vcdExtra/0000755000175100001440000000000012576452330012057 5ustar hornikusersvcdExtra/inst/0000755000175100001440000000000012576352702013036 5ustar hornikusersvcdExtra/inst/doc/0000755000175100001440000000000012576352714013606 5ustar hornikusersvcdExtra/inst/doc/vcd-tutorial.Rnw0000644000175100001440000023323612576352714016724 0ustar hornikusers% !Rnw weave = Sweave %\VignetteEngine{Sweave} %\VignetteIndexEntry{Tutorial: Working with categorical data with R and the vcd package} %\VignetteDepends{vcd,gmodels,ca} %\VignetteKeywords{contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R} %\VignettePackage{vcdExtra} \documentclass[10pt,twoside]{article} \usepackage{Sweave} \usepackage{bm} \usepackage[toc]{multitoc} % for table of contents % from Z.cls \usepackage[authoryear,round,longnamesfirst]{natbib} \bibpunct{(}{)}{;}{a}{}{,} \bibliographystyle{jss} \usepackage{hyperref} \usepackage{color} %% colors \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \hypersetup{% hyperindex = {true}, colorlinks = {true}, % linktocpage = {true}, plainpages = {false}, linkcolor = {Blue}, citecolor = {Blue}, urlcolor = {Red}, pdfstartview = {Fit}, pdfpagemode = {UseOutlines}, pdfview = {XYZ null null null} } %\AtBeginDocument{ % \hypersetup{% % pdfauthor = {Michael Friendly}, % pdftitle = {Tutorial: Working with categorical data with R and the vcd package}, % pdfkeywords = {contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R} % } %} % math stuff \newcommand*{\given}{\ensuremath{\, | \,}} \renewcommand*{\vec}[1]{\ensuremath{\bm{#1}}} \newcommand{\mat}[1]{\ensuremath{\bm{#1}}} \newcommand{\trans}{\ensuremath{^\mathsf{T}}} \newcommand{\diag}[1]{\ensuremath{\mathrm{diag} (#1)}} \def\binom#1#2{{#1 \choose #2}}% \newcommand{\implies}{ \ensuremath{\mapsto} } \newenvironment{equation*}{\displaymath}{\enddisplaymath}% \newcommand{\tabref}[1]{Table~\ref{#1}} \newcommand{\figref}[1]{Figure~\ref{#1}} \newcommand{\secref}[1]{Section~\ref{#1}} \newcommand{\loglin}{loglinear } %\usepackage{thumbpdf} % page dimensions \addtolength{\hoffset}{-1.5cm} \addtolength{\textwidth}{3cm} \addtolength{\voffset}{-1cm} \addtolength{\textheight}{2cm} % Vignette examples \newcommand*{\Example}{\fbox{\textbf{\emph{Example}}:} } % R stuff \newcommand{\var}[1]{\textit{\texttt{#1}}} \newcommand{\data}[1]{\texttt{#1}} \newcommand{\class}[1]{\textsf{"#1"}} %% \code without `-' ligatures \def\nohyphenation{\hyphenchar\font=-1 \aftergroup\restorehyphenation} \def\restorehyphenation{\hyphenchar\font=`-} {\catcode`\-=\active% \global\def\code{\bgroup% \catcode`\-=\active \let-\codedash% \Rd@code}} \def\codedash{-\discretionary{}{}{}} \def\Rd@code#1{\texttt{\nohyphenation#1}\egroup} \newcommand{\codefun}[1]{\code{#1()}} \let\proglang=\textsf \newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}} \newcommand{\Rpackage}[1]{{\textsf{#1}}} %% almost as usual \author{Michael Friendly\\York University, Toronto} \title{Working with categorical data with \proglang{R} and the \pkg{vcd} and \pkg{vcdExtra} packages} \date{\footnotesize{Using \Rpackage{vcdExtra} version \Sexpr{packageDescription("vcdExtra")[["Version"]]} and \Rpackage{vcd} version \Sexpr{packageDescription("vcd")[["Version"]]}; Date: \Sexpr{Sys.Date()}}} %% for pretty printing and a nice hypersummary also set: %\Plainauthor{Michael Friendly} %% comma-separated %\Shorttitle{vcd tutorial} %% a short title (if necessary) %\Plaintitle{Tutorial: Working with categorical data with R and the vcd package} %\SweaveOpts{engine=R,eps=TRUE,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} \SweaveOpts{engine=R,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} \SweaveOpts{prefix.string=fig/vcd-tut,eps=FALSE} \SweaveOpts{keep.source=TRUE} %\SweaveOpts{concordance=TRUE} \setkeys{Gin}{width=0.7\textwidth} <>= set.seed(1071) #library(vcd) library(vcdExtra) library(ggplot2) #data(Titanic) data(HairEyeColor) data(PreSex) data(Arthritis) art <- xtabs(~Treatment + Improved, data = Arthritis) if(!file.exists("fig")) dir.create("fig") @ %% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{document} \SweaveOpts{concordance=TRUE} \maketitle %% an abstract and keywords \begin{abstract} This tutorial describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results. The framework is provided by the \proglang{R} package \pkg{vcd}, but other packages are used to help with various tasks. The \pkg{vcdExtra} package extends the graphical and statistical methods provided by \pkg{vcd}. \end{abstract} %\keywords{contingency tables, mosaic plots, sieve plots, %categorical data, independence, conditional independence, generalized linear models, %\proglang{R}} %\Plainkeywords{contingency tables, mosaic plots, % sieve plots, categorical data, independence, % conditional independence, generalized linear models, R} {\small % \sloppy % \begin{multicols}{2} \tableofcontents % \end{multicols} } \section[Introduction]{Introduction}\label{sec:intro} %% Note: If there is markup in \(sub)section, then it has to be escape as above. This tutorial, part of the \pkg{vcdExtra} package, describes how to work with categorical data in the context of fitting statistical models in \proglang{R} and visualizing the results using the \pkg{vcd} and \pkg{vcdExtra} packages. It focuses first on methods and tools for creating and manipulating \proglang{R} data objects which represent frequency and contingency tables involving categorical variables. Further sections describe some simple methods for calculating tests of independence and measures of association amomg categorial variables, and also methods for graphically displaying results. There is much more to the analysis of categorical data than is described here, where the emphasis is on cross-tabulated tables of frequencies (``contingency tables''), statistical tests, associated \loglin\ models, and visualization of \emph{how} variables are related. A more general treatment of graphical methods for categorical data is contained in my book, \emph{Visualizing Categorical Data} \citep{vcd:Friendly:2000}, for which \pkg{vcd} is a partial \proglang{R} companion, covering topics not otherwise available in \proglang{R}. On the other hand, the implementation of graphical methods in \pkg{vcd} is more general in many respects than what I provided in \proglang{SAS}. Statistical models for categorical data in \proglang{R} have been extended considerably with the \pkg{gnm} package for generalized \emph{nonlinear} models. The \pkg{vcdExtra} package extends \pkg{vcd} methods to models fit using \codefun{glm} and \codefun{gnm}. A more complete theoretical description of these statistical methods is provided in Agresti's \citeyearpar{vcd:Agresti:2002,Agresti:2013} \emph{Categorical Data Analysis}. For this, see the \proglang{Splus/R} companion by Laura Thompson, \url{https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf} and Agresti's support web page, \url{http://www.stat.ufl.edu/~aa/cda/cda.html}. \section[Creating frequency tables]{Creating and manipulating frequency tables}\label{sec:creating} \proglang{R} provides many methods for creating frequency and contingency tables. Several are described below. In the examples below, we use some real examples and some anonymous ones, where the variables \code{A}, \code{B}, and \code{C} represent categorical variables, and \code{X} represents an arbitrary \proglang{R} data object. The first thing you need to know is that categorical data can be represented in three different forms in \proglang{R}, and it is sometimes necessary to convert from one form to another, for carrying out statistical tests, fitting models or visualizing the results. Once a data object exists in \proglang{R}, you can examine its complete structure with the \codefun{str} function, or view the names of its components with the \codefun{names} function. \begin{description} \item[case form] a data frame containing individual observations, with one or more factors, used as the classifying variables. In case form, there may also be numeric covariates. The total number of observations is \code{nrow(X)}, and the number of variables is \code{ncol(X)}. \Example The \data{Arthritis} data is available in case form in the \pkg{vcd} package. There are two explanatory factors: \code{Treatment} and \code{Sex}. \code{Age} is a numeric covariate, and \code{Improved} is the response--- an ordered factor, with levels \code{\Sexpr{paste(levels(Arthritis$Improved),collapse=' < ')}}. Excluding \code{Age}, we would have a $2 \times 2 \times 3$ contingency table for \code{Treatment}, \code{Sex} and \code{Improved}. %\code{"None" < "Some" < "Marked"}. <>= names(Arthritis) # show the variables str(Arthritis) # show the structure head(Arthritis,5) # first 5 observations, same as Arthritis[1:5,] @ \item[frequency form] a data frame containing one or more factors, and a frequency variable, often called \code{Freq} or \code{count}. The total number of observations is \verb|sum(X$Freq)|, \code{sum(X[,"Freq"])} or some equivalent form. The number of cells in the table is \code{nrow(X)}. \Example For small frequency tables, it is often convenient to enter them in frequency form using \codefun{expand.grid} for the factors and \codefun{c} to list the counts in a vector. The example below, from \cite{vcd:Agresti:2002} gives results for the 1991 General Social Survey, with respondents classified by sex and party identification. <>= # Agresti (2002), table 3.11, p. 106 GSS <- data.frame( expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")), count=c(279,165,73,47,225,191)) GSS names(GSS) str(GSS) sum(GSS$count) @ \item[table form] a matrix, array or table object, whose elements are the frequencies in an $n$-way table. The variable names (factors) and their levels are given by \code{dimnames(X)}. The total number of observations is \code{sum(X)}. The number of dimensions of the table is \code{length(dimnames(X))}, and the table sizes are given by \code{sapply(dimnames(X), length)}. \Example The \data{HairEyeColor} is stored in table form in \pkg{vcd}. <>= str(HairEyeColor) # show the structure sum(HairEyeColor) # number of cases sapply(dimnames(HairEyeColor), length) # table dimension sizes @ \Example Enter frequencies in a matrix, and assign \code{dimnames}, giving the variable names and category labels. Note that, by default, \codefun{matrix} uses the elements supplied by \emph{columns} in the result, unless you specify \code{byrow=TRUE}. <>= ## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4) dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"), satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS")) JobSat @ \data{JobSat} is a matrix, not an object of \code{class("table")}, and some functions are happier with tables than matrices. You can coerce it to a table with \codefun{as.table}, <>= JobSat <- as.table(JobSat) str(JobSat) @ \end{description} \subsection[Ordered factors]{Ordered factors and reordered tables}\label{sec:ordered-factors} In table form, the values of the table factors are ordered by their position in the table. Thus in the \data{JobSat} data, both \code{income} and \code{satisfaction} represent ordered factors, and the \emph{positions} of the values in the rows and columns reflects their ordered nature. Yet, for analysis, there are time when you need \emph{numeric} values for the levels of ordered factors in a table, e.g., to treat a factor as a quantitative variable. In such cases, you can simply re-assign the \code{dimnames} attribute of the table variables. For example, here, we assign numeric values to \code{income} as the middle of their ranges, and treat \code{satisfaction} as equally spaced with integer scores. <>= dimnames(JobSat)$income<-c(7.5,20,32.5,60) dimnames(JobSat)$satisfaction<-1:4 @ For the \data{HairEyeColor} data, hair color and eye color are ordered arbitrarily. For visualizing the data using mosaic plots and other methods described below, it turns out to be more useful to assure that both hair color and eye color are ordered from dark to light. Hair colors are actually ordered this way already, and it is easiest to re-order eye colors by indexing. Again \codefun{str} is your friend. <>= HairEyeColor <- HairEyeColor[, c(1,3,4,2), ] str(HairEyeColor) @ This is also the order for both hair color and eye color shown in the result of a correspondence analysis (\figref{fig:ca-haireye}) below. With data in case form or frequency form, when you have ordered factors represented with character values, you must ensure that they are treated as ordered in \proglang{R}.% \footnote{In \proglang{SAS}, many procedures offer the option \code{order = data | internal | formatted} to allow character values to be ordered according to (a) their order in the data set, (b) sorted internal value, or (c) sorted formatted representation provided by a \proglang{SAS} format. } Imagine that the \data{Arthritis} data was read from a text file. By default the \code{Improved} will be ordered alphabetically: \code{Marked}, \code{None}, \code{Some}--- not what we want. In this case, the function \codefun{ordered} (and others) can be useful. <>= Arthritis <- read.csv("arthritis.txt",header=TRUE) Arthritis$Improved <- ordered(Arthritis$Improved, levels=c("None", "Some", "Marked")) @ With this order of \code{Improved}, the response in this data, a mosaic display of \code{Treatment} and \code{Improved} (\figref{fig:arthritis})shows a clearly interpretable pattern. <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") @ %\setkeys{Gin}{width=0.7\textwidth} \begin{figure}[htb] \begin{center} %<>= %mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") %@ \includegraphics[width=0.7\textwidth]{fig/vcd-tut-Arthritis} \caption{Mosaic plot for the \data{Arthritis} data, showing the marginal model of independence for Treatment and Improved. Age, a covariate, and Sex are ignored here.} \label{fig:arthritis} \end{center} \end{figure} Finally, there are situations where, particularly for display purposes, you want to re-order the \emph{dimensions} of an $n$-way table, or change the labels for the variables or levels. This is easy when the data are in table form: \codefun{aperm} permutes the dimensions, and assigning to \code{names} and \code{dimnames} changes variable names and level labels respectively. We will use the following version of \data{UCBAdmissions} in \secref{sec:mantel} below.% \footnote{ Changing \code{Admit} to \code{Admit?} might be useful for display purposes, but is dangerous--- because it is then difficult to use that variable name in a model formula. See \secref{sec:tips} for options \code{labeling\_args} and \code{set\_labels} to change variable and level names for displays in the \code{strucplot} framework. } <>= UCB <- aperm(UCBAdmissions, c(2, 1, 3)) dimnames(UCB)[[2]] <- c("Yes", "No") names(dimnames(UCB)) <- c("Sex", "Admit?", "Department") ftable(UCB) @ %There is one subtle ``gotcha'' here: \codefun{aperm} returns an object of class \class{"array"}, %whereas \data{UCBAdmissions} is of class \class{"table"}, so methods defined for \code{table} %objects will not work on the permuted array. %The solution is to reassign the \code{class} of the result of \codefun{aperm}. % %<>= %class(UCBAdmissions) %class(UCB) %str(as.data.frame(UCBAdmissions)) # OK %str(as.data.frame(UCB)) # wrong % %class(UCB) <- "table" %str(as.data.frame(UCB)) # now OK %@ % \subsection[structable()]{\codefun{structable}}\label{sec:structable} For 3-way and larger tables the \codefun{structable} function in \pkg{vcd} provides a convenient and flexible tabular display. The variables assigned to the rows and columns of a two-way display can be specified by a model formula. <>= structable(HairEyeColor) # show the table: default structable(Hair+Sex ~ Eye, HairEyeColor) # specify col ~ row variables @ It also returns an object of class \code{"structable"} which may be plotted with \codefun{mosaic} (not shown here). <>= HSE < - structable(Hair+Sex ~ Eye, HairEyeColor) # save structable object mosaic(HSE) # plot it @ \subsection[table() and friends]{\codefun{table} and friends}\label{sec:table} You can generate frequency tables from factor variables using the \codefun{table} function, tables of proportions using the \codefun{prop.table} function, and marginal frequencies using \codefun{margin.table}. <>= n=500 A <- factor(sample(c("a1","a2"), n, rep=TRUE)) B <- factor(sample(c("b1","b2"), n, rep=TRUE)) C <- factor(sample(c("c1","c2"), n, rep=TRUE)) mydata <- data.frame(A,B,C) @ <>= # 2-Way Frequency Table attach(mydata) mytable <- table(A,B) # A will be rows, B will be columns mytable # print table margin.table(mytable, 1) # A frequencies (summed over B) margin.table(mytable, 2) # B frequencies (summed over A) prop.table(mytable) # cell percentages prop.table(mytable, 1) # row percentages prop.table(mytable, 2) # column percentages @ \codefun{table} can also generate multidimensional tables based on 3 or more categorical variables. In this case, use the \codefun{ftable} or \codefun{structable} function to print the results more attractively. <>= # 3-Way Frequency Table mytable <- table(A, B, C) ftable(mytable) @ \codefun{table} ignores missing values by default. To include \code{NA} as a category in counts, include the table option \code{exclude=NULL} if the variable is a vector. If the variable is a factor you have to create a new factor using \code{newfactor <- factor(oldfactor, exclude=NULL)}. \subsection[xtabs()]{\codefun{xtabs}}\label{sec:xtabs} The \codefun{xtabs} function allows you to create crosstabulations of data using formula style input. This typically works with case-form data supplied in a data frame or a matrix. The result is a contingency table in array format, whose dimensions are determined by the terms on the right side of the formula. <>= # 3-Way Frequency Table mytable <- xtabs(~A+B+C, data=mydata) ftable(mytable) # print table summary(mytable) # chi-square test of indepedence @ If a variable is included on the left side of the formula, it is assumed to be a vector of frequencies (useful if the data have already been tabulated in frequency form). <>= (GSStab <- xtabs(count ~ sex + party, data=GSS)) summary(GSStab) @ \subsection[Collapsing over factors]{Collapsing over table factors: \codefun{aggregate}, \codefun{margin.table} and \codefun{apply}} It sometimes happens that we have a data set with more variables or factors than we want to analyse, or else, having done some initial analyses, we decide that certain factors are not important, and so should be excluded from graphic displays by collapsing (summing) over them. For example, mosaic plots and fourfold displays are often simpler to construct from versions of the data collapsed over the factors which are not shown in the plots. The appropriate tools to use again depend on the form in which the data are represented--- a case-form data frame, a frequency-form data frame (\codefun{aggregate}), or a table-form array or table object (\codefun{margin.table} or \codefun{apply}). When the data are in frequency form, and we want to produce another frequency data frame, \codefun{aggregate} is a handy tool, using the argument \code{FUN=sum} to sum the frequency variable over the factors \emph{not} mentioned in the formula. \Example The data frame \data{DaytonSurvey} in the \pkg{vcdExtra} package represents a $2^5$ table giving the frequencies of reported use (``ever used?'') of alcohol, cigarettes and marijuana in a sample of high school seniors, also classified by sex and race. <>= str(DaytonSurvey) head(DaytonSurvey) @ To focus on the associations among the substances, we want to collapse over sex and race. The right-hand side of the formula used in the call to \codefun{aggregate} gives the factors to be retained in the new frequency data frame, \code{Dayton.ACM.df}. <>= # data in frequency form # collapse over sex and race Dayton.ACM.df <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum) Dayton.ACM.df @ When the data are in table form, and we want to produce another table, \codefun{apply} with \code{FUN=sum} can be used in a similar way to sum the table over dimensions not mentioned in the \code{MARGIN} argument. \codefun{margin.table} is just a wrapper for \codefun{apply} using the \codefun{sum} function. \Example To illustrate, we first convert the \data{DaytonSurvey} to a 5-way table using \codefun{xtabs}, giving \code{Dayton.tab}. <>== # in table form Dayton.tab <- xtabs(Freq~cigarette+alcohol+marijuana+sex+race, data=DaytonSurvey) structable(cigarette+alcohol+marijuana ~ sex+race, data=Dayton.tab) @ Then, use \codefun{apply} on \code{Dayton.tab} to give the 3-way table \code{Dayton.ACM.tab} summed over sex and race. The elements in this new table are the column sums for \code{Dayton.tab} shown by \codefun{structable} just above. <>== # collapse over sex and race Dayton.ACM.tab <- apply(Dayton.tab, MARGIN=1:3, FUN=sum) Dayton.ACM.tab <- margin.table(Dayton.tab, 1:3) # same result structable(cigarette+alcohol ~ marijuana, data=Dayton.ACM.tab) @ Many of these operations can be performed using the \verb|**ply()| functions in the \pkg{plyr} package. For example, with the data in a frequency form data frame, use \codefun{ddply} to collapse over unmentioned factors, and \codefun{plyr::summarise}% \footnote{ Ugh. This \pkg{plyr} function clashes with a function of the same name in \pkg{vcdExtra}. In this document I will use the explicit double-colon notation to keep them separate. } as the function to be applied to each piece. <>== Dayton.ACM.df <- ddply(DaytonSurvey, .(cigarette, alcohol, marijuana), plyr::summarise, Freq=sum(Freq)) @ \subsection[Collapsing levels]{Collapsing table levels: \codefun{collapse.table}} A related problem arises when we have a table or array and for some purpose we want to reduce the number of levels of some factors by summing subsets of the frequencies. For example, we may have initially coded Age in 10-year intervals, and decide that, either for analysis or display purposes, we want to reduce Age to 20-year intervals. The \codefun{collapse.table} function in \pkg{vcdExtra} was designed for this purpose. \Example Create a 3-way table, and collapse Age from 10-year to 20-year intervals. First, we generate a $2 \times 6 \times 3$ table of random counts from a Poisson distribution with mean of 100. <>= # create some sample data in frequency form sex <- c("Male", "Female") age <- c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69") education <- c("low", 'med', 'high') data <- expand.grid(sex=sex, age=age, education=education) counts <- rpois(36, 100) # random Possion cell frequencies data <- cbind(data, counts) # make it into a 3-way table t1 <- xtabs(counts ~ sex + age + education, data=data) structable(t1) @ Now collapse \code{age} to 20-year intervals, and \code{education} to 2 levels. In the arguments, levels of \code{age} and \code{education} given the same label are summed in the resulting smaller table. <>= # collapse age to 3 levels, education to 2 levels t2 <- collapse.table(t1, age=c("10-29", "10-29", "30-49", "30-49", "50-69", "50-69"), education=c(">= as.data.frame(GSStab) @ \Example Convert the \code{Arthritis} data in case form to a 3-way table of \code{Treatment} $\times$ \code{Sex} $\times$ \code{Improved}. Note the use of \codefun{with} to avoid having to use \code{Arthritis\$Treatment} etc. within the call to \codefun{table}.% \footnote{ \codefun{table} does not allow a \code{data} argument to provide an environment in which the table variables are to be found. In the examples in \secref{sec:table} I used \code{attach(mydata)} for this purpose, but \codefun{attach} leaves the variables in the global environment, while \codefun{with} just evaluates the \codefun{table} expression in a temporary environment of the data. } <>= Art.tab <-with(Arthritis, table(Treatment, Sex, Improved)) str(Art.tab) ftable(Art.tab) @ There may also be times that you will need an equivalent case form \code{data.frame} with factors representing the table variables rather than the frequency table. For example, the \codefun{mca} function in package \pkg{MASS} only operates on data in this format. Marc Schwartz provided code for \codefun{expand.dft} on the Rhelp mailing list for converting a table back into a case form \code{data.frame}. This function is included in \pkg{vcdExtra}. \Example Convert the \data{Arthritis} data in table form (\code{Art.tab}) back to a \code{data.frame} in case form, with factors \code{Treatment}, \code{Sex} and \code{Improved}. <>= Art.df <- expand.dft(Art.tab) str(Art.df) @ \subsection{A complex example}\label{sec:complex} If you've followed so far, you're ready for a more complicated example. The data file, \code{tv.dat} represents a 4-way table of size $5 \times 11 \times 5 \times 3$ where the table variables (unnamed in the file) are read as \code{V1} -- \code{V4}, and the cell frequency is read as \code{V5}. The file, stored in the \code{doc/extdata} directory of \pkg{vcdExtra}, can be read as follows: <>= tv.data<-read.table(system.file("doc","extdata","tv.dat",package="vcdExtra")) head(tv.data,5) @ For a local file, just use \codefun{read.table} in this form: <>= tv.data<-read.table("C:/R/data/tv.dat") @ The data \code{tv.dat} came from the initial implementation of mosaic displays in \proglang{R} by Jay Emerson. In turn, they came from the initial development of mosaic displays \citep{vcd:Hartigan+Kleiner:1984} that illustrated the method with data on a large sample of TV viewers whose behavior had been recorded for the Neilson ratings. This data set contains sample television audience data from Neilsen Media Research for the week starting November 6, 1995. \begin{flushleft} The table variables are:\\ ~~~\code{V1}-- values 1:5 correspond to the days Monday--Friday;\\ ~~~\code{V2}-- values 1:11 correspond to the quarter hour times 8:00PM through 10:30PM;\\ ~~~\code{V3}-- values 1:5 correspond to ABC, CBS, NBC, Fox, and non-network choices;\\ ~~~\code{V4}-- values 1:3 correspond to transition states: turn the television Off, Switch channels, or Persist in viewing the current channel. \end{flushleft} We are interested just the cell frequencies, and rely on the facts that the (a) the table is complete--- there are no missing cells, so \code{nrow(tv.data)}=\Sexpr{nrow(tv.data)}; (b) the observations are ordered so that \code{V1} varies most rapidly and \code{V4} most slowly. From this, we can just extract the frequency column and reshape it into an array. <>= TV <- array(tv.data[,5], dim=c(5,11,5,3)) dimnames(TV) <- list(c("Monday","Tuesday","Wednesday","Thursday","Friday"), c("8:00","8:15","8:30","8:45","9:00","9:15","9:30", "9:45","10:00","10:15","10:30"), c("ABC","CBS","NBC","Fox","Other"), c("Off","Switch","Persist")) names(dimnames(TV))<-c("Day", "Time", "Network", "State") @ More generally (even if there are missing cells), we can use \codefun{xtabs} (or \codefun{plyr::daply}) to do the cross-tabulation, using \code{V5} as the frequency variable. Here's how to do this same operation with \codefun{xtabs}: <>= TV <- xtabs(V5 ~ ., data=tv.data) dimnames(TV) <- list(Day=c("Monday","Tuesday","Wednesday","Thursday","Friday"), Time=c("8:00","8:15","8:30","8:45","9:00","9:15","9:30", "9:45","10:00","10:15","10:30"), Network=c("ABC","CBS","NBC","Fox","Other"), State=c("Off","Switch","Persist")) @ But this 4-way table is too large and awkward to work with. Among the networks, Fox and Other occur infrequently. We can also cut it down to a 3-way table by considering only viewers who persist with the current station.% \footnote{This relies on the fact that that indexing an array drops dimensions of length 1 by default, using the argument \code{drop=TRUE}; the result is coerced to the lowest possible dimension. } <>= TV <- TV[,,1:3,] # keep only ABC, CBS, NBC TV <- TV[,,,3] # keep only Persist -- now a 3 way table structable(TV) @ Finally, for some purposes, we might want to collapse the 11 times into a smaller number. Here, we use \codefun{as.data.frame.table} to convert the table back to a data frame, \codefun{levels} to re-assign the values of \code{Time}, and finally, \codefun{xtabs} to give a new, collapsed frequency table. <>= TV.df <- as.data.frame.table(TV) levels(TV.df$Time) <- c(rep("8:00-8:59",4),rep("9:00-9:59",4), rep("10:00-10:44",3)) TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df) structable(Day ~ Time+Network,TV2) @ Whew! See \figref{fig:TV-mosaic} for a mosaic plot of the \code{TV2} data. \section{Tests of Independence} \subsection{CrossTable} OK, now we're ready to do some analyses. For tabular displays, the \codefun{CrossTable} function in the \pkg{gmodels} package produces cross-tabulations modeled after \code{PROC FREQ} in \proglang{SAS} or \code{CROSSTABS} in \proglang{SPSS}. It has a wealth of options for the quantities that can be shown in each cell. <>= # 2-Way Cross Tabulation library(gmodels) CrossTable(GSStab,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE) @ There are options to report percentages (row, column, cell), specify decimal places, produce Chi-square, Fisher, and McNemar tests of independence, report expected and residual values (pearson, standardized, adjusted standardized), include missing values as valid, annotate with row and column titles, and format as \proglang{SAS} or \proglang{SPSS} style output! See \code{help(CrossTable)} for details. \subsection{Chi-square test} For 2-way tables you can use \codefun{chisq.test} to test independence of the row and column variable. By default, the $p$-value is calculated from the asymptotic chi-squared distribution of the test statistic. Optionally, the $p$-value can be derived via Monte Carlo simulation. <>= (HairEye <- margin.table(HairEyeColor, c(1, 2))) chisq.test(HairEye) @ \subsection{Fisher Exact Test}\label{sec:Fisher} \code{fisher.test(X)} provides an exact test of independence. \code{X} must be a two-way contingency table in table form. Another form, \code{fisher.test(X, Y)} takes two categorical vectors of the same length. For tables larger than $2 \times 2$ the method can be computationally intensive (or can fail) if the frequencies are not small. <>= fisher.test(GSStab) @ But this does not work because \data{HairEye} data has $n$=592 total frequency. An exact test is unnecessary in this case. <>= fisher.test(HairEye) @ %# <>= %# #cat(try(fisher.test(HairEye))) %# @ \begin{Soutput} Error in fisher.test(HairEye) : FEXACT error 6. LDKEY is too small for this problem. Try increasing the size of the workspace. \end{Soutput} \subsection[Mantel-Haenszel test]{Mantel-Haenszel test and conditional association}\label{sec:mantel} Use the \code{mantelhaen.test(X)} function to perform a Cochran-Mantel-Haenszel $\chi^2$ chi test of the null hypothesis that two nominal variables are \emph{conditionally independent}, $A \perp B \given C$, in each stratum, assuming that there is no three-way interaction. \code{X} is a 3 dimensional contingency table, where the last dimension refers to the strata. The \data{UCBAdmissions} serves as an example of a $2 \times 2 \times 6$ table, with \code{Dept} as the stratifying variable. <>= ## UC Berkeley Student Admissions mantelhaen.test(UCBAdmissions) @ The results show no evidence for association between admission and gender when adjusted for department. However, we can easily see that the assumption of equal association across the strata (no 3-way association) is probably violated. For $2 \times 2 \times k$ tables, this can be examimed from the odds ratios for each $2 \times 2$ table (\codefun{oddsratio}), and tested by using \verb|woolf_test()| in \pkg{vcd}. %<>= %oddsRatio <- function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1]) %apply(UCBAdmissions, 3, oddsRatio) % %woolf_test(UCBAdmissions) %@ <>= oddsratio(UCBAdmissions, log=FALSE) lor <- oddsratio(UCBAdmissions) # capture log odds ratios summary(lor) woolf_test(UCBAdmissions) @ We can visualize the odds ratios of Admission for each department with fourfold displays using \codefun{fourfold}. The cell frequencies $n_{ij}$ of each $2 \times 2$ table are shown as a quarter circle whose radius is proportional to $\sqrt{n_{ij}}$, so that its area is proportional to the cell frequency. Confidence rings for the odds ratio allow a visual test of the null of no association; the rings for adjacent quadrants overlap \emph{iff} the observed counts are consistent with the null hypothesis. In the extended version (the default), brighter colors are used where the odds ratio is significantly different from 1. The following lines produce \figref{fig:fourfold1}.% \footnote{The color values \code{col[3:4]} were modified from their default values to show a greater contrast between significant and insignifcant associations here.} <>= col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080") fourfold(UCB,mfrow=c(2,3), color=col) @ %\setkeys{Gin}{width=0.8\textwidth} \begin{figure}[htb] \begin{center} %<>= %col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080") %fourfold(UCB,mfrow=c(2,3), color=col) %@ \includegraphics[width=0.8\textwidth,trim=80 50 80 50]{fig/vcd-tut-fourfold1} \caption{Fourfold display for the \data{UCBAdmissions} data. Where the odds ratio differs significantly from 1.0, the confidence bands do not overlap, and the circle quadrants are shaded more intensely.} \label{fig:fourfold1} \end{center} \end{figure} Another \pkg{vcd} function, \codefun{cotabplot}, provides a more general approach to visualizing conditional associations in contingency tables, similar to trellis-like plots produced by \codefun{coplot} and lattice graphics. The \code{panel} argument supplies a function used to render each conditional subtable. The following gives a display (not shown) similar to \figref{fig:fourfold1}. <>= cotabplot(UCB, panel = cotab_fourfold) @ When we want to view the conditional probabilities of a response variable (e.g., \code{Admit}) in relation to several factors, an alternative visualization is a \codefun{doubledecker} plot. This plot is a specialized version of a mosaic plot, which highlights the levels of a response variable (plotted vertically) in relation to the factors (shown horizontally). The following call produces \figref{fig:doubledecker}, where we use indexing on the first factor (\code{Admit}) to make \code{Admitted} the highlighted level. In this plot, the association between \code{Admit} and \code{Gender} is shown where the heights of the highlighted conditional probabilities do not align. The excess of females admitted in Dept A stands out here. <>= doubledecker(Admit ~ Dept + Gender, data=UCBAdmissions[2:1,,]) @ \begin{figure}[htb] \begin{center} \includegraphics[width=0.9\textwidth]{fig/vcd-tut-doubledecker} \caption{Doubledecker display for the \data{UCBAdmissions} data. The heights of the highlighted bars show the conditional probabilities of \texttt{Admit}, given \texttt{Dept} and \texttt{Gender}.} \label{fig:doubledecker} \end{center} \end{figure} Finally, the there is a \codefun{plot} method for \code{oddsratio} objects. By default, it shows the 95\% confidence interval for the log odds ratio. \figref{fig:oddsratio} is produced by: <>= plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)") @ \setkeys{Gin}{width=0.5\textwidth} \begin{figure}[htb] \begin{center} <>= plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)") @ \caption{Log odds ratio plot for the \data{UCBAdmissions} data.} \label{fig:oddsratio} \end{center} \end{figure} \subsection[CMH tests: ordinal factors]{Cochran-Mantel-Haenszel tests for ordinal factors}\label{sec:CMH} The standard $\chi^2$ tests for association in a two-way table treat both table factors as nominal (unordered) categories. When one or both factors of a two-way table are quantitative or ordinal, more powerful tests of association may be obtaianed by taking ordinality into account, using row and or column scores to test for linear trends or differences in row or column means. More general versions of the CMH tests (Landis etal., 1978) are provided by assigning numeric scores to the row and/or column variables. For example, with two ordinal factors (assumed to be equally spaced), assigning integer scores, \code{1:R} and \code{1:C} tests the linear $\times$ linear component of association. This is statistically equivalent to the Pearson correlation between the integer-scored table variables, with $\chi^2 = (n-1) r^2$, with only 1 $df$ rather than $(R-1)\times(C-1)$ for the test of general association. When only one table variable is ordinal, these general CMH tests are analogous to an ANOVA, testing whether the row mean scores or column mean scores are equal, again consuming fewer $df$ than the test of general association. The \codefun{CMHtest} function in \pkg{vcdExtra} now calculates these various CMH tests for two possibly ordered factors, optionally stratified other factor(s). \Example Recall the $4 \times 4$ table, \code{JobSat} introduced in \secref{sec:creating}, <>= JobSat @ Treating the \code{satisfaction} levels as equally spaced, but using midpoints of the \code{income} categories as row scores gives the following results: <>= CMHtest(JobSat, rscores=c(7.5,20,32.5,60)) @ Note that with the relatively small cell frequencies, the test for general give no evidence for association. However, the the \code{cor} test for linear x linear association on 1 df is nearly significant. The \pkg{coin} contains the functions \verb|cmh_test()| and \verb|lbl_test()| for CMH tests of general association and linear x linear association respectively. \subsection{Measures of Association} There are a variety of statistical measures of \emph{strength} of association for contingency tables--- similar in spirit to $r$ or $r^2$ for continuous variables. With a large sample size, even a small degree of association can show a significant $\chi^2$, as in the example below for the \data{GSS} data. The \codefun{assocstats} function in \pkg{vcd} calculates the $\phi$ contingency coefficient, and Cramer's V for an $r \times c$ table. The input must be in table form, a two-way $r \times c$ table. It won't work with \data{GSS} in frequency form, but by now you should know how to convert. <>= assocstats(GSStab) @ For tables with ordinal variables, like \data{JobSat}, some people prefer the Goodman-Kruskal $\gamma$ statistic (\citet[\S 2.4.3]{vcd:Agresti:2002}) based on a comparison of concordant and discordant pairs of observations in the case-form equivalent of a two-way table. <>= GKgamma(JobSat) @ A web article by Richard Darlington, \url{http://www.psych.cornell.edu/Darlington/crosstab/TABLE0.HTM} gives further description of these and other measures of association. \subsection{Measures of Agreement} The \codefun{Kappa} function in the \pkg{vcd} package calculates Cohen's $\kappa$ and weighted $\kappa$ for a square two-way table with the same row and column categories \citep{Cohen:60}.% \footnote{ Don't confuse this with \codefun{kappa} in base \proglang{R} that computes something entirely different (the condition number of a matrix). } Normal-theory $z$-tests are obtained by dividing $\kappa$ by its asymptotic standard error (ASE). A \codefun{confint} method for \code{Kappa} objects provides confidence intervals. <>= (K <- Kappa(SexualFun)) confint(K) @ A visualization of agreement, both unweighted and weighted for degree of departure from exact agreement is provided by the \codefun{agreementplot} function. \figref{fig:agreesex} shows the agreementplot for the \data{SexualFun} data, produced as shown below. The Bangdiwala measures represent the proportion of the shaded areas of the diagonal rectangles, using weights $w_1$ for exact agreement, and $w_2$ for partial agreement one step from the main diagonal. <>= agree <- agreementplot(SexualFun, main="Is sex fun?") unlist(agree) @ %\setkeys{Gin}{width=0.5\textwidth} \begin{figure}[htb] \begin{center} %<>= %agree <- agreementplot(SexualFun, main="Is sex fun?") %agree %@ \includegraphics[width=0.4\textwidth,trim=50 25 50 25]{fig/vcd-tut-agreesex} \caption{Agreement plot for the \data{SexualFun} data.} \label{fig:agreesex} \end{center} \end{figure} In other examples, the agreement plot can help to show \emph{sources} of disagreement. For example, when the shaded boxes are above or below the diagonal (red) line, a lack of exact agreement can be attributed in part to different frequency of use of categories by the two raters-- lack of \emph{marginal homogeneity}. \subsection{Correspondence analysis} Use the \pkg{ca} package for correspondence analysis for visually exploring relationships between rows and columns in contingency tables. For an $r \times c$ table, the method provides a breakdown of the Pearson $\chi^2$ for association in up to $M = \min(r-1, c-1)$ dimensions, and finds scores for the row ($x_{im}$) and column ($y_{jm}$) categories such that the observations have the maximum possible correlations.% \footnote{ Related methods are the non-parametric CMH tests using assumed row/column scores (\secref{sec:CMH}), the analogous \codefun{glm} model-based methods (\secref{sec:CMH}), and the more general RC models which can be fit using \codefun{gnm}. Correspondence analysis differs in that it is a primarily descriptive/exploratory method (no significance tests), but is directly tied to informative graphic displays of the row/column categories. } Here, we carry out a simple correspondence analysis of the \data{HairEye} data. The printed results show that nearly 99\% of the association between hair color and eye color can be accounted for in 2 dimensions, of which the first dimension accounts for 90\%. <>= library(ca) ca(HairEye) @ The resulting \code{ca} object can be plotted just by running the \codefun{plot} method on the \code{ca} object, giving the result in \figref{fig:ca-haireye}. \codefun{plot.ca} does not allow labels for dimensions; these can be added with \codefun{title}. It can be seen that most of the association is accounted for by the ordering of both hair color and eye color along Dimension 1, a dark to light dimension. <>= plot(ca(HairEye), main="Hair Color and Eye Color") title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)") @ \setkeys{Gin}{width=0.7\textwidth} \begin{figure}[htb] \begin{center} <>= plot(ca(HairEye), main="Hair Color and Eye Color") title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)") @ \caption{Correspondence analysis plot for the \data{HairEye} data.} \label{fig:ca-haireye} \end{center} \end{figure} \section{Loglinear Models}\label{sec:loglin} You can use the \codefun{loglm} function in the \pkg{MASS} package to fit log-linear models. Equivalent models can also be fit (from a different perspective) as generalized linear models with the \codefun{glm} function using the \code{family='poisson'} argument, and the \pkg{gnm} package provides a wider range of generalized \emph{nonlinear} models, particularly for testing structured associations. The visualization methods for these models were originally developed for models fit using \codefun{loglm}, so this approach is emphasized here. Some extensions of these methods for models fit using \codefun{glm} and \codefun{gnm} are contained in the \pkg{vcdExtra} package and illustrated in \secref{sec:glm}. Assume we have a 3-way contingency table based on variables A, B, and C. The possible different forms of \loglin\ models for a 3-way table are shown in \tabref{tab:loglin-3way}. The \textbf{Model formula} column shows how to express each model for \codefun{loglm} in \proglang{R}.% \footnote{ For \codefun{glm}, or \codefun{gnm}, with the data in the form of a frequency data.frame, the same model is specified in the form \code{glm(Freq} $\sim$ \code{..., family="poisson")}, where \texttt{Freq} is the name of the cell frequency variable and \texttt{...