515050116100335120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.attenu (Tablicious v0.4.3)

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    tblish_002edataset_002eattitude.html000066400000000000000000000111021515050116100340270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.attitude (Tablicious v0.4.3)

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    tblish_002edataset_002eaustres.html000066400000000000000000000102211515050116100336730ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.austres (Tablicious v0.4.3)

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    tblish_002edataset_002ebeavers.html000066400000000000000000000106431515050116100336440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.beavers (Tablicious v0.4.3)

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    tblish_002edataset_002ecars.html000066400000000000000000000116711515050116100331470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.cars (Tablicious v0.4.3)

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    tblish_002edataset_002echickwts.html000066400000000000000000000114221515050116100340300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.chickwts (Tablicious v0.4.3)

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    tblish_002edataset_002eco2.html000066400000000000000000000116651515050116100327050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.co2 (Tablicious v0.4.3)

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    tblish_002edataset_002ecrimtab.html000066400000000000000000000164001515050116100336330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.crimtab (Tablicious v0.4.3)

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    tblish_002edataset_002ecupcake.html000066400000000000000000000104731515050116100336310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.cupcake (Tablicious v0.4.3)

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    tblish_002edataset_002ediscoveries.html000066400000000000000000000106211515050116100345300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.discoveries (Tablicious v0.4.3)

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    tblish_002edataset_002eesoph.html000066400000000000000000000106631515050116100333350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.esoph (Tablicious v0.4.3)

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    tblish_002edataset_002eeuro.html000066400000000000000000000116561515050116100331740ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.euro (Tablicious v0.4.3)

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    tblish_002edataset_002eeurodist.html000066400000000000000000000101131515050116100340430ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.eurodist (Tablicious v0.4.3)

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    tblish_002edataset_002efaithful.html000066400000000000000000000121121515050116100340100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.faithful (Tablicious v0.4.3)

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002efreeny.html000066400000000000000000000121051515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.freeny (Tablicious v0.4.3)

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    tblish_002edataset_002einfert.html000066400000000000000000000116021515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.infert (Tablicious v0.4.3)

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    tblish_002edataset_002eiris.html000066400000000000000000000121551515050116100331630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.iris (Tablicious v0.4.3)

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eislands.html000066400000000000000000000104001515050116100336410ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.islands (Tablicious v0.4.3)

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/tblish_002edataset_002elh.html000066400000000000000000000101731515050116100326750ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.3)

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    tblish_002edataset_002elongley.html000066400000000000000000000115411515050116100336640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.longley (Tablicious v0.4.3)

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    tblish_002edataset_002elynx.html000066400000000000000000000113351515050116100332060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.lynx (Tablicious v0.4.3)

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    tblish_002edataset_002emorley.html000066400000000000000000000121341515050116100335210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.morley (Tablicious v0.4.3)

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    tblish_002edataset_002emtcars.html000066400000000000000000000124261515050116100335070ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.mtcars (Tablicious v0.4.3)

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002enhtemp.html000066400000000000000000000104771515050116100335150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.nhtemp (Tablicious v0.4.3)

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    tblish_002edataset_002enottem.html000066400000000000000000000101411515050116100335140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.nottem (Tablicious v0.4.3)

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002enpk.html000066400000000000000000000113041515050116100330000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.npk (Tablicious v0.4.3)

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000103631515050116100361010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.occupationalStatus (Tablicious v0.4.3)

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eprecip.html000066400000000000000000000103651515050116100335000ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.precip (Tablicious v0.4.3)

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    tblish_002edataset_002epresidents.html000066400000000000000000000111711515050116100343720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.presidents (Tablicious v0.4.3)

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    tblish_002edataset_002epressure.html000066400000000000000000000112301515050116100340560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.pressure (Tablicious v0.4.3)

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    tblish_002edataset_002equakes.html000066400000000000000000000120511515050116100335010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.quakes (Tablicious v0.4.3)

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002erandu.html000066400000000000000000000111311515050116100333170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.randu (Tablicious v0.4.3)

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    tblish_002edataset_002erivers.html000066400000000000000000000102631515050116100335250ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.rivers (Tablicious v0.4.3)

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    tblish_002edataset_002erock.html000066400000000000000000000110441515050116100331470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.rock (Tablicious v0.4.3)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    tblish_002edataset_002esleep.html000066400000000000000000000113051515050116100333210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.sleep (Tablicious v0.4.3)

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    tblish_002edataset_002estackloss.html000066400000000000000000000141741515050116100342260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.stackloss (Tablicious v0.4.3)

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    tblish_002edataset_002estate.html000066400000000000000000000130011515050116100333240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.state (Tablicious v0.4.3)

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000104521515050116100355260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.sunspot_month (Tablicious v0.4.3)

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102121515050116100353330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.sunspot_year (Tablicious v0.4.3)

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    tblish_002edataset_002esunspots.html000066400000000000000000000105111515050116100341050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.sunspots (Tablicious v0.4.3)

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    tblish_002edataset_002eswiss.html000066400000000000000000000140711515050116100333640ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.swiss (Tablicious v0.4.3)

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    tblish_002edataset_002etreering.html000066400000000000000000000111731515050116100340330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.treering (Tablicious v0.4.3)

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    tblish_002edataset_002etrees.html000066400000000000000000000113431515050116100333350ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.trees (Tablicious v0.4.3)

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    tblish_002edataset_002euspop.html000066400000000000000000000102441515050116100333600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.uspop (Tablicious v0.4.3)

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    tblish_002edataset_002evolcano.html000066400000000000000000000112131515050116100336500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.volcano (Tablicious v0.4.3)

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    tblish_002edataset_002ewarpbreaks.html000066400000000000000000000111751515050116100343570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.warpbreaks (Tablicious v0.4.3)

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    tblish_002edataset_002ewomen.html000066400000000000000000000113451515050116100333420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.women (Tablicious v0.4.3)

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    tblish_002edataset_002ezCO2.html000066400000000000000000000104511515050116100327670ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.dataset.zCO2 (Tablicious v0.4.3)

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/tblish_002edatasets.html000066400000000000000000000064301515050116100320070ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.3)

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    tblish_002eevalWithTableVars.html000066400000000000000000000075651515050116100335210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.evalWithTableVars (Tablicious v0.4.3)

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    tblish_002eexamples_002eSpDb.html000066400000000000000000000077521515050116100332450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.examples.SpDb (Tablicious v0.4.3)

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    tblish_002eexamples_002ecoplot.html000066400000000000000000000150401515050116100337020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.examples.coplot (Tablicious v0.4.3)

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000117551515050116100351620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.examples.plot_pairs (Tablicious v0.4.3)

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/tblish_002esizeof2.html000066400000000000000000000124271515050116100315630ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.3)

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    tblish_002etable_002egrpstats.html000066400000000000000000000104751515050116100335310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html tblish.table.grpstats (Tablicious v0.4.3)

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/timezones.html000066400000000000000000000077701515050116100302700ustar00rootroot00000000000000 timezones (Tablicious v0.4.3)

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/todatetime.html000066400000000000000000000070061515050116100304020ustar00rootroot00000000000000 todatetime (Tablicious v0.4.3)

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/vartype.html000066400000000000000000000064671515050116100277470ustar00rootroot00000000000000 vartype (Tablicious v0.4.3)

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/vecfun.html000066400000000000000000000066261515050116100275400ustar00rootroot00000000000000 vecfun (Tablicious v0.4.3)

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/html/years.html000066400000000000000000000064271515050116100273740ustar00rootroot00000000000000 years (Tablicious v0.4.3)

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.3/user-guide/tablicious.html000066400000000000000000026726411515050116100274540ustar00rootroot00000000000000 Tablicious v0.4.3

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.3.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.3 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.3/tablicious-0.4.3.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

    Next: , Up: Durations   [Contents]

    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Sign

    The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

    Instance Variable of calendarDuration: char Years

    The number of whole calendar years in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Months

    The number of whole calendar months in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Days

    The number of whole calendar days in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Hours

    The number of whole hours in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Minutes

    The number of whole minutes in this duration. Must be integer-valued.

    Instance Variable of calendarDuration: char Seconds

    The number of seconds in this duration. May contain fractional values.

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.2.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.2.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.2.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.2.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.2.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.2.6 calendarDuration.plus

    Method: out = plus (A, B)

    Addition: add two calendarDurations.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    If B is numeric, it is converted to a calendarDuration using calendarDuration.ofDays.

    Returns a calendarDuration.

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    8.2.2.7 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    Returns a calendarDuration.

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    8.2.2.8 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj.

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    8.2.3 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.4 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.5 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.5.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.5.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.5.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.5.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

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    8.2.5.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.5.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.5.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.5.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.5.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.5.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.5.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.5.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.5.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.5.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.5.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.5.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.5.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.5.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.5.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.6 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.7 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.8 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.9 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.9.1 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.9.2 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time

    Returns double array of same size as this.

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    8.2.9.3 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.9.4 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.9.5 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.9.6 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.9.7 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.9.8 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.9.9 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.9.10 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.11 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.12 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.9.13 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.14 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.15 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.9.16 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.9.17 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.9.18 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.19 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.9.20 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.9.21 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.22 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.9.23 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.9.24 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.9.25 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.9.26 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.9.27 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns datetime array the same size as A.

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    8.2.9.28 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.9.29 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.9.30 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.9.31 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.9.32 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.9.33 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.9.34 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.11 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.12 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.13 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.13.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.13.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.13.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.13.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.13.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.13.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.16 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.17 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.18 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isfile

    Not documented

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    8.2.22 isfolder

    Not documented

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    8.2.23 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.24 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.25 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.26 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.27 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.27.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.27.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.27.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.27.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.27.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.27.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.27.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.27.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.27.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.27.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.27.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.28 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.29 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.30 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.30.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.30.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.30.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.31 mustBeA

    Not documented

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    8.2.32 mustBeCellstr

    Not documented

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    8.2.33 mustBeCharvec

    Not documented

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    8.2.34 mustBeFinite

    Not documented

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    8.2.35 mustBeInteger

    Not documented

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    8.2.36 mustBeMember

    Not documented

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    8.2.37 mustBeNonempty

    Not documented

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    8.2.38 mustBeNumeric

    Not documented

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    8.2.39 mustBeReal

    Not documented

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    8.2.40 mustBeSameSize

    Not documented

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    8.2.41 mustBeScalar

    Not documented

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    8.2.42 mustBeScalarLogical

    Not documented

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    8.2.43 mustBeVector

    Not documented

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    8.2.44 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.45 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.46 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.47 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.48 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.49 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.50 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.51 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.52 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.52.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.52.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.52.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.52.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.52.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.52.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.52.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.52.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.52.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.52.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.52.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.52.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.52.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.52.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.52.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.52.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.52.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.52.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.52.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.52.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.52.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.52.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.52.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.52.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.52.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.52.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.54 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

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    8.2.54.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.54.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.54.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.54.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.54.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.54.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.54.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.54.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.54.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.54.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.54.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.54.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.54.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.54.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.54.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.54.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.54.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.54.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.54.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.54.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.54.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.54.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.54.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.54.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.54.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.54.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.54.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.54.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.54.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.54.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.54.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

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    8.2.54.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.54.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.54.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.54.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.54.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.54.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.54.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.54.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.54.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

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    8.2.54.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.54.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.54.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.54.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.54.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.54.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.54.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.54.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.54.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.55 tableOuterFillValue

    Not documented

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    8.2.56 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.57 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.57.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.57.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.57.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.57.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.57.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.57.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.57.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.57.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.57.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.57.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.57.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.57.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.57.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.57.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.57.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.57.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.57.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.57.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.57.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.57.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.57.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.57.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.57.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.57.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.57.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.57.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.57.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.57.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.57.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.57.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.57.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.57.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.57.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.57.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.57.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.57.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.57.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.57.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.57.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.57.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.57.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.57.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.57.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.57.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.57.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.57.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.57.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.57.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.57.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.57.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.57.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.57.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.57.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.57.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.57.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.57.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.57.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.57.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.57.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.57.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.57.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.57.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.57.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.57.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.57.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.57.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.57.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.57.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.57.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.57.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.57.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.57.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.57.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.57.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.57.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.57.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.57.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.57.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.57.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.57.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.57.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.57.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.57.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.57.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.57.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.58 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.58.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.58.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.58.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.59 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.60 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.61 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.62 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.63 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.64 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.65 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.66 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.67 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.68 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.69 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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SVBvt- V,տǐ\ު#NjDmۣ/C9Åa9]8ϽFr*. W#p{4^"-]զ?j2q:Kt2|G1NfOk1,t+:-ޮڇZq7Kpj$3_,8G7qx;!/7nvY=QF܃Qv,Q%V^'w4$MyOp;u3~t_ Q$T >^,dQz.dvX+zM3qk&>opLY3Fz9.ęӛ.$8~Bj8[CG1D㛁Ȓ{:5 " ˱_BfŸs]!8Wոo>;= 25τo"&?S9FX]a{+ ֥gbsl@9B0?Z&uDmzpDlgM\2V&aӢ@܃_! {n OVx+>0gl~ &YN2> stream xYܶ~Ţ_E-D(sb.`uIt[9j#i}qpg( 8DQp8<",ZRLզls_p.dWqˢwSRՇ}~L~fgsؘu(RGRxy= Hpq^%Z1.1xwY֘-[)z-tpG/uϻ߇bК*? ش`N ?l=+6ktj;\9nQ?<(In_8<=u''hɏG˝U.>'3SmMbf3wNMS6]i[j~*ڮ8:bt%pvN9%m؝-, `F-|rL;ZI&R rſi HqӌXDOǦo/vCv˺ nKX^Mdi4Fu7n +0X LdZZur2]%bƣ_7F;є B_(|+Q |QBل];L[*0Zӑz=Y(oe 6!w[~F3w{0_ص/wEh>ك#S`E1 T s.o]Y`&%N#M{ 1D zR0V`Rl.VFKt`"mʍ=o}\sX'(*0s&hq3 aGC!9w9v1yivƐD0(4q<5?}/M t Wzs@ĩ7^9q 㩝*3b/Z_?` ̑&#9D剠e1 ѿo35=C)٣~pGZ@㜌D$-JV~4ZrHYv4;x(5xـmf1_{j=~g16x1c2%*.g Y}7sY"i;74l.L Q%\x)t+li0PeR8YNQ7<ɯĴ9`Gi ZwkKt2FTj6xOX^ՍnS"&"[2+t4̾hA{\)<-yY8H @ʈ|oAͼaZ>Rs@{2~^u" Gg sZ&dæcL>t ї& U "QKM]H Kt9.GZH ep @e``w{c;IR :lWh9 KC~/!#HҹI {M)!%Oy9m=КhvHxۘ5*w(On0Wc^ AL>ƃBR}Jekd;zkskt!{Q6|P9}9+uGvu3;S] ہ=m4d 6f'HYR vmU^^W04Pi_GiiTy*;<;>5`.BD !!9wtj(bH:Kcakv^lM彄`adD8.2>i\LMu@C* ݲy~E;^ϛiudj[,sqD[[J?-{~ M-LJOXɕ7V(Gf4aThԖEiyCc~hD80eʹ0ďcT1z-VqHhܫ⫲V҇UL|'V՘ ۛɹ" ۫3Ml> VpH+VlQ=,$s LAc8wNJN cPs@mWU'B WIQӲP௽l endstream endobj 2608 0 obj << /Length 1688 /Filter /FlateDecode >> stream xX_o6ϧ0$KR"%ۀi :yKL–|IӔ( Vuc(\3XbU$d2%iYUmAL~7j#e!<*1Bs "VP1:V̓!^:e;$Vs5컦*7^~[%T5mc9ǧKʉ)ڕ E$@Nr_nK0>V&yN˧O=|]fڳp1xs,@M)焺7Uۚ(%8hư9K4 pIGE!(7\`]BY4,YpqsZbt ѧ'@bzuwUSՠT5̇Ѩe ̹ǪE'Z_n{t]$4l^&"=$d{AyN(l ;0x}Fo?eDo I2eX#2C3N/JAѐ y6TMaz*O6 k2#ۺ!y~ ]JS3(RH(mqN!DJm6ljlknhYbx$2JK>RZ8QAoea < WݰL gHN|oyl?,z͓gVKS8ʧDrIZpk`Ǵ݅̾a;̛f.dI/F9pۖw m\ nnS\A@GؘӞuۛ7}5Li;͜zWյD4m= 6._rL!L[iuJYgM *ԻHЍzHY7 ͇=cͳmwY{=R.?gS$yAN(R:$ͦ#&cne)8j{]PNdQS^%o.$[7gtx+$9CAn}.,cؕ ۺQ|=8 /zWszbQ6Аp3MA -OK׾n< l* ث+Uc +#ؗ|mˡZT?=z.GLl~2CSs^Y#18ݺFCpxD-` sgBI-eExò#Eݛ&rH{MSNdO{D[ endstream endobj 2611 0 obj << /Length 1501 /Filter /FlateDecode >> stream xXo6_KA .ӪJKdc,- fc0qv*"e fU xūR1*gݬgE $_QgxU7]~ۗ]QJs1ƃ Wʦ(ÈKO?ޮ=9!4NI>N(u`S NÈ'$}W57JJR,B,]Ecߵ6K Q9Q/EX˷ިNR%l" % ưԱjZYε s|EYװf]c /MUL6|ovK_&1 (uSBHbfC1vy /E&(q^AiTN̖U铧fn*cQjvvH8r"daS\lFɄ63O(_Uߗs](Uc~ .dSA Gr?`3OO]sK;ײ5_.뗍4|Y n&Ɏ AELtT0WYz+h`MToB}=5=bZ` Oǂ/R *.y{}OǠA*"db𴛎F= ; m'%/tV_mwd endstream endobj 2615 0 obj << /Length 1715 /Filter /FlateDecode >> stream xX_6 OG8,=l:t> ê$EbsӏeNWy%Q$??R~|V$ >SKwGUYc? ǜƘz65/NқU%yeߗΞ➉S%seE*8,|})QLe)u8b#;6А˪\P۟"S`L@G_v\E"#X}FIT-x?,KO5}zd6at̶ /bM||7EqxQ GU.ѓ|</ otP=H\$5n̶?0ȣYJ8NBK^T$Y5CNQK+}›U`WE_ [B'႔9'g!" gǕify RdѢAB-[4DkR9"\a*;4e7!DQAkx4ic\)h(H^Db1R!1pq l AyضxR FZFRc^yy1 tN1ofX6\G{TzYgrxc=wȰrz0I5Ъ)hv,Q@UkF֓dNïa $TPVZYu=ǠOF|s͛N=kY7JyX.?`\U? :_^(-MĽ le~'? 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N&Z@@AHZ۽.> stream xXAo[7 W]d)  +6mۂ؁ 祍_ܾ{rIO$E~(Qf )P2HK j-ekP3U4dU録JP1h Z*j$6J(bf 844Zh!ۚ+vI r'c*}pV">_HW{`Af( 3$_1+FdL% TeZ1_]gcWBl暘.a~ʽŦ*[;L&,C3Dd.ag׌02WDV- @ )]X&r=7>)ȁ/{x*,rPϪ >@*.5BsH$suT Y%e 9+ R$Ԙ!9 ^K~\'Od' XjuLΐ"#+\\ŒbA$|rJ3#P$J`ڸkyvr2 mǟ+QًؐP4,nĄ@PyxZnI"u?E) sB#t߬WM87O _̞}c7 H?6ݮ:cvWWOܗY(b9D Dp@bc8 Rq mstt8eJ&wA(ы(0Zǁ 48t @iTda凉=8(5JiPx58PFq\.2(\,m#6̘ą!ɞ _L)އdFƁHAqH&A& R,`()Om?HZDzyεOP< η7 X0#gww6/W˿fWeޚIݙ ,CGoٜ !2E7/ WgdpX\_.6]mq`Ӟܢ߬YFK1k}-E 2A#)Pq q n(Ϲj򘊍'n(D6m7&q $+O,;|R=qËFECvl <$VPXpGWTζwoq "ޛ5nj|tRi IXs-'ueR!(S8= [&֒ +;eY80㦖M&4 do<3_gȞş/yG0t;Y,ࠍy_pY_-?ny ֓J$>o=e yƁ(Ppi@2ax;QC@q@/T`Xt+FNriY񛂿\шBƜP8߯J+%n}P¯ӄXƁJhD O endstream endobj 2620 0 obj << /Length 2157 /Filter /FlateDecode >> stream xYo8_1o::[Cw-z@wS8xr˘T${NAJYʝ@֪y:lJ cSx4K3tQZ M2vC+Ɂ{D~#ic] t0x[ݗ ^軨(Zj(@.Yt 5) %;A) <fp5pI❫Wo!?}8q%Zҽ̂hLw &w8),D𱸀=Yهzli-z]qpah=Z;=};_$ϑO6_V]|Pt z/\ݍ3Z+Aw ? m(\p(@jnjV%' Eϵ#Ќ,z h׸RcHJ)4f!ոPbUI1 ]?}֫Z쑑|4jGJ0W3tw lk;)AxеCdjm B<&EZUco wtG |oUP0.H2o4N9C_aL9bU>'i0']*C;[b֯}H:+J#3L U$[F~A&LMt:)+HrSTǶ׿_Evlbke8Ácǘ'sjVGk/<-Ė8xOxqia6S[C$: 4 */?{Za'5ŜJ["5wM݋kE`؏Zh;Fz|yUbCo!P4oM4;־rzIX=] ~ f1t+\ֽa th\7i SOq"$](S[6o$OEvx{P^yQL|>[C Y >ؼu2·5 Z\ Gz@P6kύߖCSoS;\=V9B5G];k5 8RL],`n_. |O+ y5 #؛\!Vj&$&Ǵn<E EԇC7Le3Н⩕mLdS)N)5gQ\^i|Zە`[؋X^;vG7M?X$}?jB7 mɰh6{g.(0vM4\l'g[U> stream xY[o6~ϯd ī{躵X[> m00%Oٯ9$% si<sC.b,^$L|zagN[j"ǗR.hL8tf!zՇh+y>[gUU^,W,2DX>u=){r(yG-('1S ]J0\IŢkv5!L"c>\ċ |n~4Q8?# 5\rvEgϝhFd+&+>)m Lͅ]J2)Z4%Yj*$~0򨓊:Q|X1qH NORl 9aE9%SJ٬r3] A ghBvkARv0n ET|ɒhk`fI#{2iV̳ h=8WFBQI)FyW~Wlc QL$8߯_R;]j7}L5xn$xY[e,_+zF= ! UU/J< +JƄyy4E{0胍zw qn&]MtAйTAi/,@@XW%JPI.Pf^f-ֽytKo𕶡ھۂ,W*?)wIZ*rS@Ԡ~ΘKwK"{SBNq4-cz)R(?ا6OO{N@*Ɖ3+]y7tE>AwЃ A L=sZ``6ҁq{)=@FD2郞^,9sixcf\M ?/J@: : ѯmkz#0Saڍ,==h*ȱcI?g4a /b=86+xbsSHƅՠq.B Vp!γ%uɆ_=Rol'pi2!ºxp$j4SM}{Ō,ƕYL(ꈌDDfƙуd&{mE|ۦ7$j/XU0Se[&s"vҰ,G-r@vz4D𚩇1jǭ$͔_E!Y08Nm]$ ].~pok|B`bQWD"` 54_MM8P/l-,2eQ[OMk sb onhcs Q@Vwx B Y/>7ęF~o9'ט@$y 1}^=pe^k6S}Y{0@xJF.%!o3AҬ3VhUm%oE:eͣv! eSbUMI5qGL:{+r]s4ns>(G[V"LS 9Fec.ێE|2C9,LȀET T=½O u1!3@d:",2e $ /1M6wzWǙvrloكi{"ryr;0_ew endstream endobj 2627 0 obj << /Length 2795 /Filter /FlateDecode >> stream xZKsܸWLtYΖ!w*In*!."9: 9K)Gh|^WJ@ea=p3-rL$_>QҾLn離n;]~n Kބa䝿f[7&ёEۿ?{y挌yq(NUP dqBgrF:$2wu)b3uQIbXâuqi])R|Ҷr;$ޱC wȻjl}Yp:^ūM)?MW(/ڤe  nw| P_qFhF7q{2mWwGI jp)h/vk6| h_z}i5*nw%w (r=Cn\.&GܳiW`0JpP6e{e2Xm8P 0/@^đPҙ]كrxۜJ;ouPͬ7iuD漞JFMzqƆ/hA:O Qcpk_^-c(vt-~엂ҤJ##6ZUa$l-N&*C;=i ŋHQpʖ=.(9up_vK}wrD]O$ ܄K#Ji G0cU\z>D 1f=6uo 2*{ ac™ 獌*%cAz12A|lMqWtL*F`ŽUSrJxP 'bDfϞ&qX ;N9)U1K ʾ4"lqf 0:Ɍ1؅Uac>0XOUG!ot~}uy:nYί.^^ܼɒV(ĦgNôao VuN>nNx! 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Bӏ PrF,Ev k6¹q\86GF">qH~GD*aDlΉl jWf`9g2DK5pHtnʮ>l]@&;ve]pMx?SYsp`ŝyS k@$R,춦z8"'ٛO‘4X8na`|려0BD'T—~ >q 4tvڢ`=ZEܣl@O{c VWSׯ(k1A amj 2P֝XtQ7 G (XAؠ:4\7xZp3n%-1 0 $ʷL<[`ynwMlHQge/-Eb$o)/N휄Mӧ-QTX~c8exzqԣ]{|[P/-K`j"nJ)nyyN;`'c1p_)g[:tD|@X. w؄95wHs:ј,)cYKpEg\GM0: S9*M7bO]9:nww*3HػL&5K~F+4lS .7_~?Go[ΓyYXx`{y65Asc>tyTuW䴲0T?O g_ۮet=}=[~Ta^ނcgAg EMC_RS' ZWWibNp7%XnSJXN~?Cg,@{OtK ^F1.UVuD^SdO/wU] x.G( q"1Ҁʨ(TgT^ֱ!F\R*C5U=$@8c PFf B4K_8?9Δ؟ `,*3 w,͌6lhAOEqLҟ_s^RjQ%8 ZZ%&\$=:10e2:r}E ׊ho 6tA(;ȋ endstream endobj 2631 0 obj << /Length 2322 /Filter /FlateDecode >> stream xY[o:~0I"]!iⴋؗDjm)%ip$+u{N(PS!g͕\ 'WX%Aea*/w+| |.0#Λ͋oh%L6Vry{ծ  ȹ->jU]'2N$ן7z񏍕* ˒U Bo7@ꔟQ˷ˢEnU'(s'Elu"'/ˇxW1V%o?H,z+pՒiVeͺD +!Y jP t_wy[ۃzU,2LM˖tBL%4@f^ |d%:HTG_y'pei+Nw1娢#$*/:\˷͵~'"zڀoKyW_^]/gjjԙC}\2@#sƇcE֒շ}$g?(tGO <|ބT=oS%<ZO~[1tpB~h1 xZ74 UP [ ~<Č  fGSF ZK%ӵ_*ڄlp Bohy6GVV]pbxW.ʒK>ԐEVTIxAzḾE\?(XݽbgRCIby ~kHE)5q=VuCӞ6f͒޾1U[xHfj@kH0Φn_"g![83P[-h-!J#Mr'+.e;|i`>via`Ov($w=[D6LǺ$9+)2Dw'ځzf7L݌pgОAz^Il&STݤto)*f^/zǩcWuW5!?I:&є` ䷼?Q`Y1v?8ơ[HUv k'Kx"L~Fkt+jtrF읆ڞq'~~߸U?dQeF
"J, , AwL@X *Q>xfn dJÑs%p>ܳ4e{ZfCdRWfAiaq] hX-+(H9gÎ4&IԤ@PX#N ]O_qǩw1~6Ap=65Z3 ~<U6cNcc}9 4dԡ> CV"fi^fR7Jw#'tfK;-l̬bݸv=KAvPWIJ%Jtnn3Ua{EvUD-cylv\8lGFJ%k'FMv44&N]s uN*E`s>ftIKHQ2𙀨Sb; Bb^qQ){:>2?uDыm[=p 闻 Ҫn8τ>Q Z̧c} d:6+ ::|R"8 :kGp6߼EsoXՊ$=,y]+:[RSTd5:VbR.ٖ}.vK8yNeiS>/ ǟ>뛈#K33uJ3&CelA :ڲ3q`1=hyj(1\%05?5$xQ&oByo"ҿ&bX|uS/ϳ 'h|:03kLv}a32/L,@GRv\e.cQoΌkj͓ k!F؋0 *VYAsE3M0Ѡ0cf+Աi)'`R9*i>91JKG33FMXip@kd_"5[ch:曹.-Ae'TE8fIL͡JafwS@źj]'WhD; xJz%'xK]$'뜷hMlTMSDo{⼛M( z>il-3nea:UެwfYDI7 endstream endobj 2634 0 obj << /Length 2159 /Filter /FlateDecode >> stream xY_6OQv=h>}pg6sQd[dEy-QI?2]<]hI.EyIh{7n[jBo\ДiNݔzx|/}.W$rB&_TNe\1MeH|d+Ql2A((`dYr\'w4{%EYVù~_hg*_S*-~XJm]lU9.)/-n2OK/ xU"iZ7X5àj^\rB%jt}co}ћ'ccʈ< =Qs$55sM 4KzTS2ȪI& /Cmnx] 88SF )- /碛`ai+H- ouY7~vKϓ1j:?YoIMQDKqؑ endstream endobj 2637 0 obj << /Length 1933 /Filter /FlateDecode >> stream xYKo6W,z+R(vޒ]R+zPh K yq4X0di,ֻP .:v ʳ/\A)f;ϳVK? 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    Next: , Up: API Reference   [Contents]