} specifies the \textbf{Model formula}. } In the \textbf{Interpretation} column, the symbol ``$\perp$'' is to be read as ``is independent of,'' and ``$\given$'' means ``conditional on,'' or ``adjusting for,'' or just ``given''. \begin{table}[htb] \caption{Log-linear Models for Three-Way Tables}\label{tab:loglin-3way} \begin{center} \begin{tabular}{llll} \hline \textbf{Model} & \textbf{Model formula} & \textbf{Symbol}& \textbf{Interpretation} \\ \hline\hline Mutual independence & \verb|~A + B + C| & $[A][B][C]$ & $A \perp B \perp C$ \\ Joint independence & \verb|~A*B + C| & $[AB][C]$ & $(A \: B) \perp C$ \\ Conditional independence & \verb|~(A+B)*C| & $[AC][BC]$ & $(A \perp B) \given C$ \\ All two-way associations & \verb|~A*B + A*C + B*C| & $[AB][AC][BC]$ & homogeneous association \\ Saturated model & \verb|~A*B*C| & $[ABC]$ & 3-way association \\ \hline \end{tabular} \end{center} \end{table} For example, the formula \verb|~A + B + C| specifies the model of \emph{mutual independence} with no associations among the three factors. In standard notation for the expected frequencies $m_{ijk}$, this corresponds to \begin{equation*} \log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C \equiv \texttt{A + B + C} \end{equation*} The parameters $\lambda_i^A , \lambda_j^B$ and $\lambda_k^C$ pertain to the differences among the one-way marginal frequencies for the factors A, B and C. Similarly, the model of \emph{joint independence}, $(A \: B) \perp C$, allows an association between A and B, but specifies that C is independent of both of these and their combinations, \begin{equation*} \log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C + \lambda_{ij}^{AB} \equiv \texttt{A * B + C} \end{equation*} where the parameters $\lambda_{ij}^{AB}$ pertain to the overall association between A and B (collapsing over C). In the literature or text books, you will often find these models expressed in shorthand symbolic notation, using brackets, \texttt{[ ]} to enclose the \emph{high-order terms} in the model. Thus, the joint independence model can be denoted \texttt{[AB][C]}, as shown in the \textbf{Symbol} column in \tabref{tab:loglin-3way}. Models of \emph{conditional independence} allow (and fit) two of the three possible two-way associations. There are three such models, depending on which variable is conditioned upon. For a given conditional independence model, e.g., \texttt{[AB][AC]}, the given variable is the one common to all terms, so this example has the interpretation $(B \perp C) \given A$. \subsection[Fitting with loglm()]{Fitting with \codefun{loglm}}\label{sec:loglm} For example, we can fit the model of mutual independence among hair color, eye color and sex in \data{HairEyeColor} as <>= library(MASS) ## Independence model of hair and eye color and sex. hec.1 <- loglm(~Hair+Eye+Sex, data=HairEyeColor) hec.1 @ Similarly, the models of conditional independence and joint independence are specified as <>= ## Conditional independence hec.2 <- loglm(~(Hair + Eye) * Sex, data=HairEyeColor) hec.2 @ <>= ## Joint independence model. hec.3 <- loglm(~Hair*Eye + Sex, data=HairEyeColor) hec.3 @ Note that printing the model gives a brief summary of the goodness of fit. A set of models can be compared using the \codefun{anova} function. <>= anova(hec.1, hec.2, hec.3) @ %Martin Theus and Stephan Lauer have written an excellent article on Visualizing %Loglinear Models, using mosaic plots. There is also great tutorial example by %Kevin Quinn on analyzing loglinear models via glm. \subsection[Fitting with glm() and gnm()]{Fitting with \codefun{glm} and \codefun{gnm}}\label{sec:glm} The \codefun{glm} approach, and extensions of this in the \pkg{gnm} package allows a much wider class of models for frequency data to be fit than can be handled by \codefun{loglm}. Of particular importance are models for ordinal factors and for square tables, where we can test more structured hypotheses about the patterns of association than are provided in the tests of general assosiation under \codefun{loglm}. These are similar in spirit to the non-parametric CMH tests described in \secref{sec:CMH}. \Example The data \code{Mental} in the \pkg{vcdExtra} package gives a two-way table in frequency form classifying young people by their mental health status and parents' socioeconomic status (SES), where both of these variables are ordered factors. <>= str(Mental) xtabs(Freq ~ mental+ses, data=Mental) # display the frequency table @ Simple ways of handling ordinal variables involve assigning scores to the table categories, and the simplest cases are to use integer scores, either for the row variable (``column effects'' model), the column variable (``row effects'' model), or both (``uniform association'' model). <>= indep <- glm(Freq ~ mental + ses, family = poisson, data = Mental) # independence model @ To fit more parsimonious models than general association, we can define numeric scores for the row and column categories <>= # Use integer scores for rows/cols Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) @ Then, the row effects model, the column effects model, and the uniform association model can be fit as follows: <>= # column effects model (ses) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) # row effects model (mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) # linear x linear association linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) @ The \codefun{Summarize} in \pkg{vcdExtra} provides a nice, compact summary of the fit statistics for a set of models, collected into a \class{glmlist} object. Smaller is better for AIC and BIC. <>= # compare models using AIC, BIC, etc vcdExtra::LRstats(glmlist(indep, roweff, coleff, linlin)) @ For specific model comparisons, we can also carry out tests of \emph{nested} models with \codefun{anova} when those models are listed from smallest to largest. Here, there are two separate paths from the most restrictive (independence) model through the model of uniform association, to those that allow only one of row effects or column effects. <>= anova(indep, linlin, coleff, test="Chisq") anova(indep, linlin, roweff, test="Chisq") @ The model of linear by linear association seems best on all accounts. For comparison, one might try the CMH tests on these data: <>= CMHtest(xtabs(Freq~ses+mental, data=Mental)) @ \subsection{Non-linear terms} The strength of the \pkg{gnm} package is that it handles a wide variety of models that handle non-linear terms, where the parameters enter the model beyond a simple linear function. The simplest example is the Goodman RC(1) model, which allows a multiplicative term to account for the association of the table variables. In the notation of generalized linear models with a log link, this can be expressed as \begin{equation*} \log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i} \delta_{j} \end{equation*} where the row-multiplicative effect parameters $\gamma_i$ and corresponding column parameters $\delta_j$ are estimated from the data.% \footnote{ This is similar in spirit to a correspondence analysis with a single dimension, but as a statistical model. } Similarly, the RC(2) model adds two multiplicative terms to the independence model, \begin{equation*} \log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i1} \delta_{j1} + \gamma_{i2} \delta_{j2} \end{equation*} In the \pkg{gnm} package, these models may be fit using the \codefun{Mult} to specify the multiplicative term, and \codefun{instances} to specify several such terms. \Example For the \code{Mental} data, we fit the RC(1) and RC(2) models, and compare these with the independence model. <>= RC1 <- gnm(Freq ~ mental + ses + Mult(mental,ses), data=Mental, family=poisson, , verbose=FALSE) RC2 <- gnm(Freq ~ mental+ses + instances(Mult(mental,ses),2), data=Mental, family=poisson, verbose=FALSE) anova(indep, RC1, RC2, test="Chisq") @ \section{Mosaic plots}\label{sec:mosaic} Mosaic plots provide an ideal method both for visualizing contingency tables and for visualizing the fit--- or more importantly--- lack of fit of a \loglin\ model. For a two-way table, \codefun{mosaic} fits a model of independence, $[A][B]$ or \verb|~A+B| as an \proglang{R} formula. For $n$-way tables, \codefun{mosaic} can fit any \loglin\ model, and can also be used to plot a model fit with \codefun{loglm}. See \citet{vcd:Friendly:1994,vcd:Friendly:1999} for the statistical ideas behind these uses of mosaic displays in connection with \loglin\ models. The essential idea is to recursively sub-divide a unit square into rectangular ``tiles'' for the cells of the table, such that the are area of each tile is proportional to the cell frequency. For a given \loglin\ model, the tiles can then be shaded in various ways to reflect the residuals (lack of fit) for a given model. The pattern of residuals can then be used to suggest a better model or understand \emph{where} a given model fits or does not fit. \codefun{mosaic} provides a wide range of options for the directions of splitting, the specification of shading, labeling, spacing, legend and many other details. It is actually implemented as a special case of a more general class of displays for $n$-way tables called \code{strucplot}, including sieve diagrams, association plots, double-decker plots as well as mosaic plots. For details, see \code{help(strucplot)} and the ``See also'' links, and also \citet{vcd:Meyer+Zeileis+Hornik:2006b}, which is available as an \proglang{R} vignette via \code{vignette("strucplot", package="vcd")}. \figref{fig:arthritis}, showing the association between \code{Treatment} and \code{Improved} was produced with the following call to \codefun{mosaic}. <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") @ Note that the residuals for the independence model were not large (as shown in the legend), yet the association between \code{Treatment} and \code{Improved} is highly significant. <>= summary(art) @ In contrast, one of the other shading schemes, from \citet{vcd:Friendly:1994} (use: \verb|gp = shading_Friendly|), uses fixed cutoffs of $\pm 2, \pm 4$, to shade cells which are \emph{individually} significant at approximately $\alpha = 0.05$ and $\alpha = 0.001$ levels, respectively. The right panel below uses \verb|gp = shading_Friendly|. \setkeys{Gin}{width=0.5\textwidth} <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: gp = shading_max") @ <>= mosaic(art, gp = shading_Friendly, split_vertical = TRUE, main="Arthritis: gp = shading_Friendly") @ \subsection[Mosaics for loglinear models]{Mosaics for \loglin\ models}\label{sec:mosaic-llm} When you have fit a \loglin\ model using \codefun{loglm}, and saved the result (as a \code{loglm} object) the simplest way to display the results is to use the \codefun{plot} method for the \code{loglm} object. Calling \code{mosaic(loglm.object)} has the same result. In \secref{sec:loglm} above, we fit several different models to the \data{HairEyeColor} data. We can produce mosaic displays of each just by plotting them: <>= # mosaic plots, using plot.loglm() method plot(hec.1, main="model: [Hair][Eye][Sex]") plot(hec.2, main="model: [HairSex][EyeSex]") plot(hec.3, main="model: [HairEye][Sex]") @ \setkeys{Gin}{width=0.32\textwidth} <>= plot(hec.1, main="model: [Hair][Eye][Sex]") @ <>= plot(hec.2, main="model: [HairSex][EyeSex]") @ <>= plot(hec.3, main="model: [HairSex][EyeSex]") @ Alternatively, you can supply the model formula to \codefun{mosaic} with the \code{expected} argument. This is passed to \codefun{loglm}, which fits the model, and returns residuals used for shading in the plot. For example, here we examine the \data{TV2} constructed in \secref{sec:complex} above. The goal is to see how Network choice depends on (varies with) Day and Time. To do this: \begin{itemize} \item We fit a model of joint independence of \code{Network} on the combinations of \code{Day} and \code{Time}, with the model formula \verb|~Day:Time + Network|. \item To make the display more easily read, we place \code{Day} and \code{Time} on the vertical axis and \code{Network} on the horizontal, \item The \code{Time} values overlap on the right vertical axis, so we use \codefun{level} to abbreviate them. \codefun{mosaic} also supports a more sophisticated set of labeling functions. Instead of changing the data table, we could have used \verb|labeling_args = list(abbreviate = c(Time = 2))| for a similar effect. \end{itemize} The following call to \codefun{mosaic} produces \figref{fig:TV-mosaic}: <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly) @ \setkeys{Gin}{width=0.75\textwidth} \begin{figure}[htb] \begin{center} <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly) @ \caption{Mosaic plot for the \data{TV} data showing model of joint independence, \texttt{Day:Time + Network} .} \label{fig:TV-mosaic} \end{center} \end{figure} From this, it is easy to read from the display how network choice varies with day and time. For example, CBS dominates in all time slots on Monday; ABC and NBC dominate on Tuesday, particularly in the later time slots; Thursday is an NBC day, while on Friday, ABC gets the greatest share. In interpreting this mosaic and other plots, it is important to understand that associations included in the model---here, that between day and time---are \emph{not} shown in the shading of the cells, because they have been fitted (taken into account) in the \loglin\ model. For comparison, you might want to try fitting the model of homogeneous association. This allows all pairs of factors to be associated, but asserts that each pairwise association is the same across the levels of the remaining factor. The resulting plot displays the contributions to a 3-way association, but is not shown here. <>= mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Day:Network + Time:Network, legend=FALSE, gp=shading_Friendly) @ \subsection[Mosaics for glm() and gnm() models]{Mosaics for \codefun{glm} and \codefun{gnm} models}\label{sec:mosglm} The \pkg{vcdExtra} package provides an additional method, \codefun{mosaic.glm} for models fit with \codefun{glm} and \codefun{gnm}.% \footnote{ Models fit with \codefun{gnm} are of \code{class = c("gnm", "glm", "lm")}, so all \code{*.glm} methods apply, unless overridden in the \pkg{gnm} package. } These are not restricted to the Poisson family, but only apply to cases where the response variable is non-negative. \Example Here, we plot the independence and the linear-by-linear association model for the Mental health data from \secref{sec:glm}. These examples illustrate some of the options for labeling (variable names and residuals printed in cells). Note that the \code{formula} supplied to \codefun{mosaic} for \class{glm} objects refers to the order of factors displayed in the plot, not the model. <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(indep, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, main="Mental health data: Independence") mosaic(linlin, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: Linear x Linear") @ \setkeys{Gin}{width=0.49\textwidth} <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(indep, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, main="Mental health data: Independence") @ <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(linlin, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: Linear x Linear") @ The \pkg{gnm} package also fits a wide variety of models with nonlinear terms or terms for structured associations of table variables. In the following, we fit the RC(1) model \begin{equation*} \log ( m_{ij} ) = \mu + \lambda_i^A + \lambda_j^B + \phi \mu_i \nu_j \end{equation*} This is similar to the linear by linear model, except that the row effect parameters ($\mu_i$) and column parameters ($\nu_j$) are estimated from the data rather than given assigned equally-spaced values. The multiplicative terms are specified by the \codefun{Mult}. <>= Mental$mental <- C(Mental$mental, treatment) Mental$ses <- C(Mental$ses, treatment) RC1model <- gnm(Freq ~ mental + ses + Mult(mental, ses), family = poisson, data = Mental) mosaic(RC1model, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: RC(1) model") @ Other forms of nonlinear terms are provided for the inverse of a predictor (\codefun{Inv}) and the exponential of a predictor (\codefun{Exp}). You should read \code{vignette("gnmOverview", package="gnm")} for further details. \subsection{Mosaic tips and techniques}\label{sec:tips} The \pkg{vcd} package implements an extremely general collection of graphical methods for $n$-way frequency tables within the strucplot framework, which includes mosaic plots (\codefun{mosaic}), as well as association plots (\codefun{assoc}), sieve diagrams (\codefun{sieve}), as well as tabular displays (\codefun{structable}). The graphical methods in \pkg{vcd} support a wide of options that control almost all of the details of the plots, but it is often difficult to determine what arguments you need to supply to achieve a given effect from the \code{help()}. As a first step, you should read the \code{vignette("strucplot")} in \pkg{vcd} to understand the overall structure of these plot methods. The notes below describe a few useful things that may not be obvious, or can be done in different ways. \subsubsection[Changing labels]{Changing the labels for variables and levels} With data in contingency table form or as a frequency data frame, it often happens that the variable names and/or the level values of the factors, while suitable for analysis, are less than adequate when used in mosaic plots and other strucplot displays. For example, we might prefer that a variable named \code{ses} appear as \code{"Socioeconomic Status"}, or a factor with levels \code{c("M", "F")} be labeled using \code{c("Male", "Female")} in a plot. Or, sometimes we start with a factor whose levels are fully spelled out (e.g., \code{c("strongly disagree", "disagree", "neutral", "agree", "strongly agree")}), only to find that the level labels overlap in graphic displays. The structplot framework in \pkg{vcd} provides an extremely large variety of functions and options for controlling almost all details of text labels in mosaics and other plots. See \code{help(labelings)} for an overview. For example, in \secref{sec:ordered-factors} we showed how to rearrange the dimensions of the \code{UCBAdmissions} table, change the names of the table variables, and relabel the levels of one of the table variables. The code below changes the actual table for plotting purposes, but we pointed out that these changes can create other problems in analysis. <>= UCB <- aperm(UCBAdmissions, c(2, 1, 3)) names(dimnames(UCB)) <- c("Sex", "Admit?", "Department") dimnames(UCB)[[2]] <- c("Yes", "No") @ The same effects can be achieved \emph{without} modifying the data using the \verb|set_varnames| and \verb|set_labels| options in \codefun{mosaic} as follows: <>= vnames <- list(set_varnames = c(Admit="Admission", Gender="Sex", Dept="Department")) lnames <- list(Admit = c("Yes", "No"), Gender = c("Males", "Females"), Dept = LETTERS[1:6]) mosaic(UCBAdmissions, labeling_args=vnames, set_labels=lnames) @ In some cases, it may be sufficient to abbreviate (or clip, or rotate) level names to avoid overlap. For example, the statements below produce another version of \figref{fig:TV-mosaic} with days of the week abbreviated to their first three letters. Section 4 in the \code{vignette("strucplot")} provides many other examples. <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly, labeling_args=list(abbreviate=c(Day=3)) ) @ %\subsubsection{Fitting complex models with glm() and gnm()} \section[Continuous predictors]{Continuous predictors}\label{sec:contin} When continuous predictors are available---and potentially important--- in explaining a categorical outcome, models for that outcome include: logistic regression (binary response), the proportional odds model (ordered polytomous response), multinomial (generalized) logistic regression. Many of these are special cases of the generalized linear model using the \code{"poisson"} or \code{"binomial"} family and their relatives. \subsection{Spine and conditional density plots}\label{sec:spine} I don't go into fitting such models here, but I would be remiss not to illustrate some visualizations in \pkg{vcd} that are helpful here. The first of these is the spine plot or spinogram \citep{vcd:Hummel:1996} (produced with \codefun{spine}). These are special cases of mosaic plots with specific spacing and shading to show how a categorical response varies with a continuous or categorical predictor. They are also a generalization of stacked bar plots where not the heights but the \emph{widths} of the bars corresponds to the relative frequencies of \code{x}. The heights of the bars then correspond to the conditional relative frequencies of {y} in every \code{x} group. \Example For the \data{Arthritis} data, we can see how \code{Improved} varies with \code{Age} as follows. \codefun{spine} takes a formula of the form \verb|y ~ x| with a single dependent factor and a single explanatory variable \code{x} (a numeric variable or a factor). The range of a numeric variable\code{x} is divided into intervals based on the \code{breaks} argument, and stacked bars are drawn to show the distribution of \code{y} as \code{x} varies. As shown below, the discrete table that is visualized is returned by the function. <>= (spine(Improved ~ Age, data = Arthritis, breaks = 3)) (spine(Improved ~ Age, data = Arthritis, breaks = "Scott")) @ \setkeys{Gin}{width=0.49\textwidth} <>= (spine(Improved ~ Age, data = Arthritis, breaks = 3)) @ <>= (spine(Improved ~ Age, data = Arthritis, breaks = "Scott")) @ The conditional density plot \citep{vcd:Hofmann+Theus} is a further generalization. This visualization technique is similar to spinograms, but uses a smoothing approach rather than discretizing the explanatory variable. As well, it uses the original \code{x} axis and not a distorted one. \setkeys{Gin}{width=0.6\textwidth} \begin{figure}[htb] \begin{center} <>= cdplot(Improved ~ Age, data = Arthritis) with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE)) @ \caption{Conditional density plot for the \data{Arthritis} data showing the variation of Improved with Age.} \label{fig:cd-plot} \end{center} \end{figure} In such plots, it is useful to also see the distribution of the observations across the horizontal axis, e.g., with a \codefun{rug} plot. \figref{fig:cd-plot} uses \codefun{cdplot} from the \pkg{graphics} package rather than \verb|cd_plot()| from \pkg{vcd}, and is produced with <>= cdplot(Improved ~ Age, data = Arthritis) with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE)) @ From \figref{fig:cd-plot} it can be easily seen that the proportion of patients reporting Some or Marked improvement increases with Age, but there are some peculiar bumps in the distribution. These may be real or artifactual, but they would be hard to see with most other visualization methods. When we switch from non-parametric data exploration to parametric statistical models, such effects are easily missed. \subsection[Model-based plots]{Model-based plots: effect plots and \pkg{ggplot2} plots}\label{sec:modelplots} The nonparametric conditional density plot uses smoothing methods to convey the distributions of the response variable, but displays that are simpler to interpret can often be obtained by plotting the predicted response from a parametric model. For complex \codefun{glm} models with interaction effects, the \pkg{effects} package provides the most useful displays, plotting the predicted values for a given term, averaging over other predictors not included in that term. I don't illustrate this here, but see \citet{effects:1,effects:2} and \code{help(package="effects")}. Here I just briefly illustrate the capabilities of the \pkg{ggplot2} package for model-smoothed plots of categorical responses in \codefun{glm} models. \Example The \data{Donner} data frame in \pkg{vcdExtra} gives details on the survival of 90 members of the Donner party, a group of people who attempted to migrate to California in 1846. They were trapped by an early blizzard on the eastern side of the Sierra Nevada mountains, and before they could be rescued, nearly half of the party had died. What factors affected who lived and who died? <>= data(Donner, package="vcdExtra") str(Donner) @ A potential model of interest is the logistic regression model for $Pr(survived)$, allowing separate fits for males and females as a function of \code{age}. The key to this is the \verb|stat_smooth()| function, using \code{method = "glm", family = binomial}. The \verb|formula = y ~ x| specifies a linear fit on the logit scale (\figref{fig:donner3}, left) <>= # separate linear fits on age for M/F ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2, aes(fill = sex)) @ Alternatively, we can allow a quadratic relation with \code{age} by specifying \verb|formula = y ~ poly(x,2)| (\figref{fig:donner3}, right). <>= # separate quadratics ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ poly(x,2), alpha = 0.2, size=2, aes(fill = sex)) @ \setkeys{Gin}{width=0.49\textwidth} \begin{figure}[htb] \begin{center} <>= ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2, aes(fill = sex)) @ <>= # separate quadratics ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ poly(x,2), alpha = 0.2, size=2, aes(fill = sex)) @ \caption{Logistic regression plots for the \data{Donner} data showing survival vs. age, by sex. Left: linear logistic model; right: quadratic model} \label{fig:donner3} \end{center} \end{figure} These plots very nicely show (a) the fitted $Pr(survived)$ for males and females; (b) confidence bands around the smoothed model fits and (c) the individual observations by jittered points at 0 and 1 for those who died and survided, respectively. \bibliography{vcd,vcdExtra} \end{document} vcdExtra/inst/doc/vcd-tutorial.pdf0000644000175100001440000300455512576352702016727 0ustar hornikusers%PDF-1.5 %ÐÔÅØ 1 0 obj << /S /GoTo /D (section.1) >> endobj 4 0 obj (Introduction) endobj 5 0 obj << /S /GoTo /D (section.2) >> endobj 8 0 obj (Creating frequency tables) endobj 9 0 obj << /S /GoTo /D (subsection.2.1) >> endobj 12 0 obj (Ordered factors) endobj 13 0 obj << /S /GoTo /D (subsection.2.2) >> endobj 16 0 obj (structable\(\)) endobj 17 0 obj << /S /GoTo /D (subsection.2.3) >> endobj 20 0 obj (table\(\) and friends) endobj 21 0 obj << /S /GoTo /D (subsection.2.4) >> endobj 24 0 obj (xtabs\(\)) 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3 11 5 3 642 4 11 5 3 498 5 11 5 3 591 vcdExtra/inst/doc/vcd-tutorial.R0000644000175100001440000006535112576352714016360 0ustar hornikusers### R code from vignette source 'vcd-tutorial.Rnw' ################################################### ### code chunk number 1: preliminaries ################################################### set.seed(1071) #library(vcd) library(vcdExtra) library(ggplot2) #data(Titanic) data(HairEyeColor) data(PreSex) data(Arthritis) art <- xtabs(~Treatment + Improved, data = Arthritis) if(!file.exists("fig")) dir.create("fig") ################################################### ### code chunk number 2: case-form ################################################### names(Arthritis) # show the variables str(Arthritis) # show the structure head(Arthritis,5) # first 5 observations, same as Arthritis[1:5,] ################################################### ### code chunk number 3: frequency-form ################################################### # Agresti (2002), table 3.11, p. 106 GSS <- data.frame( expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")), count=c(279,165,73,47,225,191)) GSS names(GSS) str(GSS) sum(GSS$count) ################################################### ### code chunk number 4: table-form1 ################################################### str(HairEyeColor) # show the structure sum(HairEyeColor) # number of cases sapply(dimnames(HairEyeColor), length) # table dimension sizes ################################################### ### code chunk number 5: table-form2 ################################################### ## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4) dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"), satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS")) JobSat ################################################### ### code chunk number 6: table-form3 ################################################### JobSat <- as.table(JobSat) str(JobSat) ################################################### ### code chunk number 7: relevel (eval = FALSE) ################################################### ## dimnames(JobSat)$income<-c(7.5,20,32.5,60) ## dimnames(JobSat)$satisfaction<-1:4 ################################################### ### code chunk number 8: reorder1 ################################################### HairEyeColor <- HairEyeColor[, c(1,3,4,2), ] str(HairEyeColor) ################################################### ### code chunk number 9: reorder2 (eval = FALSE) ################################################### ## Arthritis <- read.csv("arthritis.txt",header=TRUE) ## Arthritis$Improved <- ordered(Arthritis$Improved, levels=c("None", "Some", "Marked")) ################################################### ### code chunk number 10: Arthritis ################################################### mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") ################################################### ### code chunk number 11: reorder3 ################################################### UCB <- aperm(UCBAdmissions, c(2, 1, 3)) dimnames(UCB)[[2]] <- c("Yes", "No") names(dimnames(UCB)) <- c("Sex", "Admit?", "Department") ftable(UCB) ################################################### ### code chunk number 12: structable ################################################### structable(HairEyeColor) # show the table: default structable(Hair+Sex ~ Eye, HairEyeColor) # specify col ~ row variables ################################################### ### code chunk number 13: structable1 (eval = FALSE) ################################################### ## HSE < - structable(Hair+Sex ~ Eye, HairEyeColor) # save structable object ## mosaic(HSE) # plot it ################################################### ### code chunk number 14: setup ################################################### n=500 A <- factor(sample(c("a1","a2"), n, rep=TRUE)) B <- factor(sample(c("b1","b2"), n, rep=TRUE)) C <- factor(sample(c("c1","c2"), n, rep=TRUE)) mydata <- data.frame(A,B,C) ################################################### ### code chunk number 15: table-ex1 ################################################### # 2-Way Frequency Table attach(mydata) mytable <- table(A,B) # A will be rows, B will be columns mytable # print table margin.table(mytable, 1) # A frequencies (summed over B) margin.table(mytable, 2) # B frequencies (summed over A) prop.table(mytable) # cell percentages prop.table(mytable, 1) # row percentages prop.table(mytable, 2) # column percentages ################################################### ### code chunk number 16: table-ex2 ################################################### # 3-Way Frequency Table mytable <- table(A, B, C) ftable(mytable) ################################################### ### code chunk number 17: xtabs-ex1 ################################################### # 3-Way Frequency Table mytable <- xtabs(~A+B+C, data=mydata) ftable(mytable) # print table summary(mytable) # chi-square test of indepedence ################################################### ### code chunk number 18: xtabs-ex2 ################################################### (GSStab <- xtabs(count ~ sex + party, data=GSS)) summary(GSStab) ################################################### ### code chunk number 19: dayton1 ################################################### str(DaytonSurvey) head(DaytonSurvey) ################################################### ### code chunk number 20: dayton2 ################################################### # data in frequency form # collapse over sex and race Dayton.ACM.df <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum) Dayton.ACM.df ################################################### ### code chunk number 21: dayton3 ################################################### # in table form Dayton.tab <- xtabs(Freq~cigarette+alcohol+marijuana+sex+race, data=DaytonSurvey) structable(cigarette+alcohol+marijuana ~ sex+race, data=Dayton.tab) ################################################### ### code chunk number 22: dayton4 ################################################### # collapse over sex and race Dayton.ACM.tab <- apply(Dayton.tab, MARGIN=1:3, FUN=sum) Dayton.ACM.tab <- margin.table(Dayton.tab, 1:3) # same result structable(cigarette+alcohol ~ marijuana, data=Dayton.ACM.tab) ################################################### ### code chunk number 23: dayton5 (eval = FALSE) ################################################### ## Dayton.ACM.df <- ddply(DaytonSurvey, .(cigarette, alcohol, marijuana), ## plyr::summarise, Freq=sum(Freq)) ################################################### ### code chunk number 24: collapse1 ################################################### # create some sample data in frequency form sex <- c("Male", "Female") age <- c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69") education <- c("low", 'med', 'high') data <- expand.grid(sex=sex, age=age, education=education) counts <- rpois(36, 100) # random Possion cell frequencies data <- cbind(data, counts) # make it into a 3-way table t1 <- xtabs(counts ~ sex + age + education, data=data) structable(t1) ################################################### ### code chunk number 25: collapse2 ################################################### # collapse age to 3 levels, education to 2 levels t2 <- collapse.table(t1, age=c("10-29", "10-29", "30-49", "30-49", "50-69", "50-69"), education=c(":"ê{0΋e^$YU ¾R3}õ=R _ï¸ö´§f(ØÓžž]©¶ tá^*ÊTu`ñBQXφŸ ©Ûô§lrþ=©yVÏ<Ï“³òâ)eVÀî$MJÔ“q^Le!§Zé,™Ty¡F׊þÇ–ÿè¥ô€þƒ=qê>êµ@ñQ¿!:h€2th¡ÑŠÖÇê]÷̶õÐFÿlª4Yóm]'||sµWÛŽwñå|h»ö›¨Î>;0®>ûø‹ùCŗ¢âÙê#1 l€`÷ ,L/\V§ààF¬¾½ú¤|x»ëÑx=7þ;÷n=z¿Â 5Öú¨nžšï=(Á2ÌUä±ÉÛ°©Ì¹òsöºâøèrѶ~Ú:މg³…²!.±áòŠÉºt-×xsöûäÌ×V 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with MASS::loglm, giving comparable results to the use of these # functions with glm(..., family=poisson) models. # allow for non-integer frequencies # allow for zero frequencies, with a zero= argument logLik.loglm <- function(object, ..., zero=1E-10) { fr <- if(!is.null(object$frequencies)) unclass(object$frequencies) else { unclass(update(object, keep.frequencies = TRUE)$frequencies) } df <- prod(dim(fr)) - object$df if (any(fr==0)) { fr <- as.vector(fr) fr[fr==0] <- zero } structure(sum((log(fr) - 1) * fr - lgamma(fr + 1)) - object$deviance/2, df = df, class = "logLik") } vcdExtra/R/logseries.R0000644000175100001440000001147212576352702014406 0ustar hornikusers## Original from gmlss.dist ## I think this is working correctly 01/03/10 #LG <- function (mu.link = "logit") #{ # mstats <- checklink("mu.link", "LG", substitute(mu.link),c("logit", "probit", "cloglog", "cauchit", "log", "own")) # structure( # list(family = c("LG", "Logarithmic"), # parameters = list(mu = TRUE), # the mean # nopar = 1, # type = "Discrete", # mu.link = as.character(substitute(mu.link)), # mu.linkfun = mstats$linkfun, # mu.linkinv = mstats$linkinv, # mu.dr = mstats$mu.eta, # dldm = function(y,mu) (y/mu)+1/((1-mu)*log(1-mu)), # d2ldm2 = function(y,mu) # { # dldm <- (y/mu)+1/((1-mu)*log(1-mu)) # d2ldm2 <- -dldm^2 # d2ldm2 # }, # G.dev.incr = function(y,mu,...) -2*dLG(x = y, mu = mu, log = TRUE), # rqres = expression(rqres(pfun="pLG", type="Discrete", ymin=1, y=y, mu=mu)), # mu.initial =expression({mu <- 0.9 } ), # mu.valid = function(mu) all(mu > 0 & mu < 1), # y.valid = function(y) all(y > 0) # ), # class = c("gamlss.family","family")) #} #----------------------------------------------------------------------------------------- dlogseries<-function(x, prob = 0.5, log = FALSE) { if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", "")) if (any(x <= 0) ) stop(paste("x must be >0", "\n", "")) logfy <- x*log(prob)-log(x)-log(-log(1-prob)) if(log == FALSE) fy <- exp(logfy) else fy <- logfy fy } #---------------------------------------------------------------------------------------- plogseries <- function(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE) { if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", "")) if (any(q <= 0) ) stop(paste("q must be >0", "\n", "")) ly <- length(q) FFF <- rep(0,ly) nmu <- rep(prob, length = ly) j <- seq(along=q) for (i in j) { y.y <- q[i] mm <- nmu[i] allval <- seq(1,y.y) pdfall <- dlogseries(allval, prob = mm, log = FALSE) FFF[i] <- sum(pdfall) } cdf <- FFF cdf <- if(lower.tail==TRUE) cdf else 1-cdf cdf <- if(log.p==FALSE) cdf else log(cdf) cdf } #---------------------------------------------------------------------------------------- qlogseries <- function(p, prob=0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000) { if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", "")) if (any(p < 0) | any(p > 1.0001)) stop(paste("p must be between 0 and 1", "\n", "")) if (log.p==TRUE) p <- exp(p) else p <- p if (lower.tail==TRUE) p <- p else p <- 1-p ly <- length(p) QQQ <- rep(0,ly) nmu <- rep(prob, length = ly) for (i in seq(along=p)) { cumpro <- 0 if (p[i]+0.000000001 >= 1) QQQ[i] <- Inf else { for (j in seq(from = 1, to = max.value)) { cumpro <- plogseries(j, prob = nmu[i], log.p = FALSE) QQQ[i] <- j if (p[i] <= cumpro ) break } } } QQQ } #---------------------------------------------------------------------------------------- rlogseries <- function(n, prob = 0.5) { if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", "")) if (any(n <= 0)) stop(paste("n must be a positive integer", "\n", "")) n <- ceiling(n) p <- runif(n) r <- qlogseries(p, prob=prob) r } #---------------------------------------------------------------------------------------- vcdExtra/R/summarise-old.R0000644000175100001440000000571212576352702015173 0ustar hornikusers # summarise a glm object or glmlist summarise <- function(...) { .Deprecated("LRstats") LRstats(...) } # summarise <- function(object, ...) { # UseMethod("summarise") # } # # stat.summarise <- function(deviance, df, onames, n) { # p <- pchisq(deviance, df, lower.tail=FALSE) # aic <- deviance - 2*df # if (missing(n)) { # result <- data.frame(aic, deviance, df, p) # names(result) <- c("AIC", "LR Chisq", "Df", "Pr(>Chisq)") # } # else { # bic <- deviance - log(n)*df # result <- data.frame(aic, bic, deviance, df, p) # names(result) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)") # } # # rownames(result) <- onames # attr(result, "heading") <- "Model Summary:" # class(result) <- c("anova", "data.frame") # result # } # # # summarise.glm <-function(object, ..., test=NULL){ # dotargs <- list(...) # is.glm <- unlist(lapply(dotargs, function(x) inherits(x, "glm"))) # dotargs <- dotargs[is.glm] # if (length(dotargs)) # return(summarise.glmlist(c(list(object), dotargs), test = test)) # # oname <- as.character(sys.call())[2] # result <- stat.summarise(object$deviance, object$df.residual, oname, sum(fitted(object))) # result # } # # summarise.glmlist <-function(object, ..., test=NULL, sortby=NULL){ # nmodels <- length(object) # if (nmodels == 1) # return(summarise.glm(object[[1]], test = test)) # if (is.null(names(object))) { # oname <- as.character(sys.call())[-1] # oname <- oname[1:length(object)] # } # else oname <- names(object) # # resdf <- as.numeric(lapply(object, function(x) x$df.residual)) # resdev <- as.numeric(lapply(object, function(x) x$deviance)) # n <- as.numeric(lapply(object, function(x) sum(fitted(x)))) # result <- stat.summarise(resdev, resdf, oname, n) # if (!is.null(sortby)) { # result <- result[order(result[,sortby], decreasing=TRUE),] # } # result # } # # # summarise.loglm <-function(object, ...){ # dotargs <- list(...) # is.loglm <- unlist(lapply(dotargs, function(x) inherits(x, "loglm"))) # dotargs <- dotargs[is.loglm] # if (length(dotargs)) # return(summarise.loglmlist(c(list(object), dotargs))) # # oname <- as.character(sys.call())[2] # result <- stat.summarise(object$deviance, object$df, oname, sum(fitted(object))) # result # } # # summarise.loglmlist <-function(object, ..., sortby=NULL){ # nmodels <- length(object) # if (nmodels == 1) # return(summarise.loglm(object[[1]])) # if (is.null(names(object))) { # oname <- as.character(sys.call())[-1] # oname <- oname[1:length(object)] # } # else oname <- names(object) # # resdf <- as.numeric(lapply(object, function(x) x$df)) # resdev <- as.numeric(lapply(object, function(x) x$deviance)) # n <- as.numeric(lapply(object, function(x) sum(fitted(x)))) # result <- stat.summarise(resdev, resdf, oname, n) # if (!is.null(sortby)) { # result <- result[order(result[,sortby], decreasing=TRUE),] # } # result # } # vcdExtra/R/seq_loglm.R0000644000175100001440000000571312576352702014375 0ustar hornikusers#' Sequential loglinear models for an n-way table #' This function takes an n-way contingency table and fits a series of sequential #' models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of #' types of loglinear models. #' @param x a contingency table in array form, with optional category labels specified in the dimnames(x) attribute, #' or else a data.frame in frequency form, with the frequency variable names "Freq". #' @param type type of sequential model to fit #' @param marginals which marginals to fit? #' @param vorder order of variables #' @param k indices of conditioning variable(s) for "joint", "conditional" or order for "markov" #' @param prefix #' @param fitted keep fitted values? seq_loglm <- function( x, type = c("joint", "conditional", "mutual", "markov", "saturated"), marginals = 1:nf, # which marginals to fit? vorder = 1:nf, # order of variables in the sequential models k = NULL, # conditioning variable(s) for "joint", "conditional" or order for "markov" prefix = 'model', fitted = TRUE, # keep fitted values? ... ) { if (inherits(x, "data.frame") && "Freq" %in% colnames(x)) { x <- xtabs(Freq ~ ., data=x) } if (!inherits(x, c("table", "array"))) stop("not an xtabs, table, array or data.frame with a 'Freq' variable") nf <- length(dim(x)) x <- aperm(x, vorder) factors <- names(dimnames(x)) indices <- 1:nf type = match.arg(type) # models <- as.list(rep(NULL, length(marginals))) models <- list() for (i in marginals) { mtab <- margin.table(x, 1:i) if (i==1) { # KLUDGE: use loglin, but try to make it look like a loglm object mod <- loglin(mtab, margin=NULL, print=FALSE) mod$model.string = paste("=", factors[1]) mod$margin <- list(factors[1]) # mod$margin <- names(dimnames(mtab)) # names(mod$margin) <- factors[1] if (fitted) { fit <- mtab fit[] <- (sum(mtab) / length(mtab)) mod$fitted <- fit } mod$nobs <- length(mtab) mod$frequencies <- mtab mod$deviance <- mod$lrt class(mod) <- c("loglin", "loglm") } else { expected <- switch(type, 'conditional' = conditional(i, mtab, with=if(is.null(k)) i else k), 'joint' = joint(i, mtab, with=if(is.null(k)) i else k), 'mutual' = mutual(i, mtab), 'markov' = markov(i, mtab, order=if(is.null(k)) 1 else k), 'saturated' = saturated(i, mtab) ) form <- loglin2formula(expected) # mod <- loglm(formula=form, data=mtab, fitted=TRUE) mod <- eval(bquote(MASS::loglm(.(form), data=mtab, fitted=fitted))) mod$model.string <- loglin2string(expected, brackets=if (iChisq)") rownames(rval) <- as.character(sapply(match.call(), deparse)[-1L])[1:nmodels] rval[,1] <- -2 * ll + 2 * par rval[,2] <- -2 * ll + log(ns) * par rval[,3] <- -2 * (ll - saturated) rval[,4] <- df rval[,5] <- pchisq(rval[,3], df, lower.tail = FALSE) if (!is.null(sortby)) { rval <- rval[order(rval[,sortby], decreasing=TRUE),] } ## return structure(as.data.frame(rval), heading = "Likelihood summary table:", class = c("anova", "data.frame")) } vcdExtra/R/mosaic3d.R0000644000175100001440000001634212576352702014115 0ustar hornikusers##################################### ## Produce a 3D mosaic plot using rgl ##################################### # TODO: provide formula interface # TODO: handle zero margins (causes display to be erased in shapelist3d) # DONE: handle zero cells # DONE: generalize the calculation of residuals # DONE: allow display of type=c("observed", "expected") # DONE: if ndim>3, provide for labels at max or min # DONE: make object oriented and provide a loglm method # mosaic3d: provide observed array of counts and either residuals, expected frequencies, # or a loglin set of margins to fit mosaic3d <- function(x, ...) { UseMethod("mosaic3d") } mosaic3d.loglm <- function (x, type = c("observed", "expected"), residuals_type = c("pearson", "deviance"), # gp = shading_hcl, gp_args = list(), ...) { residuals_type <- match.arg(tolower(residuals_type), c("pearson", "deviance")) if (is.null(x$fitted)) x <- update(x, fitted = TRUE) expected <- fitted(x) residuals <- residuals(x, type = "pearson") observed <- residuals * sqrt(expected) + expected if (residuals_type == "deviance") residuals <- residuals(x, type = "deviance") # gp <- if (inherits(gp, "grapcon_generator")) # do.call("gp", c(list(observed, residuals, expected, x$df), # as.list(gp_args))) # else gp mosaic3d.default(observed, residuals = residuals, expected = expected, type = type, residuals_type = residuals_type, # gp = gp, ...) } mosaic3d.default <- function(x, expected=NULL, residuals=NULL, type = c("observed", "expected"), residuals_type = NULL, shape=rgl::cube3d(alpha=alpha), alpha=0.5, spacing=0.1, split_dir=1:3, shading=shading_basic, interpolate=c(2,4), zero_size=.05, label_edge, labeling_args=list(), newpage=TRUE, box=FALSE, ...) { if (!requireNamespace("rgl")) stop("rgl is required") type <- match.arg(type) if (is.null(residuals)) { residuals_type <- if (is.null(residuals_type)) "pearson" else match.arg(tolower(residuals_type), c("pearson", "deviance", "ft")) } ## convert structable object if (is.structable(x)) { x <- as.table(x) } ## table characteristics levels <- dim(x) ndim <- length(levels) dn <- dimnames(x) if (is.null(dn)) dn <- dimnames(x) <- lapply(levels, seq) vnames <- names(dimnames(x)) if (is.null(vnames)) vnames <- names(dn) <- names(dimnames(x)) <- LETTERS[1:ndim] ## replace NAs by 0 if (any(nas <- is.na(x))) x[nas] <- 0 ## model fitting: ## calculate expected if needed if ((is.null(expected) && is.null(residuals)) || !is.numeric(expected)) { if (inherits(expected, "formula")) { fm <- loglm(expected, x, fitted = TRUE) expected <- fitted(fm) df <- fm$df } else { if (is.null(expected)) expected <- as.list(1:ndim) fm <- loglin(x, expected, fit = TRUE, print = FALSE) expected <- fm$fit df <- fm$df } } ## compute residuals if (is.null(residuals)) residuals <- switch(residuals_type, pearson = (x - expected) / sqrt(ifelse(expected > 0, expected, 1)), deviance = { tmp <- 2 * (x * log(ifelse(x == 0, 1, x / ifelse(expected > 0, expected, 1))) - (x - expected)) tmp <- sqrt(pmax(tmp, 0)) ifelse(x > expected, tmp, -tmp) }, ft = sqrt(x) + sqrt(x + 1) - sqrt(4 * expected + 1) ) ## replace NAs by 0 if (any(nas <- is.na(residuals))) residuals[nas] <- 0 # switch observed and expected if required observed <- if (type == "observed") x else expected expected <- if (type == "observed") expected else x # replicate arguments to number of dimensions spacing <- rep(spacing, length=ndim) split_dir <- rep(split_dir, length=ndim) if(missing(label_edge)) label_edge <- rep( c('-', '+'), each=3, length=ndim) zeros <- observed <= .Machine$double.eps shapelist <- shape # sanity check if (!inherits(shapelist, "shape3d")) stop("shape must be a shape3d object") if (newpage) rgl::open3d() for (k in 1:ndim) { marg <- margin.table(observed, k:1) if (k==1) { shapelist <- split3d(shapelist, marg, split_dir[k], space=spacing[k]) label3d(shapelist, split_dir[k], dn[[k]], vnames[k], edge=label_edge[k], ...) } else { marg <- matrix(marg, nrow=levels[k]) shapelist <- split3d(shapelist, marg, split_dir[k], space=spacing[k]) names(shapelist) <- apply(as.matrix(expand.grid(dn[1:k])), 1, paste, collapse=":") L <- length(shapelist) label_cells <- if (label_edge[k]=='-') 1:levels[k] else (L-levels[k]+1):L label3d(shapelist[label_cells], split_dir[k], dn[[k]], vnames[k], edge=label_edge[k], ...) } } # assign colors # TODO: allow alpha to control transparency of side walls col <- shading(residuals, interpolate=interpolate) # display, but exclude the zero cells rgl::shapelist3d(shapelist[!as.vector(zeros)], col=col[!as.vector(zeros)], ...) # plot markers for zero cells if (any(zeros)) { ctrs <- t(sapply(shapelist, center3d)) rgl::spheres3d(ctrs[as.vector(zeros),], radius=zero_size) } # invisible(structable(observed)) invisible(shapelist) } # basic shading_Friendly, adapting the simple code used in mosaicplot() shading_basic <- function(residuals, interpolate=TRUE) { if (is.logical(interpolate)) interpolate <- c(2, 4) else if (any(interpolate <= 0) || length(interpolate) > 5) stop("invalid 'interpolate' specification") shade <- sort(interpolate) breaks <- c(-Inf, -rev(shade), 0, shade, Inf) colors <- c(hsv(0, s = seq.int(1, to = 0, length.out = length(shade) + 1)), hsv(4/6, s = seq.int(0, to = 1, length.out = length(shade) + 1))) colors[as.numeric(cut(residuals, breaks))] } # provide labels for 3D objects below/above their extent along a given dimension # FIXME: kludge for interline gap between level labels and variable name # TODO: how to pass & extract labeling_args, e.g., labeling_args=list(at='min', fontsize=10) label3d <- function(objlist, dim, text, varname, offset=.05, adj, edge="-", gap=.1, labeling_args, ...) { if(missing(adj)) { if (dim < 3) adj <- ifelse(edge == '-', c(0.5, 1), c(0.5, 0)) else adj <- ifelse(edge == '-', c(1, 0.5), c(0, 0.5)) } ranges <- lapply(objlist, range3d) loc <- t(sapply(ranges, colMeans)) # positions of labels on dimension dim min <- t(sapply(ranges, function(x) x[1,])) # other dimensions at min values max <- t(sapply(ranges, function(x) x[2,])) # other dimensions at max values xyz <- if (edge == '-') (min - offset) else (max + offset) xyz[,dim] <- loc[,dim] if(!missing(varname)) { loclab <- colMeans(loc) # NB: doesn't take space into acct xyzlab <- if (edge == '-') min[1,] - offset - gap else max[1,] + offset + gap xyzlab[dim] <- loclab[dim] xyz <- rbind(xyz, xyzlab) text <- c(text, varname) } result <- c(labels = rgl::texts3d(xyz, texts=text, adj=adj, ...)) invisible(result) } vcdExtra/R/blogits.R0000644000175100001440000000116112576352702014047 0ustar hornikusers# calculate bivariate logits and OR blogits <- function(Y, add, colnames, row.vars, rev=FALSE) { if (ncol(Y) != 4) stop("Y must have 4 columns") if (missing(add)) add <- if (any(Y==0)) 0.5 else 0 Y <- Y + add if (rev) Y <- Y[,4:1] L <- matrix(0, nrow(Y), 3) L[,1] <- log( (Y[,1] + Y[,2]) / (Y[,3] + Y[,4]) ) L[,2] <- log( (Y[,1] + Y[,3]) / (Y[,2] + Y[,4]) ) L[,3] <- log( (Y[,1] * Y[,4]) / ((Y[,2] * Y[,3])) ) cn <- c("logit1", "logit2", "logOR") colnames(L) <- if(missing(colnames)) cn else c(colnames, cn[-(1:length(colnames))]) if(!missing(row.vars)) L <- cbind(L, row.vars) L } vcdExtra/R/HLtest.R0000644000175100001440000000401212576352702013605 0ustar hornikusers# Functions for Hosmer Lemeshow test # original function downloaded from # http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html # # see also: MKmisc::gof.test for more general versions HLtest <- HosmerLemeshow <- function(model, g=10) { if (!inherits(model, "glm")) stop("requires a binomial family glm") if (!family(model)$family == 'binomial') stop("requires a binomial family glm") y <- model$y yhat <- model$fitted.values cutyhat = cut(yhat, breaks = quantile(yhat, probs=seq(0, 1, 1/g)), include.lowest=TRUE) obs = xtabs(cbind(1 - y, y) ~ cutyhat) exp = xtabs(cbind(1 - yhat, yhat) ~ cutyhat) chi = (obs - exp)/sqrt(exp) # browser() table <- data.frame(cut=dimnames(obs)$cutyhat, total= as.numeric(apply(obs, 1, sum)), obs=as.numeric(as.character(obs[,1])), exp=as.numeric(as.character(exp[,1])), chi=as.numeric(as.character(chi[,1])) ) rownames(table) <- 1:g chisq = sum(chi^2) p = 1 - pchisq(chisq, g - 2) result <- list(table=table, chisq=chisq, df=g-2, p.value=p, groups=g, call=model$call) class(result) <- "HLtest" return(result) } print.HLtest <- function(x, ...) { heading <- "Hosmer and Lemeshow Goodness-of-Fit Test" df <- data.frame("ChiSquare"=x$chisq, df=x$df, "P_value"= x$p.value) cat(heading,"\n\n") cat("Call:\n") print(x$call) print(df, row.names=FALSE) invisible(x) } # Q: how to print **s next to larg chisq components? summary.HLtest <- function(object, ...) { heading <- "Partition for Hosmer and Lemeshow Goodness-of-Fit Test" cat(heading,"\n\n") print(object$table) print(object) } ## Q: how to display any large chi residuals on the bars?? rootogram.HLtest <- function(x, ...) { rootogram(as.numeric(x$table$obs), as.numeric(x$table$exp), xlab="Fitted value group", names=1:x$groups, ...) } plot.HLtest <- function(x, ...) { rootogram.HLtest(x, ...) } vcdExtra/R/glmlist.R0000644000175100001440000000462512576352702014067 0ustar hornikusers# glmlist - make a glmlist object containing a list of fitted glm objects with their names # borrowing code from Hmisc::llist glmlist <- function(...) { args <- list(...); lname <- names(args) name <- vname <- as.character(sys.call())[-1] for (i in 1:length(args)) { vname[i] <- if (length(lname) && lname[i] != "") lname[i] else name[i] } names(args) <- vname[1:length(args)] is.glm <- unlist(lapply(args, function(x) inherits(x, "glm"))) if (!all(is.glm)) { warning("Objects ", paste(vname[!is.glm], collapse=', '), " removed because they are not glm objects") args <- args[is.glm] } class(args) <- "glmlist" return(args); } # loglmlist - do the same for loglm objects loglmlist <- function(...) { args <- list(...); lname <- names(args) name <- vname <- as.character(sys.call())[-1] for (i in 1:length(args)) { vname[i] <- if (length(lname) && lname[i] != "") lname[i] else name[i] } names(args) <- vname[1:length(args)] is.loglm <- unlist(lapply(args, function(x) inherits(x, "loglm"))) if (!all(is.loglm)) { warning("Objects ", paste(vname[!is.loglm], collapse=', '), " removed because they are not loglm objects") args <- args[is.loglm] } class(args) <- "loglmlist" return(args); } # generic version: named list nlist <- function(...) { args <- list(...); lname <- names(args) name <- vname <- as.character(sys.call())[-1] for (i in 1:length(args)) { vname[i] <- if (length(lname) && lname[i] != "") lname[i] else name[i] } names(args) <- vname[1:length(args)] return(args); } # coeficient method for a glmlist (from John Fox, r-help, 10-28-2014) coef.glmlist <- function(object, result=c("list", "matrix", "data.frame"), ...){ result <- match.arg(result) coefs <- lapply(object, coef) if (result == "list") return(coefs) coef.names <- unique(unlist(lapply(coefs, names))) n.mods <- length(object) coef.matrix <- matrix(NA, length(coef.names), n.mods) rownames(coef.matrix) <- coef.names colnames(coef.matrix) <- names(object) for (i in 1:n.mods){ coef <- coef(object[[i]]) coef.matrix[names(coef), i] <- coef } if (result == "matrix") return(coef.matrix) as.data.frame(coef.matrix) } vcdExtra/R/Kway.R0000644000175100001440000000171112576352702013320 0ustar hornikusers# Generate and fit all 1-way, 2-way, ... k-way terms in a glm Kway <- function(formula, family=poisson, data, ..., order=nt, prefix="kway") { if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame()) if (is.function(family)) family <- family() if (is.null(family$family)) { print(family) stop("'family' not recognized") } if (missing(data)) data <- environment(formula) models <- list() mod <- glm(formula, family=family, data, ...) mod$call$formula <- formula terms <- terms(formula) tl <- attr(terms, "term.labels") nt <- length(tl) models[[1]] <- mod for(i in 2:order) { models[[i]] <- update(mod, substitute(.~.^p, list(p = i))) } # null model mod0 <- update(mod, .~1) models <- c(list(mod0), models) names(models) <- paste(prefix, 0:order, sep = ".") class(models) <- "glmlist" models } vcdExtra/R/Summarise.R0000644000175100001440000000732312576352702014357 0ustar hornikusers# fixed buglet when deviance() returns a null # fixed bug: residual df calculated incorrectly # but this now depends on objects having a df.residual component # TRUE for lm, glm, polr, negbin objects # made generic, adding a glmlist method Summarise <- function(object, ...) { UseMethod("Summarise") } Summarise.glmlist <- function(object, ..., saturated = NULL, sortby=NULL) { ns <- sapply(object, function(x) length(x$residuals)) if (any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset") nmodels <- length(object) if (nmodels == 1) return(Summarise.default(object[[1L]], saturated=saturated)) rval <- lapply(object, Summarise.default, saturated=saturated) rval <- do.call(rbind, rval) if (!is.null(sortby)) { rval <- rval[order(rval[,sortby], decreasing=TRUE),] } rval } # could just do Summarise.loglmlist <- Summarise.glmlist Summarise.loglmlist <- function(object, ..., saturated = NULL, sortby=NULL) { ns <- sapply(object, function(x) length(x$residuals)) if (any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset") nmodels <- length(object) if (nmodels == 1) return(Summarise.default(object[[1L]], saturated=saturated)) rval <- lapply(object, Summarise.default, saturated=saturated) rval <- do.call(rbind, rval) if (!is.null(sortby)) { rval <- rval[order(rval[,sortby], decreasing=TRUE),] } rval } Summarise.default <- function(object, ..., saturated = NULL, sortby=NULL) { ## interface methods for logLik() and nobs() ## - use S4 methods if loaded ## - use residuals() if nobs() is not available logLik0 <- if("stats4" %in% loadedNamespaces()) stats4::logLik else logLik nobs0 <- function(x, ...) { nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs nobs2 <- function(x, ...) NROW(residuals(x, ...)) rval <- try(nobs1(x, ...), silent = TRUE) if(inherits(rval, "try-error") | is.null(rval)) rval <- nobs2(x, ...) return(rval) } dof <- function(x) { if (inherits(x, "loglm")) { rval <- x$df } else { rval <- try(x$df.residual, silent=TRUE) } if (inherits(rval, "try-error") || is.null(rval)) stop(paste("Can't determine residual df for a", class(x), "object")) rval } ## collect all objects objects <- list(object, ...) nmodels <- length(objects) ## check sample sizes ns <- sapply(objects, nobs0) if(any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset") ## extract log-likelihood and df (number of parameters) ll <- lapply(objects, logLik0) par <- as.numeric(sapply(ll, function(x) attr(x, "df"))) df <- as.numeric(sapply(objects, function(x) dof(x))) ll <- sapply(ll, as.numeric) ## compute saturated reference value (use 0 if deviance is not available) if(is.null(saturated)) { dev <- try(sapply(objects, deviance), silent = TRUE) if(inherits(dev, "try-error") || any(sapply(dev, is.null))) { saturated <- 0 } else { saturated <- ll + dev/2 } } ## setup ANOVA-style matrix rval <- matrix(rep(NA, 5 * nmodels), ncol = 5) colnames(rval) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)") rownames(rval) <- as.character(sapply(match.call(), deparse)[-1L])[1:nmodels] rval[,1] <- -2 * ll + 2 * par rval[,2] <- -2 * ll + log(ns) * par rval[,3] <- -2 * (ll - saturated) rval[,4] <- df rval[,5] <- pchisq(rval[,3], df, lower.tail = FALSE) if (!is.null(sortby)) { rval <- rval[order(rval[,sortby], decreasing=TRUE),] } ## return structure(as.data.frame(rval), heading = "Likelihood summary table:", class = c("anova", "data.frame")) } vcdExtra/R/cutfac.R0000644000175100001440000000056412576352702013657 0ustar hornikusers# Cut a variable to a factor cutfac <- function(x, breaks = NULL, q=10) { if(is.null(breaks)) breaks <- unique(quantile(x, 0:q/q)) x <- cut(x, breaks, include.lowest = TRUE, right = FALSE) levels(x) <- paste(breaks[-length(breaks)], ifelse(diff(breaks) > 1, c(paste("-", breaks[-c(1, length(breaks))] - 1, sep = ""), "+"), ""), sep = "") return(x) } vcdExtra/R/seq_mosaic.R0000644000175100001440000000415712576352702014537 0ustar hornikusers#' Sequential Mosaics and Strucplots for an N-way Table #' This function takes an n-way contingency table and plots mosaics for series of sequential #' models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of #' types of loglinear models. #' @param x a contingency table in array form, with optional category labels specified in the dimnames(x) attribute, #' or else a data.frame in frequency form, with the frequency variable names "Freq". #' @param panel panel function #' @param type type of sequential model to fit #' @param plots which marginals to plot? #' @param vorder order of variables #' @param k indices of conditioning variable(s) for "joint", "conditional" or order for "markov" #' @export seq_mosaic <- function( x, panel = mosaic, type = c("joint", "conditional", "mutual", "markov", "saturated"), plots = 1:nf, # which plots to produce? vorder = 1:nf, # order of variables in the sequential plots k = NULL, # conditioning variable(s) for "joint", "conditional" or order for "markov" ... ) { if (inherits(x, "data.frame") && "Freq" %in% colnames(x)) { x <- xtabs(Freq ~ ., data=x) } if (!inherits(x, c("table", "array"))) stop("not an xtabs, table, array or data.frame with a 'Freq' variable") nf <- length(dim(x)) x <- aperm(x, vorder) factors <- names(dimnames(x)) indices <- 1:nf type = match.arg(type) for (i in plots) { mtab <- margin.table(x, 1:i) df <- NULL if (i==1) { expected <- mtab expected[] <- sum(mtab) / length(mtab) df <- length(mtab)-1 model.string = paste("=", factors[1]) } else { expected <- switch(type, 'conditional' = conditional(i, mtab, with=if(is.null(k)) i else k), 'joint' = joint(i, mtab, with=if(is.null(k)) i else k), 'mutual' = mutual(i, mtab), 'markov' = markov(i, mtab, order=if(is.null(k)) 1 else k), 'saturated' = saturated(i, mtab) ) model.string <- loglin2string(expected, brackets=if (i= i overj <- c >= j result[[c]] <- (overi & !overj) + (overj & !overi) } result <- matrix(unlist(result), length(i), npar) colnames(result) <- paste('C', 1:npar, sep='') result } Crossings <- function(...) { dots <- list(...) if (length(dots) != 2) stop("Crossings() is defined for only two factors") if (length(dots[[1]]) != length(dots[[2]])) stop("arguments to Crossings() must all have same length") dots <- lapply(dots, as.factor) n <- nlevels(dots[[1]]) if (nlevels(dots[[2]]) != n) stop("arguments to Crossings() must all have same number of levels") result <- crossings(as.numeric(dots[[1]]), as.numeric(dots[[2]]), n) rownames(result) <- do.call("paste", c(dots, sep = "")) result } vcdExtra/R/vcdExtra-deprecated.R0000644000175100001440000000014712576352702016265 0ustar hornikusers#summarise <- function (...) { # .Deprecated("summarise", package="vcdExtra") # LRstats(...) #} # vcdExtra/R/collapse.table.R0000644000175100001440000000170212576352702015275 0ustar hornikusers# collapse a contingency table or ftable by re-assigning levels of table variables # revised to accept an array also collapse.table <- function(table, ...) { nargs <- length(args <- list(...)) if (!nargs) return(table) if (inherits(table, "ftable")) table <- as.table(table) if (inherits(table, "array")) table <- as.table(table) if (inherits(table, "table")) { tvars <- names(dimnames(table)) table <- as.data.frame.table(table) freq <- table[,"Freq"] } else stop("Argument must be a table, array or ftable object") names <- names(args) for (i in 1:nargs) { vals <- args[[i]] nm <- names[[i]] if(any(nm==tvars)) levels(table[[nm]]) <- vals else warning(nm, " is not among the table variables.") } # term <- paste(tvars, collapse = '+') # form <- as.formula(paste("freq ~", term)) # cat("term: ", term, "\n") xtabs(as.formula(paste("freq ~", paste(tvars, collapse = '+'))), data=table) } vcdExtra/R/print.Kappa.R0000644000175100001440000000103212576352702014570 0ustar hornikusers# Print method for Kappa: Add a column showing z values ## DONE: now set digits ## DONE: now include CI print.Kappa <- function (x, digits=max(getOption("digits") - 3, 3), CI=FALSE, level=0.95, ...) { tab <- rbind(x$Unweighted, x$Weighted) z <- tab[,1] / tab[,2] tab <- cbind(tab, z) if (CI) { q <- qnorm((1 + level)/2) lower <- tab[,1] - q * tab[,2] upper <- tab[,1] + q * tab[,2] tab <- cbind(tab, lower, upper) } rownames(tab) <- names(x)[1:2] print(tab, digits=digits, ...) invisible(x) } vcdExtra/R/expand.dft.R0000644000175100001440000000213112576352702014435 0ustar hornikusers# Author: Marc Schwarz # Ref: http://tolstoy.newcastle.edu.au/R/e6/help/09/01/1873.html expand.dft <- function(x, var.names = NULL, freq = "Freq", ...) { # allow: a table object, or a data frame in frequency form if(inherits(x, "table")) x <- as.data.frame.table(x, responseName = freq) freq.col <- which(colnames(x) == freq) if (length(freq.col) == 0) stop(paste(sQuote("freq"), "not found in column names")) DF <- sapply(1:nrow(x), function(i) x[rep(i, each = x[i, freq.col]), ], simplify = FALSE) DF <- do.call("rbind", DF)[, -freq.col] for (i in 1:ncol(DF)) { DF[[i]] <- type.convert(as.character(DF[[i]]), ...) } rownames(DF) <- NULL if (!is.null(var.names)) { if (length(var.names) < dim(DF)[2]) { stop(paste("Too few", sQuote("var.names"), "given.")) } else if (length(var.names) > dim(DF)[2]) { stop(paste("Too many", sQuote("var.names"), "given.")) } else { names(DF) <- var.names } } DF } # make this a synonym expand.table <- expand.dft vcdExtra/R/CMHtest.R0000644000175100001440000002251612576352702013722 0ustar hornikusers# Cochran-Mantel-Haenszel tests for ordinal factors in contingency tables # The code below follows Stokes, Davis & Koch, (2000). # "Categorical Data Analysis using the SAS System", 2nd Ed., # pp 74--75, 92--101, 124--129. # Ref: Landis, R. J., Heyman, E. R., and Koch, G. G. (1978), # Average Partial Association in Three-way Contingency Tables: # A Review and Discussion of Alternative Tests, # International Statistical Review, 46, 237-254. # See: https://onlinecourses.science.psu.edu/stat504/book/export/html/90 # http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm # DONE: this should be the main function, handling 2-way & higher-way tables # With strata, use apply() or recursion over strata # DONE: With strata, calculate overall CMH tests controlling for strata # FIXED: rmeans and cmeans tests were labeled incorrectly CMHtest <- function(x, ...) UseMethod("CMHtest") CMHtest.formula <- function(formula, data = NULL, subset = NULL, na.action = NULL, ...) { m <- match.call(expand.dots = FALSE) edata <- eval(m$data, parent.frame()) fstr <- strsplit(paste(deparse(formula), collapse = ""), "~") vars <- strsplit(strsplit(gsub(" ", "", fstr[[1]][2]), "\\|")[[1]], "\\+") varnames <- vars[[1]] condnames <- if (length(vars) > 1) vars[[2]] else NULL dep <- gsub(" ", "", fstr[[1]][1]) if (!dep %in% c("","Freq")) { if (all(varnames == ".")) { varnames <- if (is.data.frame(data)) colnames(data) else names(dimnames(as.table(data))) varnames <- varnames[-which(varnames %in% dep)] } varnames <- c(varnames, dep) } if (inherits(edata, "ftable") || inherits(edata, "table") || length(dim(edata)) > 2) { condind <- NULL dat <- as.table(data) if(all(varnames != ".")) { ind <- match(varnames, names(dimnames(dat))) if (any(is.na(ind))) stop(paste("Can't find", paste(varnames[is.na(ind)], collapse=" / "), "in", deparse(substitute(data)))) if (!is.null(condnames)) { condind <- match(condnames, names(dimnames(dat))) if (any(is.na(condind))) stop(paste("Can't find", paste(condnames[is.na(condind)], collapse=" / "), "in", deparse(substitute(data)))) ind <- c(condind, ind) } dat <- margin.table(dat, ind) } CMHtest.default(dat, strata = if (is.null(condind)) NULL else match(condnames, names(dimnames(dat))), ...) } else { m <- m[c(1, match(c("formula", "data", "subset", "na.action"), names(m), 0))] m[[1]] <- as.name("xtabs") m$formula <- formula(paste(if("Freq" %in% colnames(data)) "Freq", "~", paste(c(varnames, condnames), collapse = "+"))) tab <- eval(m, parent.frame()) CMHtest.default(tab, ...) } } CMHtest.default <- function(x, strata = NULL, rscores=1:R, cscores=1:C, types=c("cor", "rmeans", "cmeans", "general"), overall=FALSE, details=overall, ...) { snames <- function(x, strata) { sn <- dimnames(x)[strata] dn <- names(sn) apply(expand.grid(sn), 1, function(x) paste(dn, x, sep=":", collapse = "|")) } ## check dimensions L <- length(d <- dim(x)) if(any(d < 2L)) stop("All table dimensions must be 2 or greater") if(L > 2L & is.null(strata)) strata <- 3L:L if(is.character(strata)) strata <- which(names(dimnames(x)) == strata) if(L - length(strata) != 2L) stop("All but 2 dimensions must be specified as strata.") ## rearrange table to put primary dimensions first x <- aperm(x, c(setdiff(1:L, strata), strata)) d <- dim(x) R <- d[1] C <- d[2] # handle strata if (!is.null(strata)) { sn <- snames(x, strata) res <- c(apply(x, strata, CMHtest2, rscores=rscores, cscores=cscores, types=types,details=details, ...)) # DONE: fix names if there are 2+ strata names(res) <- sn for (i in seq_along(res)) res[[i]]$stratum <- sn[i] # DONE: Calculate generalized CMH, controlling for strata if (overall) { if (!details) warning("Overall CMH tests not calculated because details=FALSE") else { resall <- CMHtest3(res, types=types) res$ALL <- resall } } return(res) } else CMHtest2(x, rscores=rscores, cscores=cscores, types=types,details=details, ...) } # handle two-way case, for a given stratum # DONE: now allow rscores/cscores == 'midrank' for midrank scores # DONE: allow rscores/cscores=NULL for unordered factors, where ordinal # scores don't make sense # DONE: modified to return all A matrices as a list # DONE: cmh() moved outside CMHtest2 <- function(x, stratum=NULL, rscores=1:R, cscores=1:C, types=c("cor", "rmeans", "cmeans", "general"), details=FALSE, ...) { # left kronecker product lkronecker <- function(x, y, make.dimnames=TRUE, ...) kronecker(y, x, make.dimnames=make.dimnames, ...) # midrank scores (modified ridits) based on row/column totals midrank <- function (n) { cs <- cumsum(n) (2*cs - n +1) / (2*(cs[length(cs)]+1)) } L <- length(d <- dim(x)) R <- d[1] C <- d[2] if (is.character(rscores) && rscores=="midrank") rscores <- midrank(rowSums(x)) if (is.character(cscores) && cscores=="midrank") cscores <- midrank(colSums(x)) nt <- sum(x) pr <- rowSums(x) / nt pc <- colSums(x) / nt m <- as.vector(nt * outer(pr,pc)) # expected values under independence n <- as.vector(x) # cell frequencies V1 <- (diag(pr) - pr %*% t(pr)) V2 <- (diag(pc) - pc %*% t(pc)) V <- (nt^2/(nt-1)) * lkronecker(V1, V2, make.dimnames=TRUE) if (length(types)==1 && types=="ALL") types <- c("general", "rmeans", "cmeans", "cor" ) types <- match.arg(types, several.ok=TRUE) # handle is.null(rscores) etc here if (is.null(rscores)) types <- setdiff(types, c("cmeans", "cor")) if (is.null(cscores)) types <- setdiff(types, c("rmeans", "cor")) table <- NULL Amats <- list() if("cor" %in% types) { A <- lkronecker( t(rscores), t(cscores) ) df <- 1 table <- rbind(table, cmh(n, m, A, V, df)) Amats$cor <- A } if("rmeans" %in% types) { A <- lkronecker( cbind(diag(R-1), rep(0, R-1)), t(cscores)) df <- R-1 table <- rbind(table, cmh(n, m, A, V, df)) Amats$rmeans <- A } if("cmeans" %in% types) { A <- lkronecker( t(rscores), cbind(diag(C-1), rep(0, C-1))) df <- C-1 table <- rbind(table, cmh(n, m, A, V, df)) Amats$cmeans <- A } if ("general" %in% types) { A <- lkronecker( cbind(diag(R-1), rep(0, R-1)), cbind(diag(C-1), rep(0, C-1))) df <- (R-1)*(C-1) table <- rbind(table, cmh(n, m, A, V, df)) Amats$general <- A } colnames(table) <- c("Chisq", "Df", "Prob") rownames(table) <- types xnames <- names(dimnames(x)) result <- list(table=table, names=xnames, rscores=rscores, cscores=cscores, stratum=stratum ) if (details) result <- c(result, list(A=Amats, V=V, n=n, m=m)) class(result) <- "CMHtest" result } # do overall test, from a computed CMHtest list CMHtest3 <- function(object, types=c("cor", "rmeans", "cmeans", "general")) { nstrat <- length(object) # number of strata # extract components, each a list of nstrat terms n.list <- lapply(object, function(s) s$n) m.list <- lapply(object, function(s) s$m) V.list <- lapply(object, function(s) s$V) A.list <- lapply(object, function(s) s$A) nt <- sapply(lapply(object, function(s) s$n), sum) Df <- object[[1]]$table[,"Df"] if (length(types)==1 && types=="ALL") types <- c("general", "rmeans", "cmeans", "cor" ) types <- match.arg(types, several.ok=TRUE) table <- list() for (type in types) { AVA <- 0 Anm <- 0 for (k in 1:nstrat) { A <- A.list[[k]][[type]] V <- V.list[[k]] n <- n.list[[k]] m <- m.list[[k]] AVA <- AVA + A %*% V %*% t(A) Anm <- Anm + A %*% (n-m) } Q <- t(Anm) %*% solve(AVA) %*% Anm df <- Df[type] pvalue <- pchisq(Q, df, lower.tail=FALSE) table <- rbind(table, c(Q, df, pvalue)) } rownames(table) <- types colnames(table) <- c("Chisq", "Df", "Prob") xnames <- object[[1]]$names result=list(table=table, names=xnames, stratum="ALL") class(result) <- "CMHtest" result } # basic CMH calculation cmh <- function(n, m,A, V, df) { AVA <- A %*% V %*% t(A) Q <- t(n-m) %*% t(A) %*% solve(AVA) %*% A %*% (n-m) pvalue <- pchisq(Q, df, lower.tail=FALSE) c(Q, df, pvalue) } # DONE: incorporate stratum name in the heading # TODO: handle the printing of pvalues better print.CMHtest <- function(x, digits = max(getOption("digits") - 2, 3), ...) { heading <- "Cochran-Mantel-Haenszel Statistics" if (!is.null(x$names)) heading <- paste(heading, "for", paste(x$names, collapse=" by ")) if (!is.null(x$stratum)) heading <- paste(heading, ifelse(x$stratum=="ALL", "\n\tOverall tests, controlling for all strata", paste("\n\tin stratum", x$stratum))) # TODO: determine score types (integer, midrank) for heading df <- x$table types <- rownames(df) labels <- list(cor="Nonzero correlation", rmeans="Row mean scores differ", cmeans="Col mean scores differ", general="General association") labels <- unlist(labels[types]) # select the labels for the types df <- data.frame("AltHypothesis"=as.character(labels), df, stringsAsFactors=FALSE) cat(heading,"\n\n") print(df, digits=digits, ...) cat("\n") invisible(x) } vcdExtra/R/mosaic.glmlist.R0000644000175100001440000001137612576352702015342 0ustar hornikusers#' Mosaic Displays for a glmlist Object #' @param x a glmlist object #' @param selection the index or name of one glm in \code{x} #' @param panel panel function #' @param type a character string indicating whether the \code{"observed"} or the \code{"expected"} values of the table should be visualized #' @param legend show a legend in the mosaic displays? #' @param main either a logical, or a vector of character strings used for plotting the main title. If main is a logical and TRUE, the name of the selected glm object is used #' @param ask should the function display a menu of models, when one is not specified in \code{selection}? #' @param graphics use a graphic menu when \code{ask=TRUE}? #' @param rows,cols when \code{ask=FALSE}, the number of rows and columns in which to plot the mosaics #' @param newpage start a new page? (only applies to \code{ask=FALSE}) #' @param ... other arguments passed to \code{\link{mosaic.glm}} #' @export mosaic.glmlist <- function(x, selection, panel=mosaic, type=c("observed", "expected"), legend=ask | !missing(selection), main=NULL, ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE, ...) { # calls <- sapply(x, mod.call) # get model calls as strings models <- names(x) if (!is.null(main)) { if (is.logical(main) && main) main <- models } else main <- rep(main, length(x)) type=match.arg(type) if (!missing(selection)){ if (is.character(selection)) selection <- gsub(" ", "", selection) return(panel(x[[selection]], type=type, main=main[selection], legend=legend, ...)) } # perhaps make these model labels more explicit for the menu if (ask & interactive()){ repeat { selection <- menu(models, graphics=graphics, title="Select Model to Plot") if (selection == 0) break else panel(x[[selection]], type=type, main=main[selection], legend=legend, ...) } } else { nmodels <- length(x) mfrow <- mfrow(nmodels) if (missing(rows) || missing(cols)){ rows <- mfrow[1] cols <- mfrow[2] } if (newpage) grid.newpage() lay <- grid.layout(nrow=rows, ncol = cols) pushViewport(viewport(layout = lay, y = 0, just = "bottom")) for (i in 1:rows) { for (j in 1:cols){ if ((sel <-(i-1)*cols + j) > nmodels) break pushViewport(viewport(layout.pos.row=i, layout.pos.col=j)) panel(x[[sel]], type=type, main=main[sel], newpage=FALSE, legend=legend, ...) popViewport() } } } } mosaic.loglmlist <- function(x, selection, panel=mosaic, type=c("observed", "expected"), legend=ask | !missing(selection), main=NULL, ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE, ...) { models <- names(x) strings <- as.vector(sapply(x, function(x) x$model.string)) if (!is.null(main)) { if (is.logical(main) && main) main <- ifelse(as.vector(sapply(strings, is.null)), models, strings) } else main <- rep(main, length(x)) type=match.arg(type) if (!missing(selection)){ if (is.character(selection)) selection <- gsub(" ", "", selection) return(panel(x[[selection]], type=type, main=main[selection], legend=legend, ...)) } # perhaps make these model labels more explicit for the menu if (ask & interactive()){ repeat { selection <- menu(models, graphics=graphics, title="Select Model to Plot") if (selection == 0) break else panel(x[[selection]], type=type, main=main[selection], legend=legend, ...) } } else { nmodels <- length(x) mfrow <- mfrow(nmodels) if (missing(rows) || missing(cols)){ rows <- mfrow[1] cols <- mfrow[2] } if (newpage) grid.newpage() lay <- grid.layout(nrow=rows, ncol = cols) pushViewport(viewport(layout = lay, y = 0, just = "bottom")) for (i in 1:rows) { for (j in 1:cols){ if ((sel <-(i-1)*cols + j) > nmodels) break pushViewport(viewport(layout.pos.row=i, layout.pos.col=j)) panel(x[[sel]], type=type, main=main[sel], newpage=FALSE, legend=legend, ...) popViewport() } } } } # from effects::utilities.R mfrow <- function(n, max.plots=0){ # number of rows and columns for array of n plots if (max.plots != 0 & n > max.plots) stop(paste("number of plots =",n," exceeds maximum =", max.plots)) rows <- round(sqrt(n)) cols <- ceiling(n/rows) c(rows, cols) } # from plot.lm: get model call as a string # TODO: should use abbreviate() mod.call <- function(x) { cal <- x$call if (!is.na(m.f <- match("formula", names(cal)))) { cal <- cal[c(1, m.f)] names(cal)[2L] <- "" } cc <- deparse(cal, 80) nc <- nchar(cc[1L], "c") abbr <- length(cc) > 1 || nc > 75 cap <- if (abbr) paste(substr(cc[1L], 1L, min(75L, nc)), "...") else cc[1L] cap } vcdExtra/R/loglin-utilities.R0000644000175100001440000001375012576352702015710 0ustar hornikusers#' Loglinear Model Utilities #' These functions generate lists of terms to specify a loglinear model #' in a form compatible with loglin and provide for conversion to an #' equivalent loglm specification. They allow for a more conceptual #' way to specify such models. #' models of joint independence, of some factors wrt one or more other factors #' @param nf number of factors for which to generate model #' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}} #' @param factors names of factors used in the model when \code{table} is not specified #' @param with indices of the factors against which others are considered jointly independent #' @export joint <- function(nf, table=NULL, factors=1:nf, with=nf) { if (!is.null(table)) factors <- names(dimnames(table)) if (nf == 1) return (list(term1=factors[1])) if (nf == 2) return (list(term1=factors[1], term2=factors[2])) others <- setdiff(1:nf, with) result <- list(term1=factors[others], term2=factors[with]) result } #' models of conditional independence of some factors wrt one or more other factors #' @param nf number of factors for which to generate model #' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}} #' @param factors names of factors used in the model when \code{table} is not specified #' @param with indices of the factors against which others are considered conditionally independent #' @export conditional <- function(nf, table=NULL, factors=1:nf, with=nf) { if (!is.null(table)) factors <- names(dimnames(table)) if (nf == 1) return (list(term1=factors[1])) if (nf == 2) return (list(term1=factors[1], term2=factors[2])) main <- setdiff(1:nf, with) others <- matrix(factors[with], length(with), length(main)) result <- rbind(factors[main], others) result <- as.list(as.data.frame(result, stringsAsFactors=FALSE)) names(result) <- paste('term', 1:length(result), sep='') result } #' models of mutual independence of all factors #' @param nf number of factors for which to generate model #' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}} #' @param factors names of factors used in the model when \code{table} is not specified #' @export mutual <- function(nf, table=NULL, factors=1:nf) { if (!is.null(table)) factors <- names(dimnames(table)) result <- sapply(factors[1:nf], list) names(result) <- paste('term', 1:length(result), sep='') result } #' saturated model: highest-order interaction #' @param nf number of factors for which to generate model #' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}} #' @param factors names of factors used in the model when \code{table} is not specified #' @export saturated <- function(nf, table=NULL, factors=1:nf) { if (!is.null(table)) factors <- names(dimnames(table)) list(term1=factors[1:nf]) } # models of conditional independence, given one pair of variables ## Not needed: handled by condit, with length(with)>1 #condit2 <- function(nf, factors=1:nf, with=1:2) { # if (nf == 1) return (list(term1=factors[1])) # if (nf == 2) return (list(term1=factors[1], term2=factors[2])) # others <- setdiff(1:nf, with) # result <- rbind(factors[with], cbind(factors[others], factors[others])) # result <- as.list(as.data.frame(result, stringsAsFactors=FALSE)) # names(result) <- paste('term', 1:length(result), sep='') # result #} #' markov models of a given order #' @param nf number of factors for which to generate model #' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}} #' @param factors names of factors used in the model when \code{table} is not specified #' @param order order of the markov chain #' @export markov <- function(nf, factors=1:nf, order=1) { if (nf == 1) return (list(term1=factors[1])) if (nf == 2) return (list(term1=factors[1], term2=factors[2])) if (length(factors) < order+2) { warning(paste('Not enough factors for order', order, 'Markov chain; using order=1')) order <-1 result <- rbind(factors[1:(nf-1)], factors[2:nf]) } else { if (nf <= order+1) result <- factors[1:nf] else { result <- NULL for (i in 1:(order+1)) result <- rbind(result, factors[i:(nf-order+i-1)]) } } result <- as.list(as.data.frame(result, stringsAsFactors=FALSE)) names(result) <- paste('term', 1:length(result), sep='') result } #' convert a loglin model to a model formula for loglm #' @param x a list of terms in a loglinear model, such as returned by \code{joint}, \code{conditional}, \dots #' @param env environment in which to evaluate the formula #' @source Code from Henrique Dallazuanna, , R-help 7-4-2013 loglin2formula <- function(x, env = parent.frame()) { terms <- lapply(x, paste, collapse = ":") formula(sprintf(" ~ %s", do.call(paste, c(terms, sep = "+"))), env=env) } #' convert a loglin model to a string, using bracket notation for the high-order terms #' @param x a list of terms in a loglinear model, such as returned by \code{joint}, \code{conditional}, \dots #' @param brackets characters to use to surround model terms. Either a single character string containing two characters #' or a character vector of length two. #' @param sep characters used to separate factor names within a term #' @param collapse characters used to separate terms #' @param abbrev loglin2string <- function(x, brackets = c('[', ']'), sep=',', collapse=' ', abbrev) { if (length(brackets)==1 && (nchar(brackets)>1)) brackets <- unlist(strsplit(brackets, "")) terms <- lapply(x, paste, collapse=sep) terms <- paste(brackets[1], terms, brackets[2], sep='') paste(terms, collapse= ' ') } vcdExtra/R/GKgamma.R0000644000175100001440000000320012576352702013704 0ustar hornikusers# Calculate Goodman-Kruskal Gamma # Original from: Laura Thompson, # https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf GKgamma<-function(x, level=0.95) { # x is a matrix of counts. You can use output of crosstabs or xtabs in R. # Confidence interval calculation and output from Greg Rodd # Check for using S-PLUS and output is from crosstabs (needs >= S-PLUS 6.0) if(is.null(version$language) && inherits(x, "crosstabs")) { oldClass(x)<-NULL; attr(x, "marginals")<-NULL} ## TODO: add tests for matrix or table n <- nrow(x) m <- ncol(x) pi.c<-pi.d<-matrix(0, nrow=n, ncol=m) row.x<-row(x) col.x<-col(x) for(i in 1:(n)){ for(j in 1:(m)){ pi.c[i, j]<-sum(x[row.xi & col.x>j]) pi.d[i, j]<-sum(x[row.xj]) + sum(x[row.x>i & col.x 1, maxTitle=NULL) { # make sure requested packages are available and loaded pkgs <- .packages() for (i in seq_along(package)) { if (! package[i] %in% pkgs) if (require(package[i], character.only=TRUE, quietly=TRUE)) cat(paste("Loading package:", package[i], "\n")) else stop(paste("Package", package[i], "is not available")) } dsitems <- data(package=package)$results wanted <- if (incPackage) c('Package', 'Item','Title') else c('Item','Title') ds <- as.data.frame(dsitems[,wanted], stringsAsFactors=FALSE) # fix items with " (...)" in names, e.g., "BJsales.lead (BJsales)" in datasets ds$Item <- gsub(" .*", "", ds$Item) getDim <- function(x) { if (is.null(dim(get(x)))) length(get(x)) else paste(dim(get(x)), collapse='x') } getClass <- function(x) { cl <- class(get(x)) if (length(cl)>1 && !allClass) cl[length(cl)] else cl } ds$dim <- unlist(lapply(ds$Item, getDim )) ds$class <- unlist(lapply(ds$Item, getClass )) if (!is.null(maxTitle)) ds$Title <- substr(ds$Title, 1, maxTitle) if (incPackage) ds[c('Package', 'Item','class','dim','Title')] else ds[c('Item','class','dim','Title')] } vcdExtra/R/modFit.R0000644000175100001440000000164712576352702013637 0ustar hornikusers## ## One-line summary of model fit for a glm/loglm object ## `modFit` <- function(x, ...) UseMethod("modFit") modFit.glm <- function(x, stats="chisq", digits=2, ...) { if (!inherits(x,"glm")) stop("modFit requires a glm object") result <- NULL if ("chisq" %in% stats) result <- paste("G^2(",x$df.residual,")=", formatC(x$deviance,digits=digits,format="f"),sep="") if ("aic" %in% stats) result <- paste(result, " AIC=", formatC(x$aic,digits=digits,format="f"),sep="") result } modFit.loglm <- function(x, stats="chisq", digits=2, ...) { if (!inherits(x,"loglm")) stop("modFit requires a loglm object") result <- NULL if ("chisq" %in% stats) result <- paste("G^2(",x$df,")=", formatC(x$deviance,digits=digits,format="f"),sep="") if ("aic" %in% stats) { aic<-x$deviance-x$df*2 result <- paste(result, " AIC=", formatC(aic,digits=digits,format="f"),sep="") } result } vcdExtra/vignettes/0000755000175100001440000000000012576352714014074 5ustar hornikusersvcdExtra/vignettes/vcd-tutorial.Rnw0000644000175100001440000023323612576352702017207 0ustar hornikusers% !Rnw weave = Sweave %\VignetteEngine{Sweave} %\VignetteIndexEntry{Tutorial: Working with categorical data with R and the vcd package} %\VignetteDepends{vcd,gmodels,ca} %\VignetteKeywords{contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R} %\VignettePackage{vcdExtra} \documentclass[10pt,twoside]{article} \usepackage{Sweave} \usepackage{bm} \usepackage[toc]{multitoc} % for table of contents % from Z.cls \usepackage[authoryear,round,longnamesfirst]{natbib} \bibpunct{(}{)}{;}{a}{}{,} \bibliographystyle{jss} \usepackage{hyperref} \usepackage{color} %% colors \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \hypersetup{% hyperindex = {true}, colorlinks = {true}, % linktocpage = {true}, plainpages = {false}, linkcolor = {Blue}, citecolor = {Blue}, urlcolor = {Red}, pdfstartview = {Fit}, pdfpagemode = {UseOutlines}, pdfview = {XYZ null null null} } %\AtBeginDocument{ % \hypersetup{% % pdfauthor = {Michael Friendly}, % pdftitle = {Tutorial: Working with categorical data with R and the vcd package}, % pdfkeywords = {contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R} % } %} % math stuff \newcommand*{\given}{\ensuremath{\, | \,}} \renewcommand*{\vec}[1]{\ensuremath{\bm{#1}}} \newcommand{\mat}[1]{\ensuremath{\bm{#1}}} \newcommand{\trans}{\ensuremath{^\mathsf{T}}} \newcommand{\diag}[1]{\ensuremath{\mathrm{diag} (#1)}} \def\binom#1#2{{#1 \choose #2}}% \newcommand{\implies}{ \ensuremath{\mapsto} } \newenvironment{equation*}{\displaymath}{\enddisplaymath}% \newcommand{\tabref}[1]{Table~\ref{#1}} \newcommand{\figref}[1]{Figure~\ref{#1}} \newcommand{\secref}[1]{Section~\ref{#1}} \newcommand{\loglin}{loglinear } %\usepackage{thumbpdf} % page dimensions \addtolength{\hoffset}{-1.5cm} \addtolength{\textwidth}{3cm} \addtolength{\voffset}{-1cm} \addtolength{\textheight}{2cm} % Vignette examples \newcommand*{\Example}{\fbox{\textbf{\emph{Example}}:} } % R stuff \newcommand{\var}[1]{\textit{\texttt{#1}}} \newcommand{\data}[1]{\texttt{#1}} \newcommand{\class}[1]{\textsf{"#1"}} %% \code without `-' ligatures \def\nohyphenation{\hyphenchar\font=-1 \aftergroup\restorehyphenation} \def\restorehyphenation{\hyphenchar\font=`-} {\catcode`\-=\active% \global\def\code{\bgroup% \catcode`\-=\active \let-\codedash% \Rd@code}} \def\codedash{-\discretionary{}{}{}} \def\Rd@code#1{\texttt{\nohyphenation#1}\egroup} \newcommand{\codefun}[1]{\code{#1()}} \let\proglang=\textsf \newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}} \newcommand{\Rpackage}[1]{{\textsf{#1}}} %% almost as usual \author{Michael Friendly\\York University, Toronto} \title{Working with categorical data with \proglang{R} and the \pkg{vcd} and \pkg{vcdExtra} packages} \date{\footnotesize{Using \Rpackage{vcdExtra} version \Sexpr{packageDescription("vcdExtra")[["Version"]]} and \Rpackage{vcd} version \Sexpr{packageDescription("vcd")[["Version"]]}; Date: \Sexpr{Sys.Date()}}} %% for pretty printing and a nice hypersummary also set: %\Plainauthor{Michael Friendly} %% comma-separated %\Shorttitle{vcd tutorial} %% a short title (if necessary) %\Plaintitle{Tutorial: Working with categorical data with R and the vcd package} %\SweaveOpts{engine=R,eps=TRUE,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} \SweaveOpts{engine=R,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} \SweaveOpts{prefix.string=fig/vcd-tut,eps=FALSE} \SweaveOpts{keep.source=TRUE} %\SweaveOpts{concordance=TRUE} \setkeys{Gin}{width=0.7\textwidth} <>= set.seed(1071) #library(vcd) library(vcdExtra) library(ggplot2) #data(Titanic) data(HairEyeColor) data(PreSex) data(Arthritis) art <- xtabs(~Treatment + Improved, data = Arthritis) if(!file.exists("fig")) dir.create("fig") @ %% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{document} \SweaveOpts{concordance=TRUE} \maketitle %% an abstract and keywords \begin{abstract} This tutorial describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results. The framework is provided by the \proglang{R} package \pkg{vcd}, but other packages are used to help with various tasks. The \pkg{vcdExtra} package extends the graphical and statistical methods provided by \pkg{vcd}. \end{abstract} %\keywords{contingency tables, mosaic plots, sieve plots, %categorical data, independence, conditional independence, generalized linear models, %\proglang{R}} %\Plainkeywords{contingency tables, mosaic plots, % sieve plots, categorical data, independence, % conditional independence, generalized linear models, R} {\small % \sloppy % \begin{multicols}{2} \tableofcontents % \end{multicols} } \section[Introduction]{Introduction}\label{sec:intro} %% Note: If there is markup in \(sub)section, then it has to be escape as above. This tutorial, part of the \pkg{vcdExtra} package, describes how to work with categorical data in the context of fitting statistical models in \proglang{R} and visualizing the results using the \pkg{vcd} and \pkg{vcdExtra} packages. It focuses first on methods and tools for creating and manipulating \proglang{R} data objects which represent frequency and contingency tables involving categorical variables. Further sections describe some simple methods for calculating tests of independence and measures of association amomg categorial variables, and also methods for graphically displaying results. There is much more to the analysis of categorical data than is described here, where the emphasis is on cross-tabulated tables of frequencies (``contingency tables''), statistical tests, associated \loglin\ models, and visualization of \emph{how} variables are related. A more general treatment of graphical methods for categorical data is contained in my book, \emph{Visualizing Categorical Data} \citep{vcd:Friendly:2000}, for which \pkg{vcd} is a partial \proglang{R} companion, covering topics not otherwise available in \proglang{R}. On the other hand, the implementation of graphical methods in \pkg{vcd} is more general in many respects than what I provided in \proglang{SAS}. Statistical models for categorical data in \proglang{R} have been extended considerably with the \pkg{gnm} package for generalized \emph{nonlinear} models. The \pkg{vcdExtra} package extends \pkg{vcd} methods to models fit using \codefun{glm} and \codefun{gnm}. A more complete theoretical description of these statistical methods is provided in Agresti's \citeyearpar{vcd:Agresti:2002,Agresti:2013} \emph{Categorical Data Analysis}. For this, see the \proglang{Splus/R} companion by Laura Thompson, \url{https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf} and Agresti's support web page, \url{http://www.stat.ufl.edu/~aa/cda/cda.html}. \section[Creating frequency tables]{Creating and manipulating frequency tables}\label{sec:creating} \proglang{R} provides many methods for creating frequency and contingency tables. Several are described below. In the examples below, we use some real examples and some anonymous ones, where the variables \code{A}, \code{B}, and \code{C} represent categorical variables, and \code{X} represents an arbitrary \proglang{R} data object. The first thing you need to know is that categorical data can be represented in three different forms in \proglang{R}, and it is sometimes necessary to convert from one form to another, for carrying out statistical tests, fitting models or visualizing the results. Once a data object exists in \proglang{R}, you can examine its complete structure with the \codefun{str} function, or view the names of its components with the \codefun{names} function. \begin{description} \item[case form] a data frame containing individual observations, with one or more factors, used as the classifying variables. In case form, there may also be numeric covariates. The total number of observations is \code{nrow(X)}, and the number of variables is \code{ncol(X)}. \Example The \data{Arthritis} data is available in case form in the \pkg{vcd} package. There are two explanatory factors: \code{Treatment} and \code{Sex}. \code{Age} is a numeric covariate, and \code{Improved} is the response--- an ordered factor, with levels \code{\Sexpr{paste(levels(Arthritis$Improved),collapse=' < ')}}. Excluding \code{Age}, we would have a $2 \times 2 \times 3$ contingency table for \code{Treatment}, \code{Sex} and \code{Improved}. %\code{"None" < "Some" < "Marked"}. <>= names(Arthritis) # show the variables str(Arthritis) # show the structure head(Arthritis,5) # first 5 observations, same as Arthritis[1:5,] @ \item[frequency form] a data frame containing one or more factors, and a frequency variable, often called \code{Freq} or \code{count}. The total number of observations is \verb|sum(X$Freq)|, \code{sum(X[,"Freq"])} or some equivalent form. The number of cells in the table is \code{nrow(X)}. \Example For small frequency tables, it is often convenient to enter them in frequency form using \codefun{expand.grid} for the factors and \codefun{c} to list the counts in a vector. The example below, from \cite{vcd:Agresti:2002} gives results for the 1991 General Social Survey, with respondents classified by sex and party identification. <>= # Agresti (2002), table 3.11, p. 106 GSS <- data.frame( expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")), count=c(279,165,73,47,225,191)) GSS names(GSS) str(GSS) sum(GSS$count) @ \item[table form] a matrix, array or table object, whose elements are the frequencies in an $n$-way table. The variable names (factors) and their levels are given by \code{dimnames(X)}. The total number of observations is \code{sum(X)}. The number of dimensions of the table is \code{length(dimnames(X))}, and the table sizes are given by \code{sapply(dimnames(X), length)}. \Example The \data{HairEyeColor} is stored in table form in \pkg{vcd}. <>= str(HairEyeColor) # show the structure sum(HairEyeColor) # number of cases sapply(dimnames(HairEyeColor), length) # table dimension sizes @ \Example Enter frequencies in a matrix, and assign \code{dimnames}, giving the variable names and category labels. Note that, by default, \codefun{matrix} uses the elements supplied by \emph{columns} in the result, unless you specify \code{byrow=TRUE}. <>= ## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4) dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"), satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS")) JobSat @ \data{JobSat} is a matrix, not an object of \code{class("table")}, and some functions are happier with tables than matrices. You can coerce it to a table with \codefun{as.table}, <>= JobSat <- as.table(JobSat) str(JobSat) @ \end{description} \subsection[Ordered factors]{Ordered factors and reordered tables}\label{sec:ordered-factors} In table form, the values of the table factors are ordered by their position in the table. Thus in the \data{JobSat} data, both \code{income} and \code{satisfaction} represent ordered factors, and the \emph{positions} of the values in the rows and columns reflects their ordered nature. Yet, for analysis, there are time when you need \emph{numeric} values for the levels of ordered factors in a table, e.g., to treat a factor as a quantitative variable. In such cases, you can simply re-assign the \code{dimnames} attribute of the table variables. For example, here, we assign numeric values to \code{income} as the middle of their ranges, and treat \code{satisfaction} as equally spaced with integer scores. <>= dimnames(JobSat)$income<-c(7.5,20,32.5,60) dimnames(JobSat)$satisfaction<-1:4 @ For the \data{HairEyeColor} data, hair color and eye color are ordered arbitrarily. For visualizing the data using mosaic plots and other methods described below, it turns out to be more useful to assure that both hair color and eye color are ordered from dark to light. Hair colors are actually ordered this way already, and it is easiest to re-order eye colors by indexing. Again \codefun{str} is your friend. <>= HairEyeColor <- HairEyeColor[, c(1,3,4,2), ] str(HairEyeColor) @ This is also the order for both hair color and eye color shown in the result of a correspondence analysis (\figref{fig:ca-haireye}) below. With data in case form or frequency form, when you have ordered factors represented with character values, you must ensure that they are treated as ordered in \proglang{R}.