    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

    Next: API Alphabetically, Up: API Reference   [Contents]

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    9 Copying

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/Data-Sets-from-R.html000066400000000000000000000056651515050116100312020ustar00rootroot00000000000000 Data Sets from R (Tablicious v0.4.4)

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    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/Datenum-Compatibility.html000066400000000000000000000074561515050116100324610ustar00rootroot00000000000000 Datenum Compatibility (Tablicious v0.4.4)

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    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/Defined-Time-Zones.html000066400000000000000000000060041515050116100315670ustar00rootroot00000000000000 Defined Time Zones (Tablicious v0.4.4)

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    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.4/tablicious-0.4.4.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/Introduction.html000066400000000000000000000072341515050116100307300ustar00rootroot00000000000000 Introduction (Tablicious v0.4.4)

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.4 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Next: Durations, Previous: datetime Class, Up: Date and Time Representation   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/Validation-Functions.html000066400000000000000000000117411515050116100323050ustar00rootroot00000000000000 Validation Functions (Tablicious v0.4.4)

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/array2table.html000066400000000000000000000102251515050116100304510ustar00rootroot00000000000000 array2table (Tablicious v0.4.4)

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012
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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

    calendarDuration_002edispstrs.html000066400000000000000000000062101515050116100337620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html calendarDuration.dispstrs (Tablicious v0.4.4)

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    8.2.3.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    calendarDuration_002eismissing.html000066400000000000000000000062101515050116100341140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html calendarDuration.ismissing (Tablicious v0.4.4)

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    8.2.3.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.3.6 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

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    8.2.3.7 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

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    8.2.3.8 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

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    8.2.3.9 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/categorical_002ecellstr.html000066400000000000000000000062261515050116100326430ustar00rootroot00000000000000 categorical.cellstr (Tablicious v0.4.4)

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/categorical_002edispstrs.html000066400000000000000000000062351515050116100330460ustar00rootroot00000000000000 categorical.dispstrs (Tablicious v0.4.4)

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.10.25 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002ecolon.html000066400000000000000000000116541515050116100316250ustar00rootroot00000000000000 datetime.colon (Tablicious v0.4.4)

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

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    datetime_002econvertDatenumTimeZone.html000066400000000000000000000067531515050116100351110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html datetime.convertDatenumTimeZone (Tablicious v0.4.4)

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatenum.html000066400000000000000000000057751515050116100321570ustar00rootroot00000000000000 datetime.datenum (Tablicious v0.4.4)

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

    datetime_002edatenum2posix.html000066400000000000000000000060651515050116100332360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html datetime.datenum2posix (Tablicious v0.4.4)

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatestr.html000066400000000000000000000065741515050116100321660ustar00rootroot00000000000000 datetime.datestr (Tablicious v0.4.4)

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatestrs.html000066400000000000000000000066461515050116100323510ustar00rootroot00000000000000 datetime.datestrs (Tablicious v0.4.4)

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatestruct.html000066400000000000000000000066421515050116100326760ustar00rootroot00000000000000 datetime.datestruct (Tablicious v0.4.4)

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatetime.html000066400000000000000000000135061515050116100323050ustar00rootroot00000000000000 datetime.datetime (Tablicious v0.4.4)

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edatevec.html000066400000000000000000000056511515050116100321260ustar00rootroot00000000000000 datetime.datevec (Tablicious v0.4.4)

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    Returns double array of size [numel(obj) 6].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002ediff.html000066400000000000000000000060611515050116100314170ustar00rootroot00000000000000 datetime.diff (Tablicious v0.4.4)

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002edispstrs.html000066400000000000000000000056621515050116100323700ustar00rootroot00000000000000 datetime.dispstrs (Tablicious v0.4.4)

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eeq.html000066400000000000000000000062111515050116100311110ustar00rootroot00000000000000 datetime.eq (Tablicious v0.4.4)

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002ege.html000066400000000000000000000062241515050116100311030ustar00rootroot00000000000000 datetime.ge (Tablicious v0.4.4)

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002ehms.html000066400000000000000000000061071515050116100312770ustar00rootroot00000000000000 datetime.hms (Tablicious v0.4.4)

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eisbetween.html000066400000000000000000000063101515050116100324710ustar00rootroot00000000000000 datetime.isbetween (Tablicious v0.4.4)

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eisnan.html000066400000000000000000000060071515050116100316170ustar00rootroot00000000000000 datetime.isnan (Tablicious v0.4.4)

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    8.2.10.18 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eisnat.html000066400000000000000000000055671515050116100316370ustar00rootroot00000000000000 datetime.isnat (Tablicious v0.4.4)

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    8.2.10.19 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.10.20 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002elinspace.html000066400000000000000000000067001515050116100323050ustar00rootroot00000000000000 datetime.linspace (Tablicious v0.4.4)

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    8.2.10.21 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002elocaltime.html000066400000000000000000000066761515050116100324740ustar00rootroot00000000000000 datetime.localtime (Tablicious v0.4.4)

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    8.2.10.22 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002elt.html000066400000000000000000000062341515050116100311300ustar00rootroot00000000000000 datetime.lt (Tablicious v0.4.4)

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    8.2.10.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eminus.html000066400000000000000000000067641515050116100316540ustar00rootroot00000000000000 datetime.minus (Tablicious v0.4.4)

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    8.2.10.24 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002ene.html000066400000000000000000000062251515050116100311130ustar00rootroot00000000000000 datetime.ne (Tablicious v0.4.4)

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    8.2.10.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eofDatenum.html000066400000000000000000000060111515050116100324240ustar00rootroot00000000000000 datetime.ofDatenum (Tablicious v0.4.4)

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    8.2.10.27 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/datetime_002eofDatestruct.html000066400000000000000000000062461515050116100331630ustar00rootroot00000000000000 datetime.ofDatestruct (Tablicious v0.4.4)

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    8.2.10.28 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.10.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

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    8.2.10.30 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.10.31 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.10.32 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.10.33 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.10.34 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.10.35 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.36 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002echar.html000066400000000000000000000056411515050116100314600ustar00rootroot00000000000000 duration.char (Tablicious v0.4.4)

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002edispstrs.html000066400000000000000000000056711515050116100324210ustar00rootroot00000000000000 duration.dispstrs (Tablicious v0.4.4)

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    8.2.14.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002ehours.html000066400000000000000000000057621515050116100317070ustar00rootroot00000000000000 duration.hours (Tablicious v0.4.4)

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    8.2.14.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002elinspace.html000066400000000000000000000067371515050116100323500ustar00rootroot00000000000000 duration.linspace (Tablicious v0.4.4)

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    8.2.14.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002emilliseconds.html000066400000000000000000000060441515050116100332260ustar00rootroot00000000000000 duration.milliseconds (Tablicious v0.4.4)

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    8.2.14.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002eminutes.html000066400000000000000000000060201515050116100322170ustar00rootroot00000000000000 duration.minutes (Tablicious v0.4.4)

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    8.2.14.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002eofDays.html000066400000000000000000000062041515050116100317640ustar00rootroot00000000000000 duration.ofDays (Tablicious v0.4.4)

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    8.2.14.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002eseconds.html000066400000000000000000000057351515050116100322050ustar00rootroot00000000000000 duration.seconds (Tablicious v0.4.4)

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    8.2.14.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/duration_002eyears.html000066400000000000000000000055101515050116100316610ustar00rootroot00000000000000 duration.years (Tablicious v0.4.4)

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    8.2.14.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/eqn.html000066400000000000000000000074241515050116100270330ustar00rootroot00000000000000 eqn (Tablicious v0.4.4)

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/head.html000066400000000000000000000074421515050116100271510ustar00rootroot00000000000000 head (Tablicious v0.4.4) apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/hours.html000066400000000000000000000062551515050116100274110ustar00rootroot00000000000000 hours (Tablicious v0.4.4)

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/index.html000066400000000000000000001330171515050116100273550ustar00rootroot00000000000000 Top (Tablicious v0.4.4)

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.4.

    Table of Contents

    Next:   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/iscalendarduration.html000066400000000000000000000062151515050116100321200ustar00rootroot00000000000000 iscalendarduration (Tablicious v0.4.4)

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/iscategorical.html000066400000000000000000000061711515050116100310570ustar00rootroot00000000000000 iscategorical (Tablicious v0.4.4)

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/isdatetime.html000066400000000000000000000061011515050116100303670ustar00rootroot00000000000000 isdatetime (Tablicious v0.4.4)

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/isduration.html000066400000000000000000000060451515050116100304270ustar00rootroot00000000000000 isduration (Tablicious v0.4.4)

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/isfile.html000066400000000000000000000044461515050116100275240ustar00rootroot00000000000000 isfile (Tablicious v0.4.4)

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    8.2.22 isfile

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/isfolder.html000066400000000000000000000044361515050116100300570ustar00rootroot00000000000000 isfolder (Tablicious v0.4.4)

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    8.2.23 isfolder

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/isnanny.html000066400000000000000000000073771515050116100277360ustar00rootroot00000000000000 isnanny (Tablicious v0.4.4)

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/istable.html000066400000000000000000000064521515050116100276730ustar00rootroot00000000000000 istable (Tablicious v0.4.4)

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/istabular.html000066400000000000000000000061721515050116100302350ustar00rootroot00000000000000 istabular (Tablicious v0.4.4)

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/istimetable.html000066400000000000000000000065461515050116100305560ustar00rootroot00000000000000 istimetable (Tablicious v0.4.4)

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate.html000066400000000000000000000122041515050116100301700ustar00rootroot00000000000000 localdate (Tablicious v0.4.4)

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002eNaT.html000066400000000000000000000101051515050116100313170ustar00rootroot00000000000000 localdate.NaT (Tablicious v0.4.4)

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002edatenum.html000066400000000000000000000054561515050116100323070ustar00rootroot00000000000000 localdate.datenum (Tablicious v0.4.4)

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002edatestr.html000066400000000000000000000065721515050116100323200ustar00rootroot00000000000000 localdate.datestr (Tablicious v0.4.4)

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002edatestrs.html000066400000000000000000000066741515050116100325060ustar00rootroot00000000000000 localdate.datestrs (Tablicious v0.4.4)

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002edatestruct.html000066400000000000000000000070771515050116100330350ustar00rootroot00000000000000 localdate.datestruct (Tablicious v0.4.4)

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002edispstrs.html000066400000000000000000000057471515050116100325300ustar00rootroot00000000000000 localdate.dispstrs (Tablicious v0.4.4)

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002eisnan.html000066400000000000000000000060241515050116100317520ustar00rootroot00000000000000 localdate.isnan (Tablicious v0.4.4)

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002eisnat.html000066400000000000000000000056501515050116100317640ustar00rootroot00000000000000 localdate.isnat (Tablicious v0.4.4)

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002elocaldate.html000066400000000000000000000110351515050116100325700ustar00rootroot00000000000000 localdate.localdate (Tablicious v0.4.4)

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002eposixtime.html000066400000000000000000000062771515050116100326750ustar00rootroot00000000000000 localdate.posixtime (Tablicious v0.4.4)

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/localdate_002eymd.html000066400000000000000000000055241515050116100314370ustar00rootroot00000000000000 localdate.ymd (Tablicious v0.4.4)

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/milliseconds.html000066400000000000000000000063601515050116100307330ustar00rootroot00000000000000 milliseconds (Tablicious v0.4.4)

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/minutes.html000066400000000000000000000055711515050116100277350ustar00rootroot00000000000000 minutes (Tablicious v0.4.4)

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing.html000066400000000000000000000075011515050116100277150ustar00rootroot00000000000000 missing (Tablicious v0.4.4)

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing_002edispstrs.html000066400000000000000000000056671515050116100322520ustar00rootroot00000000000000 missing.dispstrs (Tablicious v0.4.4)

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing_002eismissing.html000066400000000000000000000060311515050116100323660ustar00rootroot00000000000000 missing.ismissing (Tablicious v0.4.4)

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing_002eisnan.html000066400000000000000000000057621515050116100315030ustar00rootroot00000000000000 missing.isnan (Tablicious v0.4.4)

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing_002eisnanny.html000066400000000000000000000057731515050116100320540ustar00rootroot00000000000000 missing.isnanny (Tablicious v0.4.4)

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/missing_002emissing.html000066400000000000000000000054341515050116100320400ustar00rootroot00000000000000 missing.missing (Tablicious v0.4.4)

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeA.html000066400000000000000000000044641515050116100276110ustar00rootroot00000000000000 mustBeA (Tablicious v0.4.4)

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    8.2.32 mustBeA

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeCellstr.html000066400000000000000000000045301515050116100310330ustar00rootroot00000000000000 mustBeCellstr (Tablicious v0.4.4)

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    8.2.33 mustBeCellstr

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeCharvec.html000066400000000000000000000045541515050116100310040ustar00rootroot00000000000000 mustBeCharvec (Tablicious v0.4.4)

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    8.2.34 mustBeCharvec

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeFinite.html000066400000000000000000000045521515050116100306450ustar00rootroot00000000000000 mustBeFinite (Tablicious v0.4.4)

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    8.2.35 mustBeFinite

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeInteger.html000066400000000000000000000045501515050116100310220ustar00rootroot00000000000000 mustBeInteger (Tablicious v0.4.4)

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    8.2.36 mustBeInteger

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeMember.html000066400000000000000000000045561515050116100306420ustar00rootroot00000000000000 mustBeMember (Tablicious v0.4.4)

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    8.2.37 mustBeMember

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeNonempty.html000066400000000000000000000045621515050116100312410ustar00rootroot00000000000000 mustBeNonempty (Tablicious v0.4.4)

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    8.2.38 mustBeNonempty

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeNumeric.html000066400000000000000000000045501515050116100310270ustar00rootroot00000000000000 mustBeNumeric (Tablicious v0.4.4)

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    8.2.39 mustBeNumeric

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeReal.html000066400000000000000000000045421515050116100303110ustar00rootroot00000000000000 mustBeReal (Tablicious v0.4.4)

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    8.2.40 mustBeReal

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeSameSize.html000066400000000000000000000045461515050116100311520ustar00rootroot00000000000000 mustBeSameSize (Tablicious v0.4.4)

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    8.2.41 mustBeSameSize

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeScalar.html000066400000000000000000000046061515050116100306340ustar00rootroot00000000000000 mustBeScalar (Tablicious v0.4.4)