% \footnote{In \proglang{SAS}, many procedures offer the option \code{order = data | internal | formatted} to allow character values to be ordered according to (a) their order in the data set, (b) sorted internal value, or (c) sorted formatted representation provided by a \proglang{SAS} format. } Imagine that the \data{Arthritis} data was read from a text file. By default the \code{Improved} will be ordered alphabetically: \code{Marked}, \code{None}, \code{Some}--- not what we want. In this case, the function \codefun{ordered} (and others) can be useful. <>= Arthritis <- read.csv("arthritis.txt",header=TRUE) Arthritis$Improved <- ordered(Arthritis$Improved, levels=c("None", "Some", "Marked")) @ With this order of \code{Improved}, the response in this data, a mosaic display of \code{Treatment} and \code{Improved} (\figref{fig:arthritis})shows a clearly interpretable pattern. <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") @ %\setkeys{Gin}{width=0.7\textwidth} \begin{figure}[htb] \begin{center} %<>= %mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") %@ \includegraphics[width=0.7\textwidth]{fig/vcd-tut-Arthritis} \caption{Mosaic plot for the \data{Arthritis} data, showing the marginal model of independence for Treatment and Improved. Age, a covariate, and Sex are ignored here.} \label{fig:arthritis} \end{center} \end{figure} Finally, there are situations where, particularly for display purposes, you want to re-order the \emph{dimensions} of an $n$-way table, or change the labels for the variables or levels. This is easy when the data are in table form: \codefun{aperm} permutes the dimensions, and assigning to \code{names} and \code{dimnames} changes variable names and level labels respectively. We will use the following version of \data{UCBAdmissions} in \secref{sec:mantel} below.% \footnote{ Changing \code{Admit} to \code{Admit?} might be useful for display purposes, but is dangerous--- because it is then difficult to use that variable name in a model formula. See \secref{sec:tips} for options \code{labeling\_args} and \code{set\_labels} to change variable and level names for displays in the \code{strucplot} framework. } <>= UCB <- aperm(UCBAdmissions, c(2, 1, 3)) dimnames(UCB)[[2]] <- c("Yes", "No") names(dimnames(UCB)) <- c("Sex", "Admit?", "Department") ftable(UCB) @ %There is one subtle ``gotcha'' here: \codefun{aperm} returns an object of class \class{"array"}, %whereas \data{UCBAdmissions} is of class \class{"table"}, so methods defined for \code{table} %objects will not work on the permuted array. %The solution is to reassign the \code{class} of the result of \codefun{aperm}. % %<>= %class(UCBAdmissions) %class(UCB) %str(as.data.frame(UCBAdmissions)) # OK %str(as.data.frame(UCB)) # wrong % %class(UCB) <- "table" %str(as.data.frame(UCB)) # now OK %@ % \subsection[structable()]{\codefun{structable}}\label{sec:structable} For 3-way and larger tables the \codefun{structable} function in \pkg{vcd} provides a convenient and flexible tabular display. The variables assigned to the rows and columns of a two-way display can be specified by a model formula. <>= structable(HairEyeColor) # show the table: default structable(Hair+Sex ~ Eye, HairEyeColor) # specify col ~ row variables @ It also returns an object of class \code{"structable"} which may be plotted with \codefun{mosaic} (not shown here). <>= HSE < - structable(Hair+Sex ~ Eye, HairEyeColor) # save structable object mosaic(HSE) # plot it @ \subsection[table() and friends]{\codefun{table} and friends}\label{sec:table} You can generate frequency tables from factor variables using the \codefun{table} function, tables of proportions using the \codefun{prop.table} function, and marginal frequencies using \codefun{margin.table}. <>= n=500 A <- factor(sample(c("a1","a2"), n, rep=TRUE)) B <- factor(sample(c("b1","b2"), n, rep=TRUE)) C <- factor(sample(c("c1","c2"), n, rep=TRUE)) mydata <- data.frame(A,B,C) @ <>= # 2-Way Frequency Table attach(mydata) mytable <- table(A,B) # A will be rows, B will be columns mytable # print table margin.table(mytable, 1) # A frequencies (summed over B) margin.table(mytable, 2) # B frequencies (summed over A) prop.table(mytable) # cell percentages prop.table(mytable, 1) # row percentages prop.table(mytable, 2) # column percentages @ \codefun{table} can also generate multidimensional tables based on 3 or more categorical variables. In this case, use the \codefun{ftable} or \codefun{structable} function to print the results more attractively. <>= # 3-Way Frequency Table mytable <- table(A, B, C) ftable(mytable) @ \codefun{table} ignores missing values by default. To include \code{NA} as a category in counts, include the table option \code{exclude=NULL} if the variable is a vector. If the variable is a factor you have to create a new factor using \code{newfactor <- factor(oldfactor, exclude=NULL)}. \subsection[xtabs()]{\codefun{xtabs}}\label{sec:xtabs} The \codefun{xtabs} function allows you to create crosstabulations of data using formula style input. This typically works with case-form data supplied in a data frame or a matrix. The result is a contingency table in array format, whose dimensions are determined by the terms on the right side of the formula. <>= # 3-Way Frequency Table mytable <- xtabs(~A+B+C, data=mydata) ftable(mytable) # print table summary(mytable) # chi-square test of indepedence @ If a variable is included on the left side of the formula, it is assumed to be a vector of frequencies (useful if the data have already been tabulated in frequency form). <>= (GSStab <- xtabs(count ~ sex + party, data=GSS)) summary(GSStab) @ \subsection[Collapsing over factors]{Collapsing over table factors: \codefun{aggregate}, \codefun{margin.table} and \codefun{apply}} It sometimes happens that we have a data set with more variables or factors than we want to analyse, or else, having done some initial analyses, we decide that certain factors are not important, and so should be excluded from graphic displays by collapsing (summing) over them. For example, mosaic plots and fourfold displays are often simpler to construct from versions of the data collapsed over the factors which are not shown in the plots. The appropriate tools to use again depend on the form in which the data are represented--- a case-form data frame, a frequency-form data frame (\codefun{aggregate}), or a table-form array or table object (\codefun{margin.table} or \codefun{apply}). When the data are in frequency form, and we want to produce another frequency data frame, \codefun{aggregate} is a handy tool, using the argument \code{FUN=sum} to sum the frequency variable over the factors \emph{not} mentioned in the formula. \Example The data frame \data{DaytonSurvey} in the \pkg{vcdExtra} package represents a $2^5$ table giving the frequencies of reported use (``ever used?'') of alcohol, cigarettes and marijuana in a sample of high school seniors, also classified by sex and race. <>= str(DaytonSurvey) head(DaytonSurvey) @ To focus on the associations among the substances, we want to collapse over sex and race. The right-hand side of the formula used in the call to \codefun{aggregate} gives the factors to be retained in the new frequency data frame, \code{Dayton.ACM.df}. <>= # data in frequency form # collapse over sex and race Dayton.ACM.df <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum) Dayton.ACM.df @ When the data are in table form, and we want to produce another table, \codefun{apply} with \code{FUN=sum} can be used in a similar way to sum the table over dimensions not mentioned in the \code{MARGIN} argument. \codefun{margin.table} is just a wrapper for \codefun{apply} using the \codefun{sum} function. \Example To illustrate, we first convert the \data{DaytonSurvey} to a 5-way table using \codefun{xtabs}, giving \code{Dayton.tab}. <>== # in table form Dayton.tab <- xtabs(Freq~cigarette+alcohol+marijuana+sex+race, data=DaytonSurvey) structable(cigarette+alcohol+marijuana ~ sex+race, data=Dayton.tab) @ Then, use \codefun{apply} on \code{Dayton.tab} to give the 3-way table \code{Dayton.ACM.tab} summed over sex and race. The elements in this new table are the column sums for \code{Dayton.tab} shown by \codefun{structable} just above. <>== # collapse over sex and race Dayton.ACM.tab <- apply(Dayton.tab, MARGIN=1:3, FUN=sum) Dayton.ACM.tab <- margin.table(Dayton.tab, 1:3) # same result structable(cigarette+alcohol ~ marijuana, data=Dayton.ACM.tab) @ Many of these operations can be performed using the \verb|**ply()| functions in the \pkg{plyr} package. For example, with the data in a frequency form data frame, use \codefun{ddply} to collapse over unmentioned factors, and \codefun{plyr::summarise}% \footnote{ Ugh. This \pkg{plyr} function clashes with a function of the same name in \pkg{vcdExtra}. In this document I will use the explicit double-colon notation to keep them separate. } as the function to be applied to each piece. <>== Dayton.ACM.df <- ddply(DaytonSurvey, .(cigarette, alcohol, marijuana), plyr::summarise, Freq=sum(Freq)) @ \subsection[Collapsing levels]{Collapsing table levels: \codefun{collapse.table}} A related problem arises when we have a table or array and for some purpose we want to reduce the number of levels of some factors by summing subsets of the frequencies. For example, we may have initially coded Age in 10-year intervals, and decide that, either for analysis or display purposes, we want to reduce Age to 20-year intervals. The \codefun{collapse.table} function in \pkg{vcdExtra} was designed for this purpose. \Example Create a 3-way table, and collapse Age from 10-year to 20-year intervals. First, we generate a $2 \times 6 \times 3$ table of random counts from a Poisson distribution with mean of 100. <>= # create some sample data in frequency form sex <- c("Male", "Female") age <- c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69") education <- c("low", 'med', 'high') data <- expand.grid(sex=sex, age=age, education=education) counts <- rpois(36, 100) # random Possion cell frequencies data <- cbind(data, counts) # make it into a 3-way table t1 <- xtabs(counts ~ sex + age + education, data=data) structable(t1) @ Now collapse \code{age} to 20-year intervals, and \code{education} to 2 levels. In the arguments, levels of \code{age} and \code{education} given the same label are summed in the resulting smaller table. <>= # collapse age to 3 levels, education to 2 levels t2 <- collapse.table(t1, age=c("10-29", "10-29", "30-49", "30-49", "50-69", "50-69"), education=c(">= as.data.frame(GSStab) @ \Example Convert the \code{Arthritis} data in case form to a 3-way table of \code{Treatment} $\times$ \code{Sex} $\times$ \code{Improved}. Note the use of \codefun{with} to avoid having to use \code{Arthritis\$Treatment} etc. within the call to \codefun{table}.% \footnote{ \codefun{table} does not allow a \code{data} argument to provide an environment in which the table variables are to be found. In the examples in \secref{sec:table} I used \code{attach(mydata)} for this purpose, but \codefun{attach} leaves the variables in the global environment, while \codefun{with} just evaluates the \codefun{table} expression in a temporary environment of the data. } <>= Art.tab <-with(Arthritis, table(Treatment, Sex, Improved)) str(Art.tab) ftable(Art.tab) @ There may also be times that you will need an equivalent case form \code{data.frame} with factors representing the table variables rather than the frequency table. For example, the \codefun{mca} function in package \pkg{MASS} only operates on data in this format. Marc Schwartz provided code for \codefun{expand.dft} on the Rhelp mailing list for converting a table back into a case form \code{data.frame}. This function is included in \pkg{vcdExtra}. \Example Convert the \data{Arthritis} data in table form (\code{Art.tab}) back to a \code{data.frame} in case form, with factors \code{Treatment}, \code{Sex} and \code{Improved}. <>= Art.df <- expand.dft(Art.tab) str(Art.df) @ \subsection{A complex example}\label{sec:complex} If you've followed so far, you're ready for a more complicated example. The data file, \code{tv.dat} represents a 4-way table of size $5 \times 11 \times 5 \times 3$ where the table variables (unnamed in the file) are read as \code{V1} -- \code{V4}, and the cell frequency is read as \code{V5}. The file, stored in the \code{doc/extdata} directory of \pkg{vcdExtra}, can be read as follows: <>= tv.data<-read.table(system.file("doc","extdata","tv.dat",package="vcdExtra")) head(tv.data,5) @ For a local file, just use \codefun{read.table} in this form: <>= tv.data<-read.table("C:/R/data/tv.dat") @ The data \code{tv.dat} came from the initial implementation of mosaic displays in \proglang{R} by Jay Emerson. In turn, they came from the initial development of mosaic displays \citep{vcd:Hartigan+Kleiner:1984} that illustrated the method with data on a large sample of TV viewers whose behavior had been recorded for the Neilson ratings. This data set contains sample television audience data from Neilsen Media Research for the week starting November 6, 1995. \begin{flushleft} The table variables are:\\ ~~~\code{V1}-- values 1:5 correspond to the days Monday--Friday;\\ ~~~\code{V2}-- values 1:11 correspond to the quarter hour times 8:00PM through 10:30PM;\\ ~~~\code{V3}-- values 1:5 correspond to ABC, CBS, NBC, Fox, and non-network choices;\\ ~~~\code{V4}-- values 1:3 correspond to transition states: turn the television Off, Switch channels, or Persist in viewing the current channel. \end{flushleft} We are interested just the cell frequencies, and rely on the facts that the (a) the table is complete--- there are no missing cells, so \code{nrow(tv.data)}=\Sexpr{nrow(tv.data)}; (b) the observations are ordered so that \code{V1} varies most rapidly and \code{V4} most slowly. From this, we can just extract the frequency column and reshape it into an array. <>= TV <- array(tv.data[,5], dim=c(5,11,5,3)) dimnames(TV) <- list(c("Monday","Tuesday","Wednesday","Thursday","Friday"), c("8:00","8:15","8:30","8:45","9:00","9:15","9:30", "9:45","10:00","10:15","10:30"), c("ABC","CBS","NBC","Fox","Other"), c("Off","Switch","Persist")) names(dimnames(TV))<-c("Day", "Time", "Network", "State") @ More generally (even if there are missing cells), we can use \codefun{xtabs} (or \codefun{plyr::daply}) to do the cross-tabulation, using \code{V5} as the frequency variable. Here's how to do this same operation with \codefun{xtabs}: <>= TV <- xtabs(V5 ~ ., data=tv.data) dimnames(TV) <- list(Day=c("Monday","Tuesday","Wednesday","Thursday","Friday"), Time=c("8:00","8:15","8:30","8:45","9:00","9:15","9:30", "9:45","10:00","10:15","10:30"), Network=c("ABC","CBS","NBC","Fox","Other"), State=c("Off","Switch","Persist")) @ But this 4-way table is too large and awkward to work with. Among the networks, Fox and Other occur infrequently. We can also cut it down to a 3-way table by considering only viewers who persist with the current station.% \footnote{This relies on the fact that that indexing an array drops dimensions of length 1 by default, using the argument \code{drop=TRUE}; the result is coerced to the lowest possible dimension. } <>= TV <- TV[,,1:3,] # keep only ABC, CBS, NBC TV <- TV[,,,3] # keep only Persist -- now a 3 way table structable(TV) @ Finally, for some purposes, we might want to collapse the 11 times into a smaller number. Here, we use \codefun{as.data.frame.table} to convert the table back to a data frame, \codefun{levels} to re-assign the values of \code{Time}, and finally, \codefun{xtabs} to give a new, collapsed frequency table. <>= TV.df <- as.data.frame.table(TV) levels(TV.df$Time) <- c(rep("8:00-8:59",4),rep("9:00-9:59",4), rep("10:00-10:44",3)) TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df) structable(Day ~ Time+Network,TV2) @ Whew! See \figref{fig:TV-mosaic} for a mosaic plot of the \code{TV2} data. \section{Tests of Independence} \subsection{CrossTable} OK, now we're ready to do some analyses. For tabular displays, the \codefun{CrossTable} function in the \pkg{gmodels} package produces cross-tabulations modeled after \code{PROC FREQ} in \proglang{SAS} or \code{CROSSTABS} in \proglang{SPSS}. It has a wealth of options for the quantities that can be shown in each cell. <>= # 2-Way Cross Tabulation library(gmodels) CrossTable(GSStab,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE) @ There are options to report percentages (row, column, cell), specify decimal places, produce Chi-square, Fisher, and McNemar tests of independence, report expected and residual values (pearson, standardized, adjusted standardized), include missing values as valid, annotate with row and column titles, and format as \proglang{SAS} or \proglang{SPSS} style output! See \code{help(CrossTable)} for details. \subsection{Chi-square test} For 2-way tables you can use \codefun{chisq.test} to test independence of the row and column variable. By default, the $p$-value is calculated from the asymptotic chi-squared distribution of the test statistic. Optionally, the $p$-value can be derived via Monte Carlo simulation. <>= (HairEye <- margin.table(HairEyeColor, c(1, 2))) chisq.test(HairEye) @ \subsection{Fisher Exact Test}\label{sec:Fisher} \code{fisher.test(X)} provides an exact test of independence. \code{X} must be a two-way contingency table in table form. Another form, \code{fisher.test(X, Y)} takes two categorical vectors of the same length. For tables larger than $2 \times 2$ the method can be computationally intensive (or can fail) if the frequencies are not small. <>= fisher.test(GSStab) @ But this does not work because \data{HairEye} data has $n$=592 total frequency. An exact test is unnecessary in this case. <>= fisher.test(HairEye) @ %# <>= %# #cat(try(fisher.test(HairEye))) %# @ \begin{Soutput} Error in fisher.test(HairEye) : FEXACT error 6. LDKEY is too small for this problem. Try increasing the size of the workspace. \end{Soutput} \subsection[Mantel-Haenszel test]{Mantel-Haenszel test and conditional association}\label{sec:mantel} Use the \code{mantelhaen.test(X)} function to perform a Cochran-Mantel-Haenszel $\chi^2$ chi test of the null hypothesis that two nominal variables are \emph{conditionally independent}, $A \perp B \given C$, in each stratum, assuming that there is no three-way interaction. \code{X} is a 3 dimensional contingency table, where the last dimension refers to the strata. The \data{UCBAdmissions} serves as an example of a $2 \times 2 \times 6$ table, with \code{Dept} as the stratifying variable. <>= ## UC Berkeley Student Admissions mantelhaen.test(UCBAdmissions) @ The results show no evidence for association between admission and gender when adjusted for department. However, we can easily see that the assumption of equal association across the strata (no 3-way association) is probably violated. For $2 \times 2 \times k$ tables, this can be examimed from the odds ratios for each $2 \times 2$ table (\codefun{oddsratio}), and tested by using \verb|woolf_test()| in \pkg{vcd}. %<>= %oddsRatio <- function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1]) %apply(UCBAdmissions, 3, oddsRatio) % %woolf_test(UCBAdmissions) %@ <>= oddsratio(UCBAdmissions, log=FALSE) lor <- oddsratio(UCBAdmissions) # capture log odds ratios summary(lor) woolf_test(UCBAdmissions) @ We can visualize the odds ratios of Admission for each department with fourfold displays using \codefun{fourfold}. The cell frequencies $n_{ij}$ of each $2 \times 2$ table are shown as a quarter circle whose radius is proportional to $\sqrt{n_{ij}}$, so that its area is proportional to the cell frequency. Confidence rings for the odds ratio allow a visual test of the null of no association; the rings for adjacent quadrants overlap \emph{iff} the observed counts are consistent with the null hypothesis. In the extended version (the default), brighter colors are used where the odds ratio is significantly different from 1. The following lines produce \figref{fig:fourfold1}.% \footnote{The color values \code{col[3:4]} were modified from their default values to show a greater contrast between significant and insignifcant associations here.} <>= col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080") fourfold(UCB,mfrow=c(2,3), color=col) @ %\setkeys{Gin}{width=0.8\textwidth} \begin{figure}[htb] \begin{center} %<>= %col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080") %fourfold(UCB,mfrow=c(2,3), color=col) %@ \includegraphics[width=0.8\textwidth,trim=80 50 80 50]{fig/vcd-tut-fourfold1} \caption{Fourfold display for the \data{UCBAdmissions} data. Where the odds ratio differs significantly from 1.0, the confidence bands do not overlap, and the circle quadrants are shaded more intensely.} \label{fig:fourfold1} \end{center} \end{figure} Another \pkg{vcd} function, \codefun{cotabplot}, provides a more general approach to visualizing conditional associations in contingency tables, similar to trellis-like plots produced by \codefun{coplot} and lattice graphics. The \code{panel} argument supplies a function used to render each conditional subtable. The following gives a display (not shown) similar to \figref{fig:fourfold1}. <>= cotabplot(UCB, panel = cotab_fourfold) @ When we want to view the conditional probabilities of a response variable (e.g., \code{Admit}) in relation to several factors, an alternative visualization is a \codefun{doubledecker} plot. This plot is a specialized version of a mosaic plot, which highlights the levels of a response variable (plotted vertically) in relation to the factors (shown horizontally). The following call produces \figref{fig:doubledecker}, where we use indexing on the first factor (\code{Admit}) to make \code{Admitted} the highlighted level. In this plot, the association between \code{Admit} and \code{Gender} is shown where the heights of the highlighted conditional probabilities do not align. The excess of females admitted in Dept A stands out here. <>= doubledecker(Admit ~ Dept + Gender, data=UCBAdmissions[2:1,,]) @ \begin{figure}[htb] \begin{center} \includegraphics[width=0.9\textwidth]{fig/vcd-tut-doubledecker} \caption{Doubledecker display for the \data{UCBAdmissions} data. The heights of the highlighted bars show the conditional probabilities of \texttt{Admit}, given \texttt{Dept} and \texttt{Gender}.} \label{fig:doubledecker} \end{center} \end{figure} Finally, the there is a \codefun{plot} method for \code{oddsratio} objects. By default, it shows the 95\% confidence interval for the log odds ratio. \figref{fig:oddsratio} is produced by: <>= plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)") @ \setkeys{Gin}{width=0.5\textwidth} \begin{figure}[htb] \begin{center} <>= plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)") @ \caption{Log odds ratio plot for the \data{UCBAdmissions} data.} \label{fig:oddsratio} \end{center} \end{figure} \subsection[CMH tests: ordinal factors]{Cochran-Mantel-Haenszel tests for ordinal factors}\label{sec:CMH} The standard $\chi^2$ tests for association in a two-way table treat both table factors as nominal (unordered) categories. When one or both factors of a two-way table are quantitative or ordinal, more powerful tests of association may be obtaianed by taking ordinality into account, using row and or column scores to test for linear trends or differences in row or column means. More general versions of the CMH tests (Landis etal., 1978) are provided by assigning numeric scores to the row and/or column variables. For example, with two ordinal factors (assumed to be equally spaced), assigning integer scores, \code{1:R} and \code{1:C} tests the linear $\times$ linear component of association. This is statistically equivalent to the Pearson correlation between the integer-scored table variables, with $\chi^2 = (n-1) r^2$, with only 1 $df$ rather than $(R-1)\times(C-1)$ for the test of general association. When only one table variable is ordinal, these general CMH tests are analogous to an ANOVA, testing whether the row mean scores or column mean scores are equal, again consuming fewer $df$ than the test of general association. The \codefun{CMHtest} function in \pkg{vcdExtra} now calculates these various CMH tests for two possibly ordered factors, optionally stratified other factor(s). \Example Recall the $4 \times 4$ table, \code{JobSat} introduced in \secref{sec:creating}, <>= JobSat @ Treating the \code{satisfaction} levels as equally spaced, but using midpoints of the \code{income} categories as row scores gives the following results: <>= CMHtest(JobSat, rscores=c(7.5,20,32.5,60)) @ Note that with the relatively small cell frequencies, the test for general give no evidence for association. However, the the \code{cor} test for linear x linear association on 1 df is nearly significant. The \pkg{coin} contains the functions \verb|cmh_test()| and \verb|lbl_test()| for CMH tests of general association and linear x linear association respectively. \subsection{Measures of Association} There are a variety of statistical measures of \emph{strength} of association for contingency tables--- similar in spirit to $r$ or $r^2$ for continuous variables. With a large sample size, even a small degree of association can show a significant $\chi^2$, as in the example below for the \data{GSS} data. The \codefun{assocstats} function in \pkg{vcd} calculates the $\phi$ contingency coefficient, and Cramer's V for an $r \times c$ table. The input must be in table form, a two-way $r \times c$ table. It won't work with \data{GSS} in frequency form, but by now you should know how to convert. <>= assocstats(GSStab) @ For tables with ordinal variables, like \data{JobSat}, some people prefer the Goodman-Kruskal $\gamma$ statistic (\citet[\S 2.4.3]{vcd:Agresti:2002}) based on a comparison of concordant and discordant pairs of observations in the case-form equivalent of a two-way table. <>= GKgamma(JobSat) @ A web article by Richard Darlington, \url{http://www.psych.cornell.edu/Darlington/crosstab/TABLE0.HTM} gives further description of these and other measures of association. \subsection{Measures of Agreement} The \codefun{Kappa} function in the \pkg{vcd} package calculates Cohen's $\kappa$ and weighted $\kappa$ for a square two-way table with the same row and column categories \citep{Cohen:60}.% \footnote{ Don't confuse this with \codefun{kappa} in base \proglang{R} that computes something entirely different (the condition number of a matrix). } Normal-theory $z$-tests are obtained by dividing $\kappa$ by its asymptotic standard error (ASE). A \codefun{confint} method for \code{Kappa} objects provides confidence intervals. <>= (K <- Kappa(SexualFun)) confint(K) @ A visualization of agreement, both unweighted and weighted for degree of departure from exact agreement is provided by the \codefun{agreementplot} function. \figref{fig:agreesex} shows the agreementplot for the \data{SexualFun} data, produced as shown below. The Bangdiwala measures represent the proportion of the shaded areas of the diagonal rectangles, using weights $w_1$ for exact agreement, and $w_2$ for partial agreement one step from the main diagonal. <>= agree <- agreementplot(SexualFun, main="Is sex fun?") unlist(agree) @ %\setkeys{Gin}{width=0.5\textwidth} \begin{figure}[htb] \begin{center} %<>= %agree <- agreementplot(SexualFun, main="Is sex fun?") %agree %@ \includegraphics[width=0.4\textwidth,trim=50 25 50 25]{fig/vcd-tut-agreesex} \caption{Agreement plot for the \data{SexualFun} data.} \label{fig:agreesex} \end{center} \end{figure} In other examples, the agreement plot can help to show \emph{sources} of disagreement. For example, when the shaded boxes are above or below the diagonal (red) line, a lack of exact agreement can be attributed in part to different frequency of use of categories by the two raters-- lack of \emph{marginal homogeneity}. \subsection{Correspondence analysis} Use the \pkg{ca} package for correspondence analysis for visually exploring relationships between rows and columns in contingency tables. For an $r \times c$ table, the method provides a breakdown of the Pearson $\chi^2$ for association in up to $M = \min(r-1, c-1)$ dimensions, and finds scores for the row ($x_{im}$) and column ($y_{jm}$) categories such that the observations have the maximum possible correlations.% \footnote{ Related methods are the non-parametric CMH tests using assumed row/column scores (\secref{sec:CMH}), the analogous \codefun{glm} model-based methods (\secref{sec:CMH}), and the more general RC models which can be fit using \codefun{gnm}. Correspondence analysis differs in that it is a primarily descriptive/exploratory method (no significance tests), but is directly tied to informative graphic displays of the row/column categories. } Here, we carry out a simple correspondence analysis of the \data{HairEye} data. The printed results show that nearly 99\% of the association between hair color and eye color can be accounted for in 2 dimensions, of which the first dimension accounts for 90\%. <>= library(ca) ca(HairEye) @ The resulting \code{ca} object can be plotted just by running the \codefun{plot} method on the \code{ca} object, giving the result in \figref{fig:ca-haireye}. \codefun{plot.ca} does not allow labels for dimensions; these can be added with \codefun{title}. It can be seen that most of the association is accounted for by the ordering of both hair color and eye color along Dimension 1, a dark to light dimension. <>= plot(ca(HairEye), main="Hair Color and Eye Color") title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)") @ \setkeys{Gin}{width=0.7\textwidth} \begin{figure}[htb] \begin{center} <>= plot(ca(HairEye), main="Hair Color and Eye Color") title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)") @ \caption{Correspondence analysis plot for the \data{HairEye} data.} \label{fig:ca-haireye} \end{center} \end{figure} \section{Loglinear Models}\label{sec:loglin} You can use the \codefun{loglm} function in the \pkg{MASS} package to fit log-linear models. Equivalent models can also be fit (from a different perspective) as generalized linear models with the \codefun{glm} function using the \code{family='poisson'} argument, and the \pkg{gnm} package provides a wider range of generalized \emph{nonlinear} models, particularly for testing structured associations. The visualization methods for these models were originally developed for models fit using \codefun{loglm}, so this approach is emphasized here. Some extensions of these methods for models fit using \codefun{glm} and \codefun{gnm} are contained in the \pkg{vcdExtra} package and illustrated in \secref{sec:glm}. Assume we have a 3-way contingency table based on variables A, B, and C. The possible different forms of \loglin\ models for a 3-way table are shown in \tabref{tab:loglin-3way}. The \textbf{Model formula} column shows how to express each model for \codefun{loglm} in \proglang{R}.% \footnote{ For \codefun{glm}, or \codefun{gnm}, with the data in the form of a frequency data.frame, the same model is specified in the form \code{glm(Freq} $\sim$ \code{..., family="poisson")}, where \texttt{Freq} is the name of the cell frequency variable and \texttt{...} specifies the \textbf{Model formula}. } In the \textbf{Interpretation} column, the symbol ``$\perp$'' is to be read as ``is independent of,'' and ``$\given$'' means ``conditional on,'' or ``adjusting for,'' or just ``given''. \begin{table}[htb] \caption{Log-linear Models for Three-Way Tables}\label{tab:loglin-3way} \begin{center} \begin{tabular}{llll} \hline \textbf{Model} & \textbf{Model formula} & \textbf{Symbol}& \textbf{Interpretation} \\ \hline\hline Mutual independence & \verb|~A + B + C| & $[A][B][C]$ & $A \perp B \perp C$ \\ Joint independence & \verb|~A*B + C| & $[AB][C]$ & $(A \: B) \perp C$ \\ Conditional independence & \verb|~(A+B)*C| & $[AC][BC]$ & $(A \perp B) \given C$ \\ All two-way associations & \verb|~A*B + A*C + B*C| & $[AB][AC][BC]$ & homogeneous association \\ Saturated model & \verb|~A*B*C| & $[ABC]$ & 3-way association \\ \hline \end{tabular} \end{center} \end{table} For example, the formula \verb|~A + B + C| specifies the model of \emph{mutual independence} with no associations among the three factors. In standard notation for the expected frequencies $m_{ijk}$, this corresponds to \begin{equation*} \log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C \equiv \texttt{A + B + C} \end{equation*} The parameters $\lambda_i^A , \lambda_j^B$ and $\lambda_k^C$ pertain to the differences among the one-way marginal frequencies for the factors A, B and C. Similarly, the model of \emph{joint independence}, $(A \: B) \perp C$, allows an association between A and B, but specifies that C is independent of both of these and their combinations, \begin{equation*} \log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C + \lambda_{ij}^{AB} \equiv \texttt{A * B + C} \end{equation*} where the parameters $\lambda_{ij}^{AB}$ pertain to the overall association between A and B (collapsing over C). In the literature or text books, you will often find these models expressed in shorthand symbolic notation, using brackets, \texttt{[ ]} to enclose the \emph{high-order terms} in the model. Thus, the joint independence model can be denoted \texttt{[AB][C]}, as shown in the \textbf{Symbol} column in \tabref{tab:loglin-3way}. Models of \emph{conditional independence} allow (and fit) two of the three possible two-way associations. There are three such models, depending on which variable is conditioned upon. For a given conditional independence model, e.g., \texttt{[AB][AC]}, the given variable is the one common to all terms, so this example has the interpretation $(B \perp C) \given A$. \subsection[Fitting with loglm()]{Fitting with \codefun{loglm}}\label{sec:loglm} For example, we can fit the model of mutual independence among hair color, eye color and sex in \data{HairEyeColor} as <>= library(MASS) ## Independence model of hair and eye color and sex. hec.1 <- loglm(~Hair+Eye+Sex, data=HairEyeColor) hec.1 @ Similarly, the models of conditional independence and joint independence are specified as <>= ## Conditional independence hec.2 <- loglm(~(Hair + Eye) * Sex, data=HairEyeColor) hec.2 @ <>= ## Joint independence model. hec.3 <- loglm(~Hair*Eye + Sex, data=HairEyeColor) hec.3 @ Note that printing the model gives a brief summary of the goodness of fit. A set of models can be compared using the \codefun{anova} function. <>= anova(hec.1, hec.2, hec.3) @ %Martin Theus and Stephan Lauer have written an excellent article on Visualizing %Loglinear Models, using mosaic plots. There is also great tutorial example by %Kevin Quinn on analyzing loglinear models via glm. \subsection[Fitting with glm() and gnm()]{Fitting with \codefun{glm} and \codefun{gnm}}\label{sec:glm} The \codefun{glm} approach, and extensions of this in the \pkg{gnm} package allows a much wider class of models for frequency data to be fit than can be handled by \codefun{loglm}. Of particular importance are models for ordinal factors and for square tables, where we can test more structured hypotheses about the patterns of association than are provided in the tests of general assosiation under \codefun{loglm}. These are similar in spirit to the non-parametric CMH tests described in \secref{sec:CMH}. \Example The data \code{Mental} in the \pkg{vcdExtra} package gives a two-way table in frequency form classifying young people by their mental health status and parents' socioeconomic status (SES), where both of these variables are ordered factors. <>= str(Mental) xtabs(Freq ~ mental+ses, data=Mental) # display the frequency table @ Simple ways of handling ordinal variables involve assigning scores to the table categories, and the simplest cases are to use integer scores, either for the row variable (``column effects'' model), the column variable (``row effects'' model), or both (``uniform association'' model). <>= indep <- glm(Freq ~ mental + ses, family = poisson, data = Mental) # independence model @ To fit more parsimonious models than general association, we can define numeric scores for the row and column categories <>= # Use integer scores for rows/cols Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) @ Then, the row effects model, the column effects model, and the uniform association model can be fit as follows: <>= # column effects model (ses) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) # row effects model (mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) # linear x linear association linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) @ The \codefun{Summarize} in \pkg{vcdExtra} provides a nice, compact summary of the fit statistics for a set of models, collected into a \class{glmlist} object. Smaller is better for AIC and BIC. <>= # compare models using AIC, BIC, etc vcdExtra::LRstats(glmlist(indep, roweff, coleff, linlin)) @ For specific model comparisons, we can also carry out tests of \emph{nested} models with \codefun{anova} when those models are listed from smallest to largest. Here, there are two separate paths from the most restrictive (independence) model through the model of uniform association, to those that allow only one of row effects or column effects. <>= anova(indep, linlin, coleff, test="Chisq") anova(indep, linlin, roweff, test="Chisq") @ The model of linear by linear association seems best on all accounts. For comparison, one might try the CMH tests on these data: <>= CMHtest(xtabs(Freq~ses+mental, data=Mental)) @ \subsection{Non-linear terms} The strength of the \pkg{gnm} package is that it handles a wide variety of models that handle non-linear terms, where the parameters enter the model beyond a simple linear function. The simplest example is the Goodman RC(1) model, which allows a multiplicative term to account for the association of the table variables. In the notation of generalized linear models with a log link, this can be expressed as \begin{equation*} \log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i} \delta_{j} \end{equation*} where the row-multiplicative effect parameters $\gamma_i$ and corresponding column parameters $\delta_j$ are estimated from the data.% \footnote{ This is similar in spirit to a correspondence analysis with a single dimension, but as a statistical model. } Similarly, the RC(2) model adds two multiplicative terms to the independence model, \begin{equation*} \log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i1} \delta_{j1} + \gamma_{i2} \delta_{j2} \end{equation*} In the \pkg{gnm} package, these models may be fit using the \codefun{Mult} to specify the multiplicative term, and \codefun{instances} to specify several such terms. \Example For the \code{Mental} data, we fit the RC(1) and RC(2) models, and compare these with the independence model. <>= RC1 <- gnm(Freq ~ mental + ses + Mult(mental,ses), data=Mental, family=poisson, , verbose=FALSE) RC2 <- gnm(Freq ~ mental+ses + instances(Mult(mental,ses),2), data=Mental, family=poisson, verbose=FALSE) anova(indep, RC1, RC2, test="Chisq") @ \section{Mosaic plots}\label{sec:mosaic} Mosaic plots provide an ideal method both for visualizing contingency tables and for visualizing the fit--- or more importantly--- lack of fit of a \loglin\ model. For a two-way table, \codefun{mosaic} fits a model of independence, $[A][B]$ or \verb|~A+B| as an \proglang{R} formula. For $n$-way tables, \codefun{mosaic} can fit any \loglin\ model, and can also be used to plot a model fit with \codefun{loglm}. See \citet{vcd:Friendly:1994,vcd:Friendly:1999} for the statistical ideas behind these uses of mosaic displays in connection with \loglin\ models. The essential idea is to recursively sub-divide a unit square into rectangular ``tiles'' for the cells of the table, such that the are area of each tile is proportional to the cell frequency. For a given \loglin\ model, the tiles can then be shaded in various ways to reflect the residuals (lack of fit) for a given model. The pattern of residuals can then be used to suggest a better model or understand \emph{where} a given model fits or does not fit. \codefun{mosaic} provides a wide range of options for the directions of splitting, the specification of shading, labeling, spacing, legend and many other details. It is actually implemented as a special case of a more general class of displays for $n$-way tables called \code{strucplot}, including sieve diagrams, association plots, double-decker plots as well as mosaic plots. For details, see \code{help(strucplot)} and the ``See also'' links, and also \citet{vcd:Meyer+Zeileis+Hornik:2006b}, which is available as an \proglang{R} vignette via \code{vignette("strucplot", package="vcd")}. \figref{fig:arthritis}, showing the association between \code{Treatment} and \code{Improved} was produced with the following call to \codefun{mosaic}. <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]") @ Note that the residuals for the independence model were not large (as shown in the legend), yet the association between \code{Treatment} and \code{Improved} is highly significant. <>= summary(art) @ In contrast, one of the other shading schemes, from \citet{vcd:Friendly:1994} (use: \verb|gp = shading_Friendly|), uses fixed cutoffs of $\pm 2, \pm 4$, to shade cells which are \emph{individually} significant at approximately $\alpha = 0.05$ and $\alpha = 0.001$ levels, respectively. The right panel below uses \verb|gp = shading_Friendly|. \setkeys{Gin}{width=0.5\textwidth} <>= mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: gp = shading_max") @ <>= mosaic(art, gp = shading_Friendly, split_vertical = TRUE, main="Arthritis: gp = shading_Friendly") @ \subsection[Mosaics for loglinear models]{Mosaics for \loglin\ models}\label{sec:mosaic-llm} When you have fit a \loglin\ model using \codefun{loglm}, and saved the result (as a \code{loglm} object) the simplest way to display the results is to use the \codefun{plot} method for the \code{loglm} object. Calling \code{mosaic(loglm.object)} has the same result. In \secref{sec:loglm} above, we fit several different models to the \data{HairEyeColor} data. We can produce mosaic displays of each just by plotting them: <>= # mosaic plots, using plot.loglm() method plot(hec.1, main="model: [Hair][Eye][Sex]") plot(hec.2, main="model: [HairSex][EyeSex]") plot(hec.3, main="model: [HairEye][Sex]") @ \setkeys{Gin}{width=0.32\textwidth} <>= plot(hec.1, main="model: [Hair][Eye][Sex]") @ <>= plot(hec.2, main="model: [HairSex][EyeSex]") @ <>= plot(hec.3, main="model: [HairSex][EyeSex]") @ Alternatively, you can supply the model formula to \codefun{mosaic} with the \code{expected} argument. This is passed to \codefun{loglm}, which fits the model, and returns residuals used for shading in the plot. For example, here we examine the \data{TV2} constructed in \secref{sec:complex} above. The goal is to see how Network choice depends on (varies with) Day and Time. To do this: \begin{itemize} \item We fit a model of joint independence of \code{Network} on the combinations of \code{Day} and \code{Time}, with the model formula \verb|~Day:Time + Network|. \item To make the display more easily read, we place \code{Day} and \code{Time} on the vertical axis and \code{Network} on the horizontal, \item The \code{Time} values overlap on the right vertical axis, so we use \codefun{level} to abbreviate them. \codefun{mosaic} also supports a more sophisticated set of labeling functions. Instead of changing the data table, we could have used \verb|labeling_args = list(abbreviate = c(Time = 2))| for a similar effect. \end{itemize} The following call to \codefun{mosaic} produces \figref{fig:TV-mosaic}: <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly) @ \setkeys{Gin}{width=0.75\textwidth} \begin{figure}[htb] \begin{center} <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly) @ \caption{Mosaic plot for the \data{TV} data showing model of joint independence, \texttt{Day:Time + Network} .} \label{fig:TV-mosaic} \end{center} \end{figure} From this, it is easy to read from the display how network choice varies with day and time. For example, CBS dominates in all time slots on Monday; ABC and NBC dominate on Tuesday, particularly in the later time slots; Thursday is an NBC day, while on Friday, ABC gets the greatest share. In interpreting this mosaic and other plots, it is important to understand that associations included in the model---here, that between day and time---are \emph{not} shown in the shading of the cells, because they have been fitted (taken into account) in the \loglin\ model. For comparison, you might want to try fitting the model of homogeneous association. This allows all pairs of factors to be associated, but asserts that each pairwise association is the same across the levels of the remaining factor. The resulting plot displays the contributions to a 3-way association, but is not shown here. <>= mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Day:Network + Time:Network, legend=FALSE, gp=shading_Friendly) @ \subsection[Mosaics for glm() and gnm() models]{Mosaics for \codefun{glm} and \codefun{gnm} models}\label{sec:mosglm} The \pkg{vcdExtra} package provides an additional method, \codefun{mosaic.glm} for models fit with \codefun{glm} and \codefun{gnm}.% \footnote{ Models fit with \codefun{gnm} are of \code{class = c("gnm", "glm", "lm")}, so all \code{*.glm} methods apply, unless overridden in the \pkg{gnm} package. } These are not restricted to the Poisson family, but only apply to cases where the response variable is non-negative. \Example Here, we plot the independence and the linear-by-linear association model for the Mental health data from \secref{sec:glm}. These examples illustrate some of the options for labeling (variable names and residuals printed in cells). Note that the \code{formula} supplied to \codefun{mosaic} for \class{glm} objects refers to the order of factors displayed in the plot, not the model. <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(indep, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, main="Mental health data: Independence") mosaic(linlin, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: Linear x Linear") @ \setkeys{Gin}{width=0.49\textwidth} <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(indep, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, main="Mental health data: Independence") @ <>= long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(linlin, ~ses+mental, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: Linear x Linear") @ The \pkg{gnm} package also fits a wide variety of models with nonlinear terms or terms for structured associations of table variables. In the following, we fit the RC(1) model \begin{equation*} \log ( m_{ij} ) = \mu + \lambda_i^A + \lambda_j^B + \phi \mu_i \nu_j \end{equation*} This is similar to the linear by linear model, except that the row effect parameters ($\mu_i$) and column parameters ($\nu_j$) are estimated from the data rather than given assigned equally-spaced values. The multiplicative terms are specified by the \codefun{Mult}. <>= Mental$mental <- C(Mental$mental, treatment) Mental$ses <- C(Mental$ses, treatment) RC1model <- gnm(Freq ~ mental + ses + Mult(mental, ses), family = poisson, data = Mental) mosaic(RC1model, residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Mental health data: RC(1) model") @ Other forms of nonlinear terms are provided for the inverse of a predictor (\codefun{Inv}) and the exponential of a predictor (\codefun{Exp}). You should read \code{vignette("gnmOverview", package="gnm")} for further details. \subsection{Mosaic tips and techniques}\label{sec:tips} The \pkg{vcd} package implements an extremely general collection of graphical methods for $n$-way frequency tables within the strucplot framework, which includes mosaic plots (\codefun{mosaic}), as well as association plots (\codefun{assoc}), sieve diagrams (\codefun{sieve}), as well as tabular displays (\codefun{structable}). The graphical methods in \pkg{vcd} support a wide of options that control almost all of the details of the plots, but it is often difficult to determine what arguments you need to supply to achieve a given effect from the \code{help()}. As a first step, you should read the \code{vignette("strucplot")} in \pkg{vcd} to understand the overall structure of these plot methods. The notes below describe a few useful things that may not be obvious, or can be done in different ways. \subsubsection[Changing labels]{Changing the labels for variables and levels} With data in contingency table form or as a frequency data frame, it often happens that the variable names and/or the level values of the factors, while suitable for analysis, are less than adequate when used in mosaic plots and other strucplot displays. For example, we might prefer that a variable named \code{ses} appear as \code{"Socioeconomic Status"}, or a factor with levels \code{c("M", "F")} be labeled using \code{c("Male", "Female")} in a plot. Or, sometimes we start with a factor whose levels are fully spelled out (e.g., \code{c("strongly disagree", "disagree", "neutral", "agree", "strongly agree")}), only to find that the level labels overlap in graphic displays. The structplot framework in \pkg{vcd} provides an extremely large variety of functions and options for controlling almost all details of text labels in mosaics and other plots. See \code{help(labelings)} for an overview. For example, in \secref{sec:ordered-factors} we showed how to rearrange the dimensions of the \code{UCBAdmissions} table, change the names of the table variables, and relabel the levels of one of the table variables. The code below changes the actual table for plotting purposes, but we pointed out that these changes can create other problems in analysis. <>= UCB <- aperm(UCBAdmissions, c(2, 1, 3)) names(dimnames(UCB)) <- c("Sex", "Admit?", "Department") dimnames(UCB)[[2]] <- c("Yes", "No") @ The same effects can be achieved \emph{without} modifying the data using the \verb|set_varnames| and \verb|set_labels| options in \codefun{mosaic} as follows: <>= vnames <- list(set_varnames = c(Admit="Admission", Gender="Sex", Dept="Department")) lnames <- list(Admit = c("Yes", "No"), Gender = c("Males", "Females"), Dept = LETTERS[1:6]) mosaic(UCBAdmissions, labeling_args=vnames, set_labels=lnames) @ In some cases, it may be sufficient to abbreviate (or clip, or rotate) level names to avoid overlap. For example, the statements below produce another version of \figref{fig:TV-mosaic} with days of the week abbreviated to their first three letters. Section 4 in the \code{vignette("strucplot")} provides many other examples. <>= dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network, legend=FALSE, gp=shading_Friendly, labeling_args=list(abbreviate=c(Day=3)) ) @ %\subsubsection{Fitting complex models with glm() and gnm()} \section[Continuous predictors]{Continuous predictors}\label{sec:contin} When continuous predictors are available---and potentially important--- in explaining a categorical outcome, models for that outcome include: logistic regression (binary response), the proportional odds model (ordered polytomous response), multinomial (generalized) logistic regression. Many of these are special cases of the generalized linear model using the \code{"poisson"} or \code{"binomial"} family and their relatives. \subsection{Spine and conditional density plots}\label{sec:spine} I don't go into fitting such models here, but I would be remiss not to illustrate some visualizations in \pkg{vcd} that are helpful here. The first of these is the spine plot or spinogram \citep{vcd:Hummel:1996} (produced with \codefun{spine}). These are special cases of mosaic plots with specific spacing and shading to show how a categorical response varies with a continuous or categorical predictor. They are also a generalization of stacked bar plots where not the heights but the \emph{widths} of the bars corresponds to the relative frequencies of \code{x}. The heights of the bars then correspond to the conditional relative frequencies of {y} in every \code{x} group. \Example For the \data{Arthritis} data, we can see how \code{Improved} varies with \code{Age} as follows. \codefun{spine} takes a formula of the form \verb|y ~ x| with a single dependent factor and a single explanatory variable \code{x} (a numeric variable or a factor). The range of a numeric variable\code{x} is divided into intervals based on the \code{breaks} argument, and stacked bars are drawn to show the distribution of \code{y} as \code{x} varies. As shown below, the discrete table that is visualized is returned by the function. <>= (spine(Improved ~ Age, data = Arthritis, breaks = 3)) (spine(Improved ~ Age, data = Arthritis, breaks = "Scott")) @ \setkeys{Gin}{width=0.49\textwidth} <>= (spine(Improved ~ Age, data = Arthritis, breaks = 3)) @ <>= (spine(Improved ~ Age, data = Arthritis, breaks = "Scott")) @ The conditional density plot \citep{vcd:Hofmann+Theus} is a further generalization. This visualization technique is similar to spinograms, but uses a smoothing approach rather than discretizing the explanatory variable. As well, it uses the original \code{x} axis and not a distorted one. \setkeys{Gin}{width=0.6\textwidth} \begin{figure}[htb] \begin{center} <>= cdplot(Improved ~ Age, data = Arthritis) with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE)) @ \caption{Conditional density plot for the \data{Arthritis} data showing the variation of Improved with Age.} \label{fig:cd-plot} \end{center} \end{figure} In such plots, it is useful to also see the distribution of the observations across the horizontal axis, e.g., with a \codefun{rug} plot. \figref{fig:cd-plot} uses \codefun{cdplot} from the \pkg{graphics} package rather than \verb|cd_plot()| from \pkg{vcd}, and is produced with <>= cdplot(Improved ~ Age, data = Arthritis) with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE)) @ From \figref{fig:cd-plot} it can be easily seen that the proportion of patients reporting Some or Marked improvement increases with Age, but there are some peculiar bumps in the distribution. These may be real or artifactual, but they would be hard to see with most other visualization methods. When we switch from non-parametric data exploration to parametric statistical models, such effects are easily missed. \subsection[Model-based plots]{Model-based plots: effect plots and \pkg{ggplot2} plots}\label{sec:modelplots} The nonparametric conditional density plot uses smoothing methods to convey the distributions of the response variable, but displays that are simpler to interpret can often be obtained by plotting the predicted response from a parametric model. For complex \codefun{glm} models with interaction effects, the \pkg{effects} package provides the most useful displays, plotting the predicted values for a given term, averaging over other predictors not included in that term. I don't illustrate this here, but see \citet{effects:1,effects:2} and \code{help(package="effects")}. Here I just briefly illustrate the capabilities of the \pkg{ggplot2} package for model-smoothed plots of categorical responses in \codefun{glm} models. \Example The \data{Donner} data frame in \pkg{vcdExtra} gives details on the survival of 90 members of the Donner party, a group of people who attempted to migrate to California in 1846. They were trapped by an early blizzard on the eastern side of the Sierra Nevada mountains, and before they could be rescued, nearly half of the party had died. What factors affected who lived and who died? <>= data(Donner, package="vcdExtra") str(Donner) @ A potential model of interest is the logistic regression model for $Pr(survived)$, allowing separate fits for males and females as a function of \code{age}. The key to this is the \verb|stat_smooth()| function, using \code{method = "glm", family = binomial}. The \verb|formula = y ~ x| specifies a linear fit on the logit scale (\figref{fig:donner3}, left) <>= # separate linear fits on age for M/F ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2, aes(fill = sex)) @ Alternatively, we can allow a quadratic relation with \code{age} by specifying \verb|formula = y ~ poly(x,2)| (\figref{fig:donner3}, right). <>= # separate quadratics ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ poly(x,2), alpha = 0.2, size=2, aes(fill = sex)) @ \setkeys{Gin}{width=0.49\textwidth} \begin{figure}[htb] \begin{center} <>= ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2, aes(fill = sex)) @ <>= # separate quadratics ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ poly(x,2), alpha = 0.2, size=2, aes(fill = sex)) @ \caption{Logistic regression plots for the \data{Donner} data showing survival vs. age, by sex. Left: linear logistic model; right: quadratic model} \label{fig:donner3} \end{center} \end{figure} These plots very nicely show (a) the fitted $Pr(survived)$ for males and females; (b) confidence bands around the smoothed model fits and (c) the individual observations by jittered points at 0 and 1 for those who died and survided, respectively. \bibliography{vcd,vcdExtra} \end{document} vcdExtra/vignettes/vcd.bib0000644000175100001440000006127012576352702015331 0ustar hornikusers%% general graphics & original methods @Article{vcd:Cohen:1980, author = {A. Cohen}, title = {On the Graphical Display of the Significant Components in a Two-Way Contingency Table}, journal = {Communications in Statistics---Theory and Methods}, year = {1980}, volume = {A9}, pages = {1025--1041} } @InProceedings{vcd:Hartigan+Kleiner:1981, author = {J. A. Hartigan and B. Kleiner}, title = {Mosaics for Contingency Tables}, booktitle = {Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface}, pages = {268--273}, year = {1981}, editor = {W. F. Eddy}, address = {New York}, publisher = {Springer-Verlag} } @Article{vcd:Hartigan+Kleiner:1984, author = {J. A. Hartigan and B. Kleiner}, title = {A Mosaic of Television Ratings}, journal = {The American Statistician}, year = {1984}, volume = {38}, pages = {32--35} } @TechReport{vcd:Young:1996, author = {Forrest W. Young}, title = {{\pkg{ViSta}}: The Visual Statistics System}, institution = {UNC L.~L.~Thurstone Psychometric Laboratory Research Memorandum}, year = 1996, number = {94--1(c)} } @Book{vcd:Cleveland:1993, author = {William S. Cleveland}, title = {Visualizing Data}, publisher = {Hobart Press}, year = 1993, address = {Summit, New Jersey} } @Article{vcd:Becker+Cleveland+Shyu:1996, author = {Richard A. Becker and William S. Cleveland and Ming-Jen Shyu}, title = {The Visual Design and Control of Trellis Display}, journal = {Journal of Computational and Graphical Statistics}, year = {1996}, volume = {5}, pages = {123--155} } @InProceedings{vcd:Riedwyl+Schuepbach:1994, author = {H. Riedwyl and M. Sch{\"u}pbach}, title = {Parquet Diagram to Plot Contingency Tables}, booktitle = {Softstat '93: Advances in Statistical Software}, pages = {293--299}, year = 1994, editor = {F. Faulbaum}, address = {New York}, publisher = {Gustav Fischer} } %% color @InProceedings{vcd:Ihaka:2003, author = {Ross Ihaka}, title = {Colour for Presentation Graphics}, booktitle = {Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria}, editor = {Kurt Hornik and Friedrich Leisch and Achim Zeileis}, year = {2003}, url = {http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/}, note = {{ISSN 1609-395X}}, } @Article{vcd:Lumley:2006, author = {Thomas Lumley}, title = {Color Coding and Color Blindness in Statistical Graphics}, journal = {ASA Statistical Computing \& Graphics Newsletter}, year = {2006}, volume = {17}, number = {2}, pages = {4--7} } @Book{vcd:Munsell:1905, author = {Albert H. Munsell}, title = {A Color Notation}, publisher = {Munsell Color Company}, year = {1905}, address = {Boston, Massachusetts} } @Article{vcd:Harrower+Brewer:2003, author = {Mark A. Harrower and Cynthia A. Brewer}, title = {\pkg{ColorBrewer.org}: An Online Tool for Selecting Color Schemes for Maps}, journal = {The Cartographic Journal}, year = {2003}, volume = {40}, pages = {27--37} } @InProceedings{vcd:Brewer:1999, author = {Cynthia A. Brewer}, title = {Color Use Guidelines for Data Representation}, booktitle = {Proceedings of the Section on Statistical Graphics, American Statistical Association}, address = {Alexandria, VA}, year = {1999}, pages = {55--60} } @Article{vcd:Cleveland+McGill:1983, author = {William S. Cleveland and Robert McGill}, title = {A Color-caused Optical Illusion on a Statistical Graph}, journal = {The American Statistician}, year = {1983}, volume = {37}, pages = {101--105} } @Book{vcd:CIE:2004, author = {{Commission Internationale de l'\'Eclairage}}, title = {Colorimetry}, edition = {3rd}, publisher = {Publication CIE 15:2004}, address = {Vienna, Austria}, year = {2004}, note = {{ISBN} 3-901-90633-9} } @InProceedings{vcd:Moretti+Lyons:2002, author = {Giovanni Moretti and Paul Lyons}, title = {Tools for the Selection of Colour Palettes}, booktitle = {Proceedings of the New Zealand Symposium On Computer-Human Interaction (SIGCHI 2002)}, address = {University of Waikato, New Zealand}, month = {July}, year = {2002} } @Article{vcd:MacAdam:1942, author = {D. L. MacAdam}, title = {Visual Sensitivities to Color Differences in Daylight}, journal = {Journal of the Optical Society of America}, year = {1942}, volume = {32}, number = {5}, pages = {247--274}, } @Book{vcd:Wyszecki+Stiles:2000, author = {G\"unter Wyszecki and W. S. Stiles}, title = {Color Science}, edition = {2nd}, publisher = {Wiley}, year = {2000}, note = {{ISBN} 0-471-39918-3} } @Misc{vcd:Poynton:2000, author = {Charles Poynton}, title = {Frequently-Asked Questions About Color}, year = {2000}, howpublished = {URL \url{http://www.poynton.com/ColorFAQ.html}}, note = {Accessed 2006-09-14}, } @Misc{vcd:Wiki+HSV:2006, author = {Wikipedia}, title = {{HSV} Color Space --- {W}ikipedia{,} The Free Encyclopedia}, year = {2006}, howpublished = {URL \url{http://en.wikipedia.org/w/index.php?title=HSV_color_space&oldid=74735552}}, note = {Accessed 2006-09-14}, } @Misc{vcd:Wiki+LUV:2006, author = {Wikipedia}, title = {{Lab} Color Space --- {W}ikipedia{,} The Free Encyclopedia}, year = {2006}, howpublished = {URL \url{http://en.wikipedia.org/w/index.php?title=Lab_color_space&oldid=72611029}}, note = {Accessed 2006-09-14}, } @Article{vcd:Smith:1978, author = {Alvy Ray Smith}, title = {Color Gamut Transform Pairs}, journal = {Computer Graphics}, pages = {12--19}, year = {1978}, volume = {12}, number = {3}, note = {ACM SIGGRAPH 78 Conference Proceedings}, } %% url = {http://www.alvyray.com/}, @Article{vcd:Meier+Spalter+Karelitz:2004, author = {Barbara J. Meier and Anne Morgan Spalter and David B. Karelitz}, title = {Interactive Color Palette Tools}, journal = {{IEEE} Computer Graphics and Applications}, volume = {24}, number = {3}, year = {2004}, pages = {64--72}, } %% url = {http://graphics.cs.brown.edu/research/color/} @InCollection{vcd:Mollon:1995, author = {J. Mollon}, editor = {T. Lamb and J. Bourriau}, booktitle = {Colour: Art and Science}, title = {Seeing Color}, publisher = {Cambridge Univesity Press}, year = 1995 } %% Friendly publications @Article{vcd:Friendly:1994, author = {Michael Friendly}, title = {Mosaic Displays for Multi-Way Contingency Tables}, journal = {Journal of the American Statistical Association}, year = {1994}, volume = {89}, pages = {190--200} } @Article{vcd:Friendly:1999, author = {Michael Friendly}, title = {Extending Mosaic Displays: Marginal, Conditional, and Partial Views of Categorical Data}, journal = {Journal of Computational and Graphical Statistics}, year = {1999}, volume = {8}, number = {3}, pages = {373--395} } @Book{vcd:Friendly:2000, author = {Michael Friendly}, title = {Visualizing Categorical Data}, publisher = {\textsf{SAS} Insitute}, year = {2000}, address = {Carey, NC}, URL = {http://www.math.yorku.ca/SCS/vcd/} } %% Augsburg publications @Article{vcd:Theus+Lauer:1999, author = {Martin Theus and Stephan R. W. Lauer}, title = {Visualizing Loglinear Models}, journal = {Journal of Computational and Graphical Statistics}, year = 1999, volume = 8, number = 3, pages = {396--412} } @Article{vcd:Hofmann:2003, author = {Heike Hofmann}, title = {Constructing and Reading Mosaicplots}, journal = {Computational Statistics \& Data Analysis}, year = {2003}, volume = {43}, pages = {565--580} } @Article{vcd:Hofmann:2001, author = {Heike Hofmann}, title = {Generalized Odds Ratios for Visual Modelling}, journal = {Journal of Computational and Graphical Statistics}, year = {2001}, volume = {10}, pages = {1--13} } @Article{vcd:Theus:2003, author = {Martin Theus}, title = {Interactive Data Visualization Using \pkg{Mondrian}}, journal = {Journal of Statistical Software}, volume = 7, number = 11, pages = {1--9}, year = 2003, url = {http://www.jstatsoft.org/v07/i11/}, } @Unpublished{vcd:Hofmann+Theus, author = {Heike Hofmann and Martin Theus}, title = {Interactive Graphics for Visualizing Conditional Distributions}, note = {Unpublished Manuscript}, year = {2005} } @Article{vcd:Hummel:1996, author = {J. Hummel}, title = {Linked Bar Charts: Analysing Categorical Data Graphically}, journal = {Computational Statistics}, year = 1996, volume = 11, pages = {23--33} } @Article{vcd:Unwin+Hawkins+Hofmann:1996, author = {Antony R. Unwin and G. Hawkins and Heike Hofmann and B. Siegl}, title = {Interactive Graphics for Data Sets with Missing Values -- \pkg{MANET}}, journal = {Journal of Computational and Graphical Statistics}, year = 1996, pages = {113--122}, volume = 4, number = 6 } @Manual{vcd:Urbanek+Wichtrey:2006, title = {\pkg{iplots}: Interactive Graphics for \textsf{R}}, author = {Simon Urbanek and Tobias Wichtrey}, year = {2006}, note = {\textsf{R} package version 1.0-3}, url = {http://www.rosuda.org/iPlots/} } %% Software @Manual{vcd:R:2006, title = {\textsf{R}: {A} Language and Environment for Statistical Computing}, author = {{\textsf{R} Development Core Team}}, organization = {\textsf{R} Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2006}, note = {{ISBN} 3-900051-00-3}, url = {http://www.R-project.org/} } @Article{vcd:Murrell:2002, author = {Paul Murrell}, title = {The \pkg{grid} Graphics Package}, journal = {\proglang{R} News}, year = 2002, volume = 2, number = 2, pages = {14--19}, month = {June}, url = {http://CRAN.R-project.org/doc/Rnews/} } @Book{vcd:Murrell:2006, author = {Paul Murrell}, title = {\textsf{R} Graphics}, publisher = {Chapmann \& Hall/CRC}, address = {Boca Raton, Florida}, year = {2006}, } @Book{vcd:Venables+Ripley:2002, author = {William N. Venables and Brian D. Ripley}, title = {Modern Applied Statistics with \textsf{S}}, edition = {4th}, publisher = {Springer-Verlag}, address = {New York}, year = {2002}, note = {{ISBN} 0-387-95457-0}, url = {http://www.stats.ox.ac.uk/pub/MASS4/} } @Manual{vcd:Ihaka:2006, title = {\pkg{colorspace}: Colorspace Manipulation}, author = {Ross Ihaka}, year = {2006}, note = {\textsf{R} package version 0.95} } @Manual{vcd:Meyer+Zeileis+Hornik:2006, title = {\pkg{vcd}: Visualizing Categorical Data}, author = {David Meyer and Achim Zeileis and Kurt Hornik}, year = {2006}, note = {\textsf{R} package version 1.0-6} } @article{vcd:Ligges+Maechler:2003, title = {\pkg{scatterplot3d} -- An {R} Package for Visualizing Multivariate Data}, author = {Uwe Ligges and Martin M{\"a}chler}, journal = {Journal of Statistical Software}, year = 2003, pages = {1--20}, number = 11, volume = 8, url = {http://www.jstatsoft.org/v08/i11/} } @Manual{vcd:SAS:2005, title = {\proglang{SAS/STAT} Version 9}, author = {\proglang{SAS} Institute Inc.}, year = {2005}, address = {Cary, NC} } @Manual{vcd:SPLUS:2005, title = {\proglang{S-PLUS} 7}, author = {{Insightful Inc.}}, year = {2005}, address = {Seattle, WA} } %% data @Article{vcd:Azzalini+Bowman:1990, author = {A. Azzalini and A. W. Bowman}, title = {A Look at Some Data on the {O}ld {F}aithful Geyser}, journal = {Applied Statistics}, year = {1990}, volume = {39}, pages = {357--365}, } @Article{vcd:Obel:1975, author = {E.B. Obel}, title = {A Comparative Study of Patients with Cancer of the Ovary Who Have Survived More or Less Than 10 Years}, journal = {Acta Obstetricia et Gynecologica Scandinavica}, year = 1975, volume = 55, pages = {429--439} } @InCollection{vcd:Koch+Edwards:1988, author = {G. Koch and S. Edwards}, title = {Clinical Efficiency Trials with Categorical Data}, booktitle = {Biopharmaceutical Statistics for Drug Development}, editor = {K. E. Peace}, publisher = {Marcel Dekker}, address = {New York}, year = {1988}, pages = {403--451} } @TechReport{vcd:Knorr-Held:1999, author = {Leonhard Knorr-Held}, title = {Dynamic Rating of Sports Teams}, institution = {SFB 386 ``Statistical Analysis of Discrete Structures''}, year = {1999}, type = {Discussion Paper}, number = {98}, url = {http://www.stat.uni-muenchen.de/sfb386/} } @Article{vcd:Snee:1974, author = {R. D. Snee}, title = {Graphical Display of Two-Way Contingency Tables}, journal = {The American Statistician}, year = 1974, volume = 28, pages = {9--12} } @Article{vcd:Bickel+Hammel+O'Connell:1975, author = {P. J. Bickel and E. A. Hammel and J. W. O'Connell}, title = {Sex Bias in Graduate Admissions: Data from {B}erkeley}, journal = {Science}, year = 1975, volume = 187, pages = {398--403} } @Book{vcd:Gilbert:1981, author = {G. N. Gilbert}, title = {Modelling Society: An Introduction to Loglinear Analysis for Social Researchers}, publisher = {Allen and Unwin}, year = 1981, address = {London} } @Book{vcd:Thornes+Collard:1979, author = {B. Thornes and J. Collard}, title = {Who Divorces?}, publisher = {Routledge \& Kegan}, year = 1979, address = {London} } @Article{vcd:Dawson:1995, author = {Robert J. MacG Dawson}, title = {The ``Unusual Episode'' Data Revisited}, journal = {Journal of Statistics Education}, year = 1995, volume = 3, url = {http://www.amstat.org/publications/jse/v3n3/datasets.dawson.html} } @Article{vcd:Haberman:1974, author = {S. J. Haberman}, title = {Log-linear Models for Frequency Tables with Ordered Classifications}, journal = {Biometrics}, year = 1974, volume = 30, pages = {689--700} } @Article{vcd:Wing:1962, author = {J. K. Wing}, title = {Institutionalism in Mental Hospitals}, journal = {British Journal of Social Clinical Psychology}, year = 1962, volume = 1, pages = {38--51} } @Book{vcd:Andersen:1991, author = {E. B. Andersen}, title = {The Statistical Analysis of Categorical Data}, publisher = {Springer-Verlag}, year = {1991}, address = {Berlin}, edition = {2nd} } @Article{vcd:Haberman:1973, author = {S. J. Haberman}, title = {The Analysis of Residuals in Cross-classified Tables}, journal = {Biometrics}, year = {1973}, volume = {29}, pages = {205--220} } @Book{vcd:Everitt+Hothorn:2006, author = {Brian S. Everitt and Torsten Hothorn}, title = {A Handbook of Statistical Analyses Using \textsf{R}}, publisher = {Chapman \& Hall/CRC}, address = {Boca Raton, Florida}, year = {2006} } @Article{vcd:Salib+Hillier:1997, author = {Emad Salib and Valerie Hillier}, title = {A Case-Control Study of Smoking and {A}lzheimer's Disease}, journal = {International Journal of Geriatric Psychiatry}, year = {1997}, volume = {12}, pages = {295--300} } %% inference @Book{vcd:Agresti:2002, author = {Alan Agresti}, title = {Categorical Data Analysis}, publisher = {John Wiley \& Sons}, year = {2002}, address = {Hoboken, New Jersey}, edition = {2nd} } @Book{vcd:Mazanec+Strasser:2000, author = {Josef A. Mazanec and Helmut Strasser}, title = {A Nonparametric Approach to Perceptions-based Market Segmentation: Foundations}, publisher = {Springer-Verlag}, year = {2000}, address = {Berlin} } @Article{vcd:Strasser+Weber:1999, author = {Helmut Strasser and Christian Weber}, title = {On the Asymptotic Theory of Permutation Statistics}, journal = {Mathematical Methods of Statistics}, volume = {8}, pages = {220--250}, year = {1999} } @Book{vcd:Pesarin:2001, author = {Fortunato Pesarin}, title = {Multivariate Permutation Tests}, year = {2001}, publisher = {John Wiley \& Sons}, address = {Chichester} } @Article{vcd:Ernst:2004, author = {Michael D. Ernst}, title = {Permutation Methods: A Basis for Exact Inference}, journal = {Statistical Science}, volume = {19}, year = {2004}, pages = {676--685} } @Article{vcd:Patefield:1981, author = {W. M. Patefield}, title = {An Efficient Method of Generating $R \times C$ Tables with Given Row and Column Totals}, note = {{A}lgorithm AS 159}, journal = {Applied Statistics}, volume = {30}, year = {1981}, pages = {91--97} } %% own @InProceedings{vcd:Meyer+Zeileis+Hornik:2003, author = {David Meyer and Achim Zeileis and Kurt Hornik}, title = {Visualizing Independence Using Extended Association Plots}, booktitle = {Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria}, editor = {Kurt Hornik and Friedrich Leisch and Achim Zeileis}, year = {2003}, url = {http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/}, note = {{ISSN 1609-395X}}, } @TechReport{vcd:Zeileis+Meyer+Hornik:2005, author = {Achim Zeileis and David Meyer and Kurt Hornik}, title = {Residual-based Shadings for Visualizing (Conditional) Independence}, institution = {Department of Statistics and Mathematics, Wirtschaftsuniversit\"at Wien, Research Report Series}, year = {2005}, type = {Report}, number = {20}, month = {August}, url = {http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_871} } @Article{vcd:Zeileis+Meyer+Hornik:2007, author = {Achim Zeileis and David Meyer and Kurt Hornik}, title = {Residual-based Shadings for Visualizing (Conditional) Independence}, journal = {Journal of Computational and Graphical Statistics}, year = {2007}, volume = {16}, number = {3}, pages = {507--525}, doi = {10.1198/106186007X237856}, url = {http://statmath.wu-wien.ac.at/~zeileis/papers/Zeileis+Meyer+Hornik-2007.pdf} } @TechReport{vcd:Meyer+Zeileis+Hornik:2005a, author = {David Meyer and Achim Zeileis and Kurt Hornik}, title = {The Strucplot Framework: Visualizing Multi-Way Contingency Tables with \pkg{vcd}}, institution = {Department of Statistics and Mathematics, Wirtschaftsuniversit\"at Wien, Research Report Series}, year = {2005}, type = {Report}, number = {22}, month = {November}, url = {http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1} } @Article{vcd:Meyer+Zeileis+Hornik:2006b, author = {David Meyer and Achim Zeileis and Kurt Hornik}, title = {The Strucplot Framework: Visualizing Multi-way Contingency Tables with \pkg{vcd}}, year = {2006}, journal = {Journal of Statistical Software}, volume = {17}, number = {3}, pages = {1--48}, url = {http://www.jstatsoft.org/v17/i03/} } @InCollection{vcd:Meyer+Zeileis+Hornik:2006a, author = {David Meyer and Achim Zeileis and Kurt Hornik}, title = {Visualizing Contingency Tables}, editor = {Chun-Houh Chen and Wolfang H\"ardle and Antony Unwin}, booktitle = {Handbook of Data Visualization}, series = {Springer Handbooks of Computational Statistics}, year = {2006}, publisher = {Springer-Verlag}, address = {New York}, note = {{ISBN} 3-540-33036-4, to appear} } @Article{vcd:Hothorn+Hornik+VanDeWiel:2006, author = {Torsten Hothorn and Kurt Hornik and Mark A. van de Wiel and Achim Zeileis}, title = {A {L}ego System for Conditional Inference}, journal = {The American Statistician}, year = {2006}, volume = {60}, number = {3}, pages = {257--263}, doi = {10.1198/000313006X118430} } @TechReport{vcd:Zeileis+Hornik:2006, author = {Achim Zeileis and Kurt Hornik}, title = {Choosing Color Palettes for Statistical Graphics}, institution = {Department of Statistics and Mathematics, Wirtschaftsuniversit\"at Wien, Research Report Series}, year = {2006}, type = {Report}, number = {41}, month = {October}, url = {http://epub.wu-wien.ac.at/} } %% bad color examples @Article{vcd:Gneiting+Sevcikova+Percival:2006, author = {Tilmann Gneiting and Hana \v{S}ev\v{c}\'ikov\'a and Donald B. Percival and Martin Schlather and Yindeng Jiang}, title = {Fast and Exact Simulation of Large Gaussian Lattice Systems in {$\mathbb{R}^2$}: Exploring the Limits}, year = {2006}, journal = {Journal of Computational and Graphical Statistics}, volume = {15}, number = {3}, pages = {483--501}, note = {Figures~1--4} } @Article{vcd:Yang+Buckley+Dudoit:2002, author = {Yee Hwa Yang and Michael J. Buckley and Sandrine Dudoit and Terence P. Speed}, title = {Comparison of Methods for Image Analysis on {cDNA} Microarray Data}, year = {2002}, journal = {Journal of Computational and Graphical Statistics}, volume = {11}, number = {1}, pages = {108--136}, note = {Figure~4a} } @Article{vcd:Kneib:2006, author = {Thomas Kneib}, title = {Mixed Model-based Inference in Geoadditive Hazard Regression for Interval-censored Survival Times}, year = {2006}, journal = {Computational Statistics \& Data Analysis}, volume = {51}, pages = {777--792}, note = {Figure~5 (left)} } @Article{vcd:Friendly:2002, author = {Michael Friendly}, title = {A Brief History of the Mosaic Display}, year = {2002}, journal = {Journal of Computational and Graphical Statistics}, volume = {11}, number = {1}, pages = {89--107}, note = {Figure~11 (left, middle)} } @Article{vcd:Celeux+Hurn+Robert:2000, author = {Gilles Celeux and Merrilee Hurn and Christian P. Robert}, title = {Computational and Inferential Difficulties with Mixture Posterior Distributions}, year = {2000}, journal = {Journal of the American Statistical Association}, volume = {95}, number = {451}, pages = {957--970}, note = {Figure~3} } %% pointers from Hadley @article{cleveland:1987, Author = {Cleveland, William and McGill, Robert}, Journal = {Journal of the Royal Statistical Society A}, Number = {3}, Pages = {192-229}, Title = {Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data}, Volume = {150}, Year = {1987}} @article{cleveland:1984, Author = {Cleveland, William S. and McGill, M. E.}, Journal = {Journal of the American Statistical Association}, Number = 387, Pages = {531-554}, Title = {Graphical Perception: Theory, Experimentation and Application to the Development of Graphical Methods}, Volume = 79, Year = 1984} @article{huang:1997, Author = {Huang, Chisheng and McDonald, John Alan and Stuetzle, Werner}, Journal = {Journal of Computational and Graphical Statistics}, Pages = {383--396}, Title = {Variable resolution bivariate plots}, Volume = {6}, Year = {1997}} @article{carr:1987, Author = {Carr, D. B. and Littlefield, R. J. and Nicholson, W. L. and Littlefield, J. S.}, Journal = {Journal of the American Statistical Association}, Number = {398}, Pages = {424-436}, Title = {Scatterplot Matrix Techniques for Large N}, Volume = {82}, Year = {1987}} @book{cleveland:1994, Author = {Cleveland, William}, Publisher = {Hobart Press}, Title = {The Elements of Graphing Data}, Year = {1994}} @book{chambers:1983, Author = {Chambers, John and Cleveland, William and Kleiner, Beat and Tukey, Paul}, Publisher = {Wadsworth}, Title = {Graphical methods for data analysis}, Year = {1983}} @book{bertin:1983, Address = {Madison, WI}, Author = {Bertin, Jacques}, Publisher = {University of Wisconsin Press}, Title = {Semiology of Graphics}, Year = {1983}} @book{wilkinson:2006, Author = {Wilkinson, Leland}, Publisher = {Springer-Verlag}, Series = {Statistics and Computing}, Title = {The Grammar of Graphics}, Year = {2005}} vcdExtra/vignettes/vcdExtra.bib0000644000175100001440000000113712576352702016331 0ustar hornikusers@ARTICLE{Cohen:60, author = {J. Cohen}, title = {A coefficient of agreement for nominal scales}, journal = {Educational and Psychological Measurement}, year = {1960}, volume = {20}, pages = {37--46}, owner = {Michael}, timestamp = {2009.01.21} } @BOOK{Agresti:2013, title = {Categorical Data Analysis}, publisher = {Wiley-Interscience [John Wiley \& Sons]}, year = {2013}, author = {Agresti, Alan}, series = {Wiley Series in Probability and Statistics}, address = {New York}, edition = {Third}, isbn = {978-0-470-46363-5}, lccn = {QA278.A353 2013} } vcdExtra/MD50000644000175100001440000001757512576452330012406 0ustar hornikusersd1a78c3d56cdff9c9b9cf663b959fe88 *DESCRIPTION 97e38bd5d4f8346c6972548a2c9cac57 *NAMESPACE 039aab0d47e36727731f160a0ee9f675 *NEWS 779bcf552908cea9c4d8aee4763b5d94 *R/CMHtest.R 1297dcac6985033047b58d1479164fbb *R/Crossings.R 1963eb3fb347cf99287b4794b4fd57dc *R/GKgamma.R a7a0bf2f6825917c8e31f99c7b25d265 *R/HLtest.R 10217e3a0582ec4cddba2bfc130a8fad *R/Kway.R c8079ae2c1058550fa5b5fda9624dc8b *R/LRstats.R 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Ø¸­÷BÞ»7g+zž™&àWž{à/¶Ú)“óF¹-à0'B€æœÑŒƒ‘Üm‘S[^‚ØAŽÿ:–rð7ýѳ{V%^•Ê—i^X‰ºö×XcƒøGÇ¡ëÀ&]`‰F¶ðGà‘zÉ­°ÆÑÄhÄ;xÕØy\¶%x©­ë°ËZá{Ð4L©{®Œ<¹SgôØ™Iå”5¤ýƒ²óTÑ1äâ ­ºIzš‘× –Jw†+å¾.Éóbùm$ó«/Õqe›y—ìuü;GZaüBCÆ?iã˜o*zßþûŠ)[ ¬vcdExtra/DESCRIPTION0000644000175100001440000000320712576452330013567 0ustar hornikusersPackage: vcdExtra Type: Package Title: 'vcd' Extensions and Additions Version: 0.6-11 Date: 2015-09-14 Authors@R: c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"), person(given = "Heather", family = "Turner", role="ctb"), person(given = "Achim", family = "Zeileis", role="ctb"), person(given = "Duncan", family = "Murdoch", role="ctb"), person(given = "David", family = "Firth", role="ctb") ) Author: Michael Friendly [aut, cre], Heather Turner [ctb], Achim Zeileis [ctb], Duncan Murdoch [ctb], David Firth [ctb] Maintainer: Michael Friendly Depends: R (>= 2.10), vcd, gnm (>= 1.0.3), grid Suggests: ca, gmodels, Fahrmeir, effects, VGAM, plyr, lmtest, nnet, ggplot2, Sleuth2, car, lattice, stats4, rgl, AER Imports: MASS, grDevices, stats, utils Description: Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer. License: GPL (>= 2) URL: http://CRAN.R-project.org/package=vcdExtra LazyLoad: yes LazyData: yes NeedsCompilation: no Packaged: 2015-09-16 20:49:16 UTC; Friendly Repository: CRAN Date/Publication: 2015-09-17 07:51:20 vcdExtra/man/0000755000175100001440000000000012576352702012634 5ustar hornikusersvcdExtra/man/expand.dft.Rd0000644000175100001440000000374012576352702015162 0ustar hornikusers\name{expand.dft} \alias{expand.dft} \alias{expand.table} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Expand a frequency table to case form} \description{ Converts a frequency table, given either as a table object or a data frame in frequency form to a data frame representing individual observations in the table. } \usage{ expand.dft(x, var.names = NULL, freq = "Freq", ...) expand.table(x, var.names = NULL, freq = "Freq", ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A table object, or a data frame in frequency form containing factors and one numeric variable representing the cell frequency for that combination of factors. } \item{var.names}{A list of variable names for the factors, if you wish to override those already in the table} \item{freq}{The name of the frequency variable in the table} \item{\dots}{Other arguments passed down to \code{type.convert}. In particular, pay attention to \code{na.strings} (default: \code{na.strings=NA} if there are missing cells) and \code{as.is} (default: \code{as.is=FALSE}, converting character vectors to factors).} } \details{ \code{expand.table} is a synonym for \code{expand.dft}. } \value{ A data frame containing the factors in the table and as many observations as are represented by the total of the \code{freq} variable. } \references{Posted on R-Help, Jan 20, 2009. %\url{http://tolstoy.newcastle.edu.au/R/e6/help/09/01/1873.html} } \author{ Mark Schwarz } %\note{ ~~further notes~~ % ~Make other sections like Warning with \section{Warning }{....} ~ %} \seealso{ \code{\link[utils]{type.convert}}, \code{\link[gnm]{expandCategorical}}} \examples{ library(vcd) art <- xtabs(~Treatment + Improved, data = Arthritis) art artdf <- expand.dft(art) str(artdf) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{manip} \keyword{array} % __ONLY ONE__ keyword per line vcdExtra/man/HospVisits.Rd0000644000175100001440000000261612576352702015243 0ustar hornikusers\name{HospVisits} \alias{HospVisits} \docType{data} \title{ Hospital Visits Data } \description{ Length of stay in hospital for 132 schizophrenic patients, classified by visiting patterns, originally from Wing (1962). } \usage{data("HospVisits")} \format{ A 3 by 3 frequency table, with format: table [1:3, 1:3] 43 6 9 16 11 18 3 10 16 - attr(*, "dimnames")=List of 2 ..$ visit: chr [1:3] "Regular" "Infrequent" "Never" ..$ stay : chr [1:3] "2-9" "10-19" "20+" } \details{ Both table variables can be considered ordinal. The variable \code{visit} refers to visiting patterns recorded hospital. The category labels are abbreviations of those given by Goodman (1983); e.g., \code{"Regular"} is short for \dQuote{received visitors regularly or patient went home}. The variable \code{stay} refers to length of stay in hospital, in year groups. } \source{ Goodman, L. A. (1983) The analysis of dependence in cross-classifications having ordered categories, using log-linear models for frequencies and log-linear models for odds. \emph{Biometrics}, 39, 149-160. } \references{ Wing, J. K. (1962). Institutionalism in Mental Hospitals, \emph{British Journal of Social and Clinical Psychology}, 1 (1), 38-51. } \examples{ data(HospVisits) mosaic(HospVisits, gp=shading_Friendly) library(ca) ca(HospVisits) # surprisingly 1D ! plot(ca(HospVisits)) } \keyword{datasets} vcdExtra/man/Summarise.Rd0000644000175100001440000000630712576352702015076 0ustar hornikusers\name{Summarise} \alias{Summarise} \alias{Summarise.glmlist} \alias{Summarise.loglmlist} \alias{Summarise.default} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Brief Summary of Model Fit for glm and loglm Models } \description{ For \code{glm} objects, the \code{print} and \code{summary} methods give too much information if all one wants to see is a brief summary of model goodness of fit, and there is no easy way to display a compact comparison of model goodness of fit for a collection of models fit to the same data. All \code{loglm} models have equivalent glm forms, but the \code{print} and \code{summary} methods give quite different results. \code{Summarise} provides a brief summary for one or more models fit to the same dataset for which \code{logLik} and \code{nobs} methods exist (e.g., \code{glm} and \code{loglm} models). %This implementation is experimental, and is subject to change. } \usage{ Summarise(object, ...) \method{Summarise}{glmlist}(object, ..., saturated = NULL, sortby = NULL) \method{Summarise}{loglmlist}(object, ..., saturated = NULL, sortby = NULL) \method{Summarise}{default}(object, ..., saturated = NULL, sortby = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood} \item{\dots}{ optionally more fitted model objects } \item{saturated}{ saturated model log likelihood reference value (use 0 if deviance is not available) } \item{sortby}{ either a numeric or character string specifying the column in the result by which the rows are sorted (in decreasing order)} } \details{ The function relies on residual degrees of freedom for the LR chisq test being available in the model object. This is true for objects inheriting from \code{lm}, \code{glm}, \code{loglm}, \code{polr} and \code{negbin}. } \value{ A data frame (also of class \code{anova}) with columns \code{c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")}. Row names are taken from the names of the model object(s). } %\references{ %% ~put references to the literature/web site here ~ %} \author{ Achim Zeileis } %\note{ %% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[stats]{logLik}}, \code{\link[stats]{glm}}, \code{\link[MASS]{loglm}}, \code{\link{logLik.loglm}}, \code{\link{modFit}} } \examples{ data(Mental) indep <- glm(Freq ~ mental+ses, family = poisson, data = Mental) Summarise(indep) Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) # compare models Summarise(indep, coleff, roweff, linlin) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/ShakeWords.Rd0000644000175100001440000000317212576352702015200 0ustar hornikusers\name{ShakeWords} \alias{ShakeWords} \docType{data} \title{ Shakespeare's Word Type Frequencies } \description{ This data set, from Efron and Thisted (1976), gives the number of distinct words types (\code{Freq}) of words that appeared exactly once, twice, etc. up to 100 times (\code{count}) in the complete works of Shakespeare. In these works, Shakespeare used 31,534 distinct words (types), comprising 884,647 words in total. Efron & Thisted used this data to ask the question, "How many words did Shakespeare know?" Put another way, suppose another new corpus of works Shakespeare were discovered, also with 884,647 words. How many new word types would appear? The answer to the main question involves contemplating an infinite number of such new corpora. } \usage{data(ShakeWords)} \format{ A data frame with 100 observations on the following 2 variables. \describe{ \item{\code{count}}{the number of times a word type appeared in Shakespeare's written works} \item{\code{Freq}}{the number of different words (types) appearing with this count.} } } \details{ In addition to the words that appear \code{1:100} times, there are 846 words that appear more than 100 times, not listed in this data set. } \source{ Bradley Efron and Ronald Thisted (1976). Estimating the Number of Unsen Species: How Many Words Did Shakespeare Know? \emph{Biometrika}, Vol. 63, No. 3, pp. 435-447, %\url{http://www.jstor.org/stable/2335721} } %\references{ %% ~~ possibly secondary sources and usages ~~ %} \examples{ data(ShakeWords) ## maybe str(ShakeWords) ; plot(ShakeWords) ... } \keyword{datasets} vcdExtra/man/GSS.Rd0000644000175100001440000000174512576352702013566 0ustar hornikusers\name{GSS} \alias{GSS} \docType{data} \title{General Social Survey-- Sex and Party affiliation} \description{ Data from the General Social Survey, 1991, on the relation between sex and party affiliation. } \usage{data(GSS)} \format{ A data frame in frequency form with 6 observations on the following 3 variables. \describe{ \item{\code{sex}}{a factor with levels \code{female} \code{male}} \item{\code{party}}{a factor with levels \code{dem} \code{indep} \code{rep}} \item{\code{count}}{a numeric vector} } } %\details{ % ~~ If necessary, more details than the __description__ above ~~ %} \source{ Agresti, A. Categorical Data Analysis John Wiley & Sons, 2002, Table 3.11, p. 106. } %\references{ % ~~ possibly secondary sources and usages ~~ %} \examples{ data(GSS) ## maybe str(GSS) ; plot(GSS) ... (GSStab <- xtabs(count ~ sex + party, data=GSS)) mod.glm <- glm(count ~ sex + party, family = poisson, data = GSS) } \keyword{datasets} vcdExtra/man/ICU.Rd0000644000175100001440000001200112576352702013535 0ustar hornikusers\name{ICU} \alias{ICU} \docType{data} \title{ ICU data set } \description{ The ICU data set consists of a sample of 200 subjects who were part of a much larger study on survival of patients following admission to an adult intensive care unit (ICU), derived from Hosmer, Lemeshow and Sturdivant (2013) and Friendly (2000). The major goal of this study was to develop a logistic regression model to predict the probability of survival to hospital discharge of these patients and to study the risk factors associated with ICU mortality. The clinical details of the study are described in Lemeshow, Teres, Avrunin, and Pastides (1988). This data set is often used to illustrate model selection methods for logistic regression. } \usage{data(ICU)} \format{ A data frame with 200 observations on the following 22 variables. \describe{ % \item{\code{id}}{Patient id code, a numeric vector} \item{\code{died}}{Died before discharge?, a factor with levels \code{No} \code{Yes}} \item{\code{age}}{Patient age, a numeric vector} \item{\code{sex}}{Patient sex, a factor with levels \code{Female} \code{Male}} \item{\code{race}}{Patient race, a factor with levels \code{Black} \code{Other} \code{White}. Also represented here as \code{white}.