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    8.2.42 mustBeScalar

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeScalarLogical.html000066400000000000000000000046141515050116100321260ustar00rootroot00000000000000 mustBeScalarLogical (Tablicious v0.4.4)

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    8.2.43 mustBeScalarLogical

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/mustBeVector.html000066400000000000000000000045321515050116100306670ustar00rootroot00000000000000 mustBeVector (Tablicious v0.4.4)

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    8.2.44 mustBeVector

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/pp.html000066400000000000000000000077071515050116100266730ustar00rootroot00000000000000 pp (Tablicious v0.4.4)

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/scalarexpand.html000066400000000000000000000072321515050116100307120ustar00rootroot00000000000000 scalarexpand (Tablicious v0.4.4)

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/seconds.html000066400000000000000000000062771515050116100277130ustar00rootroot00000000000000 seconds (Tablicious v0.4.4)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/size2str.html000066400000000000000000000064751515050116100300420ustar00rootroot00000000000000 size2str (Tablicious v0.4.4)

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/splitapply.html000066400000000000000000000113461515050116100304470ustar00rootroot00000000000000 splitapply (Tablicious v0.4.4)

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string.html000066400000000000000000000160451515050116100275550ustar00rootroot00000000000000 string (Tablicious v0.4.4)

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002ecell.html000066400000000000000000000057061515050116100311450ustar00rootroot00000000000000 string.cell (Tablicious v0.4.4)

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002echar.html000066400000000000000000000062001515050116100311310ustar00rootroot00000000000000 string.char (Tablicious v0.4.4)

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002ecmp.html000066400000000000000000000075021515050116100310010ustar00rootroot00000000000000 string.cmp (Tablicious v0.4.4)

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002edecode.html000066400000000000000000000061651515050116100314510ustar00rootroot00000000000000 string.decode (Tablicious v0.4.4)

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002edispstrs.html000066400000000000000000000064361515050116100321020ustar00rootroot00000000000000 string.dispstrs (Tablicious v0.4.4)

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002eempty.html000066400000000000000000000061231515050116100313560ustar00rootroot00000000000000 string.empty (Tablicious v0.4.4)

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002eencode.html000066400000000000000000000062761515050116100314660ustar00rootroot00000000000000 string.encode (Tablicious v0.4.4)

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002eerase.html000066400000000000000000000057761515050116100313340ustar00rootroot00000000000000 string.erase (Tablicious v0.4.4)

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    8.2.53.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002eismissing.html000066400000000000000000000061301515050116100322230ustar00rootroot00000000000000 string.ismissing (Tablicious v0.4.4)

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    8.2.53.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.53.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002eisstring.html000066400000000000000000000057011515050116100320630ustar00rootroot00000000000000 string.isstring (Tablicious v0.4.4)

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    8.2.53.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.53.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.53.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.53.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/string_002ereverse.html000066400000000000000000000061631515050116100316770ustar00rootroot00000000000000 string.reverse (Tablicious v0.4.4)

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    8.2.53.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.53.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.53.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.53.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.53.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.53.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()
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    8.2.53.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.53.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.53.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.53.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table.html000066400000000000000000000260321515050116100273330ustar00rootroot00000000000000 table (Tablicious v0.4.4)

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

    Next: tableOuterFillValue, Previous: struct2table, Up: API Alphabetically   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tableOuterFillValue.html000066400000000000000000000045201515050116100321540ustar00rootroot00000000000000 tableOuterFillValue (Tablicious v0.4.4)

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    8.2.56 tableOuterFillValue

    Not documented

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002eaddvars.html000066400000000000000000000076761515050116100314430ustar00rootroot00000000000000 table.addvars (Tablicious v0.4.4)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002ecartesian.html000066400000000000000000000076451515050116100317640ustar00rootroot00000000000000 table.cartesian (Tablicious v0.4.4)

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002egroupby.html000066400000000000000000000074561515050116100315020ustar00rootroot00000000000000 table.groupby (Tablicious v0.4.4)

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002eheight.html000066400000000000000000000057261515050116100312610ustar00rootroot00000000000000 table.height (Tablicious v0.4.4)

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002eismember.html000066400000000000000000000061011515050116100316000ustar00rootroot00000000000000 table.ismember (Tablicious v0.4.4)

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002erepmat.html000066400000000000000000000064561515050116100313020ustar00rootroot00000000000000 table.repmat (Tablicious v0.4.4)

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002erestrict.html000066400000000000000000000105361515050116100316430ustar00rootroot00000000000000 table.restrict (Tablicious v0.4.4)

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002erowfun.html000066400000000000000000000156401515050116100313250ustar00rootroot00000000000000 table.rowfun (Tablicious v0.4.4)

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

    Next: table.rows2vars, Previous: table.restrict, Up: table   [Contents]

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002erows2vars.html000066400000000000000000000106551515050116100317560ustar00rootroot00000000000000 table.rows2vars (Tablicious v0.4.4)

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esemijoin.html000066400000000000000000000071741515050116100316250ustar00rootroot00000000000000 table.semijoin (Tablicious v0.4.4)

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    table_002esetDimensionNames.html000066400000000000000000000105071515050116100333500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html table.setDimensionNames (Tablicious v0.4.4)

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esetRowNames.html000066400000000000000000000061241515050116100322510ustar00rootroot00000000000000 table.setRowNames (Tablicious v0.4.4)

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

    table_002esetVariableNames.html000066400000000000000000000075711515050116100331570ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html table.setVariableNames (Tablicious v0.4.4)

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esetdiff.html000066400000000000000000000063311515050116100314260ustar00rootroot00000000000000 table.setdiff (Tablicious v0.4.4)

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esetvar.html000066400000000000000000000071451515050116100313120ustar00rootroot00000000000000 table.setvar (Tablicious v0.4.4)

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esetxor.html000066400000000000000000000065131515050116100313300ustar00rootroot00000000000000 table.setxor (Tablicious v0.4.4)

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esize.html000066400000000000000000000072131515050116100307540ustar00rootroot00000000000000 table.size (Tablicious v0.4.4)

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esplitapply.html000066400000000000000000000070651515050116100322100ustar00rootroot00000000000000 table.splitapply (Tablicious v0.4.4)

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esplitvars.html000066400000000000000000000075351515050116100320400ustar00rootroot00000000000000 table.splitvars (Tablicious v0.4.4)

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esqueeze.html000066400000000000000000000057121515050116100314650ustar00rootroot00000000000000 table.squeeze (Tablicious v0.4.4)

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002estack.html000066400000000000000000000072201515050116100311050ustar00rootroot00000000000000 table.stack (Tablicious v0.4.4)

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002esummary.html000066400000000000000000000067331515050116100315050ustar00rootroot00000000000000 table.summary (Tablicious v0.4.4)

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002etable.html000066400000000000000000000125031515050116100310670ustar00rootroot00000000000000 table.table (Tablicious v0.4.4)

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002etable2array.html000066400000000000000000000055511515050116100322150ustar00rootroot00000000000000 table.table2array (Tablicious v0.4.4)

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002etable2cell.html000066400000000000000000000062201515050116100320100ustar00rootroot00000000000000 table.table2cell (Tablicious v0.4.4)

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002etable2struct.html000066400000000000000000000071521515050116100324220ustar00rootroot00000000000000 table.table2struct (Tablicious v0.4.4)

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002eunion.html000066400000000000000000000063771515050116100311440ustar00rootroot00000000000000 table.union (Tablicious v0.4.4)

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002evarfun.html000066400000000000000000000102641515050116100313030ustar00rootroot00000000000000 table.varfun (Tablicious v0.4.4)

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002evarnames.html000066400000000000000000000064201515050116100316150ustar00rootroot00000000000000 table.varnames (Tablicious v0.4.4)

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002evertcat.html000066400000000000000000000063751515050116100314620ustar00rootroot00000000000000 table.vertcat (Tablicious v0.4.4)

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/table_002ewidth.html000066400000000000000000000053541515050116100311250ustar00rootroot00000000000000 table.width (Tablicious v0.4.4)

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tail.html000066400000000000000000000076131515050116100272010ustar00rootroot00000000000000 tail (Tablicious v0.4.4)

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tblish_002edataset.html000066400000000000000000000243761515050116100316360ustar00rootroot00000000000000 tblish.dataset (Tablicious v0.4.4)

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

    tblish_002edataset_002eAirPassengers.html000066400000000000000000000075601515050116100347700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.AirPassengers (Tablicious v0.4.4)

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.
    tblish_002edataset_002eBJsales.html000066400000000000000000000110421515050116100335330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.BJsales (Tablicious v0.4.4)

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eBOD.html000066400000000000000000000102641515050116100326210ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.BOD (Tablicious v0.4.4)

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eChickWeight.html000066400000000000000000000113071515050116100344050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.ChickWeight (Tablicious v0.4.4)

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");
    tblish_002edataset_002eDNase.html000066400000000000000000000111741515050116100332100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.DNase (Tablicious v0.4.4)

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});
    tblish_002edataset_002eEuStockMarkets.html000066400000000000000000000111351515050116100351170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.EuStockMarkets (Tablicious v0.4.4)

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
    tblish_002edataset_002eFormaldehyde.html000066400000000000000000000114211515050116100346140ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Formaldehyde (Tablicious v0.4.4)

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot
    tblish_002edataset_002eHairEyeColor.html000066400000000000000000000151061515050116100345420ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.HairEyeColor (Tablicious v0.4.4)

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.Harman23cor, Previous: tblish.dataset.freeny, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eHarman23cor.html000066400000000000000000000102741515050116100342750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Harman23cor (Tablicious v0.4.4)

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eHarman74cor.html000066400000000000000000000103511515050116100342770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Harman74cor (Tablicious v0.4.4)

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave
    tblish_002edataset_002eIndometh.html000066400000000000000000000110071515050116100337600ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Indometh (Tablicious v0.4.4)

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    tblish_002edataset_002eInsectSprays.html000066400000000000000000000104461515050116100346460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.InsectSprays (Tablicious v0.4.4)

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots
    tblish_002edataset_002eJohnsonJohnson.html000066400000000000000000000102601515050116100351660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.JohnsonJohnson (Tablicious v0.4.4)

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.
    tblish_002edataset_002eLakeHuron.html000066400000000000000000000104761515050116100341120ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.LakeHuron (Tablicious v0.4.4)

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
    tblish_002edataset_002eLifeCycleSavings.html000066400000000000000000000126301515050116100354060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.LifeCycleSavings (Tablicious v0.4.4)

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line
    tblish_002edataset_002eLoblolly.html000066400000000000000000000111041515050116100337770ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Loblolly (Tablicious v0.4.4)

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve
    tblish_002edataset_002eNile.html000066400000000000000000000112171515050116100331030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Nile (Tablicious v0.4.4)

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave
    tblish_002edataset_002eOrange.html000066400000000000000000000115241515050116100334300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Orange (Tablicious v0.4.4)

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model
    tblish_002edataset_002eOrchardSprays.html000066400000000000000000000124761515050116100350100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.OrchardSprays (Tablicious v0.4.4)

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);
    tblish_002edataset_002ePlantGrowth.html000066400000000000000000000102201515050116100344560ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.PlantGrowth (Tablicious v0.4.4)

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave
    tblish_002edataset_002ePuromycin.html000066400000000000000000000124731515050116100342060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Puromycin (Tablicious v0.4.4)

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave
    tblish_002edataset_002eTheoph.html000066400000000000000000000142621515050116100334460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Theoph (Tablicious v0.4.4)

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

    Next: tblish.dataset.Titanic, Previous: tblish.dataset.swiss, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eTitanic.html000066400000000000000000000146051515050116100336130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.Titanic (Tablicious v0.4.4)

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

    Next: tblish.dataset.ToothGrowth, Previous: tblish.dataset.Theoph, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eToothGrowth.html000066400000000000000000000115331515050116100345050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.ToothGrowth (Tablicious v0.4.4)

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002eUCBAdmissions.html000066400000000000000000000141011515050116100346520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.UCBAdmissions (Tablicious v0.4.4)

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

    Next: tblish.dataset.UKDriverDeaths, Previous: tblish.dataset.trees, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKDriverDeaths.html000066400000000000000000000147451515050116100350510ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.UKDriverDeaths (Tablicious v0.4.4)

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

    Next: tblish.dataset.UKgas, Previous: tblish.dataset.UCBAdmissions, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUKLungDeaths.html000066400000000000000000000111001515050116100345010ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.UKLungDeaths (Tablicious v0.4.4)

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
    tblish_002edataset_002eUKgas.html000066400000000000000000000103711515050116100332260ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.UKgas (Tablicious v0.4.4)

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
    tblish_002edataset_002eUSAccDeaths.html000066400000000000000000000100551515050116100343020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.USAccDeaths (Tablicious v0.4.4)

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;
    tblish_002edataset_002eUSArrests.html000066400000000000000000000137571515050116100341220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.USArrests (Tablicious v0.4.4)

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

    Next: tblish.dataset.USJudgeRatings, Previous: tblish.dataset.USAccDeaths, Up: tblish.dataset   [Contents]

    tblish_002edataset_002eUSJudgeRatings.html000066400000000000000000000116251515050116100350550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.USJudgeRatings (Tablicious v0.4.4)

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
    tblish_002edataset_002eUSPersonalExpenditure.html000066400000000000000000000111101515050116100364540ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.USPersonalExpenditure (Tablicious v0.4.4)

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.
    tblish_002edataset_002eVADeaths.html000066400000000000000000000113271515050116100336550ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.VADeaths (Tablicious v0.4.4)

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave
    tblish_002edataset_002eWWWusage.html000066400000000000000000000103551515050116100337270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.WWWusage (Tablicious v0.4.4)

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eWorldPhones.html000066400000000000000000000105161515050116100344610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.WorldPhones (Tablicious v0.4.4)

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave
    tblish_002edataset_002eairmiles.html000066400000000000000000000072321515050116100340230ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.airmiles (Tablicious v0.4.4)

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
    tblish_002edataset_002eairquality.html000066400000000000000000000125541515050116100344050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.airquality (Tablicious v0.4.4)

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor
    tblish_002edataset_002eanscombe.html000066400000000000000000000121471515050116100340060ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.anscombe (Tablicious v0.4.4)

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);
    tblish_002edataset_002eattenu.html000066400000000000000000000114521515050116100335150ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.attenu (Tablicious v0.4.4)

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.
    tblish_002edataset_002eattitude.html000066400000000000000000000111041515050116100340320ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.attitude (Tablicious v0.4.4)

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing
    tblish_002edataset_002eaustres.html000066400000000000000000000102231515050116100336760ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.austres (Tablicious v0.4.4)

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
    tblish_002edataset_002ebeavers.html000066400000000000000000000106451515050116100336470ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.beavers (Tablicious v0.4.4)

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.
    tblish_002edataset_002ecars.html000066400000000000000000000116731515050116100331520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.cars (Tablicious v0.4.4)

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor
    tblish_002edataset_002echickwts.html000066400000000000000000000114241515050116100340330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.chickwts (Tablicious v0.4.4)

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova
    tblish_002edataset_002eco2.html000066400000000000000000000116671515050116100327100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.co2 (Tablicious v0.4.4)

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
    tblish_002edataset_002ecrimtab.html000066400000000000000000000164021515050116100336360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.crimtab (Tablicious v0.4.4)

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

    Next: tblish.dataset.cupcake, Previous: tblish.dataset.co2, Up: tblish.dataset   [Contents]

    tblish_002edataset_002ecupcake.html000066400000000000000000000104751515050116100336340ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.cupcake (Tablicious v0.4.4)

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");
    tblish_002edataset_002ediscoveries.html000066400000000000000000000106231515050116100345330ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.discoveries (Tablicious v0.4.4)

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
    tblish_002edataset_002eesoph.html000066400000000000000000000106651515050116100333400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.esoph (Tablicious v0.4.4)

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.
    tblish_002edataset_002eeuro.html000066400000000000000000000116601515050116100331700ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.euro (Tablicious v0.4.4)

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
    tblish_002edataset_002eeurodist.html000066400000000000000000000101151515050116100340460ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.eurodist (Tablicious v0.4.4)

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

    tblish_002edataset_002efaithful.html000066400000000000000000000121141515050116100340130ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.faithful (Tablicious v0.4.4)

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
    tblish_002edataset_002efreeny.html000066400000000000000000000121071515050116100335030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.freeny (Tablicious v0.4.4)

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot
    tblish_002edataset_002einfert.html000066400000000000000000000116041515050116100335030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.infert (Tablicious v0.4.4)

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave
    tblish_002edataset_002eiris.html000066400000000000000000000121571515050116100331660ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.iris (Tablicious v0.4.4)

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002eislands.html000066400000000000000000000104021515050116100336440ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.islands (Tablicious v0.4.4)

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tblish_002edataset_002elh.html000066400000000000000000000101751515050116100327000ustar00rootroot00000000000000 tblish.dataset.lh (Tablicious v0.4.4)

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
    tblish_002edataset_002elongley.html000066400000000000000000000115431515050116100336670ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.longley (Tablicious v0.4.4)

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot
    tblish_002edataset_002elynx.html000066400000000000000000000113371515050116100332110ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.lynx (Tablicious v0.4.4)

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
    tblish_002edataset_002emorley.html000066400000000000000000000121361515050116100335240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.morley (Tablicious v0.4.4)

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave
    tblish_002edataset_002emtcars.html000066400000000000000000000124301515050116100335030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.mtcars (Tablicious v0.4.4)

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R
    tblish_002edataset_002enhtemp.html000066400000000000000000000105011515050116100335020ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.nhtemp (Tablicious v0.4.4)

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
    tblish_002edataset_002enottem.html000066400000000000000000000101431515050116100335170ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.nottem (Tablicious v0.4.4)

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002enpk.html000066400000000000000000000113061515050116100330030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.npk (Tablicious v0.4.4)

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave
    tblish_002edataset_002eoccupationalStatus.html000066400000000000000000000103651515050116100361040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.occupationalStatus (Tablicious v0.4.4)

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002eprecip.html000066400000000000000000000103671515050116100335030ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.precip (Tablicious v0.4.4)

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave
    tblish_002edataset_002epresidents.html000066400000000000000000000111731515050116100343750ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.presidents (Tablicious v0.4.4)

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
    tblish_002edataset_002epressure.html000066400000000000000000000112321515050116100340610ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.pressure (Tablicious v0.4.4)

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
    tblish_002edataset_002equakes.html000066400000000000000000000120531515050116100335040ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.quakes (Tablicious v0.4.4)

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here
    tblish_002edataset_002erandu.html000066400000000000000000000111331515050116100333220ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.randu (Tablicious v0.4.4)

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;
    tblish_002edataset_002erivers.html000066400000000000000000000102651515050116100335300ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.rivers (Tablicious v0.4.4)

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)
    tblish_002edataset_002erock.html000066400000000000000000000110461515050116100331520ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.rock (Tablicious v0.4.4)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
    tblish_002edataset_002esleep.html000066400000000000000000000113071515050116100333240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.sleep (Tablicious v0.4.4)

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave
    tblish_002edataset_002estackloss.html000066400000000000000000000141761515050116100342310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.stackloss (Tablicious v0.4.4)

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

    Next: tblish.dataset.state, Previous: tblish.dataset.sleep, Up: tblish.dataset   [Contents]

    tblish_002edataset_002estate.html000066400000000000000000000130031515050116100333270ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.state (Tablicious v0.4.4)

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;
    tblish_002edataset_002esunspot_005fmonth.html000066400000000000000000000104541515050116100355310ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.sunspot_month (Tablicious v0.4.4)

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;
    tblish_002edataset_002esunspot_005fyear.html000066400000000000000000000102141515050116100353360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.sunspot_year (Tablicious v0.4.4)

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
    tblish_002edataset_002esunspots.html000066400000000000000000000105131515050116100341100ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.sunspots (Tablicious v0.4.4)

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
    tblish_002edataset_002eswiss.html000066400000000000000000000140731515050116100333670ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.swiss (Tablicious v0.4.4)

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

    Next: tblish.dataset.Theoph, Previous: tblish.dataset.sunspots, Up: tblish.dataset   [Contents]

    tblish_002edataset_002etreering.html000066400000000000000000000111751515050116100340360ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.treering (Tablicious v0.4.4)

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;
    tblish_002edataset_002etrees.html000066400000000000000000000113451515050116100333400ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.trees (Tablicious v0.4.4)

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model
    tblish_002edataset_002euspop.html000066400000000000000000000102461515050116100333630ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.uspop (Tablicious v0.4.4)

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
    tblish_002edataset_002evolcano.html000066400000000000000000000112151515050116100336530ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.volcano (Tablicious v0.4.4)

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.
    tblish_002edataset_002ewarpbreaks.html000066400000000000000000000111771515050116100343620ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.warpbreaks (Tablicious v0.4.4)

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave
    tblish_002edataset_002ewomen.html000066400000000000000000000113471515050116100333450ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.women (Tablicious v0.4.4)

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
    tblish_002edataset_002ezCO2.html000066400000000000000000000104531515050116100327720ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.dataset.zCO2 (Tablicious v0.4.4)

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave
    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tblish_002edatasets.html000066400000000000000000000064321515050116100320120ustar00rootroot00000000000000 tblish.datasets (Tablicious v0.4.4)

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

    tblish_002eevalWithTableVars.html000066400000000000000000000075671515050116100335240ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.evalWithTableVars (Tablicious v0.4.4)

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

    tblish_002eexamples_002eSpDb.html000066400000000000000000000077541515050116100332500ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.examples.SpDb (Tablicious v0.4.4)

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

    tblish_002eexamples_002ecoplot.html000066400000000000000000000150421515050116100337050ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.examples.coplot (Tablicious v0.4.4)

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

    Next: tblish.examples.plot_pairs, Previous: tblish.evalWithTableVars, Up: API Alphabetically   [Contents]

    tblish_002eexamples_002eplot_005fpairs.html000066400000000000000000000117571515050116100351650ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.examples.plot_pairs (Tablicious v0.4.4)

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/tblish_002esizeof2.html000066400000000000000000000124311515050116100315570ustar00rootroot00000000000000 tblish.sizeof2 (Tablicious v0.4.4)

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

    Next: tblish.table.grpstats, Previous: tblish.examples.SpDb, Up: API Alphabetically   [Contents]

    tblish_002etable_002egrpstats.html000066400000000000000000000104771515050116100335340ustar00rootroot00000000000000apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html tblish.table.grpstats (Tablicious v0.4.4)

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/timezones.html000066400000000000000000000077721515050116100302730ustar00rootroot00000000000000 timezones (Tablicious v0.4.4)

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/todatetime.html000066400000000000000000000070101515050116100303760ustar00rootroot00000000000000 todatetime (Tablicious v0.4.4)

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/vartype.html000066400000000000000000000064711515050116100277430ustar00rootroot00000000000000 vartype (Tablicious v0.4.4)

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/vecfun.html000066400000000000000000000066301515050116100275340ustar00rootroot00000000000000 vecfun (Tablicious v0.4.4)

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/html/years.html000066400000000000000000000064311515050116100273700ustar00rootroot00000000000000 years (Tablicious v0.4.4)

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    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

    apjanke-octave-tablicious-8a1b10f/docs/release/v0.4.4/user-guide/tablicious.html000066400000000000000000027037261515050116100274540ustar00rootroot00000000000000 Tablicious v0.4.4

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    Tablicious for GNU Octave

    This manual is for Tablicious, version 0.4.4.