} \item{\code{service}}{Service at ICU Admission, a factor with levels \code{Medical} \code{Surgical}} \item{\code{cancer}}{Cancer part of present problem?, a factor with levels \code{No} \code{Yes}} \item{\code{renal}}{History of chronic renal failure?, a factor with levels \code{No} \code{Yes}} \item{\code{infect}}{Infection probable at ICU admission?, a factor with levels \code{No} \code{Yes}} \item{\code{cpr}}{Patient received CPR prior to ICU admission?, a factor with levels \code{No} \code{Yes}} \item{\code{systolic}}{Systolic blood pressure at admission (mm Hg), a numeric vector} \item{\code{hrtrate}}{Heart rate at ICU Admission (beats/min), a numeric vector} \item{\code{previcu}}{Previous admission to an ICU within 6 Months?, a factor with levels \code{No} \code{Yes}} \item{\code{admit}}{Type of admission, a factor with levels \code{Elective} \code{Emergency}} \item{\code{fracture}}{Admission with a long bone, multiple, neck, single area, or hip fracture? a factor with levels \code{No} \code{Yes}} \item{\code{po2}}{PO2 from inital blood gases, a factor with levels \code{>60} \code{<=60}} \item{\code{ph}}{pH from inital blood gases, a factor with levels \code{>=7.25} \code{<7.25}} \item{\code{pco}}{PCO2 from inital blood gases, a factor with levels \code{<=45} \code{>45}} \item{\code{bic}}{Bicarbonate (HCO3) level from inital blood gases, a factor with levels \code{>=18} \code{<18}} \item{\code{creatin}}{Creatinine, from inital blood gases, a factor with levels \code{<=2} \code{>2}} \item{\code{coma}}{Level of unconsciousness at admission to ICU, a factor with levels \code{None} \code{Stupor} \code{Coma}} \item{\code{white}}{a recoding of \code{race}, a factor with levels \code{White} \code{Non-white}} \item{\code{uncons}}{a recoding of \code{coma} a factor with levels \code{No} \code{Yes}} } } \details{ Patient ID numbers are the rownames of the data frame. Note that the last two variables \code{white} and \code{uncons} are a recoding of respectively \code{race} and \code{coma} to binary variables. } \source{ M. Friendly (2000), \emph{Visualizing Categorical Data}, Appendix B.4. SAS Institute, Cary, NC. Hosmer, D. W. Jr., Lemeshow, S. and Sturdivant, R. X. (2013) \emph{Applied Logistic Regression}, NY: Wiley, Third Edition. } \references{ Lemeshow, S., Teres, D., Avrunin, J. S., Pastides, H. (1988). Predicting the Outcome of Intensive Care Unit Patients. \emph{Journal of the American Statistical Association}, 83, 348-356. } \examples{ data(ICU) # remove redundant variables (race, coma) ICU1 <- ICU[,-c(4,20)] # fit full model icu.full <- glm(died ~ ., data=ICU1, family=binomial) summary(icu.full) # simpler model (found from a "best" subsets procedure) icu.mod1 <- glm(died ~ age + sex + cancer + systolic + admit + uncons, data=ICU1, family=binomial) summary(icu.mod1) # even simpler model icu.mod2 <- glm(died ~ age + cancer + admit + uncons, data=ICU1, family=binomial) summary(icu.mod2) anova(icu.mod2, icu.mod1, icu.full, test="Chisq") ## Reproduce Fig 6.12 from VCD icu.fit <- data.frame(ICU, prob=predict(icu.mod2, type="response")) # combine categorical risk factors to a single string risks <- ICU[, c("cancer", "admit", "uncons")] risks[,1] <- ifelse(risks[,1]=="Yes", "Cancer", "") risks[,2] <- ifelse(risks[,2]=="Emergency", "Emerg", "") risks[,3] <- ifelse(risks[,3]=="Yes", "Uncons", "") risks <- apply(risks, 1, paste, collapse="") risks[risks==""] <- "(none)" icu.fit$risks <- risks library(ggplot2) ggplot(icu.fit, aes(x=age, y=prob, color=risks)) + geom_point(size=2) + geom_line(size=1.25, alpha=0.5) + theme_bw() + ylab("Probability of death") } \keyword{datasets} vcdExtra/man/LRstats.Rd0000644000175100001440000000625712576352702014531 0ustar hornikusers\name{LRstats} \alias{LRstats} \alias{LRstats.glmlist} \alias{LRstats.loglmlist} \alias{LRstats.default} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Brief Summary of Model Fit for glm and loglm Models } \description{ For \code{glm} objects, the \code{print} and \code{summary} methods give too much information if all one wants to see is a brief summary of model goodness of fit, and there is no easy way to display a compact comparison of model goodness of fit for a collection of models fit to the same data. All \code{loglm} models have equivalent glm forms, but the \code{print} and \code{summary} methods give quite different results. \code{LRstats} provides a brief summary for one or more models fit to the same dataset for which \code{logLik} and \code{nobs} methods exist (e.g., \code{glm} and \code{loglm} models). %This implementation is experimental, and is subject to change. } \usage{ LRstats(object, ...) \method{LRstats}{glmlist}(object, ..., saturated = NULL, sortby = NULL) \method{LRstats}{loglmlist}(object, ..., saturated = NULL, sortby = NULL) \method{LRstats}{default}(object, ..., saturated = NULL, sortby = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood} \item{\dots}{ optionally more fitted model objects } \item{saturated}{ saturated model log likelihood reference value (use 0 if deviance is not available) } \item{sortby}{ either a numeric or character string specifying the column in the result by which the rows are sorted (in decreasing order)} } \details{ The function relies on residual degrees of freedom for the LR chisq test being available in the model object. This is true for objects inheriting from \code{lm}, \code{glm}, \code{loglm}, \code{polr} and \code{negbin}. } \value{ A data frame (also of class \code{anova}) with columns \code{c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")}. Row names are taken from the names of the model object(s). } %\references{ %% ~put references to the literature/web site here ~ %} \author{ Achim Zeileis } %\note{ %% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[stats]{logLik}}, \code{\link[stats]{glm}}, \code{\link[MASS]{loglm}}, \code{\link{logLik.loglm}}, \code{\link{modFit}} } \examples{ data(Mental) indep <- glm(Freq ~ mental+ses, family = poisson, data = Mental) LRstats(indep) Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) # compare models LRstats(indep, coleff, roweff, linlin) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Titanicp.Rd0000644000175100001440000000404012576352702014674 0ustar hornikusers\name{Titanicp} \alias{Titanicp} \docType{data} \title{ Passengers on the Titanic } \description{ Data on passengers on the RMS Titanic, excluding the Crew and some individual identifier variables. } \usage{data(Titanicp)} \format{ A data frame with 1309 observations on the following 6 variables. \describe{ \item{\code{pclass}}{a factor with levels \code{1st} \code{2nd} \code{3rd}} \item{\code{survived}}{a factor with levels \code{died} \code{survived}} \item{\code{sex}}{a factor with levels \code{female} \code{male}} \item{\code{age}}{passenger age in years (or fractions of a year, for children), a numeric vector; age is missing for 263 of the passengers} \item{\code{sibsp}}{number of siblings or spouses aboard, integer: \code{0:8}} \item{\code{parch}}{number of parents or children aboard, integer: \code{0:6}} } } \details{ There are a number of related versions of the Titanic data, in various formats. This version was derived from \code{ptitanic} in the \pkg{rpart.plot} package, modifying it to remove the \code{Class 'labelled'} attributes for some variables (inherited from Frank Harrell's \code{titanic3} version) which caused problems with some applications, notably \code{ggplot2}. Other versions: \code{\link[datasets]{Titanic}} is the 4-way frequency table of all 2201 people aboard the Titanic, including passengers and crew. } \source{ The original R source for this dataset was compiled by Frank Harrell and Robert Dawson: \url{http://biostat.mc.vanderbilt.edu/twiki/pub/Main/DataSets/titanic.html}, described in more detail in \url{http://biostat.mc.vanderbilt.edu/twiki/pub/Main/DataSets/titanic3info.txt} For this version of the Titanic data, passenger details were deleted, survived was cast as a factor, and the name changed to \code{Titanicp} to minimize confusion with other versions. } %\references{ %%% ~~ possibly secondary sources and usages ~~ %} \examples{ data(Titanicp) ## maybe str(Titanicp) ; plot(Titanicp) ... } \keyword{datasets} vcdExtra/man/modFit.Rd0000644000175100001440000000373312576352702014353 0ustar hornikusers\name{modFit} \Rdversion{1.1} \alias{modFit} \alias{modFit.loglm} \alias{modFit.glm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Brief Summary of Model Fit for a glm or loglm Object } \description{ Formats a brief summary of model fit for a \code{glm} or \code{loglm} object, showing the likelihood ratio Chisq (df) value and or AIC. Useful for inclusion in a plot title or annotation. } \usage{ modFit(x, ...) \method{modFit}{glm}(x, stats="chisq", digits=2, ...) \method{modFit}{loglm}(x, stats="chisq", digits=2, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A \code{glm} or \code{loglm} object } \item{\dots}{ Arguments passed down } \item{stats}{ One or more of \code{chisq} or \code{aic}, determining the statistics displayed. } \item{digits}{ Number of digits after the decimal point in displayed statistics. } } %\details{ %%% ~~ If necessary, more details than the description above ~~ %} \value{ A character string containing the formatted values of the chosen statistics. %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{Summarise}} (soon to be deprecated), \code{\link{LRstats}} } \examples{ data(Mental) require(MASS) (Mental.tab <- xtabs(Freq ~ ses+mental, data=Mental)) (Mental.mod <- loglm(~ses+mental, Mental.tab)) Mental.mod modFit(Mental.mod) # use to label mosaic() mosaic(Mental.mod, main=paste("Independence model,", modFit(Mental.mod))) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{utilities} \keyword{models}% __ONLY ONE__ keyword per line vcdExtra/man/Yamaguchi87.Rd0000644000175100001440000000756512576352702015226 0ustar hornikusers\name{Yamaguchi87} \alias{Yamaguchi87} \docType{data} \title{ Occupational Mobility in Three Countries } \description{ Yamaguchi (1987) presented this three-way frequency table, cross-classifying occupational categories of sons and fathers in the United States, United Kingdom and Japan. This data set has become a classic for models comparing two-way mobility tables across layers corresponding to countries, groups or time (e.g., Goodman and Hout, 1998; Xie, 1992). The US data were derived from the 1973 OCG-II survey; those for the UK from the 1972 Oxford Social Mobility Survey; those for Japan came from the 1975 Social Stratification and Mobility survey. They pertain to men aged 20-64. } \usage{data(Yamaguchi87)} \format{ A frequency data frame with 75 observations on the following 4 variables. The total sample size is 28887. \describe{ \item{\code{Son}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}} \item{\code{Father}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}} \item{\code{Country}}{a factor with levels \code{US} \code{UK} \code{Japan}} \item{\code{Freq}}{a numeric vector} } } \details{ Five status categories -- upper and lower nonmanuals (\code{UpNM}, \code{LoNM}), upper and lower manuals (\code{UpM}, \code{LoM}), and \code{Farm}) are used for both fathers' occupations and sons' occupations. Upper nonmanuals are professionals, managers, and officials; lower nonmanuals are proprietors, sales workers, and clerical workers; upper manuals are skilled workers; lower manuals are semi-skilled and unskilled nonfarm workers; and farm workers are farmers and farm laborers. Some of the models from Xie (1992), Table 1, are fit in \code{demo(yamaguchi-xie)}. } \source{ Yamaguchi, K. (1987). Models for comparing mobility tables: toward parsimony and substance, \emph{American Sociological Review}, vol. 52 (Aug.), 482-494, Table 1 } \references{ Goodman, L. A. and Hout, M. (1998). Statistical Methods and Graphical Displays for Analyzing How the Association Between Two Qualitative Variables Differs Among Countries, Among Groups, Or Over Time: A Modified Regression-Type Approach. \emph{Sociological Methodology}, 28 (1), 175-230. Xie, Yu (1992). The log-multiplicative layer effect model for comparing mobility tables. \emph{American Sociological Review}, 57 (June), 380-395. } \examples{ data(Yamaguchi87) # reproduce Table 1 structable(~ Father + Son + Country, Yamaguchi87) # create table form Yama.tab <- xtabs(Freq ~ Son + Father + Country, data=Yamaguchi87) # define mosaic labeling_args for convenient reuse in 3-way displays largs <- list(rot_labels=c(right=0), offset_varnames = c(right = 0.6), offset_labels = c(right = 0.2), set_varnames = c(Son="Son's status", Father="Father's status") ) ################################### # Fit some models & display mosaics # Mutual independence yama.indep <- glm(Freq ~ Son + Father + Country, data=Yamaguchi87, family=poisson) anova(yama.indep) mosaic(yama.indep, ~Son+Father, main="[S][F] ignoring country") mosaic(yama.indep, ~Country + Son + Father, condvars="Country", labeling_args=largs, main='[S][F][C] Mutual independence') # no association between S and F given country ('perfect mobility') # asserts same associations for all countries yama.noRC <- glm(Freq ~ (Son + Father) * Country, data=Yamaguchi87, family=poisson) anova(yama.noRC) mosaic(yama.noRC, ~~Country + Son + Father, condvars="Country", labeling_args=largs, main="[SC][FC] No [SF] (perfect mobility)") # ignore diagonal cells yama.quasi <- update(yama.noRC, ~ . + Diag(Son,Father):Country) anova(yama.quasi) mosaic(yama.quasi, ~Son+Father, main="Quasi [S][F]") ## see also: # demo(yamaguchi-xie) ## } \keyword{datasets} vcdExtra/man/Crossings.Rd0000644000175100001440000000366612576352702015110 0ustar hornikusers\name{Crossings} \alias{Crossings} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Crossings Interaction of Factors } \description{ Given two ordered factors in a square, n x n frequency table, \code{Crossings} creates an n-1 column matrix corresponding to different degrees of difficulty in crossing from one level to the next, as described by Goodman (1972). } \usage{ Crossings(...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{\dots}{ Two factors } } %\details{ %% ~~ If necessary, more details than the description above ~~ %} \value{ For two factors of \code{n} levels, returns a binary indicator matrix of \code{n*n} rows and \code{n-1} columns. } \references{ Goodman, L. (1972). Some multiplicative models for the analysis of cross-classified data. In: \emph{Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability}, Berkeley, CA: University of California Press, pp. 649-696. } \author{ Michael Friendly and Heather Turner } %\note{ %% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[stats]{glm}}, \code{\link[gnm]{gnm}} for model fitting functions for frequency tables \code{\link[gnm]{Diag}}, \code{\link[gnm]{Mult}}, \code{\link[gnm]{Symm}}, \code{\link[gnm]{Topo}} for similar extensions to terms in model formulas. } \examples{ data(Hauser79) # display table structable(~Father+Son, data=Hauser79) hauser.indep <- gnm(Freq ~ Father + Son, data=Hauser79, family=poisson) hauser.CR <- update(hauser.indep, ~ . + Crossings(Father,Son)) LRstats(hauser.CR) hauser.CRdiag <- update(hauser.indep, ~ . + Crossings(Father,Son) + Diag(Father,Son)) LRstats(hauser.CRdiag) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} \keyword{manip}% __ONLY ONE__ keyword per line vcdExtra/man/Vote1980.Rd0000644000175100001440000000303012576352702014356 0ustar hornikusers\name{Vote1980} \alias{Vote1980} \docType{data} \title{ Race and Politics in the 1980 Presidential Vote } \description{ Data from the 1982 General Social Survey on votes in the 1980 U.S. presidential election in relation to race and political conservatism. } \usage{data(Vote1980)} \format{ A frequency data frame representing a 2 x 7 x 2 table, with 28 observations on the following 4 variables. \describe{ \item{\code{race}}{a factor with levels \code{NonWhite} \code{White}} \item{\code{conservatism}}{ a factor with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7}, \code{1}=most liberal, \code{7}=most conservative} \item{\code{votefor}}{a factor with levels \code{Carter} \code{Reagan}; \code{Carter} represents Jimmy Carter or other.} \item{\code{Freq}}{a numeric vector} } } \details{ The data contains a number of sampling zeros in the frequencies of NonWhites voting for Ronald Reagan. } \source{ Clogg, C. & Shockey, J. W. (1988). In Nesselroade, J. R. & Cattell, R. B. (ed.) Multivariate Analysis of Discrete Data, \emph{Handbook of Multivariate Experimental Psychology}, New York: Plenum Press. } \references{ Agresti, A. (1990) \emph{Categorical Data Analysis}, Table 4.12 New York: Wiley-Interscience. Friendly, M. (2000) \emph{Visualizing Categorical Data}, Example 7.5 Cary, NC: SAS Institute. } \examples{ data(Vote1980) fourfold(xtabs(Freq ~ race + votefor + conservatism, data=Vote1980), mfrow=c(2,4)) } \keyword{datasets} vcdExtra/man/loglin-utilities.Rd0000644000175100001440000001615412576352702016427 0ustar hornikusers\name{loglin-utilities} \alias{loglin-utilities} \alias{conditional} \alias{joint} \alias{loglin2formula} \alias{loglin2string} \alias{markov} \alias{mutual} \alias{saturated} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Loglinear Model Utilities } \description{ These functions generate lists of terms to specify a loglinear model in a form compatible with \code{\link[stats]{loglin}} and also provide for conversion to an equivalent \code{\link[MASS]{loglm}} specification or a shorthand character string representation. They allow for a more conceptual way to specify such models by a function for their type, as opposed to just an uninterpreted list of model terms and also allow easy specification of marginal models for a given contingency table. They are intended to be used as tools in higher-level modeling and graphics functions, but can also be used directly. } \usage{ conditional(nf, table = NULL, factors = 1:nf, with = nf) joint(nf, table = NULL, factors = 1:nf, with = nf) markov(nf, factors = 1:nf, order = 1) mutual(nf, table = NULL, factors = 1:nf) saturated(nf, table = NULL, factors = 1:nf) loglin2formula(x, env = parent.frame()) loglin2string(x, brackets = c("[", "]"), sep = ",", collapse = " ", abbrev) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{nf}{ number of factors for which to generate the model } \item{table}{ a contingency table used only for factor names in the model, typically the output from \code{\link[base]{table}} and possibly permuted with \code{aperm} } \item{factors}{ names of factors used in the model formula when \code{table} is not specified } \item{with}{ For \code{joint} and \code{conditional} models, \code{with} gives the indices of the factors against which all others are considered jointly or conditionally independent } \item{order}{ For \code{markov}, this gives the order of the Markov chain model for the factors. An \code{order=1} Markov chain allows associations among sequential pairs of factors, e.g., \code{[A,B], [B,C], [C,D]} \dots. An \code{order=2} Markov chain allows associations among sequential triples. } \item{x}{ For the \code{loglin2*} functions, a list of terms in a loglinear model, such as returned by \code{conditional}, \code{joint}, \dots } \item{env}{ For \code{loglin2formula}, environment in which to evaluate the formula } \item{brackets}{ For \code{loglin2string}, characters to use to surround model terms. Either a single character string containing two characters (e.g., \code{'[]'} or a character vector of length two. } \item{sep}{ For \code{loglin2string}, the separator character string used for factor names within a given model term } \item{collapse}{ For \code{loglin2string}, the character string used between terms in the the model string } \item{abbrev}{ For \code{loglin2string}, whether and how to abbreviate the terms in the string representation. This has not yet been implemented. } } \details{ The main model specification functions, \code{conditional}, \code{joint}, \code{markov}, \dots, \code{saturated}, return a list of vectors indicating the marginal totals to be fit, via the \code{margin} argument to \code{\link[stats]{loglin}}. Each element of this list corresponds to a high-order term in a hierarchical loglinear model, where, e.g., a term like \code{c("A", "B")} is equivalent to the \code{\link[MASS]{loglm}} term \code{"A:B"} and hence automatically includes all low-order terms. Note that these can be used to supply the \code{expected} argument for the default \code{\link[vcd]{mosaic}} function, when the data is supplied as a contingency table. The table below shows some typical results in terms of the standard shorthand notation for loglinear models, with factors A, B, C, \dots, where brackets are used to delimit the high-order terms in the loglinear model. \tabular{llll}{ \strong{function} \tab \strong{3-way} \tab \strong{4-way} \tab \strong{5-way} \cr \code{mutual} \tab [A] [B] [C] \tab [A] [B] [C] [D] \tab [A] [B] [C] [D] [E] \cr \code{joint} \tab [AB] [C] \tab [ABC] [D] \tab [ABCE] [E] \cr \code{joint (with=1)} \tab [A] [BC] \tab [A] [BCD] \tab [A] [BCDE] \cr \code{conditional} \tab [AC] [BC] \tab [AD] [BD] [CD] \tab [AE] [BE] [CE] [DE] \cr \code{condit (with=1)} \tab [AB] [AC] \tab [AB] [AC] [AD] \tab [AB] [AC] [AD] [AE] \cr \code{markov (order=1)} \tab [AB] [BC] \tab [AB] [BC] [CD] \tab [AB] [BC] [CD] [DE] \cr \code{markov (order=2)} \tab [A] [B] [C] \tab [ABC] [BCD] \tab [ABC] [BCD] [CDE] \cr \code{saturated} \tab [ABC] \tab [ABCD] \tab [ABCDE] \cr } \code{loglin2formula} converts the output of one of these to a model formula suitable as the \code{formula} for of \code{\link[MASS]{loglm}}. \code{loglin2string} converts the output of one of these to a string describing the loglinear model in the shorthand bracket notation, e.g., \code{"[A,B] [A,C]"}. } \value{ For the main model specification functions, \code{conditional}, \code{joint}, \code{markov}, \dots, the result is a list of vectors (terms), where the elements in each vector are the names of the factors. The elements of the list are given names \code{term1, term2, \dots}. } \references{ These functions were inspired by the original SAS implementation of mosaic displays, described in the \emph{User's Guide}, \url{http://www.datavis.ca/mosaics/mosaics.pdf} } \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[stats]{loglin}}, \code{\link[MASS]{loglm}} } \examples{ joint(3, table=HairEyeColor) # as a formula or string loglin2formula(joint(3, table=HairEyeColor)) loglin2string(joint(3, table=HairEyeColor)) joint(2, HairEyeColor) # marginal model for [Hair] [Eye] # other possibilities joint(4, factors=letters, with=1) joint(5, factors=LETTERS) joint(5, factors=LETTERS, with=4:5) conditional(4) conditional(4, with=3:4) # use in mosaic displays or other strucplots mosaic(HairEyeColor, expected=joint(3)) mosaic(HairEyeColor, expected=conditional(3)) # use with MASS::loglm cond3 <- loglin2formula(conditional(3, table=HairEyeColor)) cond3 <- loglin2formula(conditional(3)) # same, with factors 1,2,3 require(MASS) loglm(cond3, data=HairEyeColor) saturated(3, HairEyeColor) loglin2formula(saturated(3, HairEyeColor)) loglin2string(saturated(3, HairEyeColor)) loglin2string(saturated(3, HairEyeColor), brackets='{}', sep=', ') } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Bartlett.Rd0000644000175100001440000000266612576352702014716 0ustar hornikusers\name{Bartlett} \Rdversion{1.1} \alias{Bartlett} \docType{data} \title{Bartlett data on plum root cuttings} \description{In an experiment to investigate the effect of cutting length (two levels) and planting time (two levels) on the survival of plum root cuttings, 240 cuttings were planted for each of the 2 x 2 combinations of these factors, and their survival was later recorded. Bartlett (1935) used these data to illustrate a method for testing for no three-way interaction in a contingency table.} \usage{ data(Bartlett) } \format{ A 3-dimensional array resulting from cross-tabulating 3 variables for 960 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Alive}\tab \code{"Alive", "Dead"}\cr 2\tab \code{Time}\tab \code{"Now", "Spring"}\cr 3\tab \code{Length}\tab \code{"Long", "Short"}\cr } } %\details { } \source{ % \cite{Hand-etal:94 [p.15 #19]} Hand, D. and Daly, F. and Lunn, A. D.and McConway, K. J. and Ostrowski, E. (1994). \emph{A Handbook of Small Data Sets}. London: Chapman & Hall, p. 15, # 19. } \references{ % \cite{Bartlett:35} Bartlett, M. S. (1935). Contingency Table Interactions \emph{Journal of the Royal Statistical Society}, Supplement, 1935, 2, 248-252. } %\seealso { } \examples{ data(Bartlett) fourfold(Bartlett, mfrow=c(1,2)) mosaic(Bartlett, shade=TRUE) pairs(Bartlett, gp=shading_Friendly) } \keyword{datasets} vcdExtra/man/glmlist.Rd0000644000175100001440000000663212576352702014605 0ustar hornikusers\name{glmlist} \Rdversion{1.1} \alias{glmlist} \alias{loglmlist} \alias{coef.glmlist} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Create a Model List Object } \description{ \code{glmlist} creates a \code{glmlist} object containing a list of fitted \code{glm} objects with their names. \code{loglmlist} does the same for \code{loglm} objects. The intention is to provide object classes to facilitate model comparison, extraction, summary and plotting of model components, etc., perhaps using \code{\link[base]{lapply}} or similar. There exists a \code{\link[stats]{anova.glm}} method for \code{glmlist} objects. Here, a \code{coef} method is also defined, collecting the coefficients from all models in a single object of type determined by \code{result}. } \usage{ glmlist(...) loglmlist(...) \method{coef}{glmlist}(object, result=c("list", "matrix", "data.frame"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{\dots}{ One or more model objects, as appropriate to the function, optionally assigned names as in \code{list}. } \item{object}{a \code{glmlist} object} \item{result}{type of the result to be returned} } \details{ The arguments to \code{glmlist} or \code{loglmlist} are of the form \code{value} or \code{name=value}. Any objects which do not inherit the appropriate class \code{glm} or \code{loglm} are excluded, with a warning. In the \code{coef} method, coefficients from the different models are matched by name in the list of unique names across all models. } \value{ An object of class \code{glmlist} \code{loglmlist}, just like a \code{list}, except that each model is given a \code{name} attribute. %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Michael Friendly; \code{coef} method by John Fox } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ The function \code{\link[Hmisc]{llist}} in package \code{Hmisc} is similar, but perplexingly more general. The function \code{\link[stats]{anova.glm}} also handles \code{glmlist objects} \code{\link{LRstats}} gives LR statistics and tests for a \code{glmlist} object. } \examples{ data(Mental) indep <- glm(Freq ~ mental+ses, family = poisson, data = Mental) Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) # use object names mods <- glmlist(indep, coleff, roweff, linlin) names(mods) # assign new names mods <- glmlist(Indep=indep, Col=coleff, Row=roweff, LinxLin=linlin) names(mods) LRstats(mods) coef(mods, result='data.frame') #extract model components unlist(lapply(mods, deviance)) res <- lapply(mods, residuals) boxplot(as.data.frame(res), main="Residuals from various models") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{utilities} \keyword{models}% __ONLY ONE__ keyword per line vcdExtra/man/AirCrash.Rd0000644000175100001440000000414012576352702014616 0ustar hornikusers\name{AirCrash} \alias{AirCrash} \docType{data} \title{ Air Crash Data } \description{ Data on all fatal commercial airplane crashes from 1993--2015. Excludes small planes (less than 6 passengers) and non-commercial (cargo, military, private) aircraft. } \usage{data("AirCrash")} \format{ A data frame with 439 observations on the following 5 variables. \describe{ \item{\code{Phase}}{phase of the flight, a factor with levels \code{en route} \code{landing} \code{standing} \code{take-off} \code{unknown}} \item{\code{Cause}}{a factor with levels \code{criminal} \code{human error} \code{mechanical} \code{unknown} \code{weather}} \item{\code{date}}{date of crash, a Date} \item{\code{Fatalities}}{number of fatalities, a numeric vector} \item{\code{Year}}{year, a numeric vector} } } \details{ \code{Phase} of the flight was cleaned by combining related variants, spelling, etc. } \source{ Originally from David McCandless, \url{http://www.informationisbeautiful.net/visualizations/plane-truth-every-single-commercial-plane-crash-visualized/}, with the data at \url{https://docs.google.com/spreadsheet/ccc?key=0AjOUPqcIwvnjdEx2akx5ZjJXSk9oM1E3dWpqZFJ6Nmc&usp=drive_web#gid=1}, downloaded April 14, 2015. } \references{ Rick Wicklin, \url{http://blogs.sas.com/content/iml/2015/03/30/visualizing-airline-crashes.html} } \examples{ data(AirCrash) aircrash.tab <- xtabs(~Phase + Cause, data=AirCrash) mosaic(aircrash.tab, shade=TRUE) # fix label overlap mosaic(aircrash.tab, shade=TRUE, labeling_args=list(rot_labels=c(30, 30, 30, 30))) # reorder by Phase phase.ord <- rev(c(3,4,1,2,5)) mosaic(aircrash.tab[phase.ord,], shade=TRUE, labeling_args=list(rot_labels=c(30, 30, 30, 30)), offset_varnames=0.5) # reorder by frequency phase.ord <- order(rowSums(aircrash.tab), decreasing=TRUE) cause.ord <- order(colSums(aircrash.tab), decreasing=TRUE) mosaic(aircrash.tab[phase.ord,cause.ord], shade=TRUE, labeling_args=list(rot_labels=c(30, 30, 30, 30))) library(ca) aircrash.ca <- ca(aircrash.tab) plot(aircrash.ca) } \keyword{datasets} vcdExtra/man/DaytonSurvey.Rd0000644000175100001440000000463212576352702015604 0ustar hornikusers\name{DaytonSurvey} \alias{DaytonSurvey} \docType{data} \title{ Dayton Student Survey on Substance Use } \description{ This data, from Agresti (2002), Table 9.1, gives the result of a 1992 survey in Dayton Ohio of 2276 high school seniors on whether they had ever used alcohol, cigarettes and marijuana. } \usage{data(DaytonSurvey)} \format{ A frequency data frame with 32 observations on the following 6 variables. \describe{ \item{\code{cigarette}}{a factor with levels \code{Yes} \code{No}} \item{\code{alcohol}}{a factor with levels \code{Yes} \code{No}} \item{\code{marijuana}}{a factor with levels \code{Yes} \code{No}} \item{\code{sex}}{a factor with levels \code{female} \code{male}} \item{\code{race}}{a factor with levels \code{white} \code{other}} \item{\code{Freq}}{a numeric vector} } } \details{ Agresti uses the letters G (\code{sex}), R (\code{race}), A (\code{alcohol}), C (\code{cigarette}), M (\code{marijuana}) to refer to the table variables, and this usage is followed in the examples below. Background variables include \code{sex} and \code{race} of the respondent (GR), typically treated as explanatory, so that any model for the full table should include the term \code{sex:race}. Models for the reduced table, collapsed over \code{sex} and \code{race} are not entirely unreasonable, but don't permit the estimation of the effects of these variables on the responses. The full 5-way table contains a number of cells with counts of 0 or 1, as well as many cells with large counts, and even the ACM table collapsed over GR has some small cell counts. Consequently, residuals for these models in mosaic displays are best represented as standardized (adjusted) residuals. } \source{ Agresti, A. (2002). \emph{Categorical Data Analysis}, 2nd Ed., New York: Wiley-Interscience, Table 9.1, p. 362. } \references{ Thompson, L. (2009). \emph{R (and S-PLUS) Manual to Accompany Agresti's Categorical Data}, \url{https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf} } \examples{ data(DaytonSurvey) mod.GR <- glm(Freq ~ . + sex*race, data=DaytonSurvey, family=poisson) # mutual independence + GR mod.homog.assoc <- glm(Freq ~ .^2, data=DaytonSurvey, family=poisson) # homogeneous association # collapse over sex and race Dayton.ACM <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum) } \keyword{datasets} vcdExtra/man/CyclingDeaths.Rd0000644000175100001440000000311312576352702015642 0ustar hornikusers\name{CyclingDeaths} \alias{CyclingDeaths} \docType{data} \title{ London Cycling Deaths } \description{ A data frame containing the number of deaths of cyclists in London from 2005 through 2012 in each fortnightly period. Aberdein & Spiegelhalter (2013) discuss these data in relation to the observation that six cyclists died in London between Nov. 5 and Nov. 13, 2013. } \usage{data(CyclingDeaths)} \format{ A data frame with 208 observations on the following 2 variables. \describe{ \item{\code{date}}{a Date} \item{\code{deaths}}{number of deaths, a numeric vector} } } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ \url{http://data.gov.uk/dataset/road-accidents-safety-data}, STATS 19 data, 2005-2012, using the files \code{Casualty0512.csv} and \code{Accidents0512.csv} } \references{ Aberdein, Jody and Spiegelhalter, David (2013). Have London's roads become more dangerous for cyclists? \emph{Significance}, 10(6), 46--48. } \examples{ data(CyclingDeaths) plot(deaths ~ date, data=CyclingDeaths, type="h", lwd=3, ylab="Number of deaths", axes=FALSE) axis(1, at=seq(as.Date('2005-01-01'), by='years', length.out=9), labels=2005:2013) axis(2, at=0:3) # make a one-way frequency table CyclingDeaths.tab <- table(CyclingDeaths$deaths) gf <- goodfit(CyclingDeaths.tab) gf summary(gf) rootogram(gf, xlab="Number of Deaths") distplot(CyclingDeaths.tab) # prob of 6 or more deaths in one fortnight lambda <- gf$par$lambda ppois(5, lambda, lower.tail=FALSE) } \keyword{datasets} vcdExtra/man/datasets.Rd0000644000175100001440000000424212576352702014735 0ustar hornikusers\name{datasets} \alias{datasets} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Information on Data Sets in Packages } \description{ The \code{\link[utils]{data}} function is used both to load data sets from packages, and give a display of the names and titles of data sets in one or more packages, however it does not return a result that can be easily used to get additional information about the nature of data sets in packages. The \code{datasets()} function is designed to produce a more useful summary display of data sets in one or more packages. It extracts the \code{class} and dimension information (\code{dim} or code{length}) of each item, and formats these to provide additional descriptors. } \usage{ datasets(package, allClass=FALSE, incPackage=length(package) > 1, maxTitle=NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{package}{ a character vector giving the package(s) to look in } \item{allClass}{ include all classes of the item (\code{TRUE}) or just the last class (\code{FALSE})? } \item{incPackage}{ include the package name in result? } \item{maxTitle}{ maximum length of data set Title } } \details{ The requested packages must be installed, and are silently loaded in order to extract \code{class} and size information. } \value{ A \code{data.frame} whose rows correspond to data sets found in \code{package}. The columns (for a single package) are: \item{Item}{data set name} \item{class}{class} \item{dim}{an abbreviation of the dimensions of the data set} \item{Title}{data set title} } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Michael Friendly, with R-help from Curt Seeliger } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[utils]{data}}, } \examples{ datasets("vcdExtra") datasets(c("vcd", "vcdExtra")) datasets("datasets", maxTitle=50) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{package} \keyword{data}% __ONLY ONE__ keyword per line vcdExtra/man/Accident.Rd0000644000175100001440000000707712576352702014650 0ustar hornikusers\name{Accident} \alias{Accident} \docType{data} \title{ Traffic Accident Victims in France in 1958 } \description{ Bertin (1983) used these data to illustrate the cross-classification of data by numerous variables, each of which could have various types and could be assigned to various visual attributes. For modeling and visualization purposes, the data can be treated as a 4-way table using loglinear models and mosaic displays, or as a frequency-weighted data frame using a binomial response for \code{result} (\code{"Died"} vs. \code{"Injured"}) and plots of predicted probabilities. } \usage{data(Accident)} \format{ A data frame in frequency form (comprising a 5 x 2 x 4 x 2 table) with 80 observations on the following 5 variables. \describe{ \item{\code{age}}{an ordered factor with levels \code{0-9} < \code{10-19} < \code{20-29} < \code{30-49} < \code{50+}} \item{\code{result}}{a factor with levels \code{Died} \code{Injured}} \item{\code{mode}}{mode of transportation, a factor with levels \code{4-Wheeled} \code{Bicycle} \code{Motorcycle} \code{Pedestrian}} \item{\code{gender}}{a factor with levels \code{Female} \code{Male}} \item{\code{Freq}}{a numeric vector} } } \details{ \code{age} is an ordered factor, but arguably, \code{mode} should be treated as ordered, with levels \code{Pedestrian} < \code{Bicycle} < \code{Motorcycle} < \code{4-Wheeled} as Bertin does. This affects the parameterization in models, so we don't do this directly in the data frame. } \source{ Bertin (1983), p. 30; original data from the Ministere des Travaux Publics } \references{ Bertin, J. (1983), \emph{Semiology of Graphics}, University of Wisconsin Press. } \examples{ # examples data(Accident) head(Accident) # for graphs, reorder mode Accident$mode <- ordered(Accident$mode, levels=levels(Accident$mode)[c(4,2,3,1)]) # Bertin's table accident_tab <- xtabs(Freq ~ gender+mode+age+result, data=Accident) structable(mode+gender ~ age+result, data=accident_tab) ## Loglinear models ## ---------------- # mutual independence acc.mod0 <- glm(Freq ~ age+result+mode+gender, data=Accident, family=poisson) LRstats(acc.mod0) mosaic(acc.mod0, ~mode+age+gender+result) # result as a response acc.mod1 <- glm(Freq ~ age*mode*gender + result, data=Accident, family=poisson) LRstats(acc.mod1) mosaic(acc.mod1, ~mode+age+gender+result, labeling_args = list(abbreviate = c(gender=1, result=4))) # allow two-way association of result with each explanatory variable acc.mod2 <- glm(Freq ~ age*mode*gender + result*(age+mode+gender), data=Accident, family=poisson) LRstats(acc.mod2) mosaic(acc.mod2, ~mode+age+gender+result, labeling_args = list(abbreviate = c(gender=1, result=4))) acc.mods <- glmlist(acc.mod0, acc.mod1, acc.mod2) LRstats(acc.mods) ## Binomial (logistic regression) models for result ## ------------------------------------------------ library(car) # for Anova() acc.bin1 <- glm(result=='Died' ~ age+mode+gender, weights=Freq, data=Accident, family=binomial) Anova(acc.bin1) acc.bin2 <- glm(result=='Died' ~ (age+mode+gender)^2, weights=Freq, data=Accident, family=binomial) Anova(acc.bin2) acc.bin3 <- glm(result=='Died' ~ (age+mode+gender)^3, weights=Freq, data=Accident, family=binomial) Anova(acc.bin3) # compare models anova(acc.bin1, acc.bin2, acc.bin3, test="Chisq") # visualize probability of death with effect plots library(effects) plot(allEffects(acc.bin1), ylab='Pr (Died)') plot(allEffects(acc.bin2), ylab='Pr (Died)') #} \keyword{datasets} vcdExtra/man/Gilby.Rd0000644000175100001440000000333212576352702014172 0ustar hornikusers\name{Gilby} \Rdversion{1.1} \alias{Gilby} \docType{data} \title{Clothing and Intelligence Rating of Children} \description{Schoolboys were classified according to their clothing and to their teachers rating of "dullness" (lack of intelligence), in a 5 x 7 table originally from Gilby (1911). Anscombe (1981) presents a slightly collapsed 4 x 6 table, used here, where the last two categories of clothing were pooled as were the first two categories of dullness due to small counts. Both \code{Dullnes} and \code{Clothing} are ordered categories, so models and methods that examine their association in terms of ordinal categories are profitable. } \usage{ data(Gilby) } \format{ A 2-dimensional array resulting from cross-tabulating 2 variables for 1725 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Dullness}\tab \code{"Ment. defective", "Slow", "Slow Intell", "Fairly Intell", "Capable", "V.Able"}\cr 2\tab \code{Clothing}\tab \code{"V.Well clad", "Well clad", "Passable", "Insufficient"}\cr } } %\details{ } \source{ Anscombe, F. J. (1981). \emph{Computing in Statistical Science Through APL}. New York: Springer-Verlag, p. 302 } \references{ % \cite{Gilby & Pearson 1911, from Anscombe 1981, p 302} Gilby, W. H. (1911). On the significance of the teacher's appreciation of general intelligence. \emph{Biometrika}, 8, 93-108 (esp. p. 94). [Quoted by Kendall (1943,..., 1953) Table 13.1, p 320.] } %\seealso{ } \examples{ data(Gilby) mosaic(Gilby, shade=TRUE) # correspondence analysis to see relations among categories if(require(ca)){ ca(Gilby) plot(ca(Gilby)) title(xlab="Dimension 1", ylab="Dimension 2") } } \keyword{datasets} vcdExtra/man/Cancer.Rd0000644000175100001440000000203512576352702014316 0ustar hornikusers\name{Cancer} \Rdversion{1.1} \alias{Cancer} \docType{data} \title{Survival of Breast Cancer Patients} \description{Three year survival of 474 breast cancer patients according to nuclear grade and diagnostic center.} \usage{ data(Cancer) } \format{ A 3-dimensional array resulting from cross-tabulating 3 variables for 474 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Survival}\tab \code{"Died", "Surv"}\cr 2\tab \code{Grade}\tab \code{"Malignant", "Benign"}\cr 3\tab \code{Center}\tab \code{"Boston", "Glamorgan"}\cr } } %\details { } \source{ % \cite{Lindsey:95 [p38]} % \cite{Whittaker:90} Lindsey, J. K. (1995). Analysis of Frequency and Count Data Oxford, UK: Oxford University Press. p. 38, Table 2.5. Whittaker, J. (1990) Graphical Models in Applied Multivariate Statistics New York: John Wiley and Sons, p. 220. } %\references{ % \cite{Morrison etal} %} %\seealso { } \examples{ data(Cancer) # example goes here } \keyword{datasets} vcdExtra/man/collapse.table.Rd0000644000175100001440000000720412576352702016016 0ustar hornikusers\name{collapse.table} \alias{collapse.table} %- Also NEED an '\alias' for EACH other topic documented here. \title{Collapse Levels of a Table} \description{ Collapse (or re-label) variables in a a contingency table, array or \code{ftable} object by re-assigning levels of the table variables. } \usage{ collapse.table(table, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{table}{A \code{\link[base]{table}}, \code{\link[base]{array}} or \code{\link[stats]{ftable}} object} \item{\dots}{ A collection of one or more assignments of factors of the table to a list of levels } } \details{ Each of the \code{\dots} arguments must be of the form \code{variable = levels}, where \code{variable} is the name of one of the table dimensions, and \code{levels} is a character or numeric vector of length equal to the corresponding dimension of the table. } \value{ A \code{xtabs} and \code{table} object, representing the original table with one or more of its factors collapsed or rearranged into other levels. } %\references{ ~put references to the literature/web site here ~ } \author{Michael Friendly} %\note{ ~~further notes~~ % % ~Make other sections like Warning with \section{Warning }{....} ~ %} \seealso{ \code{\link{expand.dft}} expands a frequency data frame to case form. \code{\link[base]{margin.table}} "collapses" a table in a different way, by summing over table dimensions. } \examples{ # create some sample data in table form sex <- c("Male", "Female") age <- letters[1:6] education <- c("low", 'med', 'high') data <- expand.grid(sex=sex, age=age, education=education) counts <- rpois(36, 100) data <- cbind(data, counts) t1 <- xtabs(counts ~ sex + age + education, data=data) structable(t1) ## age a b c d e f ## sex education ## Male low 119 101 109 85 99 93 ## med 94 98 103 108 84 84 ## high 81 88 96 110 100 92 ## Female low 107 104 95 86 103 96 ## med 104 98 94 95 110 106 ## high 93 85 90 109 99 86 # collapse age to 3 levels t2 <- collapse.table(t1, age=c("A", "A", "B", "B", "C", "C")) structable(t2) ## age A B C ## sex education ## Male low 220 194 192 ## med 192 211 168 ## high 169 206 192 ## Female low 211 181 199 ## med 202 189 216 ## high 178 199 185 # collapse age to 3 levels and pool education: "low" and "med" to "low" t3 <- collapse.table(t1, age=c("A", "A", "B", "B", "C", "C"), education=c("low", "low", "high")) structable(t3) ## age A B C ## sex education ## Male low 412 405 360 ## high 169 206 192 ## Female low 413 370 415 ## high 178 199 185 # change labels for levels of education to 1:3 t4 <- collapse.table(t1, education=1:3) structable(t4) structable(t4) ## age a b c d e f ## sex education ## Male 1 119 101 109 85 99 93 ## 2 94 98 103 108 84 84 ## 3 81 88 96 110 100 92 ## Female 1 107 104 95 86 103 96 ## 2 104 98 94 95 110 106 ## 3 93 85 90 109 99 86 } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{manip} \keyword{attribute}% __ONLY ONE__ keyword per line vcdExtra/man/Draft1970table.Rd0000644000175100001440000000527112576352702015521 0ustar hornikusers\name{Draft1970table} \alias{Draft1970table} \docType{data} \title{ USA 1970 Draft Lottery Table } \description{ This data set gives the results of the 1970 US draft lottery, in the form of a frequency table. The rows are months of the year, Jan--Dec and columns give the number of days in that month which fall into each of three draft risk categories High, Medium, and Low, corresponding to the chances of being called to serve in the US army. } \usage{data(Draft1970table)} \format{ The format is: 'table' int [1:12, 1:3] 9 7 5 8 9 11 12 13 10 9 ... - attr(*, "dimnames")=List of 2 ..$ Month: chr [1:12] "Jan" "Feb" "Mar" "Apr" ... ..$ Risk : chr [1:3] "High" "Med" "Low" } \details{ The lottery numbers are divided into three categories of risk of being called for the draft -- High, Medium, and Low -- each representing roughly one third of the days in a year. Those birthdays having the highest risk have lottery numbers 1-122, medium risk have numbers 123-244, and the lowest risk category contains lottery numbers 245-366. } \source{ This data is available in several forms, but the table version was obtained from \url{http://sas.uwaterloo.ca/~rwoldfor/software/eikosograms/data/draft-70} } \references{ Fienberg, S. E. (1971), "Randomization and Social Affairs: The 1970 Draft Lottery," \emph{Science}, 171, 255-261. Starr, N. (1997). Nonrandom Risk: The 1970 Draft Lottery, \emph{Journal of Statistics Education}, v.5, n.2 \url{http://www.amstat.org/publications/jse/v5n2/datasets.starr.html} } \seealso{\code{\link{Draft1970}} } \examples{ data(Draft1970table) chisq.test(Draft1970table) # plot.table -> graphics:::mosaicplot plot(Draft1970table, shade=TRUE) mosaic(Draft1970table, gp=shading_Friendly) # correspondence analysis if(require(ca)) { ca(Draft1970table) plot(ca(Draft1970table)) } # convert to a frequency data frame with ordered factors Draft1970df <- as.data.frame(Draft1970table) Draft1970df <- within(Draft1970df, { Month <- ordered(Month) Risk <- ordered(Risk, levels=rev(levels(Risk))) }) str(Draft1970df) # similar model, as a Poisson GLM indep <- glm(Freq ~ Month + Risk, family = poisson, data = Draft1970df) mosaic(indep, residuals_type="rstandard", gp=shading_Friendly) # numeric scores for tests of ordinal factors Cscore <- as.numeric(Draft1970df$Risk) Rscore <- as.numeric(Draft1970df$Month) # linear x linear association between Month and Risk linlin <- glm(Freq ~ Month + Risk + Rscore:Cscore, family = poisson, data = Draft1970df) # compare models anova(indep, linlin, test="Chisq") mosaic(linlin, residuals_type="rstandard", gp=shading_Friendly) } \keyword{datasets} vcdExtra/man/print.Kappa.Rd0000644000175100001440000000235112576352702015313 0ustar hornikusers\name{print.Kappa} \alias{print.Kappa} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Print Kappa } \description{ This is a replacement for the \code{print.Kappa} method in \code{vcd}, adding display of \code{z} values to the \code{vcd} version and optional confidence intervals. } \usage{ \method{print}{Kappa}(x, digits=max(getOption("digits") - 3, 3), CI=FALSE, level=0.95, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A Kappa object} \item{digits}{number of digits to print} \item{CI}{Include confidence intervals in the display?} \item{level}{confidence level} \item{\dots}{ Other arguments } } %\details{ % ~~ If necessary, more details than the description above ~~ %} \value{ Returns the Kappa object, invisibly. } %\references{ ~put references to the literature/web site here ~ } \author{ Michael Friendly} \seealso{ \code{\link[vcd]{confint.Kappa}} } \examples{ data("SexualFun") Kappa(SexualFun) print(Kappa(SexualFun), CI=TRUE) # stratified 3-way table apply(MSPatients, 3, Kappa) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{htest} \keyword{category} vcdExtra/man/Depends.Rd0000644000175100001440000000314112576352702014504 0ustar hornikusers\name{Depends} \alias{Depends} \docType{data} \title{ Dependencies of R Packages } \description{ This one-way table gives the type-token distribution of the number of dependencies declared in 4983 packages listed on CRAN on January 17, 2014. } \usage{data(Depends)} \format{ The format is: 'table' int [1:15(1d)] 986 1347 993 685 375 298 155 65 32 19 ... - attr(*, "dimnames")=List of 1 ..$ Depends: chr [1:15] "0" "1" "2" "3" ... } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ Using code from \url{http://blog.revolutionanalytics.com/2013/12/a-look-at-the-distribution-of-r-package-dependencies.html} } %\references{ %%% ~~ possibly secondary sources and usages ~~ %} \examples{ data(Depends) plot(Depends, xlab="Number of Dependencies", ylab="Number of R Packages", lwd=8) \dontrun{ # The code below, from Joseph Rickert, downloads and tabulates the data p <- as.data.frame(available.packages(),stringsAsFactors=FALSE) names(p) pkgs <- data.frame(p[,c(1,4)]) # Pick out Package names and Depends row.names(pkgs) <- NULL # Get rid of row names pkgs <- pkgs[complete.cases(pkgs[,2]),] # Remove NAs pkgs$Depends2 <-strsplit(pkgs$Depends,",") # split list of Depends pkgs$numDepends <- as.numeric(lapply(pkgs$Depends2,length)) # Count number of dependencies in list zeros <- c(rep(0,dim(p)[1] - dim(pkgs)[1])) # Account for packages with no dependencies Deps <- as.vector(c(zeros,pkgs$numDepends)) # Set up to tablate Depends <- table(Deps) } } \keyword{datasets} vcdExtra/man/Hoyt.Rd0000644000175100001440000000634012576352702014051 0ustar hornikusers\name{Hoyt} \Rdversion{1.1} \alias{Hoyt} \docType{data} \title{Minnesota High School Graduates} \description{Minnesota high school graduates of June 1930 were classified with respect to (a) \code{Rank} by thirds in their graduating class, (b) post-high school \code{Status} in April 1939 (4 levels), (c) \code{Sex}, (d) father's \code{Occupation}al status (7 levels, from 1=High to 7=Low). The data were first presented by Hoyt et al. (1959) and have been analyzed by Fienberg(1980), Plackett(1974) and others. } \usage{ data(Hoyt) } \format{ A 4-dimensional array resulting from cross-tabulating 4 variables for 13968 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Status}\tab \code{"College", "School", "Job", "Other"}\cr 2\tab \code{Rank}\tab \code{"Low", "Middle", "High"}\cr 3\tab \code{Occupation}\tab \code{"1", "2", "3", "4", "5", "6", "7"}\cr 4\tab \code{Sex}\tab \code{"Male", "Female"}\cr } } \details{Post high-school \code{Status} is natural to consider as the response. \code{Rank} and father's \code{Occupation} are ordinal variables.} \source{ % \cite{Hoyt-etal:59} % \cite{Fienberg:80 [pp.91-92]} % \cite{Plackett:74} % \cite{minn38{MASS}} Fienberg, S. E. (1980). \emph{The Analysis of Cross-Classified Categorical Data}. Cambridge, MA: MIT Press, p. 91-92. R. L. Plackett, (1974). \emph{The Analysis of Categorical Data}. London: Griffin. } \references{ Hoyt, C. J., Krishnaiah, P. R. and Torrance, E. P. (1959) Analysis of complex contingency tables, \emph{Journal of Experimental Education} 27, 187-194. } \seealso{ \code{\link[MASS]{minn38}} provides the same data as a data frame. } \examples{ data(Hoyt) # display the table structable(Status+Sex ~ Rank+Occupation, data=Hoyt) # mosaic for independence model plot(Hoyt, shade=TRUE) # examine all pairwise mosaics pairs(Hoyt, shade=TRUE) # collapse Status to College vs. Non-College Hoyt1 <- collapse.table(Hoyt, Status=c("College", rep("Non-College",3))) plot(Hoyt1, shade=TRUE) ################################################# # fitting models with loglm, plotting with mosaic ################################################# # fit baseline log-linear model for Status as response require(MASS) hoyt.mod0 <- loglm(~ Status + (Sex*Rank*Occupation), data=Hoyt1) hoyt.mod0 mosaic(hoyt.mod0, gp=shading_Friendly, main="Baseline model: Status + (Sex*Rank*Occ)") # add one-way association of Status with factors hoyt.mod1 <- loglm(~ Status * (Sex + Rank + Occupation) + (Sex*Rank*Occupation), data=Hoyt1) hoyt.mod1 mosaic(hoyt.mod1, gp=shading_Friendly, main="Status * (Sex + Rank + Occ)") # can we drop any terms? drop1(hoyt.mod1, test="Chisq") # assess model fit anova(hoyt.mod0, hoyt.mod1) # what terms to add? add1(hoyt.mod1, ~.^2, test="Chisq") # add interaction of Sex:Occupation on Status hoyt.mod2 <- update(hoyt.mod1, ~.+Status:Sex:Occupation) mosaic(hoyt.mod2, gp=shading_Friendly, main="Adding Status:Sex:Occupation") # compare model fits anova(hoyt.mod0, hoyt.mod1, hoyt.mod2) # Alternatively, try stepwise analysis, heading toward the saturated model steps <- step(hoyt.mod0, direction="forward", scope=~Status*Sex*Rank*Occupation) # display anova steps$anova } \keyword{datasets} vcdExtra/man/CMHtest.Rd0000644000175100001440000002046012576352702014434 0ustar hornikusers\name{CMHtest} \alias{CMHtest} \alias{CMHtest.formula} \alias{CMHtest.default} \alias{Cochran Mantel Haenszel test} \alias{print.CMHtest} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Generalized Cochran-Mantel-Haenszel Tests } \description{ Provides generalized Cochran-Mantel-Haenszel tests of association of two possibly ordered factors, optionally stratified other factor(s). With strata, \code{CMHtest} calculates these tests for each level of the statifying variables and also provides overall tests controlling for the strata. For ordinal factors, more powerful tests than the test for general association (independence) are obtained by assigning scores to the row and columm categories. } \usage{ CMHtest(x, ...) \method{CMHtest}{formula}(formula, data = NULL, subset = NULL, na.action = NULL, ...) \method{CMHtest}{default}(x, strata = NULL, rscores = 1:R, cscores = 1:C, types = c("cor", "rmeans", "cmeans", "general"), overall=FALSE, details=overall, ...) \method{print}{CMHtest}(x, digits = max(getOption("digits") - 2, 3), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A 2+ way contingency table in array form, or a class \code{"table"} object with optional category labels specified in the dimnames(x) attribute. } \item{formula}{a formula specifying the variables used to create a contingency table from \code{data}. This should be a one-sided formula when \code{data} is in array form, and a two-sided formula with a response \code{Freq} if \code{data} is a data frame with a cell frequency variable. For convenience, conditioning formulas can be specified indicating strata. } \item{data}{either a data frame, or an object of class \code{"table"} or \code{"ftable"}. } \item{subset}{an optional vector specifying a subset of observations to be used. } \item{na.action}{a function which indicates what should happen when the data contain \code{NA}s. Ignored if \code{data} is a contingency table } \item{strata}{ For a 3- or higher-way table, the names or numbers of the factors to be treated as strata. By default, the first 2 factors are treated as the main table variables, and all others considered stratifying factors. } \item{rscores}{ Row scores. Either a set of numbers (typically integers, \code{1:R}) or the string \code{"midrank"} for standardized midrank scores, or \code{NULL} to exclude tests that depend on row scores. } \item{cscores}{ Column scores. Same as for row scores. } \item{types}{ Types of CMH tests to compute: Any one or more of \code{c("cor", "cmeans", "rmeans", "general")}, or \code{"ALL"} for all of these. } \item{overall}{ logical. Whether to calculate overall tests, controlling for the stratifying factors. } \item{details}{ logical. Whether to include computational details in the result } \item{\dots}{ Other arguments passed to default method. } \item{digits}{ Digits to print. } } \details{ The standard \eqn{\chi^2} tests for association in a two-way table treat both table factors as nominal (unordered) categories. When one or both factors of a two-way table are quantitative or ordinal, more powerful tests of association may be obtained by taking ordinality into account using row and or column scores to test for linear trends or differences in row or column means. The CMH analysis for a two-way table produces generalized Cochran-Mantel-Haenszel statistics (Landis etal., 1978). These include the CMH \bold{correlation} statistic (\code{"cor"}), treating both factors as ordered. For a given statum, with equally spaced row and column scores, this CMH statistic reduces to \eqn{(n-1) r^2}, where \eqn{r} is the Pearson correlation between X and Y. With \code{"midrank"} scores, this CMH statistic is analogous to \eqn{(n-1) r_S^2}, using the Spearman rank correlation. The \bold{ANOVA} (row mean scores and column mean scores) statistics, treat the columns and rows respectively as ordinal, and are sensitive to mean shifts over columns or rows. These are transforms of the \eqn{F} statistics from one-way ANOVAs with equally spaced scores and to Kruskal-Wallis tests with \code{"midrank"} scores. The CMH \bold{general} association statistic treat both factors as unordered, and give a test closely related to the Pearson \eqn{\chi^2} test. When there is more than one stratum, the overall general CMH statistic gives a stratum-adjusted Pearson \eqn{\chi^2}, equivalent to what is calculated by \code{\link[stats]{mantelhaen.test}}. For a 3+ way table, one table of CMH tests is produced for each combination of the factors identified as \code{strata}. If \code{overall=TRUE}, an additional table is calculated for the same two primary variables, controlling for (pooling over) the \code{strata} variables. These overall tests implicitly assume no interactions between the primary variables and the strata and they will have low power in the presence of interactions. } \value{ An object of class \code{"CMHtest"} , a list with the following 4 components: \item{table}{A matrix containing the test statistics, with columns \code{Chisq}, \code{Df} and \code{Prob} } \item{names}{The names of the table row and column variables} \item{rscore}{Row scores} \item{cscore}{Column scores} If \code{details==TRUE}, additional components are included. If there are strata, the result is a list of \code{"CMHtest"} objects. If \code{overall=TRUE} another component, labeled \code{ALL} is appended to the list. } \references{ Stokes, M. E. & Davis, C. S. & Koch, G., (2000). \emph{Categorical Data Analysis using the SAS System}, 2nd Ed., Cary, NC: SAS Institute, pp 74-75, 92-101, 124-129. Details of the computation are given at: \url{http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm } Cochran, W. G. (1954), Some Methods for Strengthening the Common \eqn{\chi^2} Tests, \emph{Biometrics}, 10, 417-451. Landis, R. J., Heyman, E. R., and Koch, G. G. (1978). Average Partial Association in Three-way Contingency Tables: A Review and Discussion of Alternative Tests, \emph{International Statistical Review}, \bold{46}, 237-254. Mantel, N. (1963), Chi-square Tests with One Degree of Freedom: Extensions of the Mantel-Haenszel Procedure," \emph{Journal of the American Statistical Association}, 58, 690-700. } \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[coin]{cmh_test}} provides the CMH test of general association; \code{\link[coin]{lbl_test}} provides the CMH correlation test of linear by linear association. \code{\link[stats]{mantelhaen.test}} provides the overall general Cochran-Mantel-Haenszel chi-squared test of the null that two nominal variables are conditionally independent in each stratum, assuming that there is no three-way interaction } \examples{ data(JobSat, package="vcdExtra") CMHtest(JobSat) CMHtest(JobSat, rscores="midrank", cscores="midrank") # formula interface CMHtest(~ ., data=JobSat) # A 3-way table (both factors ordinal) data(MSPatients, package="vcd") CMHtest(MSPatients) # also calculate overall tests, controlling for Patient CMHtest(MSPatients, overall=TRUE) # compare with mantelhaen.test mantelhaen.test(MSPatients) # formula interface CMHtest(~ ., data=MSPatients, overall=TRUE) # using a frequency data.frame CMHtest(xtabs(Freq~ses+mental, data=Mental)) # or, more simply CMHtest(Freq~ses+mental, data=Mental) # conditioning formulae CMHtest(Freq~right+left|gender, data=VisualAcuity) CMHtest(Freq ~ attitude+memory|education+age, data=Punishment) # Stokes etal, Table 5.1, p 92: two unordered factors parties <- matrix( c(221, 160, 360, 140, 200, 291, 160, 311, 208, 106, 316, 97), nrow=3, ncol=4, byrow=TRUE) dimnames(parties) <- list(party=c("Dem", "Indep", "Rep"), neighborhood=c("Bayside", "Highland", "Longview", "Sheffield")) CMHtest(parties, rscores=NULL, cscores=NULL) # compare with Pearson chisquare chisq.test(parties) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{htest} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Kway.Rd0000644000175100001440000001037712576352702014046 0ustar hornikusers\name{Kway} \Rdversion{1.1} \alias{Kway} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Fit All K-way Models in a GLM } \description{ Generate and fit all 0-way, 1-way, 2-way, ... k-way terms in a glm. This function is designed mainly for hierarchical loglinear models (or \code{glm}s in the poission family), where it is desired to find the highest-order terms necessary to achieve a satisfactory fit. Using \code{\link[stats]{anova}} on the resulting \code{\link{glmlist}} object will then give sequential tests of the pooled contributions of all terms of degree \eqn{k+1} over and above those of degree \eqn{k}. This function is also intended as an example of a generating function for \code{\link{glmlist}} objects, to facilitate model comparison, extraction, summary and plotting of model components, etc., perhaps using \code{lapply} or similar. } \usage{ Kway(formula, family=poisson, data, ..., order = nt, prefix = "kway") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ a two-sided formula for the 1-way effects in the model. The LHS should be the response, and the RHS should be the first-order terms connected by \code{+} signs. } \item{family}{ a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See \code{\link[stats]{family}} for details of family functions.) } \item{data}{ an optional data frame, list or environment (or object coercible by \code{\link[base]{as.data.frame}} to a data frame) containing the variables in the model. If not found in data, the variables are taken from \code{environment(formula)}, typically the environment from which \code{glm} is called. } \item{\dots}{ Other arguments passed to \code{glm} } \item{order}{ Highest order interaction of the models generated. Defaults to the number of terms in the model formula. } \item{prefix}{ Prefix used to label the models fit in the \code{glmlist} object. } } \details{ With \code{y} as the response in the \code{formula}, the 0-way (null) model is \code{y ~ 1}. The 1-way ("main effects") model is that specified in the \code{formula} argument. The k-way model is generated using the formula \code{. ~ .^k}. With the default \code{order = nt}, the final model is the saturated model. As presently written, the function requires a two-sided formula with an explicit response on the LHS. For frequency data in table form (e.g., produced by \code{xtabs}) you the \code{data} argument is coerced to a data.frame, so you should supply the \code{formula} in the form \code{Freq ~ } \dots. } \value{ An object of class \code{glmlist}, of length \code{order+1} containing the 0-way, 1-way, ... models up to degree \code{order}. } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Michael Friendly and Heather Turner } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{glmlist}}, \code{\link{Summarise}} (soon to be deprecated), \code{\link{LRstats}} } \examples{ ## artificial data factors <- expand.grid(A=factor(1:3), B=factor(1:2), C=factor(1:3), D=factor(1:2)) Freq <- rpois(nrow(factors), lambda=40) df <- cbind(factors, Freq) mods3 <- Kway(Freq ~ A + B + C, data=df, family=poisson) LRstats(mods3) mods4 <- Kway(Freq ~ A + B + C + D, data=df, family=poisson) LRstats(mods4) # JobSatisfaction data data(JobSatisfaction, package="vcd") modSat <- Kway(Freq ~ management+supervisor+own, data=JobSatisfaction, family=poisson, prefix="JobSat") LRstats(modSat) anova(modSat, test="Chisq") # Rochdale data: very sparse, in table form data(Rochdale, package="vcd") \dontrun{ modRoch <- Kway(Freq~EconActive + Age + HusbandEmployed + Child + Education + HusbandEducation + Asian + HouseholdWorking, data=Rochdale, family=poisson) LRstats(modRoch) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/JobSat.Rd0000644000175100001440000000217312576352702014310 0ustar hornikusers\name{JobSat} \Rdversion{1.1} \alias{JobSat} \docType{data} \title{Cross-classification of job satisfaction by income} \description{ This data set is a contingency table of job satisfaction by income for a small sample of black males from the 1996 General Social Survey, as used by Agresti (2002) for an example. } \usage{data(JobSat)} \format{ A 4 x 4 contingency table of \code{income} by \code{satisfaction}, with the following structure: \preformatted{ table [1:4, 1:4] 1 2 1 0 3 3 6 1 10 10 ... - attr(*, "dimnames")=List of 2 ..$ income : chr [1:4] "< 15k" "15-25k" "25-40k" "> 40k" ..$ satisfaction: chr [1:4] "VeryD" "LittleD" "ModerateS" "VeryS" } } \details{ Both \code{income} and \code{satisfaction} are ordinal variables, and are so ordered in the table. Measures of association, visualizations, and models should take ordinality into account. } \source{ Agresti, A. Categorical Data Analysis John Wiley & Sons, 2002, Table 2.8, p. 57. } %\references{ % ~~ possibly secondary sources and usages ~~ %} \examples{ data(JobSat) assocstats(JobSat) GKgamma(JobSat) } \keyword{datasets} vcdExtra/man/Fungicide.Rd0000644000175100001440000000353112576352702015022 0ustar hornikusers\name{Fungicide} \alias{Fungicide} \docType{data} \title{ Carcinogenic Effects of a Fungicide } \description{ Data from Gart (1971) on the carcinogenic effects of a certain fungicide in two strains of mice. Of interest is how the association between \code{group} (Control, Treated) and \code{outcome} (Tumor, No Tumor) varies with \code{sex} and \code{strain} of the mice. Breslow (1976) used this data to illustrate the application of linear models to log odds ratios. } \usage{data(Fungicide)} \format{ The data comprise a set of four 2 x 2 tables classifying 403 mice, either Control or Treated and whether or not a tumor was later observed. The four groups represent the combinations of sex and strain of mice. The format is: num [1:2, 1:2, 1:2, 1:2] 5 4 74 12 3 2 84 14 10 4 ... - attr(*, "dimnames")=List of 4 ..$ group : chr [1:2] "Control" "Treated" ..$ outcome: chr [1:2] "Tumor" "NoTumor" ..$ sex : chr [1:2] "M" "F" ..$ strain : chr [1:2] "1" "2" } \details{ All tables have some small cells, so a continuity correction is recommended. } \source{ Gart, J. J. (1971). The comparison of proportions: a review of significance tests, confidence intervals and adjustments for stratification. \emph{International Statistical Review}, 39, 148-169. } \references{ Brewlow, N. (1976), Regression analysis of the log odds ratio: A method for retrospective studies, \emph{Biometrics}, 32(3), 409-416. } \examples{ data(Fungicide) # loddsratio was moved to vcd; requires vcd_1.3-3+ \dontrun{ if (require(vcd)) { fung.lor <- loddsratio(Fungicide, correct=TRUE) fung.lor confint(fung.lor) } } # visualize odds ratios in fourfold plots cotabplot(Fungicide, panel=cotab_fourfold) # -- fourfold() requires vcd >= 1.2-10 fourfold(Fungicide, p_adjust_method="none") } \keyword{datasets} vcdExtra/man/Geissler.Rd0000644000175100001440000000434412576352702014705 0ustar hornikusers\name{Geissler} \alias{Geissler} \docType{data} \title{ Geissler's Data on the Human Sex Ratio } \description{ Geissler (1889) published data on the distributions of boys and girls in families in Saxony, collected for the period 1876-1885. The \code{Geissler} data tabulates the family composition of 991,958 families by the number of boys and girls listed in the table supplied by Edwards (1958, Table 1). } \usage{data(Geissler)} \format{ A data frame with 90 observations on the following 4 variables. The rows represent the non-NA entries in Edwards' table. \describe{ \item{\code{boys}}{number of boys in the family, \code{0:12}} \item{\code{girls}}{number of girls in the family, \code{0:12}} \item{\code{size}}{family size: \code{boys+girls}} \item{\code{Freq}}{number of families with this sex composition} } } \details{ The data on family composition was available because, on the birth of a child, the parents had to state the sex of all their children on the birth certificate. These family records are not necessarily independent, because a given family may have had several children during this 10 year period. } \source{ Edwards, A. W. F. (1958). An Analysis Of Geissler's Data On The Human Sex Ratio. \emph{Annals of Human Genetics}, 23, 6-15 } \references{ Geissler, A. (1889). \emph{Beitrage zur Frage des Geschlechts verhaltnisses der Geborenen} Z. K. Sachsischen Statistischen Bureaus, 35, n.p. Lindsey, J. K. & Altham, P. M. E. (1998). Analysis of the human sex ratio by using overdispersion models. \emph{Journal of the Royal Statistical Society: Series C (Applied Statistics)}, 47, 149-157. } \seealso{ \code{\link[vcd]{Saxony}}, containing the data for families of size 12. } \examples{ data(Geissler) ## maybe str(Geissler) ; plot(Geissler) ... # reproduce Saxony data, families of size 12 Saxony12<-subset(Geissler, size==12, select=c(boys, Freq)) rownames(Saxony12)<-NULL # make a 1-way table xtabs(Freq~boys, Saxony12) # extract data for other family sizes Saxony11<-subset(Geissler, size==11, select=c(boys, Freq)) rownames(Saxony11)<-NULL Saxony10<-subset(Geissler, size==10, select=c(boys, Freq)) rownames(Saxony10)<-NULL } \keyword{datasets} vcdExtra/man/logLik.loglm.Rd0000644000175100001440000000617012576352702015461 0ustar hornikusers\name{logLik.loglm} \alias{logLik.loglm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Log-Likelihood of a loglm Object } \description{ Calculates the log-likelihood value of the \code{loglm} model represented by \code{object} evaluated at the estimated coefficients. It allows the use of \code{\link[stats]{AIC}} and \code{\link[stats]{BIC}}, which require that a \code{logLik} method exists to extract the corresponding log-likelihood for the model. } \usage{ \method{logLik}{loglm}(object, ..., zero=1E-10) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ A \code{loglm} object } \item{\dots}{ For compatibility with the S3 generic; not used here } \item{zero}{value used to replace zero frequencies in calculating the log-likelihood} } \details{ If cell frequencies have not been stored with the \code{loglm} object (via the argument \code{keep.frequencies = TRUE}), they are obtained using \code{update}. This function calculates the log-likelihood in a way that allows for non-integer frequencies, such as the case where 0.5 has been added to all cell frequencies to allow for sampling zeros. If the frequencies still contain zero values, those are replaced by the value of \code{start}. For integer frequencies, it gives the same result as the corresponding model fit using \code{\link[stats]{glm}}, whereas \code{\link[stats]{glm}} returns \code{-Inf} if there are any non-integer frequencies. } \value{ Returns an object of class \code{logLik}. This is a number with one attribute, \code{"df"} (degrees of freedom), giving the number of (estimated) parameters in the model. } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Achim Zeileis } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[MASS]{loglm}}, \code{\link[stats]{AIC}}, \code{\link[stats]{BIC}}, } \examples{ data(Titanic, package="datasets") require(MASS) titanic.mod1 <- loglm(~ (Class * Age * Sex) + Survived, data=Titanic) titanic.mod2 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age + Sex), data=Titanic) titanic.mod3 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age * Sex), data=Titanic) logLik(titanic.mod1) AIC(titanic.mod1, titanic.mod2, titanic.mod3) BIC(titanic.mod1, titanic.mod2, titanic.mod3) # compare with models fit using glm() titanic <- as.data.frame(Titanic) titanic.glm1 <- glm(Freq ~ (Class * Age * Sex) + Survived, data=titanic, family=poisson) titanic.glm2 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age + Sex), data=titanic, family=poisson) titanic.glm3 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age * Sex), data=titanic, family=poisson) logLik(titanic.glm1) AIC(titanic.glm1, titanic.glm2, titanic.glm3) BIC(titanic.glm1, titanic.glm2, titanic.glm3) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} \keyword{htest}% __ONLY ONE__ keyword per line vcdExtra/man/Heckman.Rd0000644000175100001440000000473212576352702014477 0ustar hornikusers\name{Heckman} \Rdversion{1.1} \alias{Heckman} \docType{data} \title{Labour Force Participation of Married Women 1967-1971} \description{1583 married women were surveyed over the years 1967-1971, recording whether or not they were employed in the labor force. The data, originally from Heckman & Willis (1977) provide an example of modeling longitudinal categorical data, e.g., with markov chain models for dependence over time. } \usage{ data(Heckman) } \format{ A 5-dimensional array resulting from cross-tabulating 5 variables for 1583 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{e1971}\tab \code{"71Yes", "No"}\cr 2\tab \code{e1970}\tab \code{"70Yes", "No"}\cr 3\tab \code{e1969}\tab \code{"69Yes", "No"}\cr 4\tab \code{e1968}\tab \code{"68Yes", "No"}\cr 5\tab \code{e1967}\tab \code{"67Yes", "No"}\cr } } \details{ Lindsey (1993) fits an initial set of logistic regression models examining the dependence of employment in 1971 (\code{e1971}) on successive subsets of the previous years, \code{e1970}, \code{e1969}, \dots \code{e1967}. Alternatively, one can examine markov chain models of first-order (dependence on previous year), second-order (dependence on previous two years), etc. } \source{ % \cite{Lindsey:93 [p. 185]} Lindsey, J. K. (1993). \emph{Models for Repeated Measurements} Oxford, UK: Oxford University Press, p. 185. } \references{ % \cite{HeckmanWillis:77} Heckman, J.J. & Willis, R.J. (1977). "A beta-logistic model for the analysis of sequential labor force participation by married women." \emph{Journal of Political Economy}, 85: 27-58 } %\seealso{ } \examples{ data(Heckman) # independence model mosaic(Heckman, shade=TRUE) # same, as a loglm() require(MASS) (heckman.mod0 <- loglm(~ e1971+e1970+e1969+e1968+e1967, data=Heckman)) mosaic(heckman.mod0, main="Independence model") # first-order markov chain: bad fit (heckman.mod1 <- loglm(~ e1971*e1970 + e1970*e1969 +e1969*e1968 + e1968*e1967, data=Heckman)) mosaic(heckman.mod1, main="1st order markov chain model") # second-order markov chain: bad fit (heckman.mod2 <- loglm(~ e1971*e1970*e1969 + e1970*e1969*e1968 +e1969*e1968*e1967, data=Heckman)) mosaic(heckman.mod2, main="2nd order markov chain model") # third-order markov chain: fits OK (heckman.mod3 <- loglm(~ e1971*e1970*e1969*e1968 + e1970*e1969*e1968*e1967, data=Heckman)) mosaic(heckman.mod2, main="3rd order markov chain model") } \keyword{datasets} vcdExtra/man/blogits.Rd0000644000175100001440000000505612576352702014574 0ustar hornikusers\name{blogits} \alias{blogits} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Bivariate Logits and Log Odds Ratio } \description{ This function calculates the log odds and log odds ratio for two binary responses classified by one or more stratifying variables. It is useful for plotting the results of bivariate logistic regression models, such as those fit using \code{\link[VGAM]{vglm}} in the \pkg{VGAM}. } \usage{ blogits(Y, add, colnames, row.vars, rev=FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{Y}{ A four-column matrix or data frame whose columns correspond to the 2 x 2 combinations of two binary responses. } \item{add}{ Constant added to all cells to allow for zero frequencies. The default is 0.5 if \code{any(Y)==0} and 0 otherwise. } \item{colnames}{ Names for the columns of the results. The default is \code{c("logit1", "logit2", "logOR")}. If less than three names are supplied, the remaining ones are filled in from the default. } \item{row.vars}{ A data frame or matrix giving the factor levels of one or more factors corresponding to the rows of \code{Y} } \item{rev}{A logical, indicating whether the order of the columns in \code{Y} should be reversed.} } \details{ For two binary variables with levels 0,1 the logits are calculated assuming the columns in \code{Y} are given in the order 11, 10, 01, 00, so the logits give the log odds of the 1 response compared to 0. If this is not the case, either use \code{rev=TRUE} or supply \code{Y[,4:1]} as the first argument. } \value{ A data frame with \code{nrow(Y)} rows and \code{3 + ncol(row.vars)} columns } %\references{ %%% ~put references to the literature/web site here ~ %} \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[VGAM]{vglm}} } \examples{ data(Toxaemia) tox.tab <- xtabs(Freq~class + smoke + hyper + urea, Toxaemia) # reshape to 4-column matrix toxaemia <- t(matrix(aperm(tox.tab), 4, 15)) colnames(toxaemia) <- c("hu", "hU", "Hu", "HU") rowlabs <- expand.grid(smoke=c("0", "1-19", "20+"), class=factor(1:5)) toxaemia <- cbind(toxaemia, rowlabs) # logits for H and U logitsTox <- blogits(toxaemia[,4:1], add=0.5, colnames=c("logitH", "logitW"), row.vars=rowlabs) logitsTox } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{manip} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Caesar.Rd0000644000175100001440000000474212576352702014330 0ustar hornikusers\name{Caesar} \alias{Caesar} \docType{data} \title{Risk Factors for Infection in Caesarian Births} \description{Data from infection from birth by Caesarian section, classified by \code{Risk} (two levels), whether \code{Antibiotics} were used (two levels) and whether the Caesarian section was \code{Planned} or not. The outcome is \code{Infection} (three levels).} \usage{ data(Caesar) } \format{ A 4-dimensional array resulting from cross-tabulating 4 variables for 251 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Infection}\tab \code{"Type 1", "Type 2", "None"}\cr 2\tab \code{Risk}\tab \code{"Yes", "No"} (presence of risk factors)\cr 3\tab \code{Antibiotics}\tab \code{"Yes", "No"} (were antibiotics given?)\cr 4\tab \code{Planned}\tab \code{"Yes", "No"} (was the C section planned?)\cr } } \details{ \code{Infection} is regarded as the response variable here. There are quite a few 0 cells here, particularly when \code{Risk} is absent and the Caesarian section was unplanned. Should these be treated as structural or sampling zeros? } \source{ % \cite{Fahrmeir:94} Fahrmeir, L. & Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models New York: Springer Verlag, Table 1.1. } %\references{ %} \seealso{\code{\link[Fahrmeir]{caesar}} for the same data recorded as a frequency data frame with other variables.} \examples{ data(Caesar) #display table; note that there are quite a few 0 cells structable(Caesar) require(MASS) # baseline model, Infection as response Caesar.mod0 <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar) # NB: Pearson chisq cannot be computed due to the 0 cells Caesar.mod0 mosaic(Caesar.mod0, main="Baseline model") # Illustrate handling structural zeros zeros <- 0+ (Caesar >0) zeros[1,,1,1] <- 1 structable(zeros) # fit model excluding possible structural zeros Caesar.mod0s <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar, start=zeros) Caesar.mod0s anova(Caesar.mod0, Caesar.mod0s, test="Chisq") mosaic (Caesar.mod0s) # what terms to add? add1(Caesar.mod0, ~.^2, test="Chisq") # add Association of Infection:Antibiotics Caesar.mod1 <- update(Caesar.mod0, ~.+Infection:Antibiotics) anova(Caesar.mod0, Caesar.mod1, test="Chisq") mosaic(Caesar.mod1, gp=shading_Friendly, main="Adding Infection:Antibiotics") } \keyword{datasets} vcdExtra/man/Mammograms.Rd0000644000175100001440000000220712576352702015224 0ustar hornikusers\name{Mammograms} \alias{Mammograms} \docType{data} \title{ Mammogram Ratings } \description{ Kundel & Polansky (2003) give (possibly contrived) data on a set of 110 mammograms rated by two readers. } \usage{data(Mammograms)} \format{ A frequency table in matrix form. The format is: num [1:4, 1:4] 34 6 2 0 10 8 5 1 2 8 ... - attr(*, "dimnames")=List of 2 ..$ Reader2: chr [1:4] "Absent" "Minimal" "Moderate" "Severe" ..$ Reader1: chr [1:4] "Absent" "Minimal" "Moderate" "Severe" } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ Kundel, H. L. & Polansky, M. (2003), "Measurement of Observer Agreement", \emph{Radiology}, \bold{228}, 303-308, Table A1 } %\references{ %%% ~~ possibly secondary sources and usages ~~ %} \examples{ data(Mammograms) B <- agreementplot(Mammograms, main="Mammogram ratings") # agreement measures B Kappa(Mammograms) ## other displays mosaic(Mammograms, shade=TRUE) sieve(Mammograms, pop = FALSE, shade = TRUE) labeling_cells(text = Mammograms, gp_text = gpar(fontface = 2, cex=1.75))(as.table(Mammograms)) } \keyword{datasets} vcdExtra/man/PhdPubs.Rd0000644000175100001440000000315412576352702014473 0ustar hornikusers\name{PhdPubs} \alias{PhdPubs} \docType{data} \title{ Publications of PhD Candidates } \description{ A data set giving the number of publications by doctoral candidates in biochemistry in relation to various predictors, originally from Long (1997). There is a large number of zero counts. Is there evidence for a separate group of non-publishers? } \usage{data(PhdPubs)} \format{ A data frame with 915 observations on the following 6 variables. \describe{ \item{\code{articles}}{number of articles published in the final three years of PhD studies} \item{\code{female}}{dummy variable for gender, coded \code{1} for female} \item{\code{married}}{dummy variable for marital status, coded \code{1} for married} \item{\code{kid5}}{number of young children, age 5 and under} \item{\code{phdprestige}}{prestige of the PhD department} \item{\code{mentor}}{number of publications by the mentor in the preceeding three years} } } %\details{ %% ~~ If necessary, more details than the __description__ above ~~ %} \source{ Long, J. S. (1997) \emph{Regression Models for Categorical and Limited Dependent Variables}, Sage. } %\references{ %% ~~ possibly secondary sources and usages ~~ %} \examples{ data(PhdPubs) # very uninformative hist(PhdPubs$articles, breaks=0:19, col="pink", xlim=c(0,20), xlab="Number of Articles") library(vcd) rootogram(goodfit(PhdPubs$articles), xlab="Number of Articles") # compare with negative binomial rootogram(goodfit(PhdPubs$articles, type="nbinomial"), xlab="Number of Articles", main="Negative binomial") } \keyword{datasets} vcdExtra/man/WorkerSat.Rd0000644000175100001440000000230312576352702015042 0ustar hornikusers\name{WorkerSat} \alias{WorkerSat} \docType{data} \title{ Worker Satisfaction Data } \description{ Blue collar workers job satisfaction from large scale investigation in Denmark in 1968 (Andersen, 1991). } \usage{data("WorkerSat")} \format{ A frequency data frame with 8 observations on the following 4 variables, representing the 2 x 2 x 2 classification of 715 cases. \describe{ \item{\code{Manage}}{Quality of management, an ordered factor with levels \code{bad} < \code{good}} \item{\code{Super}}{Supervisor satisfaction, an ordered factor with levels \code{low} < \code{high}} \item{\code{Worker}}{Worker job satisfaction, an ordered factor with levels \code{low} < \code{high}} \item{\code{Freq}}{a numeric vector} } } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ \url{https://onlinecourses.science.psu.edu/stat504/node/131} } \references{ Andersen, E. B. (1991) Statistical Analysis of Categorical Data, 2nd Ed., Springer-Verlag. } \examples{ data(WorkerSat) worker.tab <- xtabs(Freq ~ Worker + Super + Manage, data=WorkerSat) fourfold(worker.tab) mosaic(worker.tab, shade=TRUE) } \keyword{datasets} vcdExtra/man/split3d.Rd0000644000175100001440000000605412576352702014512 0ustar hornikusers\name{split3d} \Rdversion{1.1} \alias{split3d} \alias{split3d.shape3d} \alias{split3d.list} \alias{range3d} \alias{center3d} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Subdivide a 3D Object } \description{ Subdivides a \code{shape3d} object or a list of \code{shape3d} objects into objects of the same shape along a given dimension according to the proportions or frequencies specified in vector(s). \code{split3d} is the basic workhorse used in \code{\link{mosaic3d}}, but may be useful in other contexts. \code{range3d} and \code{center3d} are utility functions, also useful in other contexts. } \usage{ split3d(obj, ...) \method{split3d}{shape3d}(obj, p, dim, space = 0.1, ...) \method{split3d}{list}(obj, p, dim, space = 0.1, ...) range3d(obj) center3d(obj) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{obj}{ A \code{shape3d} object, or a list composed of them } \item{\dots}{ Other arguments for split3d methods } \item{p}{ For a single \code{shade3d} object, a vector of proportions (or a vector of non-negative numbers which will be normed to proportions) indicating the number of subdivisions and their scaling along dimension \code{dim}. For a list of \code{shade3d} objects, a matrix whose columns indicate the subdivisions of each object. } \item{dim}{ The dimension along which the object is to be subdivided. Either an integer: 1, 2, or 3, or a character: "x", "y", or "z". } \item{space}{ The total space used to separate the copies of the object along dimension \code{dim}. The unit inter-object space is therefore \code{space/(length(p)-1)}. } } \details{ The resulting list of \code{shape3d} objects is actually composed of \emph{copies} of the input object(s), scaled according to the proportions in \code{p} and then translated to make their range along the splitting dimension equal to that of the input object(s). } \value{ \code{split3d} returns a list of \code{shape3d} objects. \code{range3d} returns a 2 x 3 matrix, whose first row contains the minima on dimensions x, y, z, and whose second row contains the maxima. \code{center3d} returns a numeric vector containing the means of the minima and maxima on dimensions x, y, z. } \author{ Duncan Murdoch, with refinements by Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{mosaic3d}} \code{\link[rgl]{shapelist3d}} for the plotting of lists of \code{shape3d} objects. } \examples{ if (require(rgl)) { open3d() cube <- cube3d(alpha=0.4) sl1 <- split3d(cube, c(.2, .3, .5), 1) col <- c("#FF000080", "#E5E5E580", "#0000FF80") shapelist3d(sl1, col=col) open3d() p <- matrix(c(.6, .4, .5, .5, .2, .8), nrow=2) sl2 <- split3d(sl1, p, 2) shapelist3d(sl2, col=col) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{dplot} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Heart.Rd0000644000175100001440000000141612576352702014170 0ustar hornikusers\name{Heart} \Rdversion{1.1} \alias{Heart} \docType{data} \title{Sex, Occupation and Heart Disease} \description{Classification of individuals by gender, occupational category and occurrence of heart disease} \usage{ data(Heart) } \format{ A 3-dimensional array resulting from cross-tabulating 3 variables for 21522 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Disease}\tab \code{"Disease", "None"}\cr 2\tab \code{Gender}\tab \code{"Male", "Female"}\cr 3\tab \code{Occup}\tab \code{"Unempl", "WhiteCol", "BlueCol"}\cr } } %\details{ } \source{ % \cite{Karger, 1980} Karger, (1980). } %\references{ %} %\seealso{ } \examples{ data(Heart) # example goes here } \keyword{datasets} vcdExtra/man/Cormorants.Rd0000644000175100001440000000601012576352702015247 0ustar hornikusers\name{Cormorants} \alias{Cormorants} \docType{data} \title{ Advertising Behavior by Males Cormorants } \description{ Male double-crested cormorants use advertising behavior to attract females for breeding. In this study by Meagan Mc Rae (2015), cormorants were observed two or three times a week at six stations in a tree-nesting colony for an entire season, April 10, 2014-July 10, 2014. The number of advertising birds was counted and these observations were classified by characteristics of the trees and nests. The goal is to determine how this behavior varies temporally over the season and spatially, as well as with characteristics of nesting sites. } \usage{data("Cormorants")} \format{ A data frame with 343 observations on the following 8 variables. \describe{ \item{\code{category}}{Time of season, divided into 3 categories based on breeding chronology, an ordered factor with levels \code{Pre} < \code{Incubation} < \code{Chicks Present}} \item{\code{week}}{Week of the season} \item{\code{station}}{Station of observations on two different peninsulas in a park, a factor with levels \code{B1} \code{B2} \code{C1} \code{C2} \code{C3} \code{C4}} \item{\code{nest}}{Type of nest, an ordered factor with levels \code{no} < \code{partial} < \code{full}} \item{\code{height}}{Relative height of bird in the tree, an ordered factor with levels \code{low} < \code{mid} < \code{high}} \item{\code{density}}{Number of other nests in the tree, an ordered factor with levels \code{zero} < \code{few} < \code{moderate} < \code{high}} \item{\code{tree_health}}{Health of the tree the bird is advertising in, a factor with levels \code{dead} \code{healthy}} \item{\code{count}}{Number of birds advertising, a numeric vector} } } \details{ Observations were made on only 2 days in weeks 3 and 4, but 3 days in all other weeks. One should use log(days) as an offset, so that the response measures rate. \code{Cormorants$days <- ifelse(Cormorants$week \%in\% 3:4, 2, 3)} } \source{ Mc Rae, M. (2015). Spatial, Habitat and Frequency Changes in Double-crested Cormorant Advertising Display in a Tree-nesting Colony. Unpublished MA project, Environmental Studies, York University. } %\references{ %%% ~~ possibly secondary sources and usages ~~ %} \examples{ data(Cormorants) str(Cormorants) library(ggplot2) ggplot(Cormorants, aes(count)) + geom_histogram(binwidth=0.5) + labs(x="Number of birds advertising") # Quick look at the data, on the log scale, for plots of `count ~ week`, # stratified by something else. library(ggplot2) ggplot(Cormorants, aes(week, count, color=height)) + geom_jitter() + stat_smooth(method="loess", size=2) + scale_y_log10(breaks=c(1,2,5,10)) + geom_vline(xintercept=c(4.5, 9.5)) # ### models using week fit1 <-glm(count ~ week + station + nest + height + density + tree_health, data=Cormorants, family = poisson) library(car) Anova(fit1) # plot fitted effects library(effects) plot(allEffects(fit1)) } \keyword{datasets} vcdExtra/man/seq_loglm.Rd0000644000175100001440000000737212576352702015116 0ustar hornikusers\name{seq_loglm} \alias{seq_loglm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Sequential Loglinear Models for an N-way Table } \description{ This function takes an n-way contingency table and fits a series of sequential models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of types of loglinear models. } \usage{ seq_loglm(x, type = c("joint", "conditional", "mutual", "markov", "saturated"), marginals = 1:nf, vorder = 1:nf, k = NULL, prefix = "model", fitted = TRUE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ a contingency table in array form, with optional category labels specified in the dimnames(x) attribute, or else a data.frame in frequency form, with the frequency variable named \code{"Freq"}. } \item{type}{ type of sequential model to fit, a character string. One of \code{"joint"}, \code{"conditional"}, \code{"mutual"}, \code{"markov"}, or \code{"saturated"}. } \item{marginals}{ which marginal sub-tables to fit? A vector of a (sub)set of the integers, \code{1:nf} where \code{nf} is the number of factors in the full n-way table. } \item{vorder}{ order of variables, a permutation of the integers \code{1:nf}, used to reorder the variables in the original table for the purpose of fitting sequential marginal models. } \item{k}{ conditioning variable(s) for \code{type} = \code{"joint"}, \code{"conditional"} or Markov chain order for \code{type} = \code{"markov"} } \item{prefix}{ prefix used to give names to the sequential models } \item{fitted}{ argument passed to \code{loglm} to store the fitted values in the model objects } \item{\dots}{ other arguments, passed down } } \details{ Sequential marginal models for an n-way tables begin with the model of equal-probability for the one-way margin (equivalent to a \code{\link[stats]{chisq.test}}) and add successive variables one at a time in the order specified by \code{vorder}. All model types give the same result for the two-way margin, namely the test of independence for the first two factors. Sequential models of \emph{joint independence} (\code{type="joint"}) have a particularly