    Table of Contents

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    1 Introduction

    Time is an illusion. Lunchtime doubly so.

    — Douglas Adams

    This is the manual for the Tablicious package version 0.4.4 for GNU Octave.

    Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a table class with support for filtering and join operations; datetime, duration, and related classes; Missing Data support; string and categorical data types; and other miscellaneous things.

    This document is a work in progress. You are invited to help improve it and submit patches.

    Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

    Tablicious was written by Andrew Janke <>. Support can be found on the Tablicious project GitHub page.

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    2 Getting Started

    The easiest way to obtain Tablicious is by using Octave’s pkg package manager. To install the development prerelease of Tablicious, run this in Octave: 

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.4/tablicious-0.4.4.tar.gz

    (Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

    For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.

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    3 Table Representation

    Tablicious provides the table class for representing tabular data.

    A table is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

    A table is composed of multiple “variables”, each with a name, which all have the same number of rows. (A table variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

    Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a table is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

    Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using table arrays. It’s like having your own in-memory SQL engine.

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    3.1 Table Construction

    There are two main ways to construct a table array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a table.

    To build an array from multiple variables, use the table(…) constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new table object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the 'VariableNames' option.

    To convert a tabular-organized array of another type into a table, use the conversion functions like array2table, struct2table and cell2table. array2table and cell2table take each column of the input array and turn it into a separate table variable in the resulting table. struct2table takes the fields of a struct and puts them into table variables.

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    3.2 Tables vs SQL

    Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave table operations.

    In this table, t is a variable holding a table array, and ix is some indexing expression.

    SQLRelationalOctave table
    SELECTPROJECTsubsetvars, t(:,ix)
    WHERERESTRICTsubsetrows, t(ix,:)
    INNER JOINJOINinnerjoin
    OUTER JOINOUTER JOINouterjoin
    FROM table1, table2, …Cartesian productcartesian
    GROUP BYSUMMARIZEgroupby
    DISTINCT(automatic)unique(t)

    Note that there is one big difference between relational algebra and SQL & Octave table: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit DISTINCT/unique() on its results, and there‘s no ORDER BY/sort(). This is not the case in SQL or Octave table.

    Note for users coming from Matlab: Matlab does not provide a general groupby function. Instead, you have to variously use rowfun, grpstats, groupsummary, and manual code to accomplish “group by” operations.

    Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

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    4 Date and Time Representation

    Tablicious provides the datetime class for representing points in time.

    There’s also duration and calendarDuration for representing periods or durations of time. Like vector quantities along the time line, as opposed to datetime being a point along the time line.

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    4.1 datetime Class

    A datetime is an array object that represents points in time in the familiar Gregorian calendar.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    The underlying representation is that of a datenum (a double containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a datetime inclues an optional TimeZone property indicating what time zone the datetimes are in.

    So, basically, a datetime is an object wrapper around a datenum array, plus time zone support.

    Up: datetime Class   [Contents]

    4.1.1 Datenum Compatibility

    While the underlying data representation of datetime is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

    This is because of the signature of the datetime constructor. When combining objects and primitive types like double, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for datetime does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

    To combine datetimes with datenums, you can convert the datenums to datetimes by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you can convert the datetimes to datenums by accessing its dnums field with x.dnums.

    Examples: 

    dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
    ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
    ⇒ 04-Mar-2011   01-Jan-2017

    Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums do not carry time zone information.

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    4.2 Time Zones

    Tablicious has support for representing dates in time zones and for converting between time zones.

    A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone associated with it. This is represented by an empty TimeZone property on the datetime object. A zoneless datetime represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

    A zoned datetime is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone property. A zoned datetime represents the local time in that time zone.

    By default, the datetime constructor creates unzoned datetimes. To make a zoned datetime, either pass the 'TimeZone' option to the constructor, or set the TimeZone property after object creation. Setting the TimeZone property on a zoneless datetime declares that it’s a local time in that time zone. Setting the TimeZone property on a zoned datetime turns it back into a zoneless datetime without changing the local time it represents.

    You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned datetimes. 

    % Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
    ⇒  04-Mar-2011 06:00:00

% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    ⇒  04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
    ⇒  04-Mar-2011 06:00:00 America/New_York

% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
    ⇒  04-Mar-2011 05:00:00 America/Chicago

    When you combine two zoned datetimes via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input. 

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
    ⇒ 03:00:00

    You cannot combine a zoned and an unzoned datetime. This results in an error being raised.

    Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

    Up: Time Zones   [Contents]

    4.2.1 Defined Time Zones

    Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

    You can use the timezones function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

    Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

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    4.3 Durations

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    4.3.1 duration Class

    A duration represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

    A duration has a resolution of about a nanosecond for typical dates. The underlying representation is a double representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

    You can add or subtract a duration to a datetime to get another datetime. You can also add or subtract durations to each other.

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    4.3.2 calendarDuration Class

    A calendarDuration represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A calendarDuration does arithmetic with "whole" calendar periods.

    calendarDurations and durations cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow durations to be interpreted as numbers of days when combined with calendarDurations.) 

    d = datetime('2011-03-04 00:00:00')
    ⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
    ⇒ 1y 3mo
d2 = d + cdur
    ⇒ 04-Jun-2012

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    5 Validation Functions

    Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

    These validation functions are named following the pattern mustBeXxx, where Xxx is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

    The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

    Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

    When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with inputname(), so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested. 

    % Validation of a simple variable does not need a label
mustBeScalar (x);
% Validation of a field or property reference does need a label
mustBeScalar (this.foo, 'this.foo');

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    6 Example Data Sets

    Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the tblish.datasets and tblish.dataset classes.

    To see a list of the available data sets, run tblish.datasets.list(). Then to load one of the example data sets, run tblish.datasets.load('examplename'). For example: 

    tblish.datasets.list
t = tblish.datasets.load('cupcake')

    You can also load it by calling tblish.dataset.<name>. This does the same thing. For example: 

    t = tblish.dataset.cupcake

    When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

    Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the doc command on tblish.dataset.<name>, where <name> is the name of the data set.

    For example: 

    doc tblish.dataset.cupcake

    (The command help tblish.dataset.<name> ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s help command.)

    Up: Example Data Sets   [Contents]

    6.1 Data Sets from R

    Many of Tablicious’ example data sets are based on the example datasets found in R’s datasets package. R can be found at https://www.r-project.org/, and documentation for its datasets is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

    Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

    Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

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    7 Missing Functionality

    Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

    • File I/O like readtable() and writetable()
    • summary() categorical
    • Assignment to table variables using .-indexing
    • timetable
    • POSIX time zone support for years outside the IANA time zone database coverage
    • Week-of-year (ISO calendar) calculations
    • Various 'ConvertFrom' forms for datetime and duration constructors
    • Support for LDML formatting for datetime
    • Various functions:
      • between
      • caldiff
      • dateshift
      • week
    • isdst, isweekend
    • calendarDuration.split
    • duration.Format support
    • Moving window methods in fillmissing
    • UTCOffset and DSTOffset fields in the output of timezones()
    • Plotting support

    It is the author’s hope that many these will be implemented some day.

    These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

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    8 API Reference

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    8.1 API by Category

    8.1.1 Tables

    table

    Tabular data array containing multiple columnar variables.

    See table.

    array2table

    Convert an array to a table.

    See array2table.

    cell2table

    Convert a cell array to a table.

    See cell2table.

    struct2table

    Convert struct to a table.

    See struct2table.

    tableOuterFillValue

    See tableOuterFillValue.

    vartype

    Filter by variable type for use in suscripting.

    See vartype.

    istable

    True if input is a ‘table’ array or other table-like type, false otherwise.

    See istable.

    istimetable

    True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

    See istimetable.

    istabular

    True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

    See istabular.

    tblish.evalWithTableVars

    Evaluate an expression against a table array’s variables.

    See tblish.evalWithTableVars.

    tblish.table.grpstats

    Statistics by group for a table array.

    See tblish.table.grpstats.

    8.1.2 Strings and Categoricals

    string

    A string array of Unicode strings.

    See string.

    NaS

    “Not-a-String".

    See NaS.

    contains

    Test if strings contain a pattern.

    See contains.

    dispstrs

    Display strings for array.

    See dispstrs.

    categorical

    Categorical variable array.

    See categorical.

    iscategorical

    True if input is a ‘categorical’ array, false otherwise.

    See iscategorical.

    NaC

    “Not-a-Categorical".

    See NaC.

    discretize

    Group data into discrete bins or categories.

    See discretize.

    8.1.3 Dates and Times

    datetime

    Represents points in time using the Gregorian calendar.

    See datetime.

    NaT

    “Not-a-Time”.

    See NaT.

    todatetime

    Convert input to a Tablicious datetime array, with convenient interface.

    See todatetime.

    localdate

    Represents a complete day using the Gregorian calendar.

    See localdate.

    isdatetime

    True if input is a ‘datetime’ array, false otherwise.

    See isdatetime.

    calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

    See calendarDuration.

    iscalendarduration

    True if input is a ‘calendarDuration’ array, false otherwise.

    See iscalendarduration.

    calmonths

    Create a ‘calendarDuration’ that is a given number of calendar months long.

    See calmonths.

    calyears

    Construct a ‘calendarDuration’ a given number of years long.

    See calyears.

    days

    Duration in days.

    See days.

    duration

    Represents durations or periods of time as an amount of fixed-length time (i.e.

    See duration.

    hours

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See hours.

    isduration

    True if input is a ‘duration’ array, false otherwise.

    See isduration.

    milliseconds

    Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

    See milliseconds.

    minutes

    Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

    See minutes.

    seconds

    Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

    See seconds.

    timezones

    List all the time zones defined on this system.

    See timezones.

    years

    Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

    See years.

    8.1.4 Missing Data

    missing

    Generic auto-converting missing value.

    See missing.

    isnanny

    Test if elements are NaN or NaN-like

    See isnanny.

    eqn

    Determine element-wise equality, treating NaNs as equal

    See eqn.

    8.1.5 Validation Functions

    mustBeA

    See mustBeA.

    mustBeCellstr

    See mustBeCellstr.

    mustBeCharvec

    See mustBeCharvec.

    mustBeFinite

    See mustBeFinite.

    mustBeInteger

    See mustBeInteger.

    mustBeMember

    See mustBeMember.

    mustBeNonempty

    See mustBeNonempty.

    mustBeNumeric

    See mustBeNumeric.

    mustBeReal

    See mustBeReal.

    mustBeSameSize

    See mustBeSameSize.

    mustBeScalar

    See mustBeScalar.

    mustBeScalarLogical

    See mustBeScalarLogical.

    mustBeVector

    See mustBeVector.

    8.1.6 Miscellaneous

    colvecfun

    Apply a function to column vectors in array.

    See colvecfun.

    dispstrs

    Display strings for array.

    See dispstrs.

    head

    Get first K rows of an array.

    See head.

    isfile

    See isfile.

    isfolder

    See isfolder.

    pp

    Alias for prettyprint, for interactive use.

    See pp.

    scalarexpand

    Expand scalar inputs to match size of non-scalar inputs.

    See scalarexpand.

    size2str

    Format an array size for display.

    See size2str.

    splitapply

    Split data into groups and apply function.

    See splitapply.

    tail

    Get last K rows of an array.

    See tail.

    vecfun

    Apply function to vectors in array along arbitrary dimension.

    See vecfun.

    tblish.sizeof2

    Approximate size of an array in bytes, with object support.

    See tblish.sizeof2.

    8.1.7 Example Datasets

    tblish.datasets

    Example dataset collection.

    See tblish.datasets.

    tblish.dataset

    The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

    See tblish.dataset.

    8.1.8 Example Code

    tblish.examples.coplot

    Conditioning plot.

    See tblish.examples.coplot.

    tblish.examples.plot_pairs

    Plot pairs of variables against each other.

    See tblish.examples.plot_pairs.

    tblish.examples.SpDb

    The classic Suppliers-Parts example database.

    See tblish.examples.SpDb.

    8.1.9 Uncategorized

    caldays

    Create a ‘calendarDuration’ that is a given number of calendar days long.

    See caldays.

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    8.2 API Alphabetically

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    8.2.1 array2table

    Function: out = array2table (c)
    Function: out = array2table (…, 'VariableNames', VariableNames)
    Function: out = array2table (…, 'RowNames', RowNames)

    Convert an array to a table.

    Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

    You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use cell2table instead, columns of the cell array which can be condensed into primitive arrays will be. With array2table, they won’t be.

    See also: cell2table, table, struct2table

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    8.2.2 caldays

    Function File: out = caldays (x)

    Create a calendarDuration that is a given number of calendar days long.

    Input x is a numeric array specifying the number of calendar days.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, 0, x).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.3 calendarDuration

    Class: calendarDuration

    Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

    Instance Variable of calendarDuration: char Format

    The format to display this calendarDuration in. Currently unsupported.

    This is a single value that applies to the whole array.

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    8.2.3.1 calendarDuration.calendarDuration

    Constructor: obj = calendarDuration ()

    Constructs a new scalar calendarDuration of zero elapsed time.

    Constructor: obj = calendarDuration (Y, M, D)
    Constructor: obj = calendarDuration (Y, M, D, H, MI, S)

    Constructs new calendarDuration arrays based on input values.

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    8.2.3.2 calendarDuration.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.3.3 calendarDuration.ismissing

    Method: out = ismissing (obj)

    True if input elements are missing.

    This is equivalent to ismissing.

    Returns logical array the same size as obj.

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    8.2.3.4 calendarDuration.isnan

    Method: out = isnan (obj)

    True if input elements are NaN.

    This is equivalent to ismissing, and is provided for compatibility and polymorphic programming purposes.

    Returns logical array the same size as obj.

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    8.2.3.5 calendarDuration.minus

    Method: out = times (A, B)

    Subtraction: Subtracts one calendarDuration from another.

    Returns a calendarDuration.

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    8.2.3.6 calendarDuration.mtimes

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor.

    This does not do true matrix multiplication, so at least one of the input arguments must be scalar.

    Returns a calendarDuration.

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    8.2.3.7 calendarDuration.plus

    Method: out = plus (obj, B)

    Addition: add to a calendarDuration.

    All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

    B may be a calendarDuration, duration, or numeric. If B is numeric, it is converted to a calendarDuration using caldays(B).

    Returns a calendarDuration.

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    8.2.3.8 calendarDuration.times

    Method: out = times (obj, B)

    Multiplication: Multiplies a calendarDuration by a numeric factor B.

    Returns a calendarDuration.

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    8.2.3.9 calendarDuration.uminus

    Method: out = uminus (obj)

    Unary minus. Negates the sign of obj, which is equivalent to negating each of its components.

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    8.2.4 calmonths

    Function File: out = calmonths (x)

    Create a calendarDuration that is a given number of calendar months long.

    Input x is a numeric array specifying the number of calendar months.

    This is a shorthand alternative to calling the calendarDuration constructor with calendarDuration(0, x, 0).

    Returns a new calendarDuration object of the same size as x.

    See calendarDuration.

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    8.2.5 calyears

    Function: out = calyears (x)

    Construct a calendarDuration a given number of years long.

    This is a shorthand for calling calendarDuration(x, 0, 0).

    See calendarDuration.

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    8.2.6 categorical

    Class: categorical

    Categorical variable array.

    A categorical array represents an array of values of a categorical variable. Each categorical array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

    In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a NaN value.

    This class is not fully implemented yet. Missing stuff:

    • gt, ge, lt, le
    • Ordinal support in general
    • countcats
    • summary
    Instance Variable of categorical: uint16 code

    The numeric codes of the array element values. These are indexes into the cats category list.

    This is a planar property.

    Instance Variable of categorical: logical tfMissing

    A logical mask indicating whether each element of the array is missing (that is, undefined).

    This is a planar property.

    Instance Variable of categorical: cellstr cats

    The names of the categories in this array. This is the list into which the code values are indexes.

    Instance Variable of categorical: scalar_logical isOrdinal

    A scalar logical indicating whether the categories in this array have an ordinal relationship.

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    8.2.6.1 categorical.addcats

    Method: out = addcats (obj, newcats)

    Add categories to categorical array.

    Adds the specified categories to obj, without changing any of its values.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.2 categorical.categorical

    Constructor: obj = categorical ()

    Constructs a new scalar categorical whose value is undefined.

    Constructor: obj = categorical (vals)
    Constructor: obj = categorical (vals, valueset)
    Constructor: obj = categorical (vals, valueset, category_names)
    Constructor: obj = categorical (…, 'Ordinal', Ordinal)
    Constructor: obj = categorical (…, 'Protected', Protected)

    Constructs a new categorical array from the given values.

    vals is the array of values to convert to categoricals.

    valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

    category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

    Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

    Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

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    8.2.6.3 categorical.categories

    Method: out = categories (obj)

    Get a list of the categories in obj.

    Gets a list of the categories in obj, identified by their category names.

    Returns a cellstr column vector.

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    8.2.6.4 categorical.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr array. The strings will be the category names for corresponding values, or '' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.5 categorical.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj. The display strings are either the category string, or '<undefined>' for undefined values.

    Returns a cellstr array the same size as obj.

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    8.2.6.6 categorical.double

    Method: out = double (obj)

    Convert to double array, by getting the underlying code values.

    Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or NaN for undefined values.

    The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

    Returns a double array the same size as obj.

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    8.2.6.7 categorical.iscategory

    Method: out = iscategory (obj, catnames)

    Test whether input is a category on a categorical array.

    catnames is a cellstr listing the category names to check against obj.

    Returns a logical array the same size as catnames.

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    8.2.6.8 categorical.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing.

    For categorical arrays, undefined elements are considered to be missing.

    Returns a logical array the same size as obj.

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    8.2.6.9 categorical.isnanny

    Method: out = isnanny (obj)

    Test whethere elements are NaN-ish.

    Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

    Returns a logical array the same size as obj.

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    8.2.6.10 categorical.isordinal

    Method: out = isordinal (obj)

    Whether obj is ordinal.

    Returns true if obj is ordinal (as determined by its IsOrdinal property), and false otherwise.

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    8.2.6.11 categorical.isundefined

    Method: out = isundefined (obj)

    Test whether elements are undefined.

    Checks whether each element in obj is undefined. "Undefined" is a special value defined by categorical. It is equivalent to a NaN or a missing value.

    Returns a logical array the same size as obj.

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    8.2.6.12 categorical.mergecats

    Method: out = mergecats (obj, oldcats)
    Method: out = mergecats (obj, oldcats, newcat)

    Merge multiple categories.

    Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

    If newcat is not provided, all of odcats are merged into oldcats{1}.

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    8.2.6.13 categorical.missing

    Static Method: out = categorical.missing ()
    Static Method: out = categorical.missing (sz)

    Create an array of missing (undefined) categoricals.

    Creates a categorical array whose elements are all missing (<undefined>).

    This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

    Returns a categorical array.

    See also: categorical.undefined

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    8.2.6.14 categorical.removecats

    Method: out = removecats (obj)

    Removes all unused categories from obj. This is equivalent to out = squeezecats (obj).

    Method: out = removecats (obj, oldcats)

    Remove categories from categorical array.

    Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

    newcats is a cellstr listing the category names to add to obj.

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    8.2.6.15 categorical.renamecats

    Method: out = renamecats (obj, newcats)
    Method: out = renamecats (obj, oldcats, newcats)

    Rename categories.

    Renames some or all of the categories in obj, without changing any of its values.

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    8.2.6.16 categorical.reordercats

    Method: out = reordercats (obj)
    Method: out = reordercats (obj, newcats)

    Reorder categories.

    Reorders the categories in obj to match newcats.

    newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

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    8.2.6.17 categorical.setcats

    Method: out = setcats (obj, newcats)

    Set categories for categorical array.

    Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

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    8.2.6.18 categorical.squeezecats

    Method: out = squeezecats (obj)

    Remove unused categories.

    Removes all categories which have no corresponding values in obj’s elements.

    This is currently unimplemented.

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    8.2.6.19 categorical.string

    Method: out = string (obj)

    Convert to string array.

    Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

    Returns a string array the same size as obj.

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    8.2.6.20 categorical.summary

    Method: summary (obj)

    Display summary of array’s values.

    Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

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    8.2.6.21 categorical.undefined

    Static Method: out = categorical.undefined ()
    Static Method: out = categorical.undefined (sz)

    Create an array of undefined categoricals.

    Creates a categorical array whose elements are all <undefined>.

    sz is the size of the array to create. If omitted or empty, creates a scalar.

    Returns a categorical array.

    See also: categorical.missing

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    8.2.7 cell2table

    Function: out = cell2table (c)
    Function: out = cell2table (…, 'VariableNames', VariableNames)
    Function: out = cell2table (…, 'RowNames', RowNames)

    Convert a cell array to a table.

    Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of cat-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

    See also: array2table, table, struct2table

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    8.2.8 colvecfun

    Function: out = colvecfun (fcn, x)

    Apply a function to column vectors in array.

    Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

    fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

    Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

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    8.2.9 contains

    Function: out = colvecfun (str, pattern)
    Function: out = colvecfun (…, 'IgnoreCase', IgnoreCase)

    Test if strings contain a pattern.

    Tests whether the given strings contain the given pattern(s).

    str (char, cellstr, or string) is a list of strings to compare against pattern.

    pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

    Returns a logical array of the same size as the string array represented by str.

    See also: startsWith, endsWith

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    8.2.10 datetime

    Class: datetime

    Represents points in time using the Gregorian calendar.

    The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

    A datetime array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

    This is an attempt to reproduce the functionality of Matlab’s datetime. It also contains some Octave-specific extensions.

    Instance Variable of datetime: double dnums

    The underlying datenums that represent the points in time. These are always in UTC.

    This is a planar property: the size of dnums is the same size as the containing datetime array object.

    Instance Variable of datetime: char TimeZone

    The time zone this datetime array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

    Setting the TimeZone of a datetime array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

    Instance Variable of datetime: char Format

    The format to display this datetime in. Currently unsupported.

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    8.2.10.1 datetime.colon

    Method: out = colon (lo, hi)
    Method: out = colon (hi, inc, hi)

    Generate a sequence of uniformly-spaced values.

    This method implements the behavior for the colon operator (lo:hi or lo:inc:hi calls) for the datetime type.

    "Uniformly-spaced" means uniform in terms of the duration or calendarDuration value used as the increment. Calendar durations are not necessarily equal-sized in terms of the amount of actual time contained in them, so when using a calendarDuration as the increment, the resulting vector may not be, and often will not be, uniformly spaced in terms of actual (non-"calendar") time.

    The inc argument may be a duration, calendarDuration, or numeric. Numerics are taken to be a number of days (uniform-size days, not calendar days), and are converted to a duration object with duration.ofDays (inc). The default value for inc, used in the two-arg lo:hi is 1, that is, 1 day of exactly 24 hours.

    Returns a datetime vector.

    WARNING: There are issues with negative-direction sequences. When hi is less than lo, this will always produce an empty array, even if inc is a negative value. And there are cases with calendarDurations that have both Months, Days and/or Times with mixed signs that values may move in the "wrong" direction, or produce an infinite loop. If these problem cases can be correctly identified, but not corrected, those cases may raise an error future releases of Tablicious.

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    8.2.10.2 datetime.convertDatenumTimeZone

    Static Method: out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId)

    Convert a datenum from one time zone to another.

    dnum is a datenum array to convert.

    fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

    toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

    Returns a datenum array the same size as dnum.

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    8.2.10.3 datetime.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent the same local time.

    Returns double array of same size as this.

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    8.2.10.4 datetime.datenum2posix

    Static Method: out = datetime.datenum2posix (dnums)

    Converts Octave datenums to Unix dates.

    The input datenums are assumed to be in UTC.

    Returns a double, which may have fractional seconds.

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    8.2.10.5 datetime.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.10.6 datetime.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.10.7 datetime.datestruct

    Method: out = datestruct (obj)

    Converts this to a "datestruct" broken-down time structure.

    A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

    The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

    Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

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    8.2.10.8 datetime.datetime

    Constructor: obj = datetime ()

    Constructs a new scalar datetime containing the current local time, with no time zone attached.

    Constructor: obj = datetime (datevec)
    Constructor: obj = datetime (datestrs)
    Constructor: obj = datetime (in, 'ConvertFrom', inType)
    Constructor: obj = datetime (Y, M, D, H, MI, S)
    Constructor: obj = datetime (Y, M, D, H, MI, MS)
    Constructor: obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone)

    Constructs a new datetime array based on input values.

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    8.2.10.9 datetime.datevec

    Method: out = datevec (obj)

    Convert this to a datevec that represent the same local wall time.

    Returns double array of size [numel(obj) 6].

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    8.2.10.10 datetime.diff

    Method: out = diff (obj)

    Differences between elements.

    Computes the difference between each successive element in obj, as a duration.

    Returns a duration array the same size as obj.

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    8.2.10.11 datetime.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.10.12 datetime.eq

    Method: out = eq (A, B)

    True if A is equal to B. This defines the == operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.13 datetime.ge

    Method: out = ge (A, B)

    True if A is greater than or equal to B. This defines the >= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.14 datetime.gmtime

    Method: out = gmtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

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    8.2.10.15 datetime.gt

    Method: out = gt (A, B)

    True if A is greater than B. This defines the > operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.16 datetime.hms

    Method: [h, m, s] = hms (obj)

    Get the Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.17 datetime.isbetween

    Method: out = isbetween (obj, lower, upper)

    Tests whether the elements of obj are between lower and upper.

    All inputs are implicitly converted to datetime arrays, and are subject to scalar expansion.

    Returns a logical array the same size as the scalar expansion of the inputs.

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    8.2.10.18 datetime.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.10.19 datetime.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.10.20 datetime.le

    Method: out = le (A, B)

    True if A is less than or equal toB. This defines the <= operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.21 datetime.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in date/time space.

    Constructs a vector of datetimes that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to datetimes.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.10.22 datetime.localtime

    Method: out = localtime (obj)

    Convert to TM_STRUCT structure in UTC time.

    Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

    If obj is unzoned, it is assumed to be in UTC time.

    Returns a struct array in TM_STRUCT style.

    Example: 

    dt = datetime;
dt.TimeZone = datetime.SystemTimeZone;
tm_struct = localtime (dt);

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    8.2.10.23 datetime.lt

    Method: out = lt (A, B)

    True if A is less than B. This defines the < operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.24 datetime.minus

    Method: out = minus (A, B)

    Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes from each other.

    If both inputs are datetime, then the output is a duration. Otherwise, the output is a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    Returns an array the same size as A.

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    8.2.10.25 datetime.NaT

    Static Method: out = datetime.NaT ()
    Static Method: out = datetime.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

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    8.2.10.26 datetime.ne

    Method: out = ne (A, B)

    True if A is not equal to B. This defines the != operator for datetimes.

    Inputs are implicitly converted to datetime using the one-arg constructor or conversion method.

    Returns logical array the same size as obj.

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    8.2.10.27 datetime.ofDatenum

    Static Method: obj = datetime.ofDatenum (dnums)

    Converts a datenum array to a datetime array.

    Returns an unzoned datetime array of the same size as the input.

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    8.2.10.28 datetime.ofDatestruct

    Static Method: obj = datetime.ofDatestruct (dstruct)

    Converts a datestruct to a datetime array.

    A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

    Returns an unzoned datetime array.

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    8.2.10.29 datetime.plus

    Method: out = plus (A, B)

    Addition (+ operator). Adds a duration, calendarDuration, or numeric B to a datetime A.

    A must be a datetime.

    Numeric B inputs are implicitly converted to duration using duration.ofDays.

    WARNING: Arithmetic with calendarDuration arguments on datetimes in time zones which use Daylight Saving Time may be buggy.

    Returns datetime array the same size as A.

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    8.2.10.30 datetime.posix2datenum

    Static Method: dnums = datetime.posix2datenum (pdates)

    Converts POSIX (Unix) times to datenums

    Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

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    8.2.10.31 datetime.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values (seconds since the Unix epoch)

    Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

    Returns double array of same size as this.

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    8.2.10.32 datetime.proxyKeys

    Method: [keysA, keysB] = proxyKeys (a, b)

    Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

    This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

    Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

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    8.2.10.33 datetime.timeofday

    Method: out = timeofday (obj)

    Get the time of day (elapsed time since midnight).

    For zoned datetimes, these will be local times in the associated time zone.

    Returns a duration array the same size as obj.

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    8.2.10.34 datetime.week

    Method: out = week (obj)

    Get the week of the year.

    This method is unimplemented.

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    8.2.10.35 datetime.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.10.36 datetime.ymdhms

    Method: [y, m, d, h, mi, s] = ymdhms (obj)

    Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

    For zoned datetimes, these will be local times in the associated time zone.

    Returns double arrays the same size as obj.

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    8.2.11 days

    Function: out = days (x)

    Duration in days.

    If x is numeric, then out is a duration array in units of fixed-length 24-hour days, with the same size as x.

    If x is a duration, then returns a double array the same size as x indicating the number of fixed-length days that each duration is.

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    8.2.12 discretize

    Function: [Y, E] = discretize (X, n)
    Function: [Y, E] = discretize (X, edges)
    Function: [Y, E] = discretize (X, dur)
    Function: [Y, E] = discretize (…, 'categorical')
    Function: [Y, E] = discretize (…, 'IncludedEdge', IncludedEdge)

    Group data into discrete bins or categories.

    n is the number of bins to group the values into.

    edges is an array of edge values defining the bins.

    dur is a duration value indicating the length of time of each bin.

    If 'categorical' is specified, the resulting values are a categorical array instead of a numeric array of bin indexes.

    Returns: Y - the bin index or category of each value from X E - the list of bin edge values

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    8.2.13 dispstrs

    Function: out = dispstrs (x)

    Display strings for array.

    Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

    The default implementation of dispstrs can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

    This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

    Returns a cell array the same size as x.

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    8.2.14 duration

    Class: duration

    Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

    This is an attempt to reproduce the functionality of Matlab’s duration. It also contains some Octave-specific extensions.

    Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

    Instance Variable of duration: double days

    The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

    This is a planar property: the size of days is the same size as the containing duration array object.

    Instance Variable of duration: char Format

    The format to display this duration in. Currently unsupported.

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    8.2.14.1 duration.char

    Method: out = char (obj)

    Convert to char. The contents of the strings will be the same as returned by dispstrs.

    This is primarily a convenience method for use on scalar objs.

    Returns a 2-D char array with one row per element in obj.

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    8.2.14.2 duration.dispstrs

    Method: out = duration (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.14.3 duration.hours

    Method: out = hours (obj)

    Equivalent number of hours.

    Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.4 duration.linspace

    Method: out = linspace (from, to, n)

    Linearly-spaced values in time duration space.

    Constructs a vector of durations that represent linearly spaced points starting at from and going up to to, with n points in the vector.

    from and to are implicitly converted to durations.

    n is how many points to use. If omitted, defaults to 100.

    Returns an n-long datetime vector.

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    8.2.14.5 duration.milliseconds

    Method: out = milliseconds (obj)

    Equivalent number of milliseconds.

    Gets the number of milliseconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.6 duration.minutes

    Method: out = minutes (obj)

    Equivalent number of minutes.

    Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.7 duration.ofDays

    Static Method: obj = duration.ofDays (dnums)

    Converts a double array representing durations in whole and fractional days to a duration array. This is the method that is used for implicit conversion of numerics in many cases.

    Returns a duration array of the same size as the input.

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    8.2.14.8 duration.seconds

    Method: out = seconds (obj)

    Equivalent number of seconds.

    Gets the number of seconds that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.14.9 duration.years

    Method: out = years (obj)

    Equivalent number of years.

    Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

    Returns double array the same size as obj.

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    8.2.15 eqn

    Function: out = eqn (A, B)

    Determine element-wise equality, treating NaNs as equal

    out = eqn (A, B)

    eqn is just like eq (the function that implements the == operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of isequaln.

    eqn uses isnanny to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

    Developer’s note: the name “eqn” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the isequaln pattern of appending an “n” to the corresponding non-NaN-equivocating function.

    See also: eq, isequaln, isnanny

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    8.2.17 hours

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

    If input is numeric, returns a duration array that is that many hours in time.

    If input is a duration, converts the duration to a number of hours.

    Returns an array the same size as x.

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    8.2.18 iscalendarduration

    Function: out = iscalendarduration (x)

    True if input is a calendarDuration array, false otherwise.

    Respects iscalendarduration override methods on user-defined classes, even if they do not inherit from calendarDuration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.19 iscategorical

    Function: out = iscategorical (x)

    True if input is a categorical array, false otherwise.

    Respects iscategorical override methods on user-defined classes, even if they do not inherit from categorical or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.20 isdatetime

    Function: out = isdatetime (x)

    True if input is a datetime array, false otherwise.

    Respects isdatetime override methods on user-defined classes, even if they do not inherit from datetime or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.21 isduration

    Function: out = isduration (x)

    True if input is a duration array, false otherwise.

    Respects isduration override methods on user-defined classes, even if they do not inherit from duration or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.22 isfile

    Not documented

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    8.2.23 isfolder

    Not documented

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    8.2.24 isnanny

    Function: out = isnanny (X)

    Test if elements are NaN or NaN-like

    Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if isnan() or isnat() returns true, and is false for types that do not support isnan() or isnat().

    This function only exists because:

    1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
    2. isnan() errors out for some types that do not support isnan(), like cells.

    isnanny() smooths over those differences so you can call it polymorphically on any input type. Hopefully.

    Under normal operation, isnanny() should not throw an error for any type or value of input.

    See also: ismissing, isnan, isnat, eqn, isequaln

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    8.2.25 istable

    Function: out = istable (x)

    True if input is a table array or other table-like type, false otherwise.

    Respects istable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.26 istabular

    Function: out = istabular (x)

    True if input is eitehr a table or timetable array, or an object like them.

    Respects istable and istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    Returns a scalar logical.

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    8.2.27 istimetable

    Function: out = istimetable (x)

    True if input is a timetable array or other timetable-like type, false otherwise.

    Respects istimetable override methods on user-defined classes, even if they do not inherit from table or were known to Tablicious at authoring time.

    User-defined classes should only override istimetable to return true if they conform to the table public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

    Returns a scalar logical.

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    8.2.28 localdate

    Class: localdate

    Represents a complete day using the Gregorian calendar.

    This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

    Instance Variable of localdate: double dnums

    The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

    These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

    Instance Variable of localdate: char Format

    The format to display this localdate in. Currently unsupported.

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    8.2.28.1 localdate.datenum

    Method: out = datenum (obj)

    Convert this to datenums that represent midnight on obj’s days.

    Returns double array of same size as this.

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    8.2.28.2 localdate.datestr

    Method: out = datestr (obj)
    Method: out = datestr (obj, …)

    Format obj as date strings. Supports all arguments that core Octave’s datestr does.

    Returns date strings as a 2-D char array.

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    8.2.28.3 localdate.datestrs

    Method: out = datestrs (obj)
    Method: out = datestrs (obj, …)

    Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s datestr does.

    Returns a cellstr array the same size as obj.

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    8.2.28.4 localdate.datestruct

    Method: out = datestruct (obj)

    Converts this to a “datestruct” broken-down time structure.

    A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by datetime in that it lacks Hour, Minute, and Second components. This is done for efficiency.

    The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

    Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

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    8.2.28.5 localdate.dispstrs

    Method: out = dispstrs (obj)

    Get display strings for each element of obj.

    Returns a cellstr the same size as obj.

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    8.2.28.6 localdate.isnan

    Method: out = isnan (obj)

    True if input elements are NaT. This is an alias for isnat to support type compatibility and polymorphic programming.

    Returns logical array the same size as obj.

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    8.2.28.7 localdate.isnat

    Method: out = isnat (obj)

    True if input elements are NaT.

    Returns logical array the same size as obj.

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    8.2.28.8 localdate.localdate

    Constructor: obj = localdate ()

    Constructs a new scalar localdate containing the current local date.

    Constructor: obj = localdate (datenums)
    Constructor: obj = localdate (datestrs)
    Constructor: obj = localdate (Y, M, D)
    Constructor: obj = localdate (…, 'Format', Format)

    Constructs a new localdate array based on input values.

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    8.2.28.9 localdate.NaT

    Static Method: out = localdate.NaT ()
    Static Method: out = localdate.NaT (sz)

    “Not-a-Time”: Creates NaT-valued arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    This static method is provided because the global NaT function creates datetimes, not localdates

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    8.2.28.10 localdate.posixtime

    Method: out = posixtime (obj)

    Converts this to POSIX time values for midnight of obj’s days.

    Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

    Returns double array of same size as this.

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    8.2.28.11 localdate.ymd

    Method: [y, m, d] = ymd (obj)

    Get the Year, Month, and Day components of obj.

    Returns double arrays the same size as obj.

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    8.2.29 milliseconds

    Function File: out = milliseconds (x)

    Create a duration x milliseconds long, or get the milliseconds in a duration x.

    If input is numeric, returns a duration array that is that many milliseconds in time.

    If input is a duration, converts the duration to a number of milliseconds.

    Returns an array the same size as x.

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    8.2.30 minutes

    Function File: out = hours (x)

    Create a duration x hours long, or get the hours in a duration x.

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    8.2.31 missing

    Class: missing

    Generic auto-converting missing value.

    missing is a generic missing value that auto-converts to other types.

    A missing array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

    This class is currently EXPERIMENTAL. Use at your own risk.

    Note: This class does not actually work for assignment. If you do this: 

      x = 1:5
  x(3) = missing

    It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

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    8.2.31.1 missing.dispstrs

    Method: out = dispstrs (obj)

    Display strings.

    Gets display strings for each element in obj.

    For missing, the display strings are always '<missing>'.

    Returns a cellstr the same size as obj.

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    8.2.31.2 missing.ismissing

    Method: out = ismissing (obj)

    Test whether elements are missing values.

    ismissing is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.3 missing.isnan

    Method: out = isnan (obj)

    Test whether elements are NaN.

    isnan is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.4 missing.isnanny

    Method: out = isnanny (obj)

    Test whether elements are NaN-like.

    isnanny is always true for missing arrays.

    Returns a logical array the same size as obj.

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    8.2.31.5 missing.missing

    Constructor: obj = missing ()

    Constructs a scalar missing array.

    The constructor takes no arguments, since there’s only one missing value.

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    8.2.32 mustBeA

    Not documented

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    8.2.33 mustBeCellstr

    Not documented

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    8.2.34 mustBeCharvec

    Not documented

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    8.2.35 mustBeFinite

    Not documented

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    8.2.36 mustBeInteger

    Not documented

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    8.2.37 mustBeMember

    Not documented

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    8.2.38 mustBeNonempty

    Not documented

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    8.2.39 mustBeNumeric

    Not documented

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    8.2.40 mustBeReal

    Not documented

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    8.2.41 mustBeSameSize

    Not documented

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    8.2.42 mustBeScalar

    Not documented

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    8.2.43 mustBeScalarLogical

    Not documented

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    8.2.44 mustBeVector

    Not documented

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    8.2.45 NaC

    Function: out = NaC ()
    Function: out = NaC (sz)

    “Not-a-Categorical". Creates missing-valued categorical arrays.

    Returns a new categorical array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

    NaC is the categorical equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaC values never compare equal to any value, including other NaCs.

    NaC is a convenience function which is strictly a wrapper around categorical.undefined and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: categorical.undefined

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    8.2.46 NaS

    Function: out = NaS ()
    Function: out = NaS (sz)

    “Not-a-String". Creates missing-valued string arrays.

    Returns a new string array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

    NaS is the string equivalent of NaN or NaT. It represents a missing, invalid, or null value. NaS values never compare equal to any value, including other NaSs.

    NaS is a convenience function which is strictly a wrapper around string.missing and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

    See also: string.missing

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    8.2.47 NaT

    Function: out = NaT ()
    Function: out = NaT (sz)

    “Not-a-Time”. Creates missing-valued datetime arrays.

    Constructs a new datetime array of all NaT values of the given size. If no input sz is given, the result is a scalar NaT.

    NaT is the datetime equivalent of NaN. It represents a missing or invalid value. NaT values never compare equal to, greater than, or less than any value, including other NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

    NaT currently cannot create NaT arrays of type localdate. To do that, use localdate.NaT instead.

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    8.2.48 pp

    Function: pp (X)
    Function: pp (A, B, C, …)
    Function: pp ('A', 'B', 'C', …)
    Function: pp A B C …

    Alias for prettyprint, for interactive use.

    This is an alias for prettyprint(), with additional name-conversion magic.

    If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

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    8.2.49 scalarexpand

    Function: [out1, out2, …, outN] = scalarexpand (x1, x2, …, xN)

    Expand scalar inputs to match size of non-scalar inputs.

    Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. repmat is used to do the expansion.

    Works on any input types that support size, isscalar, and repmat.

    It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

    Returns as many output arguments as there were input arguments.

    Examples: 

    x1 = rand(3);
x2 = 42;
x3 = magic(3);
[x1, x2, x3] = scalarexpand (x1, x2, x3)

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    8.2.50 seconds

    Function File: out = seconds (x)

    Create a duration x seconds long, or get the seconds in a duration x.

    If input is numeric, returns a duration array that is that many seconds in time.

    If input is a duration, converts the duration to a number of seconds.

    Returns an array the same size as x.

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    8.2.51 size2str

    Function: out = size2str (sz)

    Format an array size for display.

    Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

    sz is an array of dimension sizes, in the format returned by the size function.

    Returns a charvec.

    Examples: 

    str = size2str (size (magic (4)))
    ⇒ str = 4-by-4

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    8.2.52 splitapply

    Function: out = splitapply (func, X, G)
    Function: out = splitapply (func, X1, …, XN, G)
    Function: [Y1, …, YM] = splitapply (…)

    Split data into groups and apply function.

    func is a function handle to call on each group of inputs in turn.

    X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a table, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

    G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

    Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

    Returns the concatenated outputs of applying func to each group.

    See also: table.groupby, table.splitapply

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    8.2.53 string

    Class: string

    A string array of Unicode strings.

    A string array is an array of strings, where each array element is a single string.

    The string class represents strings, where:

    • Each element of a string array is a single string
    • A single string is a 1-dimensional row vector of Unicode characters
    • Those characters are encoded in UTF-8
      • This last bit depends on the fact that Octave chars are UTF-8 now

    This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

    String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

    This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

    Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

    This class really serves three roles:

    1. It is a type-safe object wrapper around Octave’s base primitive character types.
    2. It adds ismissing() semantics.
    3. And it introduces Unicode support.

    Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

    “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

    TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

    TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

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    8.2.53.1 string.cell

    Method: out = cell (obj)

    Convert to cell array.

    Converts this to a cell, which will be a cellstr. Missing values are converted to ''.

    This method returns the same values as cellstr(obj); it is just provided for interface compatibility purposes.

    Returns a cell array of the same size as obj.

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    8.2.53.2 string.cellstr

    Method: out = cellstr (obj)

    Convert to cellstr.

    Converts obj to a cellstr. Missing values are converted to ''.

    Returns a cellstr array of the same size as obj.

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    8.2.53.3 string.char

    Method: out = char (obj)

    Convert to char array.

    Converts obj to a 2-D char array. It will have as many rows as obj has elements.

    It is an error to convert missing-valued string arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

    Returns 2-D char array.

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    8.2.53.4 string.cmp

    Method: [out, outA, outB] = cmp (A, B)

    Value ordering comparison, returning -1/0/+1.

    Compares each element of A and B, returning for each element i whether A(i) was less than (-1), equal to (0), or greater than (1) the corresponding B(i).

    TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

    Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

    Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

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    8.2.53.5 string.decode

    Static Method: out = string.decode (bytes, charsetName)

    Decode encoded text from bytes.

    Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

    Returns a scalar string.

    See also: string.encode

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    8.2.53.6 string.dispstrs

    Method: out = dispstrs (obj)

    Display strings for array elements.

    Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in "...", and with CR/LF characters replaced with '\r' and '\n' escape sequences, or "<missing>" for missing values.

    Returns a cellstr of the same size as obj.

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    8.2.53.7 string.empty

    Function: out = empty (sz)

    Get an empty string array of a specified size.

    The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

    The size may also be supplied as multiple arguments containing scalar numerics.

    Returns an empty string array of the requested size.

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    8.2.53.8 string.encode

    Method: out = encode (obj, charsetName)

    Encode string in a given character encoding.

    obj must be scalar.

    charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

    Returns the encoded string as a uint8 vector.

    See also: string.decode.

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    8.2.53.9 string.erase

    Method: out = erase (obj, match)

    Erase matching substring.

    Erases the substrings in obj which match the match input.

    Returns a string array of the same size as obj.

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    8.2.53.10 string.ismissing

    Method: out = ismissing (obj)

    Test whether array elements are missing.

    For string arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

    Returns a logical array the same size as obj.

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    8.2.53.11 string.isnanny

    Method: out = isnanny (obj)

    Test whether array elements are NaN-like.

    Missing values are considered nannish; any other string value is not.

    Returns a logical array of the same size as obj.

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    8.2.53.12 string.isstring

    Method: out = isstring (obj)

    Test if input is a string array.

    isstring is always true for string inputs.

    Returns a scalar logical.

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    8.2.53.13 string.lower

    Method: out = lower (obj)

    Convert to lower case.

    Converts all the characters in all the strings in obj to lower case.

    This currently delegates to Octave’s own lower() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.53.14 string.missing

    Static Method: out = string.missing (sz)

    Missing string value.

    Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

    Returns a string array of size sz or [1 1].

    See also: NaS

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    8.2.53.15 string.plus

    Method: out = plus (a, b)

    String concatenation via plus operator.

    Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

    Returns a string array the same size as the scalar expansion of its inputs.

    See also: string.strcat

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    8.2.53.16 string.regexprep

    Method: out = regexprep (obj, pat, repstr)
    Method: out = regexprep (…, varargin)

    Replace based on regular expression matching.

    Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

    Returns a string array of the same size as obj.

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    8.2.53.17 string.reverse

    Method: out = reverse (obj)

    Reverse string, character-wise.

    Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

    Returns a string array the same size as obj.

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    8.2.53.18 string.reverse_bytes

    Method: out = reverse_bytes (obj)

    Reverse string, byte-wise.

    Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

    This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

    You probably do not want to use this method. You probably want to use string.reverse instead.

    Returns a string array the same size as obj.

    See also: string.reverse

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    8.2.53.19 string.strcat

    Method: out = strcat (varargin)

    String concatenation.

    Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

    The semantics of concatenating missing strings with non-missing strings has not been determined yet.

    Returns a string array the same size as the scalar expansion of its inputs.

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    8.2.53.20 string.strcmp

    Method: out = strcmp (A, B)

    String comparison.

    Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

    This does the same comparison as A == B, but is not polymorphic. Generally, there is no reason to use strcmp instead of == or eq on string arrays, unless you want to be compatible with cellstr inputs as well.

    Returns logical array the size of the scalar expansion of A and B.

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    8.2.53.21 string.strfind

    Method: out = strfind (obj, pattern)
    Method: out = strfind (…, varargin)

    Find pattern in string.

    Finds the locations where pattern occurs in the strings of obj.

    TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

    Returns either an index vector, or a cell array of index vectors.

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    8.2.53.22 string.string

    Constructor: obj = string ()
    Constructor: obj = string (in)

    Construct a new string array.

    The zero-argument constructor creates a new scalar string array whose value is the empty string.

    The other constructors construct a new string array by converting various types of inputs.

    • chars and cellstrs are converted via cellstr()
    • numerics are converted via num2str()
    • datetimes are converted via datestr()

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    8.2.53.23 string.strlength

    Method: out = strlength (obj)

    String length in characters (actually, UTF-16 code units).

    Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

    The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

    This is the string length method you probably want to use, not strlength_bytes.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength_bytes

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    8.2.53.24 string.strlength_bytes

    Method: out = strlength_bytes (obj)

    String length in bytes.

    Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as numel(str) for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want strlength instead.

    Returns double array of the same size as obj. Returns NaNs for missing strings.

    See also: string.strlength

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    8.2.53.25 string.strrep

    Method: out = strrep (obj, match, replacement)
    Method: out = strrep (…, varargin)

    Replace occurrences of pattern with other string.

    Replaces matching substrings in obj with a given replacement string.

    varargin is passed along to the core Octave strrep function. This supports whatever options it does. TODO: Maybe document what those options are.

    Returns a string array of the same size as obj.

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    8.2.53.26 string.upper

    Method: out = upper (obj)

    Convert to upper case.

    Converts all the characters in all the strings in obj to upper case.

    This currently delegates to Octave’s own upper() function to do the conversion, so whatever character class handling it has, this has.

    Returns a string array of the same size as obj.

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    8.2.54 struct2table

    Function: out = struct2table (s)
    Function: out = struct2table (…, 'AsArray', AsArray)

    Convert struct to a table.

    Converts the input struct s to a table.

    s may be a scalar struct or a nonscalar struct array.

    The AsArray option is not implemented yet.

    Returns a table.

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    8.2.55 table

    Class: table

    Tabular data array containing multiple columnar variables.

    A table is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

    A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

    A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

    A note on accessing properties of a table array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do t.Properties.<property> for a table t. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a table with a variable named Properties. Try to avoid that.)

    WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

    Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

    We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

    See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

    Instance Variable of table: cellstr VariableNames

    The names of the variables in the table, as a cellstr row vector.

    Instance Variable of table: cell VariableValues

    A cell vector containing the values for each of the variables. VariableValues(i) corresponds to VariableNames(i).

    Instance Variable of table: cellstr RowNames

    An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

    Instance Variable of table: cellstr DimensionNames

    Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to {"Row", "Variables"}. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

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    8.2.55.1 table.addvars

    Method: out = addvars (obj, var1, …, varN)
    Method: out = addvars (…, 'Before', Before)
    Method: out = addvars (…, 'After', After)
    Method: out = addvars (…, 'NewVariableNames', NewVariableNames)

    Add variables to table.

    Adds the specified variables to a table.

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    8.2.55.2 table.antijoin

    Method: [outA, ixA, outB, ixB] = antijoin (A, B)

    Natural antijoin (AKA “semidifference”).

    Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

    Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.3 table.cartesian

    Method: [out, ixs] = cartesian (A, B)

    Cartesian product of two tables.

    Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

    Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

    The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

    This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

    The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.4 table.convertvars

    Method: out = convertvars (obj, vars, dataType)

    Convert variables to specified data type.

    Converts the variables in obj specified by vars to the specified data type.

    vars is a cellstr or numeric vector specifying which variables to convert.

    dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

    Returns a table with the same variable names as obj, but with converted types.

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    8.2.55.5 table.findgroups

    Method: [G, TID] = findgroups (obj)

    Find groups within a table’s row values.

    Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

    Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

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    8.2.55.6 table.getvar

    Method: [out, name] = getvar (obj, varRef)

    Get value and name for single table variable.

    varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

    Returns: out – the value of the referenced table variable name – the name of the referenced table variable

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    8.2.55.7 table.getvars

    Method: [out1, …] = getvars (obj, varRef)

    Get values for one ore more table variables.

    varRef is a variable reference in the form of variable names or indexes.

    Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

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    8.2.55.8 table.groupby

    Method: [out] = groupby (obj, groupvars, aggcalcs)

    Find groups in table data and apply functions to variables within groups.

    This works like an SQL "SELECT ... GROUP BY ..." statement.

    groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

    aggcalcs is a specification of the aggregate calculations to perform on them, in the form {out_var, fcn, in_vars; ...}, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

    Returns a table.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.9 table.height

    Method: out = height (obj)

    Number of rows in table.

    For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

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    8.2.55.10 table.horzcat

    Method: out = horzcat (varargin)

    Horizontal concatenation.

    Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

    Output has the same RowNames as varargin{1}. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

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    8.2.55.11 table.innerjoin

    Method: [out, ixa, ixb] = innerjoin (A, B)
    Method: […] = innerjoin (A, B, …)

    Combine two tables by rows using key variables.

    Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

    TODO: Document options.

    Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

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    8.2.55.12 table.intersect

    Method: [C, ia, ib] = intersect (A, B)

    Set intersection.

    Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

    Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.13 table.isempty

    Method: out = isempty (obj)

    Test whether array is empty.

    For tables, isempty is true if the number of rows is 0 or the number of variables is 0.

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    8.2.55.14 table.ismember

    Method: [tf, loc] = ismember (A, B)

    Set membership.

    Finds rows in A that are members of B.

    Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

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    8.2.55.15 table.ismissing

    Method: out = ismissing (obj)
    Method: out = ismissing (obj, indicator)

    Find missing values.

    Finds missing values in obj’s variables.

    If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

    All variables in this must be vectors. (This is due to the requirement that size(out) == size(obj).)

    Returns a logical array the same size as obj.

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    8.2.55.16 table.istable

    Method: tf = istable (obj)

    True if input is a table.

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    8.2.55.17 table.join

    Method: [C, ib] = join (A, B)
    Method: [C, ib] = join (A, B, …)

    Combine two tables by rows using key variables, in a restricted form.

    This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

    You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

    See also: table.innerjoin, table.outerjoin

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    8.2.55.18 table.mergevars

    Method: out = mergevars (obj, vars)
    Method: out = mergevars (…, 'NewVariableName', NewVariableName)
    Method: out = mergevars (…, 'MergeAsTable', MergeAsTable)

    Merge table variables into a single variable.

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    8.2.55.19 table.movevars

    Method: out = movevars (obj, vars, relLocation, location)

    Move around variables in a table.

    vars is a list of variables to move, specified by name or index.

    relLocation is 'Before' or 'After'.

    location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

    Returns a table with the same variables as obj, but in a different order.

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    8.2.55.20 table.ndims

    Method: out = ndims (obj)

    Number of dimensions

    For tables, ndims(obj) is always 2, because table arrays are always 2-D (rows-by-columns).

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    8.2.55.21 table.numel

    Method: out = numel (obj)

    Total number of elements in table (actually 1).

    For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

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    8.2.55.22 table.outerfillvals

    Method: out = outerfillvals (obj)

    Get fill values for outer join.

    Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

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    8.2.55.23 table.outerjoin

    Method: [out, ixa, ixb] = outerjoin (A, B)
    Method: […] = outerjoin (A, B, …)

    Combine two tables by rows using key variables, retaining unmatched rows.

    Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

    TODO: Document options.

    Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

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    8.2.55.24 table.prettyprint

    Method: prettyprint (obj)

    Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

    Variables which contain objects are displayed using the strings returned by their dispstrs method, if they define one.

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    8.2.55.25 table.realjoin

    Method: [out, ixs] = realjoin (A, B)
    Method: […] = realjoin (A, B, …)

    "Real" relational inner join, without key restrictions

    Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

    Currently does not support tables which have RowNames. This may be added in the future.

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

    FIXME: Document those options.

    Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

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    8.2.55.26 table.removevars

    Method: out = removevars (obj, vars)

    Remove variables from table.

    Deletes the variables specified by vars from obj.

    vars may be a char, cellstr, numeric index vector, or logical index vector.

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    8.2.55.27 table.renamevars

    Method: out = renamevars (obj, renameMap)

    Rename variables in a table.

    Renames selected variables in the table obj based on the mapping provided in renameMap.

    renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

    Variables which are not included in renameMap are not modified.

    It is an error if any variables named in the first column of renameMap are not present in obj.

    Renames

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    8.2.55.28 table.repelem

    Method: out = repelem (obj, R)
    Method: out = repelem (obj, R_1, R_2)

    Replicate elements of matrix.

    Replicates elements of this table matrix by applying repelem to each of its variables. This

    Only two dimensions are supported for repelem on tables.

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    8.2.55.29 table.repmat

    Method: out = repmat (obj, sz)

    Replicate matrix.

    Repmats a table by repmatting each of its variables vertically.

    For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

    Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

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    8.2.55.30 table.restrict

    Method: out = restrict (obj, expr)
    Method: out = restrict (obj, ix)

    Subset rows using variable expression or index.

    Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

    If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

    If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with tblish.evalWithTableVars. The output of expr must be a numeric or logical index vector (This form is a shorthand for out = subsetrows (this, tblish.evalWithTableVars (this, expr)).)

    TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb;
prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

    See also: tblish.evalWithTableVars

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    8.2.55.31 table.rowfun

    Method: out = varfun (func, obj)
    Method: out = varfun (…, 'OptionName', OptionValue, …)

    Apply function to rows in table and collect outputs.

    This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

    func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify 'SeparateInputs', false to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the 'NumOutputs' or 'OutputVariableNames' options.

    Supported name/value options:

    'OutputVariableNames'

    Names of table variables to store combined function output arguments in.

    'NumOutputs'

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

    'SeparateInputs'

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

    'ErrorHandler'

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

    'ExtractCellContents'

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

    'InputVariables'

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

    'GroupingVariables'

    Not yet implemented.

    'OutputFormat'

    The format of the output. May be 'table' (the default), 'uniform', or 'cell'. If it is 'uniform' or 'cell', the output variables are returned in multiple output arguments from 'rowfun'.

    Returns a table whose variables are the collected output arguments of func if OutputFormat is 'table'. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is 'uniform') or cells (if OutputFormat is 'cell').

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    8.2.55.32 table.rows2vars

    Method: out = rows2vars (obj)
    Method: out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource)
    Method: out = rows2vars (…, 'DataVariables', DataVariables)

    Reorient table, swapping rows and variables dimensions.

    This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

    The variable names become a new variable named “OriginalVariableNames”.

    The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

    If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

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    8.2.55.33 table.semijoin

    Method: [outA, ixA, outB, ixB] = semijoin (A, B)

    Natural semijoin.

    Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

    This method also computes the semijoin of B and A, for convenience.

    Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

    This is a Tablicious/Octave extension, not defined in the Matlab table interface.

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    8.2.55.34 table.setdiff

    Method: [C, ia] = setdiff (A, B)

    Set difference.

    Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

    Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

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    8.2.55.35 table.setDimensionNames

    Method: out = setDimensionNames (obj, names)
    Method: out = setDimensionNames (obj, ix, names)

    Set dimension names.

    Sets the DimensionNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

    ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

    This method exists because the obj.Properties.DimensionNames = … assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the obj.Properties.DimensionNames = … assignment form.

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    8.2.55.36 table.setRowNames

    Method: out = setRowNames (obj, names)

    Set row names.

    Sets the row names on obj to names.

    names is a cellstr column vector, with the same number of rows as obj has.

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    8.2.55.37 table.setvar

    Method: out = setvar (obj, varRef, value)

    Set value for a variable in table.

    This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

    This method exists primarily because I cannot get obj.foo = value to work, apparently due to an issue with Octave’s subsasgn support.

    varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

    value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

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    8.2.55.38 table.setVariableNames

    Method: out = setVariableNames (obj, names)
    Method: out = setVariableNames (obj, ix, names)

    Set variable names.

    Sets the VariableNames for this table to a new list of names.

    names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

    ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

    This method exists because the obj.Properties.VariableNames = … assignment form does not work, possibly due to an Octave bug.

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    8.2.55.39 table.setxor

    Method: [C, ia, ib] = setxor (A, B)

    Set exclusive OR.

    Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

    Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.40 table.size

    Method: sz = size (obj)
    Method: [nr, nv] = size (obj)
    Method: [nr, nv, …] = size (obj)

    Gets the size of a table.

    For tables, the size is [number-of-rows x number-of-variables]. This is the same as [height(obj), width(obj)].

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    8.2.55.41 table.splitapply

    Method: out = splitapply (func, obj, G)
    Method: [Y1, …, YM] = splitapply (func, obj, G)

    Split table data into groups and apply function.

    Performs a splitapply, using the variables in obj as the input X variables to the splitapply function call.

    See also: splitapply, table.groupby, tblish.table.grpstats

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    8.2.55.42 table.splitvars

    Method: out = splitvars (obj)
    Method: out = splitvars (obj, vars)
    Method: out = splitvars (…, 'NewVariableNames', NewVariableNames)

    Split multicolumn table variables.

    Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

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    8.2.55.43 table.squeeze

    Method: obj = squeeze (obj)

    Remove singleton dimensions.

    For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

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    8.2.55.44 table.stack

    Method: out = stack (obj, vars)
    Method: out = stack (…, 'NewDataVariableName', NewDataVariableName)
    Method: out = stack (…, 'IndexVariableName', IndexVariableName)

    Stack multiple table variables into a single variable.

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    8.2.55.45 table.summary

    Method: summary (obj)

    Display a summary of a table’s data.

    Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

    This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

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    8.2.55.46 table.table

    Constructor: obj = table ()

    Constructs a new empty (0 rows by 0 variables) table.

    Constructor: obj = table (var1, var2, …, varN)

    Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

    Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

    Constructor: obj = table ('Size', sz, 'VariableTypes', varTypes)

    Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

    Constructor: obj = table (…, 'VariableNames', varNames)
    Constructor: obj = table (…, 'RowNames', rowNames)

    Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

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    8.2.55.47 table.table2array

    Method: s = table2struct (obj)

    Converts obj to a homogeneous array.

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    8.2.55.48 table.table2cell

    Method: c = table2cell (obj)

    Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

    Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

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    8.2.55.49 table.table2struct

    Method: s = table2struct (obj)
    Method: s = table2struct (…, 'ToScalar', trueOrFalse)

    Converts obj to a scalar structure or structure array.

    Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

    Returns a scalar struct or struct array, depending on the value of the ToScalar option.

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    8.2.55.50 table.union

    Method: [C, ia, ib] = union (A, B)

    Set union.

    Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

    Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

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    8.2.55.51 table.varfun

    Method: out = varfun (fcn, obj)
    Method: out = varfun (…, 'OutputFormat', outputFormat)
    Method: out = varfun (…, 'InputVariables', vars)
    Method: out = varfun (…, 'ErrorHandler', errorFcn)

    Apply function to table variables.

    Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

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    8.2.55.52 table.varnames

    Method: out = varnames (obj)
    Method: out = varnames (obj, varNames)

    Get or set variable names for a table.

    Returns cellstr in the getter form. Returns an updated datetime in the setter form.

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    8.2.55.53 table.vertcat

    Method: out = vertcat (varargin)

    Vertical concatenation.

    Combines tables by vertically concatenating them.

    Inputs that are not tables are automatically converted to tables by calling table() on them.

    The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

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    8.2.55.54 table.width

    Method: out = width (obj)

    Number of variables in table.

    Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

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    8.2.56 tableOuterFillValue

    Not documented

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    8.2.57 tail

    Function: out = tail (A)
    Function: out = tail (A, k)

    Get last K rows of an array.

    Returns the array A, subsetted to its last k rows. This means subsetting it to the last (min (k, size (A, 1))) elements along dimension 1, and leaving all other dimensions unrestricted.

    A is the array to subset.

    k is the number of rows to get. k defaults to 8 if it is omitted or empty.

    If there are less than k rows in A, returns all rows.

    Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

    See also: head

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    8.2.58 tblish.dataset

    Class: tblish.dataset

    The tblish.dataset class provides convenient access to the various datasets included with Tablicious.

    This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

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    8.2.58.1 tblish.dataset.airmiles

    Static Method: out = airmiles ()

    Passenger Miles on Commercial US Airlines, 1937-1960

    Description

    The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

    Source

    F.A.A. Statistical Handbook of Aviation.

    Examples

    t = tblish.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");

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    8.2.58.2 tblish.dataset.AirPassengers

    Static Method: out = AirPassengers ()

    Monthly Airline Passenger Numbers 1949-1960

    Description

    The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

    Source

    Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

    Examples

    ## TODO: This example needs to be ported from R.

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    8.2.58.3 tblish.dataset.airquality

    Static Method: out = airquality ()

    New York Air Quality Measurements from 1973

    Description

    Daily air quality measurements in New York, May to September 1973.

    Format

    Ozone

    Ozone concentration (ppb)

    SolarR

    Solar R (lang)

    Wind

    Wind (mph)

    Temp

    Temperature (degrees F)

    Month

    Month (1-12)

    Day

    Day of month (1-31)

    Source

    New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

    References

    Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

    Examples

    t = tblish.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
  for j = 1:n_vars
    if (i == j)
      continue
    endif
    ix_subplot = (n_vars * (j - 1) + i);
    hax = subplot (n_vars, n_vars, ix_subplot);
    var_x = vars{i};
    var_y = vars{j};
    x = t.(var_x);
    y = t.(var_y);
    scatter (hax, x, y, 10);
    # Fit a cubic line to these points
    # TODO: Find out exactly what kind of fitted line R's example is using, and
    # port that.
    hold on
    p = polyfit (x, y, 3);
    x_hat = unique(x);
    p_y = polyval (p, x_hat);
    plot (hax, x_hat, p_y, "r");
  endfor
endfor

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    8.2.58.4 tblish.dataset.anscombe

    Static Method: out = anscombe ()

    Anscombe’s Quartet of “Identical” Simple Linear Regressions

    Description

    Four sets of x/y pairs which have the same statistical properties, but are very different.

    Format

    The data comes in an array of 4 structs, each with fields as follows:

    x

    The X values for this pair.

    y

    The Y values for this pair.

    Source

    Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

    References

    Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

    Examples

    data = tblish.dataset.anscombe

# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];

# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
  x = data(i_pair).x;
  y = data(i_pair).y;
  # TODO: Port the anova and other characterizations from the R code
  # TODO: Do a linear regression and plot its line
  hax = subplot (2, 2, i_pair);
  haxs(i_pair) = hax;
  xlabel (sprintf ("x%d", i_pair));
  ylabel (sprintf ("y%d", i_pair));
  scatter (x, y, "r");
endfor

# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim (haxs(1), x_limits);
ylim (haxs(1), y_limits);

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    8.2.58.5 tblish.dataset.attenu

    Static Method: out = attenu ()

    Joyner-Boore Earthquake Attenuation Data

    Description

    Event data for 23 earthquakes in California, showing peak accelerations.

    Format

    event

    Event number

    mag

    Moment magnitude

    station

    Station identifier

    dist

    Station-hypocenter distance (km)

    accel

    Peak acceleration (g)

    Source

    Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

    References

    Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

    Examples

    # TODO: Port the example code from R
# It does coplot() and pairs(), which are higher-level plotting tools
# than core Octave provides. This could turn into a long example if we
# just use base Octave here.

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    8.2.58.6 tblish.dataset.attitude

    Static Method: out = attitude ()

    The Chatterjee-Price Attitude Data

    Description

    Aggregated data from a survey of clerical employees at a large financial organization.

    Format

    rating

    Overall rating.

    complaints

    Handling of employee complaints.

    privileges

    Does not allow special privileges.

    learning

    Opportunity to learn.

    raises

    Raises based on performance.

    critical

    Too critical.

    advance

    Advancement.

    Source

    Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

    Examples

    t = tblish.dataset.attitude

tblish.examples.plot_pairs (t);

# TODO: Display table summary

# TODO: Whatever those statistical linear-model plots are that R is doing

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    8.2.58.7 tblish.dataset.austres

    Static Method: out = austres ()

    Australian Population

    Description

    Numbers of Australian residents measured quarterly from March 1971 to March 1994.

    Format

    date

    The month of the observation.

    residents

    The number of residents.

    Source

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.austres

plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

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    8.2.58.8 tblish.dataset.beavers

    Static Method: out = beavers ()

    Body Temperature Series of Two Beavers

    Description

    Body temperature readings for two beavers.

    Format

    day

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

    time

    Time of observation, in the form 0330 for 3:30am

    temp

    Measured body temperature in degrees Celsius.

    activ

    Indicator of activity outside the retreat.

    Source

    P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

    Examples

    # TODO: This example needs to be ported from R.

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    8.2.58.9 tblish.dataset.BJsales

    Static Method: out = BJsales ()

    Sales Data with Leading Indicator

    Description

    Sales Data with Leading Indicator

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.10 tblish.dataset.BOD

    Static Method: out = BOD ()

    Biochemical Oxygen Demand

    Description

    Contains biochemical oxygen demand versus time in an evaluation of water quality.

    Format

    Time

    Time of the measurement (in days).

    demand

    Biochemical oxygen demand (mg/l).

    Source

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

    Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

    Examples

    # TODO: Port this example from R

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    8.2.58.11 tblish.dataset.cars

    Static Method: out = cars ()

    Speed and Stopping Distances of Cars

    Description

    Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

    Format

    speed

    Speed (mph).

    dist

    Stopping distance (ft).

    Source

    Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    
t = tblish.dataset.cars;


# TODO: Add Lowess smoothed lines to the plots

figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data");

figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars data (logarithmic scales)");

# TODO: Do the linear model plot

# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
  [P, S, mu] = polyfit (t.speed, t.dist, degree);
  y2 = polyval(P, x2, [], mu);
  plot (x2, y2);
endfor

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    8.2.58.12 tblish.dataset.ChickWeight

    Static Method: out = ChickWeight ()

    Weight versus age of chicks on different diets

    Format

    weight

    a numeric vector giving the body weight of the chick (gm).

    Time

    a numeric vector giving the number of days since birth when the measurement was made.

    Chick

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

    Diet

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

    Source

    Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

    Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

    Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.ChickWeight

tblish.examples.coplot (t, "Time", "weight", "Chick");

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    8.2.58.13 tblish.dataset.chickwts

    Static Method: out = chickwts ()

    Chicken Weights by Feed Type

    Description

    An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

    Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

    Format

    weight

    Chick weight at six weeks (gm).

    feed

    Feed type.

    Source

    Anonymous (1948) Biometrika, 35, 214.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    # This example requires the statistics package from Octave Forge

t = tblish.dataset.chickwts

# Boxplot by group
figure
g = groupby (t, "feed", {
  "weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);

# Linear model
# TODO: This linear model thing and anova

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    8.2.58.14 tblish.dataset.co2

    Static Method: out = co2 ()

    Mauna Loa Atmospheric CO2 Concentration

    Description

    Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

    Format

    date

    Date of the month of the observation, as datetime.

    co2

    CO2 concentration (ppm).

    Details

    The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

    Source

    Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

    ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

    References

    Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.

    Examples

    t = tblish.dataset.co2;

plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");

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    8.2.58.15 tblish.dataset.crimtab

    Static Method: out = crimtab ()

    Student’s 3000 Criminals Data

    Description

    Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

    Format

    This dataset contains three separate variables. The finger_length and body_height variables correspond to the rows and columns of the count matrix.

    finger_length

    Midpoints of intervals of finger lengths (cm).

    body_height

    Body heights (cm).

    count

    Number of prisoners in this bin.

    Details

    Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

    “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

    The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

    MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

    Source

    http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

    References

    Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

    MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

    Student (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Examples

    # TODO: Port this from R

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    8.2.58.16 tblish.dataset.cupcake

    Static Method: out = cupcake ()

    Google Search popularity for "cupcake", 2004-2019

    Description

    Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

    Format

    Month

    Month when searches took place

    Cupcake

    An indicator of search volume, in unknown units

    Source

    Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

    Examples

    t = tblish.dataset.cupcake
plot (datenum (t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

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    8.2.58.17 tblish.dataset.discoveries

    Static Method: out = discoveries ()

    Yearly Numbers of Important Discoveries

    Description

    The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

    Format

    year

    Year.

    discoveries

    Number of “great” discoveries that year.

    Source

    The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.discoveries;

plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");

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    8.2.58.18 tblish.dataset.DNase

    Static Method: out = DNase ()

    Elisa assay of DNase

    Description

    Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

    Format

    Run

    Ordered categorical indicating the assay run.

    conc

    Known concentration of the protein (ng/ml).

    density

    Measured optical density in the assay (dimensionless).

    Source

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.DNase;

# TODO: Port this from R

tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
  "PlotArgs", {"o"});

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    8.2.58.19 tblish.dataset.esoph

    Static Method: out = esoph ()

    Smoking, Alcohol and Esophageal Cancer

    Description

    Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

    Format

    item

    Age group (years).

    alcgp

    Alcohol consumption (gm/day).

    tobgp

    Tobacco consumption (gm/day).

    ncases

    Number of cases.

    ncontrols

    Number of controls

    Source

    Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

    Examples

    # TODO: Port this from R

# TODO: Port the anova output

# TODO: Port the fancy plot
# This involves a "mosaic plot", which is not supported by Octave, so this will
# take some work.

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    8.2.58.20 tblish.dataset.euro

    Static Method: out = euro ()

    Conversion Rates of Euro Currencies

    Description

    Conversion rates between the various Euro currencies.

    Format

    This data comes in two separate variables.

    euro

    An 11-long vector of the value of 1 Euro in all participating currencies.

    euro_cross

    An 11-by-11 matrix of conversion rates between various Euro currencies.

    euro_date

    The date upon which these Euro conversion rates were fixed.

    Details

    The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

    Source

    Unknown.

    This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

    Examples

    # TODO: Port this from R

# TODO: Example conversion

# TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

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    8.2.58.21 tblish.dataset.eurodist

    Static Method: out = eurodist ()

    Distances Between European Cities and Between US Cities

    Description

    eurodist gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

    UScitiesD gives “straight line” distances between 10 cities in the US.

    Format

    eurodist

    ?????

    TODO: Finish this.

    Source

    Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

    The US cities distances were provided by Pierre Legendre.

    Examples

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    8.2.58.22 tblish.dataset.EuStockMarkets

    Static Method: out = EuStockMarkets ()

    Daily Closing Prices of Major European Stock Indices

    Description

    Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

    Format

    A multivariate time series with 1860 observations on 4 variables.

    The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

    Source

    The data were kindly provided by Erste Bank AG, Vienna, Austria.

    Examples

    
t = tblish.dataset.EuStockMarkets;

# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place

t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);

price0 = price(1,:);

rel_price = price ./ repmat (price0, [size(price, 1) 1]);

figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");

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    8.2.58.23 tblish.dataset.faithful

    Static Method: out = faithful ()

    Old Faithful Geyser Data

    Description

    Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

    Format

    eruptions

    Eruption time (mins).

    waiting

    Waiting time to next eruption (mins).

    Source

    W. Härdle.

    References

    Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

    Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

    Examples

    t = tblish.dataset.faithful;

# Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary

# Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")

# Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave

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    8.2.58.24 tblish.dataset.Formaldehyde

    Static Method: out = Formaldehyde ()

    Determination of Formaldehyde

    Description

    These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

    Format

    record

    Observation record number.

    carb

    Carbohydrate (ml).

    optden

    Optical Density

    Source

    Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.Formaldehyde;

figure
scatter (t.carb, t.optden)
# TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")

# TODO: Add linear model summary output
# TOD: Add linear model summary plot

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    8.2.58.25 tblish.dataset.freeny

    Static Method: out = freeny ()

    Freeny’s Revenue Data

    Description

    Freeny’s data on quarterly revenue and explanatory variables.

    Format

    Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

    date

    Start date of the quarter for the observation.

    y

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

    lag_quarterly_revenue

    Quarterly revenue (y), lagged 1 quarter.

    price_index

    A price index

    income_level

    ??? TODO: Fill this in

    market_potential

    ??? TODO: Fill this in

    Source

    Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.freeny;

summary (t)

tblish.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

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    8.2.58.26 tblish.dataset.HairEyeColor

    Static Method: out = HairEyeColor ()

    Hair and Eye Color of Statistics Students

    Description

    Distribution of hair and eye color and sex in 592 statistics students.

    Format

    This data set comes in multiple variables

    n

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

    hair

    Hair colors for the indexes along dimension 1.

    eye

    Eye colors for the indexes along dimension 2.

    sex

    Sexes for the indexes along dimension 3.

    Details

    The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

    This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

    Source

    http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

    Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

    References

    Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

    Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

    Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

    Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

    Examples

    tblish.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

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    8.2.58.27 tblish.dataset.Harman23cor

    Static Method: out = Harman23cor ()

    Harman Example 2.3

    Description

    A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

    Format

    cov

    An 8-by-8 correlation matrix.

    names

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

    Examples

    tblish.dataset.Harman23cor;

# TODO: Port factanal to Octave

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    8.2.58.28 tblish.dataset.Harman74cor

    Static Method: out = Harman74cor ()

    Harman Example 7.4

    Description

    A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

    Format

    cov

    A 2-dimensional correlation matrix.

    vars

    Names of the variables corresponding to the indexes along the dimensions of cov.

    Source

    Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

    Examples

    tblish.dataset.Harman74cor;

# TODO: Port factanal to Octave

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    8.2.58.29 tblish.dataset.Indometh

    Static Method: out = Indometh ()

    Pharmacokinetics of Indomethacin

    Description

    Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

    Format

    Subject

    Subject identifier.

    time

    Time since drug administration at which samples were drawn (hours).

    conc

    Plasma concentration of indomethacin (mcg/ml).

    Details

    Each of the six subjects were given an intravenous injection of indometacin.

    Source

    Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

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    8.2.58.30 tblish.dataset.infert

    Static Method: out = infert ()

    Infertility after Spontaneous and Induced Abortion

    Description

    This is a matched case-control study dating from before the availability of conditional logistic regression.

    Format

    education

    Index of the record.

    age

    Age in years of case.

    parity

    Count.

    induced

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

    case_status

    0 = control, 1 = case.

    spontaneous

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

    stratum

    Matched set number.

    pooled_stratum

    Stratum number.

    Note

    One case with two prior spontaneous abortions and two prior induced abortions is omitted.

    Source

    Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

    Examples

    t = tblish.dataset.infert;

# TODO: Port glm() (generalized linear model) stuff to Octave

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    8.2.58.31 tblish.dataset.InsectSprays

    Static Method: out = InsectSprays ()

    Effectiveness of Insect Sprays

    Description

    The counts of insects in agricultural experimental units treated with different insecticides.

    Format

    spray

    The type of spray.

    count

    Insect count.

    Source

    Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

    References

    McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.InsectSprays;

# TODO: boxplot

# TODO: AOV plots

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    8.2.58.32 tblish.dataset.iris

    Static Method: out = iris ()

    The Fisher Iris dataset: measurements of various flowers

    Description

    This is the classic Fisher Iris dataset.

    Format

    Species

    The species of flower being measured.

    SepalLength

    Length of sepals, in centimeters.

    SepalWidth

    Width of sepals, in centimeters.

    PetalLength

    Length of petals, in centimeters.

    PetalWidth

    Width of petals, in centimeters.

    Source

    http://archive.ics.uci.edu/ml/datasets/Iris

    References

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

    Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

    The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

    Examples

    # TODO: Port this example from R

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    8.2.58.33 tblish.dataset.islands

    Static Method: out = islands ()

    Areas of the World’s Major Landmasses

    Description

    The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

    Format

    name

    The name of the island.

    area

    The area, in thousands of square miles.

    Source

    The World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.islands;

# TODO: Port dot chart to Octave

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    8.2.58.34 tblish.dataset.JohnsonJohnson

    Static Method: out = JohnsonJohnson ()

    Quarterly Earnings per Johnson & Johnson Share

    Description

    Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

    Format

    date

    Start date of the quarter.

    earnings

    Earnings per share (USD).

    Source

    Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

    Examples

    t = tblish.dataset.JohnsonJohnson

# TODO: Yikes, look at all those plots. Port them to Octave.

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    8.2.58.35 tblish.dataset.LakeHuron

    Static Method: out = LakeHuron ()

    Level of Lake Huron 1875-1972

    Description

    Annual measurements of the level, in feet, of Lake Huron 1875–1972.

    Format

    year

    Year of the measurement

    level

    Lake level (ft).

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

    Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

    Examples

    t = tblish.dataset.LakeHuron;

plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")

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    8.2.58.36 tblish.dataset.lh

    Static Method: out = lh ()

    Luteinizing Hormone in Blood Samples

    Description

    A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

    Format

    sample

    The number of the observation.

    lh

    Level of luteinizing hormone.

    Source

    P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

    Examples

    t = tblish.dataset.lh;

plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");

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    8.2.58.37 tblish.dataset.LifeCycleSavings

    Static Method: out = LifeCycleSavings ()

    Intercountry Life-Cycle Savings Data

    Description

    Data on the savings ratio 1960–1970.

    Format

    country

    Name of the country.

    sr

    Aggregate personal savings.

    pop15

    Percentage of population under 15.

    pop75

    Percentage of population over 75.

    dpi

    Real per-capita disposable income.

    ddpi

    Percent growth rate of dpi.

    Details

    Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

    Source

    The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

    References

    Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

    Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

    Examples

    t = tblish.dataset.LifeCycleSavings;

# TODO: linear model

# TODO: pairs plot with Lowess smoothed line

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    8.2.58.38 tblish.dataset.Loblolly

    Static Method: out = Loblolly ()

    Growth of Loblolly pine trees

    Description

    Records of the growth of Loblolly pine trees.

    Format

    height

    Tree height (ft).

    age

    Tree age (years).

    Seed

    Seed source for the tree. Ordering is according to increasing maximum height.

    Source

    Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Loblolly;

t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")

# TODO: Compute and plot fitted curve

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    8.2.58.39 tblish.dataset.longley

    Static Method: out = longley ()

    Longley’s Economic Regression Data

    Description

    A macroeconomic data set which provides a well-known example for a highly collinear regression.

    Format

    Year

    The year.

    GNP_deflator

    GNP implicit price deflator (1954=100).

    GNP

    Gross National Product.

    Unemployed

    Number of unemployed.

    Armed_Forces

    Number of people in the armed forces.

    Population

    “Noninstitutionalized” population ≥ 14 years of age.

    Employed

    Number of people employed.

    Source

    J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.longley;

# TODO: Linear model
# TODO: opar plot

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    8.2.58.40 tblish.dataset.lynx

    Static Method: out = lynx ()

    Annual Canadian Lynx trappings 1821-1934

    Description

    Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

    Format

    year

    Year of the record.

    lynx

    Number of lynx trapped.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

    Examples

    t = tblish.dataset.lynx;

plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");

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    8.2.58.41 tblish.dataset.morley

    Static Method: out = morley ()

    Michelson Speed of Light Data

    Description

    A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

    Format

    Expt

    The experiment number, from 1 to 5.

    Run

    The run number within each experiment.

    Speed

    Speed-of-light measurement.

    Details

    The data is here viewed as a randomized block experiment with experiment and run as the factors. run may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

    Source

    A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

    S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

    A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

    Examples

    t = tblish.dataset.morley;

# TODO: Port to Octave

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    8.2.58.42 tblish.dataset.mtcars

    Static Method: out = mtcars ()

    Motor Trend 1974 Car Road Tests

    Description

    The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

    Format

    mpg

    Fuel efficiency in miles/gallon

    cyl

    Number of cylinders

    disp

    Displacement (cu. in.)

    hp

    Gross horsepower

    drat

    Rear axle ratio

    wt

    Weight (1,000 lbs)

    qsec

    1/4 mile time

    vs

    Engine type (0 = V-shaped, 1 = straight)

    am

    Transmission type (0 = automatic, 1 = manual)

    gear

    Number of forward gears

    carb

    Number of carburetors

    Note

    Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

    Source

    Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

    Examples

    # TODO: Port this example from R

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    8.2.58.43 tblish.dataset.nhtemp

    Static Method: out = nhtemp ()

    Average Yearly Temperatures in New Haven

    Description

    The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

    Format

    year

    Year of the observation.

    temp

    Mean annual temperature (degrees F).

    Source

    Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.nhtemp;

plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

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    8.2.58.44 tblish.dataset.Nile

    Static Method: out = Nile ()

    Flow of the River Nile

    Description

    Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

    Format

    year

    Year of the record.

    flow

    Annual flow (cubic meters).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

    References

    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

    Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

    Examples

    t = tblish.dataset.Nile;

figure
plot (t.year, t.flow);

# TODO: Port the rest of the example to Octave

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    8.2.58.45 tblish.dataset.nottem

    Static Method: out = nottem ()

    Average Monthly Temperatures at Nottingham, 1920-1939

    Description

    A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

    Format

    record

    Index of the record.

    lead

    Leading indicator.

    sales

    Sales volume.

    Source

    Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

    Examples

    # TODO: Come up with example code here

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    8.2.58.46 tblish.dataset.npk

    Static Method: out = npk ()

    Classical N, P, K Factorial Experiment

    Description

    A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

    Format

    block

    Which block (1 to 6).

    N

    Indicator (0/1) for the application of nitrogen.

    P

    Indicator (0/1) for the application of phosphate.

    K

    Indicator (0/1) for the application of potassium.

    yield

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

    Source

    Imperial College, London, M.Sc. exercise sheet.

    References

    Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

    Examples

    t = tblish.dataset.npk;

# TODO: Port aov() and LM to Octave

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    8.2.58.47 tblish.dataset.occupationalStatus

    Static Method: out = occupationalStatus ()

    Occupational Status of Fathers and their Sons

    Description

    Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

    Format

    An 8-by-8 matrix of counts, with classifying fators origin (father’s occupational status, levels 1:8) and destination (son’s occupational status, levels 1:8).

    Source

    Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

    Examples

    # TODO: Come up with example code here

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    8.2.58.48 tblish.dataset.Orange

    Static Method: out = Orange ()

    Growth of Orange Trees

    Description

    Records of the growth of orange trees.

    Format

    Tree

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

    age

    Age of the tree (days since 1968-12-31).

    circumference

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.Orange;

# TODO: Port coplot to Octave

# TODO: Linear model

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    8.2.58.49 tblish.dataset.OrchardSprays

    Static Method: out = OrchardSprays ()

    Potency of Orchard Sprays

    Description

    An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

    Format

    rowpos

    Row of the design.

    colpos

    Column of the design

    treatment

    Treatment level.

    decrease

    Response.

    Details

    Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

    The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

    An 8 x 8 Latin square design was used and the treatments were coded as follows:

    A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

    Source

    Finney, D. J. (1947). Probit Analysis. Cambridge.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.OrchardSprays;

tblish.examples.plot_pairs (t);

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    8.2.58.50 tblish.dataset.PlantGrowth

    Static Method: out = PlantGrowth ()

    Results from an Experiment on Plant Growth

    Description

    Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

    Format

    group

    Treatment condition group.

    weight

    Weight of plants.

    Source

    Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

    Examples

    t = tblish.dataset.PlantGrowth;

# TODO: Port anova to Octave

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    8.2.58.51 tblish.dataset.precip

    Static Method: out = precip ()

    Annual Precipitation in US Cities

    Description

    The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

    Format

    city

    City observed.

    precip

    Annual precipitation (in).

    Source

    Statistical Abstracts of the United States, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.precip;

# TODO: Port dot plot to Octave

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    8.2.58.52 tblish.dataset.presidents

    Static Method: out = presidents ()

    Quarterly Approval Ratings of US Presidents

    Description

    The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

    Format

    date

    Approximate date of the observation.

    approval

    Approval rating (%).

    Details

    The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

    Source

    The Gallup Organisation.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.presidents;

figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")

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    8.2.58.53 tblish.dataset.pressure

    Static Method: out = pressure ()

    Vapor Pressure of Mercury as a Function of Temperature

    Description

    Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

    Format

    temperature

    Temperature (deg C).

    pressure

    Pressure (mm Hg).

    Source

    Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.pressure;

figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")

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    8.2.58.54 tblish.dataset.Puromycin

    Static Method: out = Puromycin ()

    Reaction Velocity of an Enzymatic Reaction

    Description

    Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

    Format

    state

    Whether the cell was treated.

    conc

    Substrate concentrations (ppm).

    rate

    Instantaneous reaction rates (counts/min/min).

    Details

    Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

    Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

    Examples

    t = tblish.dataset.Puromycin;

# TODO: Port example to Octave

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    8.2.58.55 tblish.dataset.quakes

    Static Method: out = quakes ()

    Locations of Earthquakes off Fiji

    Description

    The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

    Format

    lat

    Latitude of event.

    long

    Longitude of event.

    depth

    Depth (km).

    mag

    Richter magnitude.

    stations

    Number of stations reporting.

    Details

    There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

    Source

    This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

    References

    G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    # TODO: Come up with example code here

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    8.2.58.56 tblish.dataset.randu

    Static Method: out = randu ()

    Random Numbers from Congruential Generator RANDU

    Description

    400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

    Format

    record

    Index of the record.

    x

    X value of the triple.

    y

    Y value of the triple.

    z

    Z value of the triple.

    Details

    In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

    These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

    Under VMS versions 2.0 and higher, this problem has been fixed.

    Source

    David Donoho

    Examples

    t = tblish.dataset.randu;

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    8.2.58.57 tblish.dataset.rivers

    Static Method: out = rivers ()

    Lengths of Major North American Rivers

    Description

    This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

    Format

    rivers

    A vector containing 141 observations.

    Source

    World Almanac and Book of Facts, 1975, page 406.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.rivers;

longest_river = max (rivers)
shortest_river = min (rivers)

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    8.2.58.58 tblish.dataset.rock

    Static Method: out = rock ()

    Measurements on Petroleum Rock Samples

    Description

    Measurements on 48 rock samples from a petroleum reservoir.

    Format

    area

    Area of pores space, in pixels out of 256 by 256.

    peri

    Perimeter in pixels.

    shape

    Perimeter/sqrt(area).

    perm

    Permeability in milli-Darcies.

    Details

    Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

    Source

    Data from BP Research, image analysis by Ronit Katz, U. Oxford.

    Examples

    t = tblish.dataset.rock;

figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")

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    8.2.58.59 tblish.dataset.sleep

    Static Method: out = sleep ()

    Student’s Sleep Data

    Description

    Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    Format

    id

    Patient ID.

    group

    Drug given.

    extra

    Increase in hours of sleep.

    Details

    The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

    Source

    Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

    Student (1908). The probable error of the mean. Biometrika, 6, 20.

    References

    Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

    Examples

    t = tblish.dataset.sleep;

# TODO: Port to Octave

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    8.2.58.60 tblish.dataset.stackloss

    Static Method: out = stackloss ()

    Brownlee’s Stack Loss Plant Data

    Description

    Operational data of a plant for the oxidation of ammonia to nitric acid.

    Format

    AirFlow

    Flow of cooling air.

    WaterTemp

    Cooling Water Inlet temperature.

    AcidConc

    Concentration of acid (per 1000, minus 500).

    StackLoss

    Stack loss

    Details

    “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

    AirFlow represents the rate of operation of the plant. WaterTemp is the temperature of cooling water circulated through coils in the absorption tower. AcidConc is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

    Source

    Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

    Examples

    t = tblish.dataset.stackloss;

# TODO: Create linear model and print summary

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    8.2.58.61 tblish.dataset.state

    Static Method: out = state ()

    US State Facts and Figures

    Description

    Data related to the 50 states of the United States of America.

    Format

    abb

    State abbreviation.

    name

    State name.

    area

    Area (sq mi).

    lat

    Approximate center (latitude).

    lon

    Approximate center (longitude).

    division

    State division.

    revion

    State region.

    Population

    Population estimate as of July 1, 1975.

    Income

    Per capita income (1974).

    Illiteracy

    Illiteracy as of 1970 (percent of population).

    LifeExp

    Lfe expectancy in years (1969-71).

    Murder

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

    HSGrad

    Percent high-school graduates (1970).

    Frost

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

    Source

    U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

    U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.state;

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    8.2.58.62 tblish.dataset.sunspot_month

    Static Method: out = sunspot_month ()

    Monthly Sunspot Data, from 1749 to “Present”

    Description

    Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

    Format

    month

    Month of the observation.

    sunspots

    Number of sunspots.

    Source

    WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

    Examples

    t = tblish.dataset.sunspot_month;

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    8.2.58.63 tblish.dataset.sunspot_year

    Static Method: out = sunspot_year ()

    Yearly Sunspot Data, 1700-1988

    Description

    Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

    Format

    year

    Year of the observation.

    sunspots

    Number of sunspots.

    Source

    H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

    Examples

    t = tblish.dataset.sunspot_year;

figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")

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    8.2.58.64 tblish.dataset.sunspots

    Static Method: out = sunspots ()

    Monthly Sunspot Numbers, 1749-1983

    Description

    Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

    Format

    month

    Month of the observation.

    sunspots

    Number of observed sunspots.

    Source

    Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

    Examples

    t = tblish.dataset.sunspots;

figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")

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    8.2.58.65 tblish.dataset.swiss

    Static Method: out = swiss ()

    Swiss Fertility and Socioeconomic Indicators (1888) Data

    Description

    Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

    Format

    Fertility

    Ig, ‘common standardized fertility measure’.

    Agriculture

    % of males involved in agriculture as occupation.

    Examination

    % draftees receiving highest mark on army examination.

    Education

    % education beyond primary school for draftees.

    Catholic

    % ‘Catholic’ (as opposed to ‘Protestant’).

    InfantMortality

    Live births who live less than 1 year.

    All variables but ‘Fertility’ give proportions of the population.

    Source

    (paraphrasing Mosteller and Tukey):

    Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

    The data collected are for 47 French-speaking “provinces” at about 1888.

    Here, all variables are scaled to [0, 100], where in the original, all but Catholic were scaled to [0, 1].

    Note

    Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

    They state that variables Examination and Education are averages for 1887, 1888 and 1889.

    References

    Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

    Examples

    t = tblish.dataset.swiss;

# TODO: Port linear model to Octave

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    8.2.58.66 tblish.dataset.Theoph

    Static Method: out = Theoph ()

    Pharmacokinetics of Theophylline

    Description

    An experiment on the pharmacokinetics of theophylline.

    Format

    Subject

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

    Wt

    Weight of the subject (kg).

    Dose

    Dose of theophylline administerred orally to the subject (mg/kg).

    Time

    Time since drug administration when the sample was drawn (hr).

    conc

    Theophylline concentration in the sample (mg/L).

    Details

    Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

    These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

    Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

    Examples

    t = tblish.dataset.Theoph;

# TODO: Coplot
# TODO: Yet another linear model to port to Octave

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    8.2.58.67 tblish.dataset.Titanic

    Static Method: out = Titanic ()

    Survival of passengers on the Titanic

    Description

    This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

    Format

    n is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

    Class

    1st, 2nd, 3rd, Cre.

    Sex

    Male, Female.

    Age

    Child, Adult.

    Survived

    No, Yes.

    Details

    The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

    These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

    Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

    Source

    Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

    The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

    British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

    Examples

    tblish.dataset.Titanic;

# TODO: Port mosaic plot to Octave

# TODO: Check for higher survival rates in children and females

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    8.2.58.68 tblish.dataset.ToothGrowth

    Static Method: out = ToothGrowth ()

    The Effect of Vitamin C on Tooth Growth in Guinea Pigs

    Description

    The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as VC).

    Format

    supp

    Supplement type.

    dose

    Dose (mg/day).

    len

    Tooth length.

    Source

    C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

    Examples

    t = tblish.dataset.ToothGrowth;

tblish.examples.coplot (t, "dose", "len", "supp");

# TODO: Port Lowess smoothing to Octave

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    8.2.58.69 tblish.dataset.treering

    Static Method: out = treering ()

    Yearly Treering Data, -6000-1979

    Description

    Contains normalized tree-ring widths in dimensionless units.

    Format

    A univariate time series with 7981 observations.

    Each tree ring corresponds to one year.

    Details

    The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

    Source

    Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

    References

    For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

    Examples

    t = tblish.dataset.treering;

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    8.2.58.70 tblish.dataset.trees

    Static Method: out = trees ()

    Diameter, Height and Volume for Black Cherry Trees

    Description

    This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

    Format

    Girth

    Tree diameter (rather than girth, actually) in inches.

    Height

    Height in ft.

    Volume

    Volume of timber in cubic feet.

    Source

    Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

    References

    Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

    Examples

    t = tblish.dataset.trees;

figure
tblish.examples.plot_pairs (t);

figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")

# TODO: Transform to log space for the coplot

# TODO: Linear model

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    8.2.58.71 tblish.dataset.UCBAdmissions

    Static Method: out = UCBAdmissions ()

    Student Admissions at UC Berkeley

    Description

    Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

    Format

    A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

    Admit

    Admitted, Rejected.

    Gender

    Male, Female.

    Dept

    A, B, C, D, E, F.

    Details

    This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

    Source

    The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

    References

    Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

    Examples

    tblish.dataset.UCBAdmissions;

# TODO: Port mosaic plot to Octave

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    8.2.58.72 tblish.dataset.UKDriverDeaths

    Static Method: out = UKDriverDeaths ()

    Road Casualties in Great Britain 1969-84

    Description

    UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

    Seatbelts is more information on the same problem.

    Format

    UKDriverDeaths is a table with the following variables:

    month

    Month of the observation.

    deaths

    Number of deaths.

    Seatbelts is a table with the following variables:

    month

    Month of the observation.

    DriversKilled

    Car drivers killed.

    drivers

    Same as UKDriverDeaths deaths count.

    front

    Front-seat passengers killed or seriously injured.

    rear

    Rear-seat passengers killed or seriously injured.

    kms

    Distance driven.

    PetrolPrice

    Petrol price.

    VanKilled

    Number of van (“light goods vehicle”) drivers killed.

    law

    0/1: was the seatbelt law in effect that month?

    Source

    Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

    Examples

    tblish.dataset.UKDriverDeaths;
d = UKDriverDeaths;
s = Seatbelts;

# TODO: Port the model and plots to Octave

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    8.2.58.73 tblish.dataset.UKgas

    Static Method: out = UKgas ()

    UK Quarterly Gas Consumption

    Description

    Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

    Format

    date

    Quarter of the observation

    gas

    Gas consumption (MM therms).

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

    Examples

    t = tblish.dataset.UKgas;

plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")

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    8.2.58.74 tblish.dataset.UKLungDeaths

    Static Method: out = UKLungDeaths ()

    Monthly Deaths from Lung Diseases in the UK

    Description

    Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

    Format

    date

    Month of the observation.

    ldeaths

    Total lung deaths.

    fdeaths

    Lung deaths among females.

    mdeaths

    Lung deaths among males.

    Source

    P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

    Examples

    t = tblish.dataset.UKLungDeaths;

figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")

figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")

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    8.2.58.75 tblish.dataset.USAccDeaths

    Static Method: out = USAccDeaths ()

    Accidental Deaths in the US 1973-1978

    Description

    A time series giving the monthly totals of accidental deaths in the USA.

    Format

    month

    Month of the observation.

    deaths

    Accidental deaths.

    Source

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

    Examples

    t = tblish.dataset.USAccDeaths;

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    8.2.58.76 tblish.dataset.USArrests

    Static Method: out = USArrests ()

    Violent Crime Rates by US State

    Description

    This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

    Format

    State

    State name.

    Murder

    Murder arrests (per 100,000).

    Assault

    Assault arrests (per 100,000).

    UrbanPop

    Percent urban population.

    Rape

    Rape arrests (per 100,000).

    Note

    USArrests contains the data as in McNeil’s monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

    See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

    Source

    World Almanac and Book of Facts 1975. (Crime rates).

    Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.USArrests;

summary (t);

tblish.examples.plot_pairs (t(:,2:end));

# TODO: Difference between USArrests and its correction

# TODO: +/- 0.5 to restore the original <n>.5 percentages

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    8.2.58.77 tblish.dataset.USJudgeRatings

    Static Method: out = USJudgeRatings ()

    Lawyers’ Ratings of State Judges in the US Superior Court

    Description

    Lawyers’ ratings of state judges in the US Superior Court.

    Format

    CONT

    Number of contacts of lawyer with judge.

    INTG

    Judicial integrity.

    DMNR

    Demeanor.

    DILG

    Diligence.

    CFMG

    Case flow managing.

    DECI

    Prompt decisions.

    PREP

    Preparation for trial.

    FAMI

    Familiarity with law.

    ORAL

    Sound oral rulings.

    WRIT

    Sound written rulings.

    PHYS

    Physical ability.

    RTEN

    Worthy of retention.

    Source

    New Haven Register, 14 January, 1977 (from John Hartigan).

    Examples

    t = tblish.dataset.USJudgeRatings;

figure
tblish.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")

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    8.2.58.78 tblish.dataset.USPersonalExpenditure

    Static Method: out = USPersonalExpenditure ()

    Personal Expenditure Data

    Description

    This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

    Format

    A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.

    Source

    The World Almanac and Book of Facts, 1962, page 756.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.USPersonalExpenditure;

# TODO: Port medpolish() from R, whatever that is.

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    8.2.58.79 tblish.dataset.uspop

    Static Method: out = uspop ()

    Populations Recorded by the US Census

    Description

    This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

    Format

    year

    Year of the census.

    population

    Population, in millions.

    Source

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.uspop;

figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")

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    8.2.58.80 tblish.dataset.VADeaths

    Static Method: out = VADeaths ()

    Death Rates in Virginia (1940)

    Description

    Death rates per 1000 in Virginia in 1940.

    Format

    A 2-dimensional matrix deaths, with age group along dimension 1 and demographic group along dimension 2.

    Details

    The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

    This provides a rather nice 3-way analysis of variance example.

    Source

    Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.VADeaths;

# TODO: Port to Octave

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    8.2.58.81 tblish.dataset.volcano

    Static Method: out = volcano ()

    Topographic Information on Auckland’s Maunga Whau Volcano

    Description

    Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

    Format

    A matrix volcano with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

    Source

    Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

    References

    Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

    Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

    Examples

    tblish.dataset.volcano;

# TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
# should be fine. And then maybe do a 3-d mesh plot.

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    8.2.58.82 tblish.dataset.warpbreaks

    Static Method: out = warpbreaks ()

    The Number of Breaks in Yarn during Weaving

    Description

    This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

    Format

    wool

    Type of wool (A or B).

    tension

    The level of tension (L, M, H).

    breaks

    Number of breaks.

    There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

    Source

    Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

    References

    Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.warpbreaks;

summary (t)

# TODO: Port the plotting code and OPAR to Octave

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    8.2.58.83 tblish.dataset.women

    Static Method: out = women ()

    Average Heights and Weights for American Women

    Description

    This data set gives the average heights and weights for American women aged 30–39.

    Format

    height

    Height (in).

    weight

    Weight (lbs).

    Details

    The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

    The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

    Source

    The World Almanac and Book of Facts, 1975.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    t = tblish.dataset.women;

figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")

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    8.2.58.84 tblish.dataset.WorldPhones

    Static Method: out = WorldPhones ()

    The World’s Telephones

    Description

    The number of telephones in various regions of the world (in thousands).

    Format

    A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

    The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

    The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

    Source

    AT&T (1961) The World’s Telephones.

    References

    McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

    Examples

    tblish.dataset.WorldPhones;

# TODO: Port matplot() to Octave

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    8.2.58.85 tblish.dataset.WWWusage

    Static Method: out = WWWusage ()

    WWWusage

    Description

    A time series of the numbers of users connected to the Internet through a server every minute.

    Format

    A time series of length 100.

    Source

    Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

    References

    Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

    Examples

    # TODO: Come up with example code here

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    8.2.58.86 tblish.dataset.zCO2

    Static Method: out = zCO2 ()

    Carbon Dioxide Uptake in Grass Plants

    Description

    The CO2 data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

    Format

    Details

    The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

    Source

    Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

    Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

    Examples

    t = tblish.dataset.zCO2;

# TODO: Coplot
# TODO: Port the linear model to Octave

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    8.2.59 tblish.datasets

    Class: tblish.datasets

    Example dataset collection.

    tblish.datasets is a collection of example datasets to go with the Tablicious package.

    The tblish.datasets class provides methods for listing and loading the example datasets.

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    8.2.59.1 datasets.description

    Static Method: description (datasetName)
    Static Method: out = description (datasetName)

    Get or display the description for a dataset.

    Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

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    8.2.59.2 datasets.list

    Static Method: list ()
    Static Method: out = list ()

    List all datasets.

    Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

    Returns a table with variables Name, Description, and possibly more.

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    8.2.59.3 datasets.load

    Static Method: load (datasetName)
    Static Method: out = load (datasetName)

    Load a specified dataset.

    datasetName is the name of the dataset to load, as found in the Name column of the dataset list.

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    8.2.60 tblish.evalWithTableVars

    Function: out = tblish.evalWithTableVars (tbl, expr)

    Evaluate an expression against a table array’s variables.

    Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

    expr is a charvec containing an Octave expression.

    As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

    Returns the result of the evaluation.

    Examples: 

    [s,p,sp] = tblish.examples.SpDb
tmp = join (sp, p);
shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

    See also: table.restrict

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    8.2.61 tblish.examples.coplot

    Function: [fig, hax] = tblish.examples.coplot (tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (fig, tbl, xvar, yvar, gvar)
    Function: [fig, hax] = tblish.examples.coplot (…, OptionName, OptionValue, …)

    Conditioning plot.

    tblish.examples.coplot produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

    tbl is a table containing the data to plot.

    xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

    yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

    gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

    fig is the figure handle to plot into. If fig is not provided, a new figure is created.

    Name/Value options:

    PlotFcn

    The plotting function to use, supplied as a function handle. Defaults to @plot. It must be a function that provides the signature fcn(hax, X, Y, …).

    PlotArgs

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

    Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

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    8.2.62 tblish.examples.plot_pairs

    Function: out = tblish.examples.plot_pairs (data)
    Function: out = tblish.examples.plot_pairs (data, plot_type)
    Function: out = tblish.examples.plot_pairs (fig, …)

    Plot pairs of variables against each other.

    data is the data holding the variables to plot. It may be either a table or a struct. Each variable or field in the table or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

    plot_type is a charvec indicating what plot type to do in each subplot. ("scatter" is the default.) Valid plot_type values are:

    "scatter"

    A plain scatter plot.

    "smooth"

    A scatter plot + fitted line, like R’s panel.smooth does.

    fig is an optional figure handle to plot into. If omitted, a new figure is created.

    Returns the created figure, if the output is captured.

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    8.2.63 tblish.examples.SpDb

    Function: spdb = tblish.examples.SpDb ()
    Function: [s, p, sp] = tblish.examples.SpDb ()

    The classic Suppliers-Parts example database.

    Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

    Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

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    8.2.64 tblish.sizeof2

    Function: out = tblish.sizeof2 (x)

    Approximate size of an array in bytes, with object support.

    This is an alternative to Octave’s sizeof function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

    This may be supplemented or replaced by sizeof override methods on Tablicious’s classes. I’m not sure whether Octave’s sizeof supports extension by method overrides, so I’m not doing that yet. If that happens, this sizeof2 function will stick around in a deprecated state for a while, and it will respect those override methods.

    For tables, this returns the sum of sizeof for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

    This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

    This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

    x is an array of any type.

    Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

    See also: sizeof

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    8.2.65 tblish.table.grpstats

    Function: [out] = tblish.table.grpstats (tbl, groupvar)
    Function: [out] = tblish.table.grpstats (…, 'DataVars', DataVars)

    Statistics by group for a table array.

    This is a table-specific implementation of grpstats that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global grpstats function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its grpstats function. This function is supplied as an alternative you can use in an environment where table arrays are not supported by the grpstats that you have, though you need to make code changes and call it as tblish.table.grpstats(tbl) instead of with a plain grpstats(tbl).

    See also: table.groupby, table.findgroups, table.splitapply

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    8.2.66 timezones

    Function: out = timezones ()
    Function: out = timezones (area)

    List all the time zones defined on this system.

    This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.

    If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

    Name

    The IANA zone name, as cellstr.

    Area

    The geographical area the zone is in, as cellstr.

    Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

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    8.2.67 todatetime

    Function: out = todatetime (x)

    Convert input to a Tablicious datetime array, with convenient interface.

    This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

    This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

    Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

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    8.2.68 vartype

    Function: out = vartype (type)

    Filter by variable type for use in suscripting.

    Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

    type is the name of a type as charvec. This may be anything that the isa function accepts, or 'cellstr' to select cellstrs, as determined by iscellstr.

    Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a table object.

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    8.2.69 vecfun

    Function: out = vecfun (fcn, x, dim)

    Apply function to vectors in array along arbitrary dimension.

    This function is not implemented yet.

    Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

    fcn is a function handle to apply.

    x is an array of arbitrary type which is to be sliced and passed in to fcn.

    dim is the dimension along which the vector slices lay.

    Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

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    8.2.70 years

    Function File: out = years (x)

    Create a duration x years long, or get the years in a duration x.

    If input is numeric, returns a duration array in units of fixed-length years of 365.2425 days each.

    If input is a duration, converts the duration to a number of fixed-length years as double.

    Note: years creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use calyears.

    See calyears.

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    9 Copying

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