simple interpretation, because they decompose the likelihood ratio test for the model of mutual independence in the full n-way table, and hence account for "total" association in terms of portions attributable to the conditional probabilities of each new variable, given all prior variables. } \value{ An object of class \code{"loglmlist"}, each of which is a class \code{"loglm"} object %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ These functions were inspired by the original SAS implementation of mosaic displays, described in the \emph{User's Guide}, \url{http://www.datavis.ca/mosaics/mosaics.pdf} } \author{ Michael Friendly } \note{ One-way marginal tables are a bit of a problem here, because they cannot be fit directly using \code{\link[MASS]{loglm}}. The present version uses \code{\link[stats]{loglin}}, and repairs the result to look like a \code{loglm} object (sort of). } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{loglin-utilities}} for descriptions of sequential models, \code{\link{conditional}}, \code{\link{joint}}, \code{\link{mutual}}, \dots \code{\link{loglmlist}}, } \examples{ data(Titanic, package="datasets") # variables are in the order Class, Sex, Age, Survived tt <- seq_loglm(Titanic) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{models} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Vietnam.Rd0000644000175100001440000000330312576352702014525 0ustar hornikusers\name{Vietnam} \alias{Vietnam} \docType{data} \title{ Student Opinion about the Vietnam War } \description{ A survey of student opinion on the Vietnam War was taken at the University of North Carolina at Chapel Hill in May 1967 and published in the student newspaper. Students were asked to fill in ballot papers stating which policy out of A,B,C or D they supported. Responses were cross-classified by gender/year. The response categories were: \describe{ \item{\code{A}}{Defeat North Vietnam by widespread bombing and land invasion} \item{\code{B}}{Maintain the present policy} \item{\code{C}}{De-escalate military activity, stop bombing and begin negotiations} \item{\code{D}}{Withdraw military forces Immediately} } } \usage{data(Vietnam)} \format{ A frequency data frame with 40 observations representing a 2 x 5 x 4 contingency table on the following 4 variables. \describe{ \item{\code{sex}}{a factor with levels \code{Female} \code{Male}} \item{\code{year}}{year of study, an ordered factor with levels \code{Freshmen}, \code{Sophomore}, \code{Junior}, \code{Senior}, \code{Grad student}} \item{\code{response}}{a factor with levels \code{A} \code{B} \code{C} \code{D}} \item{\code{Freq}}{cell frequency, a numeric vector} } } \details{ For some analyses, it is useful to treat \code{year} as numeric, and possibly assign grad students a value \code{year=7}. } \source{ Aitken, M. etal, 1989, \emph{Statistical Modelling in GLIM} } \references{ Friendly, M. (2000), \emph{Visualizing Categorical Data}, SAS Institute, Cary, NC, Example 7.9. } \examples{ data(Vietnam) ## maybe str(Vietnam) ; plot(Vietnam) ... } \keyword{datasets} vcdExtra/man/Hauser79.Rd0000644000175100001440000000737612576352702014547 0ustar hornikusers\name{Hauser79} \alias{Hauser79} \docType{data} \title{ Hauser (1979) Data on Social Mobility } \description{ Hauser (1979) presented this two-way frequency table, cross-classifying occupational categories of sons and fathers in the United States. } \usage{data(Hauser79)} \format{ A frequency data frame with 25 observations on the following 3 variables, representing the cross-classification of 19912 individuals by father's occupation and son's first occupation. \describe{ \item{\code{Son}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}} \item{\code{Father}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}} \item{\code{Freq}}{a numeric vector} } } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ R.M. Hauser (1979), Some exploratory methods for modeling mobility tables and other cross-classified data. In: K.F. Schuessler (Ed.), \emph{Sociological Methodology}, 1980, Jossey-Bass, San Francisco, pp. 413-458. } \references{ Powers, D.A. and Xie, Y. (2008). \emph{Statistical Methods for Categorical Data Analysis}, Bingley, UK: Emerald. } \examples{ data(Hauser79) str(Hauser79) # display table structable(~Father+Son, data=Hauser79) #Examples from Powers & Xie, Table 4.15 # independence model mosaic(Freq ~ Father + Son, data=Hauser79, shade=TRUE) hauser.indep <- gnm(Freq ~ Father + Son, data=Hauser79, family=poisson) mosaic(hauser.indep, ~Father+Son, main="Independence model", gp=shading_Friendly) hauser.quasi <- update(hauser.indep, ~ . + Diag(Father,Son)) mosaic(hauser.quasi, ~Father+Son, main="Quasi-independence model", gp=shading_Friendly) hauser.qsymm <- update(hauser.indep, ~ . + Diag(Father,Son) + Symm(Father,Son)) mosaic(hauser.qsymm, ~Father+Son, main="Quasi-symmetry model", gp=shading_Friendly) #mosaic(hauser.qsymm, ~Father+Son, main="Quasi-symmetry model") # numeric scores for row/column effects Sscore <- as.numeric(Hauser79$Son) Fscore <- as.numeric(Hauser79$Father) # row effects model hauser.roweff <- update(hauser.indep, ~ . + Father*Sscore) LRstats(hauser.roweff) # uniform association hauser.UA <- update(hauser.indep, ~ . + Fscore*Sscore) LRstats(hauser.UA) # uniform association, omitting diagonals hauser.UAdiag <- update(hauser.indep, ~ . + Fscore*Sscore + Diag(Father,Son)) LRstats(hauser.UAdiag) # Levels for Hauser 5-level model levels <- matrix(c( 2, 4, 5, 5, 5, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 4, 1 ), 5, 5, byrow=TRUE) hauser.topo <- update(hauser.indep, ~ . + Topo(Father, Son, spec=levels)) mosaic(hauser.topo, ~Father+Son, main="Topological model", gp=shading_Friendly) hauser.RC <- update(hauser.indep, ~ . + Mult(Father, Son), verbose=FALSE) mosaic(hauser.RC, ~Father+Son, main="RC model", gp=shading_Friendly) LRstats(hauser.RC) # crossings models hauser.CR <- update(hauser.indep, ~ . + Crossings(Father,Son)) mosaic(hauser.topo, ~Father+Son, main="Crossings model", gp=shading_Friendly) LRstats(hauser.CR) hauser.CRdiag <- update(hauser.indep, ~ . + Crossings(Father,Son) + Diag(Father,Son)) LRstats(hauser.CRdiag) # compare model fit statistics modlist <- glmlist(hauser.indep, hauser.roweff, hauser.UA, hauser.UAdiag, hauser.quasi, hauser.qsymm, hauser.topo, hauser.RC, hauser.CR, hauser.CRdiag) sumry <- LRstats(modlist) sumry[order(sumry$AIC, decreasing=TRUE),] # or, more simply LRstats(modlist, sortby="AIC") mods <- substring(rownames(sumry),8) with(sumry, {plot(Df, AIC, cex=1.3, pch=19, xlab='Degrees of freedom', ylab='AIC') text(Df, AIC, mods, adj=c(0.5,-.5), col='red', xpd=TRUE) }) } \keyword{datasets} vcdExtra/man/Alligator.Rd0000644000175100001440000000410512576352702015041 0ustar hornikusers\name{Alligator} \alias{Alligator} \docType{data} \title{ Alligator Food Choice } \description{ The Alligator data, from Agresti (2002), comes from a study of the primary food choices of alligators in four Florida lakes. Researchers classified the stomach contents of 219 captured alligators into five categories: Fish (the most common primary food choice), Invertebrate (snails, insects, crayfish, etc.), Reptile (turtles, alligators), Bird, and Other (amphibians, plants, household pets, stones, and other debris). } \usage{data(Alligator)} \format{ A frequency data frame with 80 observations on the following 5 variables. \describe{ \item{\code{lake}}{a factor with levels \code{George} \code{Hancock} \code{Oklawaha} \code{Trafford}} \item{\code{sex}}{a factor with levels \code{female} \code{male}} \item{\code{size}}{alligator size, a factor with levels \code{large} (>2.3m) \code{small} (<=2.3m)} \item{\code{food}}{primary food choice, a factor with levels \code{bird} \code{fish} \code{invert} \code{other} \code{reptile}} \item{\code{count}}{cell frequency, a numeric vector} } } \details{ The table contains a fair number of 0 counts. \code{food} is the response variable. \code{fish} is the most frequent choice, and often taken as a baseline category in multinomial response models. } \source{ Agresti, A. (2002). \emph{Categorical Data Analysis}, New York: Wiley, 2nd Ed., Table 7.1 } %\references{ %%% ~~ possibly secondary sources and usages ~~ %} \examples{ data(Alligator) # change from frequency data.frame to table allitable <- xtabs(count~lake+sex+size+food, data=Alligator) # Agresti's Table 7.1 structable(food~lake+sex+size, allitable) plot(allitable, shade=TRUE) # mutual independence model mosaic(~food+lake+size, allitable, shade=TRUE) # food jointly independent of lake and size mosaic(~food+lake+size, allitable, shade=TRUE, expected=~lake:size+food) if (require(nnet)) { # multinomial logit model mod1 <- multinom(food ~ lake+size+sex, data=Alligator, weights=count) } } \keyword{datasets} vcdExtra/man/cutfac.Rd0000644000175100001440000000562012576352702014373 0ustar hornikusers\name{cutfac} \alias{cutfac} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Cut a Numeric Variable to a Factor } \description{ \code{cutfac} acts like \code{\link[base]{cut}}, dividing the range of \code{x} into intervals and coding the values in \code{x} according in which interval they fall. However, it gives nicer labels for the factor levels and by default chooses convenient breaks among the values based on deciles. It is particularly useful for plots in which one wants to make a numeric variable discrete for the purpose of getting boxplots, spinograms or mosaic plots. } \usage{ cutfac(x, breaks = NULL, q = 10) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{a numeric vector which is to be converted to a factor by cutting } \item{breaks}{ either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which \code{x} is to be cut. } \item{q}{ the number of quantile groups used to define \code{breaks}, if that has not been specified. } } \details{ By default, \code{\link[base]{cut}} chooses breaks by equal lengths of the range of \code{x}, whereas \code{cutfac} uses \code{\link[stats]{quantile}} to choose breaks of roughly equal count. } \value{ A \code{\link[base]{factor}} corresponding to \code{x} is returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } %\references{ %% ~put references to the literature/web site here ~ %} \author{ Achim Zeileis } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[base]{cut}}, \code{\link[stats]{quantile}} } \examples{ if (require(AER)) { data("NMES1988", package="AER") nmes <- NMES1988[, c(1, 6:8, 13, 15, 18)] plot(log(visits+1) ~ cutfac(chronic), data = nmes, ylab = "Physician office visits (log scale)", xlab = "Number of chronic conditions", main = "chronic") plot(log(visits+1) ~ cutfac(hospital, c(0:2, 8)), data = nmes, ylab = "Physician office visits (log scale)", xlab = "Number of hospital stays", main = "hospital") } %\donttest{ %# countreg not yet on CRAN %if (require(countreg)) { %data("CrabSatellites", package = "countreg") % %# jittered scatterplot %plot(jitter(satellites) ~ width, data=CrabSatellites, % ylab="Number of satellites (jittered)", xlab="Carapace width", % cex.lab=1.25) %with(CrabSatellites, lines(lowess(width, satellites), col="red", lwd=2)) % %# boxplot, using deciles %plot(satellites ~ cutfac(width), data=CrabSatellites, % ylab="Number of satellites", xlab="Carapace width (deciles)") %} } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{manip} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/HairEyePlace.Rd0000644000175100001440000000332312576352702015417 0ustar hornikusers\name{HairEyePlace} \alias{HairEyePlace} \docType{data} \title{ Hair Color and Eye Color in Caithness and Aberdeen } \description{ A three-way frequency table crossing eye color and hair color in two places, Caithness and Aberdeen, Scotland. These data were of interest to Fisher (1940) and others because there are mixtures of people of Nordic, Celtic and Anglo-Saxon origin. One or both tables have been widely analyzed in conjunction with RC and canonical correlation models for categorical data, e.g., Becker and Clogg (1989). } \usage{data(HairEyePlace)} \format{ The format is: num [1:4, 1:5, 1:2] 326 688 343 98 38 116 84 48 241 584 ... - attr(*, "dimnames")=List of 3 ..$ Eye : chr [1:4] "Blue" "Light" "Medium" "Dark" ..$ Hair : chr [1:5] "Fair" "Red" "Medium" "Dark" ... ..$ Place: chr [1:2] "Caithness" "Aberdeen" } \details{ The hair and eye colors are ordered as in the original source, suggesting that they form ordered categories. } \source{ This data was taken from the \code{colors} data in \pkg{logmult}. } \references{ Becker, M. P., and Clogg, C. C. (1989). Analysis of Sets of Two-Way Contingency Tables Using Association Models. \emph{Journal of the American Statistical Association}, 84(405), 142-151. Fisher, R.A. (1940) The precision of discriminant functions. \emph{Annals of Eugenics}, 10, 422-429. } \examples{ data(HairEyePlace) # separate mosaics mosaic(HairEyePlace[,,1], shade=TRUE, main="Caithness") mosaic(HairEyePlace[,,2], shade=TRUE, main="Aberdeen") # condition on Place mosaic(~Hair + Eye |Place, data=HairEyePlace, shade=TRUE, legend=FALSE) cotabplot(~Hair+Eye|Place, data=HairEyePlace, shade=TRUE, legend=FALSE) } \keyword{datasets} vcdExtra/man/mosaic.glm.Rd0000644000175100001440000001760412576352702015164 0ustar hornikusers\name{mosaic.glm} \alias{mosaic.glm} \alias{sieve.glm} \alias{assoc.glm} %- Also NEED an '\alias' for EACH other topic documented here. \title{Mosaic plots for fitted generalized linear and generalized nonlinear models } \description{ Procduces mosaic plots (and other plots in the \code{\link[vcd]{strucplot}} framework) for a log-linear model fitted with \code{\link[stats]{glm}} or for a generalized nonlinear model fitted with \code{\link[gnm]{gnm}}. These methods extend the range of strucplot visualizations well beyond the models that can be fit with \code{\link[MASS]{loglm}}. They are intended for models for counts using the Poisson family (or quasi-poisson), but should be sensible as long as (a) the response variable is non-negative and (b) the predictors visualized in the \code{strucplot} are discrete factors. } \usage{ \method{mosaic}{glm}(x, formula = NULL, panel = mosaic, type = c("observed", "expected"), residuals = NULL, residuals_type = c("pearson", "deviance", "rstandard"), gp = shading_hcl, gp_args = list(), ...) \method{sieve}{glm}(x, ...) \method{assoc}{glm}(x, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A \code{glm} or \code{gnm} object. The response variable, typically a cell frequency, should be non-negative. } \item{formula}{ A one-sided formula with the indexing factors of the plot separated by '+', determining the order in which the variables are used in the mosaic. A formula must be provided unless \code{x$data} inherits from class \code{"table"} -- in which case the indexing factors of this table are used, or the factors in \code{x$data} (or model.frame(x) if \code{x$data} is an environment) exactly cross-classify the data -- in which case this set of cross-classifying factors are used. } \item{panel}{Panel function used to draw the plot for visualizing the observed values, residuals and expected values. Currently, one of \code{"mosaic"}, \code{"assoc"}, or \code{"sieve"} in \code{vcd}.} \item{type}{A character string indicating whether the \code{"observed"} or the \code{"expected"} values of the table should be visualized by the area of the tiles or bars.} \item{residuals}{ An optional array or vector of residuals corresponding to the cells in the data, for example, as calculated by \code{residuals.glm(x)}, \code{residuals.gnm(x)}.} \item{residuals_type}{If the \code{residuals} argument is \code{NULL}, residuals are calculated internally and used in the display. In this case, \code{residual_type} can be \code{"pearson"}, \code{"deviance"} or \code{"rstandard"}. Otherwise (when \code{residuals} is supplied), \code{residuals_type} is used as a label for the legend in the plot. } \item{gp}{Object of class \code{"gpar"}, shading function or a corresponding generating function (see \code{\link[vcd]{strucplot}} Details and \code{\link[vcd]{shadings}}). Ignored if shade = FALSE.} \item{gp_args}{A list of arguments for the shading-generating function, if specified.} \item{\dots}{ Other arguments passed to the \code{panel} function e.g., \code{\link[vcd]{mosaic}} } } \details{ For both poisson family generalized linear models and loglinear models, standardized residuals provided by \code{rstandard} (sometimes called adjusted residuals) are often preferred because they have constant unit asymptotic variance. The \code{sieve} and \code{assoc} methods are simple convenience interfaces to this plot method, setting the panel argument accordingly. } %\note{ %In the current version, the \code{glm} or \code{gnm} object \emph{must} have been fit using %the \code{data} argument to supply a data.frame or table, rather than with variables %in the global environment. %} \value{ The \code{structable} visualized by \code{\link[vcd]{strucplot}} is returned invisibly. } %\references{ ~put references to the literature/web site here ~ } \author{Heather Turner, Michael Friendly, with help from Achim Zeileis} %\note{ %} \seealso{ \code{\link[stats]{glm}}, \code{\link[gnm]{gnm}}, \code{\link[vcd]{plot.loglm}}, \code{\link[vcd]{mosaic}} } \examples{ GSStab <- xtabs(count ~ sex + party, data=GSS) # using the data in table form mod.glm1 <- glm(Freq ~ sex + party, family = poisson, data = GSStab) res <- residuals(mod.glm1) std <- rstandard(mod.glm1) # For mosaic.default(), need to re-shape residuals to conform to data stdtab <- array(std, dim=dim(GSStab), dimnames=dimnames(GSStab)) mosaic(GSStab, gp=shading_Friendly, residuals=stdtab, residuals_type="Std\nresiduals", labeling = labeling_residuals) # Using externally calculated residuals with the glm() object mosaic.glm(mod.glm1, residuals=std, labeling = labeling_residuals, shade=TRUE) # Using residuals_type mosaic.glm(mod.glm1, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE) ## Ordinal factors and structured associations data(Mental) xtabs(Freq ~ mental+ses, data=Mental) long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) # fit independence model # Residual deviance: 47.418 on 15 degrees of freedom indep <- glm(Freq ~ mental+ses, family = poisson, data = Mental) long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES")) mosaic(indep,residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals) # or, show as a sieve diagram mosaic(indep, labeling_args = long.labels, panel=sieve, gp=shading_Friendly) # fit linear x linear (uniform) association. Use integer scores for rows/cols Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) mosaic(linlin,residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="Lin x Lin model") ## Goodman Row-Column association model fits even better (deviance 3.57, df 8) if (require(gnm)) { Mental$mental <- C(Mental$mental, treatment) Mental$ses <- C(Mental$ses, treatment) RC1model <- gnm(Freq ~ ses + mental + Mult(ses, mental), family = poisson, data = Mental) mosaic(RC1model,residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly, main="RC1 model") } ############# UCB Admissions data, fit using glm() structable(Dept ~ Admit+Gender,UCBAdmissions) berkeley <- as.data.frame(UCBAdmissions) berk.glm1 <- glm(Freq ~ Dept * (Gender+Admit), data=berkeley, family="poisson") summary(berk.glm1) mosaic(berk.glm1, gp=shading_Friendly, labeling=labeling_residuals, formula=~Admit+Dept+Gender) # the same, displaying studentized residuals; note use of formula to reorder factors in the mosaic mosaic(berk.glm1, residuals_type="rstandard", labeling=labeling_residuals, shade=TRUE, formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit]") ## all two-way model berk.glm2 <- glm(Freq ~ (Dept + Gender + Admit)^2, data=berkeley, family="poisson") summary(berk.glm2) mosaic.glm(berk.glm2, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE, formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit][AdmitGender]") anova(berk.glm1, berk.glm2, test="Chisq") # Add 1 df term for association of [GenderAdmit] only in Dept A berkeley <- within(berkeley, dept1AG <- (Dept=='A')*(Gender=='Female')*(Admit=='Admitted')) berkeley[1:6,] berk.glm3 <- glm(Freq ~ Dept * (Gender+Admit) + dept1AG, data=berkeley, family="poisson") summary(berk.glm3) mosaic.glm(berk.glm3, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE, formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit] + DeptA*[GA]") anova(berk.glm1, berk.glm3, test="Chisq") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{hplot} \keyword{models} \keyword{multivariate} % __ONLY ONE__ keyword per line vcdExtra/man/vcdExtra-package.Rd0000644000175100001440000001156612576352702016305 0ustar hornikusers\name{vcdExtra-package} \alias{vcdExtra-package} \alias{vcdExtra} \docType{package} \title{ Extensions and additions to vcd: Visualizing Categorical Data } \description{ This package provides additional data sets, documentation, and a few functions designed to extend the \code{vcd} package for Visualizing Categorical Data and the \code{gnm} package for Generalized Nonlinear Models. In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and adds a 3D version in \code{\link{mosaic3d}}. } \details{ \tabular{ll}{ Package: \tab vcdExtra\cr Type: \tab Package\cr Version: \tab 0.6-11\cr Date: \tab 2015-09-14\cr License: \tab GPL version 2 or newer\cr LazyLoad: \tab yes\cr } The main purpose of this package is to serve as a sandbox for introducing extensions of mosaic plots and related graphical methods that apply to loglinear models fitted using \code{glm()} and related, generalized nonlinear models fitted with \code{gnm()} in the \code{\link[gnm]{gnm-package}} package. A related purpose is to fill in some holes in the analysis of categorical data in R, not provided in base R, the \pkg{vcd}, or other commonly used packages. The method \code{\link{mosaic.glm}} extends the \code{\link[vcd]{mosaic.loglm}} method in the \pkg{vcd} package to this wider class of models. This method also works for the generalized nonlinear models fit with the \code{\link[gnm]{gnm-package}} package, including models for square tables and models with multiplicative associations. \code{\link{mosaic3d}} introduces a 3D generalization of mosaic displays using the \pkg{rgl} package. In addition, there are several new data sets, a tutorial vignette, \describe{ \item{vcd-tutorial}{Working with categorical data with R and the vcd package, \code{vignette("vcd-tutorial", package = "vcdExtra") }} } and a few functions for manipulating categorical data sets and working with models for categorical data. A new class, \code{\link{glmlist}}, is introduced for working with collections of \code{glm} objects, e.g., \code{\link{Kway}} for fitting all K-way models from a basic marginal model, and \code{\link{LRstats}} for brief statistical summaries of goodnes-of-fit for a collection of models. For square tables with ordered factors, \code{\link{Crossings}} supplements the specification of terms in model formulas using \code{\link[gnm]{Symm}}, \code{\link[gnm]{Diag}}, \code{\link[gnm]{Topo}}, etc. in the \code{\link[gnm]{gnm-package}}. Some of these extensions may be migrated into vcd or gnm. A collection of demos is included to illustrate fitting and visualizing a wide variety of models: \describe{ \item{mental-glm}{Mental health data: mosaics for glm() and gnm() models} \item{occStatus}{Occupational status data: Compare mosaic using expected= to mosaic.glm} \item{ucb-glm}{UCBAdmissions data: Conditional independence via loglm() and glm()} \item{vision-quasi}{VisualAcuity data: Quasi- and Symmetry models} \item{yaish-unidiff}{Yaish data: Unidiff model for 3-way table} \item{Wong2-3}{Political views and support for women to work (U, R, C, R+C and RC(1) models)} \item{Wong3-1}{Political views, support for women to work and national welfare spending (3-way, marginal, and conditional independence models)} \item{housing}{Visualize glm(), multinom() and polr() models from \code{example(housing, package="MASS")}} } Use \code{ demo(package="vcdExtra")} for a complete current list. The \pkg{vcdExtra} package now contains a large number of data sets illustrating various forms of categorical data analysis and related visualizations, from simple to advanced. Use \code{data(package="vcdExtra")} for a complete list, or \code{datasets(package="vcdExtra")} for an annotated one showing the \code{class} and \code{dim} for each data set. } \author{ Michael Friendly Maintainer: Michael Friendly } \references{ Friendly, M. \emph{Visualizing Categorical Data}, Cary NC: SAS Insitute, 2000. Web materials: \url{http://www.datavis.ca/books/vcd/}. Meyer, D.; Zeileis, A. & Hornik, K. The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd \emph{Journal of Statistical Software}, 2006, \bold{17}, 1-48. Available in R via \code{vignette("strucplot", package = "vcd")} Turner, H. and Firth, D. \emph{Generalized nonlinear models in R: An overview of the gnm package}, 2007, \url{http://eprints.ncrm.ac.uk/472/}. Available in R via \code{vignette("gnmOverview", package = "gnm")}. } \keyword{ package } \seealso{ \code{\link[gnm]{gnm-package}}, for an extended range of models for contingency tables \code{\link[vcd]{mosaic}} for details on mosaic displays within the strucplot framework. %~~ Optional links to other man pages, e.g. ~~ %~~ \code{\link[:-package]{}} ~~ } \examples{ example(mosaic.glm) demo("mental-glm") } vcdExtra/man/Abortion.Rd0000644000175100001440000000254512576352702014706 0ustar hornikusers\name{Abortion} \Rdversion{1.1} \alias{Abortion} \docType{data} \title{Abortion Opinion Data} \description{Opinions about abortion classified by gender and SES} \usage{ data(Abortion) } \format{ A 3-dimensional array resulting from cross-tabulating 3 variables for 1100 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Sex}\tab \code{"Female", "Male"}\cr 2\tab \code{Status}\tab \code{"Lo", "Hi"}\cr 3\tab \code{Support_Abortion}\tab \code{"Yes", "No"}\cr } } \details{ The combinations of \code{Sex} and \code{Status} represent four independent samples, having fixed \code{Sex}-\code{Status} marginal totals. Thus the \code{Sex:Status} association must be included in any loglinear model. \code{Support_Abortion} is a natural response variable. } \source{ % \cite{Christensen:90 [p. 92]} Christensen, R. (1990). \emph{Log-Linear Models}, New York, NY: Springer-Verlag, p. 92, Example 3.5.2. Christensen, R. (1997). \emph{Log-Linear Models and Logistic Regression}, New York, NY: Springer, p. 100, Example 3.5.2. } %\references{ %} %\seealso { } \examples{ data(Abortion) # example goes here ftable(Abortion) mosaic(Abortion, shade=TRUE) # stratified by Sex fourfold(aperm(Abortion, 3:1)) # stratified by Status fourfold(aperm(Abortion, c(3,1,2))) } \keyword{datasets} vcdExtra/man/HLtest.Rd0000644000175100001440000000707112576352702014333 0ustar hornikusers\name{HLtest} \alias{HosmerLemeshow} \alias{HLtest} \alias{plot.HLtest} \alias{print.HLtest} \alias{rootogram.HLtest} \alias{summary.HLtest} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Hosmer-Lemeshow Goodness of Fit Test } \description{ The \code{HLtest} function computes the classical Hosmer-Lemeshow (1980) goodness of fit test for a binomial \code{glm} object in logistic regression The general idea is to assesses whether or not the observed event rates match expected event rates in subgroups of the model population. The Hosmer-Lemeshow test specifically identifies subgroups as the deciles of fitted event values, or other quantiles as determined by the \code{g} argument. Given these subgroups, a simple chisquare test on \code{g-2} df is used. In addition to \code{print} and \code{summary} methods, a \code{plot} method is supplied to visualize the discrepancies between observed and fitted frequencies. } \usage{ HosmerLemeshow(model, g = 10) HLtest(model, g = 10) \method{print}{HLtest}(x, ...) \method{summary}{HLtest}(object, ...) \method{plot}{HLtest}(x, ...) \method{rootogram}{HLtest}(x, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{model}{ A \code{glm} model object in the \code{binomial} family } \item{g}{ Number of groups used to partition the fitted values for the GOF test. } \item{x, object}{ A \code{HLtest} object } \item{\dots}{ Other arguments passed down to methods } } %\details{ %%% ~~ If necessary, more details than the description above ~~ %} \value{ A class \code{HLtest} object with the following components: %% If it is a LIST, use \item{table}{A data.frame describing the results of partitioning the data into \code{g} groups with the following columns: \code{cut}, \code{total}, \code{obs}, \code{exp}, \code{chi}} \item{chisq}{The chisquared statistics} \item{df}{Degrees of freedom} \item{p.value}{p value} \item{groups}{Number of groups} \item{call}{\code{model} call} %% ... } \references{ Hosmer, David W., Lemeshow, Stanley (1980). A goodness-of-fit test for multiple logistic regression model. \emph{Communications in Statistics, Series A}, 9, 1043-1069. Hosmer, David W., Lemeshow, Stanley (2000). \emph{Applied Logistic Regression}, New York: Wiley, ISBN 0-471-61553-6 Lemeshow, S. and Hosmer, D.W. (1982). A review of goodness of fit statistics for use in the development of logistic regression models. \emph{American Journal of Epidemiology}, 115(1), 92-106. } \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[vcd]{rootogram}}, ~~~ } \examples{ data(birthwt, package="MASS") # how to do this without attach? attach(birthwt) race = factor(race, labels = c("white", "black", "other")) ptd = factor(ptl > 0) ftv = factor(ftv) levels(ftv)[-(1:2)] = "2+" bwt <- data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0), ptd, ht = (ht > 0), ui = (ui > 0), ftv) detach(birthwt) options(contrasts = c("contr.treatment", "contr.poly")) BWmod <- glm(low ~ ., family=binomial, data=bwt) (hlt <- HLtest(BWmod)) str(hlt) summary(hlt) plot(hlt) # basic model BWmod0 <- glm(low ~ age, family=binomial, data=bwt) (hlt0 <- HLtest(BWmod0)) str(hlt0) summary(hlt0) plot(hlt0) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{htest} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Donner.Rd0000644000175100001440000000703312576352702014353 0ustar hornikusers\name{Donner} \alias{Donner} \docType{data} \title{ Survival in the Donner Party } \description{ This data frame contains information on the members of the Donner Party, a group of people who attempted to migrate to California in 1846. They were trapped by an early blizzard on the eastern side of the Sierra Nevada mountains, and before they could be rescued, nearly half of the party had died. What factors affected who lived and who died? } \usage{data(Donner)} \format{ A data frame with 90 observations on the following 5 variables. \describe{ \item{\code{family}}{family name, a factor with 10 levels } \item{\code{age}}{age of person, a numeric vector} \item{\code{sex}}{a factor with levels \code{Female} \code{Male}} \item{\code{survived}}{a numeric vector, 0 or 1} \item{\code{death}}{date of death for those who died before rescue, a POSIXct} } } \details{ This data frame uses the person's name as row labels. \code{family} reflects a recoding of the last names of individuals to reduce the number of factor levels. The main families in the Donner party were: Donner, Graves, Breen and Reed. The families of Murphy, Foster and Pike are grouped as \code{'MurFosPik'}, those of Fosdick and Wolfinger are coded as \code{'FosdWolf'}, and all others as \code{'Other'}. } \source{ D. K. Grayson, 1990, "Donner party deaths: A demographic assessment", \emph{J. Anthropological Research}, \bold{46}, 223-242. Johnson, K. (1996). \emph{Unfortunate Emigrants: Narratives of the Donner Party}. Logan, UT: Utah State University Press. Additions, and dates of death from \url{http://user.xmission.com/~octa/DonnerParty/Roster.htm}. } \references{ Ramsey, F.L. and Schafer, D.W. (2002). \emph{The Statistical Sleuth: A Course in Methods of Data Analysis}, (2nd ed), Duxbury. } \seealso{ \code{\link[alr3]{donner}} in \pkg{alr3}, \code{\link[Sleuth2]{case2001}} in \pkg{Sleuth2}(adults only) provide similar data sets. } \examples{ # conditional density plots op <- par(mfrow=c(1,2), cex.lab=1.5) cdplot(factor(survived) ~ age, subset=sex=='Male', data=Donner, main="Donner party: Males", ylevels=2:1, ylab="Survived", yaxlabels=c("yes", "no")) with(Donner, rug(jitter(age[sex=="Male"]), col="white", quiet=TRUE)) cdplot(factor(survived) ~ age, subset=sex=='Female', data=Donner, main="Donner party: Females", ylevels=2:1, ylab="Survived", yaxlabels=c("yes", "no")) with(Donner, rug(jitter(age[sex=="Female"]), col="white", quiet=TRUE)) par(op) # fit some models (mod1 <- glm(survived ~ age + sex, data=Donner, family=binomial)) (mod2 <- glm(survived ~ age * sex, data=Donner, family=binomial)) anova(mod2, test="Chisq") (mod3 <- glm(survived ~ poly(age,2) * sex, data=Donner, family=binomial)) anova(mod3, test="Chisq") LRstats(glmlist(mod1, mod2, mod3)) # plot fitted probabilities from mod2 and mod3 # idea from: http://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html library(ggplot2) # separate linear fits on age for M/F ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2, aes(fill = sex)) # separate quadratics ggplot(Donner, aes(age, survived, color = sex)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ poly(x,2), alpha = 0.2, size=2, aes(fill = sex)) } \keyword{datasets} vcdExtra/man/GKgamma.Rd0000644000175100001440000000431012576352702014425 0ustar hornikusers\name{GKgamma} \alias{GKgamma} \alias{print.GKgamma} %- Also NEED an '\alias' for EACH other topic documented here. \title{Calculate Goodman-Kruskal Gamma for ordered tables} \description{ The Goodman-Kruskal \eqn{\gamma}{gamma} statistic is a measure of association for ordinal factors in a two-way table proposed by Goodman and Kruskal (1954). } \usage{ GKgamma(x, level = 0.95) %\method{print}{GKgamma}{x, digits = 3, ...} } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A two-way frequency table, in matrix or table form. The rows and columns are considered to be ordinal factors} \item{level}{Confidence level for a significance test of \eqn{\gamma \ne =}{gamma !=0}} % \item{digits}{Number of digits printed by the print method} % \item{...}{Other arguments} } %\details{ % ~~ If necessary, more details than the description above ~~ %} \value{ Returns an object of class \code{"GKgamma"} with 6 components, as follows % If it is a LIST, use %\describe{ \item{gamma}{The gamma statistic} \item{C}{Total number of concordant pairs in the table} \item{D}{Total number of disconcordant pairs in the table} \item{sigma}{Standard error of gamma } \item{CIlevel}{Confidence level} \item{CI}{Confidence interval} % } } \references{ Agresti, A. \emph{Categorical Data Analysis}. John Wiley & Sons, 2002, pp. 57--59. Goodman, L. A., & Kruskal, W. H. (1954). Measures of association for cross classifications. \emph{Journal of the American Statistical Association}, 49, 732-764. Goodman, L. A., & Kruskal, W. H. (1963). Measures of association for cross classifications III: Approximate sampling theory. \emph{Journal of the American Statistical Association}, 58, 310-364. } \author{Michael Friendly; original version by Laura Thompson} %\note{ ~~further notes~~ % % ~Make other sections like Warning with \section{Warning }{....} ~ %} \seealso{\code{\link[vcd]{assocstats}}, \link[vcd]{Kappa}} \examples{ data(JobSat) GKgamma(JobSat) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{htest} \keyword{category} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Toxaemia.Rd0000644000175100001440000000421212576352702014671 0ustar hornikusers\name{Toxaemia} \alias{Toxaemia} \docType{data} \title{ Toxaemia Symptoms in Pregnancy } \description{ Brown et al (1983) gave these data on two signs of toxaemia, an abnormal condition during pregnancy characterized by high blood pressure (hypertension) and high levels of protein in the urine. If untreated, both the mother and baby are at risk of complications or death. The data frame \code{Toxaemia} represents 13384 expectant mothers in Bradford, England in their first pregnancy, who were also classified according to social class and the number of cigarettes smoked per day. } \usage{data(Toxaemia)} \format{ A data frame in frequency form representing a 5 x 3 x 2 x 2 contingency table, with 60 observations on the following 5 variables. \describe{ \item{\code{class}}{Social class of mother, a factor with levels \code{1} \code{2} \code{3} \code{4} \code{5}} \item{\code{smoke}}{Cigarettes smoked per day during pregnancy, a factor with levels \code{0} \code{1-19} \code{20+}} \item{\code{hyper}}{Hypertension level, a factor with levels \code{Low} \code{High}} \item{\code{urea}}{Protein urea level, a factor with levels \code{Low} \code{High}} \item{\code{Freq}}{frequency in each cell, a numeric vector} } } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ Brown, P. J., Stone, J. and Ord-Smith, C. (1983), Toxaemic signs during pregnancy. \emph{JRSS, Series C, Applied Statistics}, 32, 69-72 } \references{ Friendly, M. (2000), \emph{Visualizing Categorical Data}, SAS Institute, Cary, NC, Example 7.15. } \examples{ data(Toxaemia) tox.tab <- xtabs(Freq~class+smoke+hyper+urea,Toxaemia) ftable(tox.tab, row.vars=1) # symptoms by smoking mosaic(~smoke+hyper+urea, data=tox.tab, shade=TRUE) # symptoms by social class mosaic(~class+hyper+urea, data=tox.tab, shade=TRUE) # predictors mosaic(~smoke+class, data=tox.tab, shade=TRUE) # responses mosaic(~hyper+urea, data=tox.tab, shade=TRUE) # log odds ratios for urea and hypertension, by class and smoke \dontrun{ LOR <-loddsratio(aperm(tox.tab)) LOR } } \keyword{datasets} vcdExtra/man/mosaic.glmlist.Rd0000644000175100001440000001270512576352702016055 0ustar hornikusers\name{mosaic.glmlist} \alias{mosaic.glmlist} \alias{mosaic.loglmlist} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Mosaic Displays for \code{glmlist} and \code{logllmlist} Objects } \description{ This function provides a convenient interface for viewing mosaic displays associated with a collection of glm models for freqency tables that have been stored in a \code{glmlist} or \code{loglmlist} object. You can plot either selected models individually, or mosaics for all models in an array of viewports. } \usage{ \method{mosaic}{glmlist}(x, selection, panel=mosaic, type=c("observed", "expected"), legend=ask | !missing(selection), main=NULL, ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE, ...) \method{mosaic}{loglmlist}(x, selection, panel=mosaic, type=c("observed", "expected"), legend=ask | !missing(selection), main=NULL, ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ a \code{glmlist} or \code{loglmlist} object } \item{selection}{ the index or name of one \code{glm} or \code{loglm} object in \code{x}. If no selection is specified, a menu of models is presented or all models are plotted. } \item{panel}{ a \code{\link[vcd]{strucplot}} panel function, typicially \code{\link[vcd]{mosaic}} or \code{\link[vcd]{sieve}} } \item{type}{ a character string indicating whether the \code{"observed"} or the \code{"expected"} values of the table should be visualized } \item{legend}{ logical: show a legend for residuals in the mosaic display(s)? The default behavior is to include a legend when only a single plot is shown, i.e., if \code{ask} is \code{TRUE} or a \code{selection} has been specified. } \item{main}{ either a logical, or a vector of character strings used for plotting the main title. If main is a logical and \code{TRUE}, the name of the selected glm object is used. } \item{ask}{ logical: should the function display a menu of models, when one is not specified in \code{selection}? If \code{selection} is not supplied and \code{ask} is \code{TRUE} (the default), a menu of model names is presented; if \code{ask} is \code{FALSE}, mosaics for all models are plotted in an array. } \item{graphics}{ logical: use a graphic dialog box when \code{ask=TRUE}? } \item{rows,cols}{ when \code{ask=FALSE}, the number of rows and columns in which to plot the mosaics. } \item{newpage}{ start a new page? (only applies to \code{ask=FALSE}) } \item{\dots}{ other arguments passed to \code{\link{mosaic.glm}} and ultimately to \code{\link[vcd]{mosaic}}. } } \details{ Most details of the plots produced can be controlled via \dots arguments as shown in some of the examples below. In particular, with \code{panel=sieve} you need to also pass \code{gp=shading_Friendly} to get a color version. } \value{ Returns the result of \code{\link{mosaic.glm}}. %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ David Meyer, Achim Zeileis, and Kurt Hornik (2006). The Strucplot Framework: Visualizing Multi-Way Contingency Tables with vcd. \emph{Journal of Statistical Software}, 17(3), 1-48. \url{http://www.jstatsoft.org/v17/i03/}, available as \code{vignette("strucplot", package="vcd")}. } \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{glmlist}}, \code{\link{loglmlist}}, \code{\link{Kway}} \code{\link{mosaic.glm}}, \code{\link[vcd]{mosaic}}, \code{\link[vcd]{strucplot}}, for the many parameters that control the details of mosaic plots. } \examples{ data(JobSatisfaction, package="vcd") # view all pairwise mosaics pairs(xtabs(Freq~management+supervisor+own, data=JobSatisfaction), shade=TRUE, diag_panel=pairs_diagonal_mosaic) modSat <- Kway(Freq ~ management+supervisor+own, data=JobSatisfaction, family=poisson, prefix="JobSat") names(modSat) \dontrun{ mosaic(modSat) # uses menu, if interactive() } mosaic(modSat, "JobSat.1") # model label mosaic(modSat, 2) # model index # supply a formula to determine the order of variables in the mosaic mosaic(modSat, 2, formula=~own+supervisor+management) mosaic(modSat, ask=FALSE) # uses viewports # use a different panel function, label the observed valued in the cells mosaic(modSat, 1, main=TRUE, panel=sieve, gp=shading_Friendly, labeling=labeling_values) data(Mental) indep <- glm(Freq ~ mental+ses, family = poisson, data = Mental) Cscore <- as.numeric(Mental$ses) Rscore <- as.numeric(Mental$mental) coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental) roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental) linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental) # assign names for the plot labels modMental <- glmlist(Indep=indep, ColEff=coleff, RowEff=roweff, `Lin x Lin`=linlin) mosaic(modMental, ask=FALSE, margins=c(3,1,1,2), labeling_args=list(abbreviate_labs=5)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{hplot} \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Mobility.Rd0000644000175100001440000000210512576352702014711 0ustar hornikusers\name{Mobility} \Rdversion{1.1} \alias{Mobility} \docType{data} \title{Social Mobility data} \description{Data on social mobility, recording the occupational category of fathers and their sons. } \usage{ data(Mobility) } \format{ A 2-dimensional array resulting from cross-tabulating 2 variables for 19912 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Son's_Occupation}\tab \code{"UpNonMan", "LoNonMan", "UpManual", "LoManual", "Farm"}\cr 2\tab \code{Father's_Occupation}\tab \code{"UpNonMan", "LoNonMan", "UpManual", "LoManual", "Farm"}\cr } } %\details{ } \source{ Falguerolles, A. de and Mathieu, J. R. (1988). \emph{Proceedings of COMPSTAT 88}, Copenhagen, Denmark, Springer-Verlag. % \cite{FeathermanHauser:78} Featherman, D. L. and Hauser, R. M. Occupations and social mobility in the United States. \emph{Sociological Microjournal}, 12, Fiche 62. Copenhagen: Sociological Institute. } %\references{ %} %\seealso{ } \examples{ data(Mobility) # example goes here } \keyword{datasets} vcdExtra/man/Detergent.Rd0000644000175100001440000000317212576352702015047 0ustar hornikusers\name{Detergent} \Rdversion{1.1} \alias{Detergent} \docType{data} \title{Detergent preference data} \description{Cross-classification of a sample of 1008 consumers according to (a) the softness of the laundry water used, (b) previous use of detergent Brand M, (c) the termperature of laundry water used and (d) expressed preference for Brand X or Brand M in a blind trial.} \usage{ data(Detergent) } \format{ A 4-dimensional array resulting from cross-tabulating 4 variables for 1008 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Temperature}\tab \code{"High", "Low"}\cr 2\tab \code{M_User}\tab \code{"Yes", "No"}\cr 3\tab \code{Preference}\tab \code{"Brand X", "Brand M"}\cr 4\tab \code{Water_softness}\tab \code{"Soft", "Medium", "Hard"}\cr } } %\details{ } \source{ % \cite{Fienberg:80 [p. 71]} Fienberg, S. E. (1980). \emph{The Analysis of Cross-Classified Categorical Data} Cambridge, MA: MIT Press, p. 71. } \references{ % \cite{RiesSmith:63} Ries, P. N. & Smith, H. (1963). The use of chi-square for preference testing in multidimensional problems. \emph{Chemical Engineering Progress}, 59, 39-43. } %\seealso{ } \examples{ data(Detergent) # example goes here mosaic(Detergent, shade=TRUE) require(MASS) (det.mod0 <- loglm(~ Preference + Temperature + M_User + Water_softness, data=Detergent)) # examine addition of two-way terms add1(det.mod0, ~ .^2, test="Chisq") # model for Preference as a response (det.mod1 <- loglm(~ Preference + (Temperature * M_User * Water_softness), data=Detergent)) mosaic(det.mod0) } \keyword{datasets} vcdExtra/man/mosaic3d.Rd0000644000175100001440000001654412576352702014637 0ustar hornikusers\name{mosaic3d} \Rdversion{1.1} \alias{mosaic3d} \alias{mosaic3d.default} \alias{mosaic3d.loglm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ 3D Mosaic Plots } \description{ Produces a 3D mosaic plot for a contingency table (or a \code{link[MASS]{loglm}} model) using the \code{\link[rgl]{rgl-package}}. Generalizing the 2D mosaic plot, this begins with a given 3D shape (a unit cube), and successively sub-divides it along the X, Y, Z dimensions according to the table margins, generating a nested set of 3D tiles. The volume of the resulting tiles is therefore proportional to the frequency represented in the table cells. Residuals from a given loglinear model are then used to color or shade each of the tiles. This is a developing implementation. The arguments and details are subject to change. } \usage{ mosaic3d(x, ...) \method{mosaic3d}{loglm}(x, type = c("observed", "expected"), residuals_type = c("pearson", "deviance"), ...) \method{mosaic3d}{default}(x, expected = NULL, residuals = NULL, type = c("observed", "expected"), residuals_type = NULL, shape = rgl::cube3d(alpha = alpha), alpha = 0.5, spacing = 0.1, split_dir = 1:3, shading = shading_basic, interpolate=c(2,4), zero_size=.05, label_edge, labeling_args = list(), newpage = TRUE, box=FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A \code{link[MASS]{loglm}} model object. Alternatively, a multidimensional \code{array} or \code{table} or \code{\link[vcd]{structable}} of frequencies in a contingency table. In the present implementation, the dimensions are taken in sequential order. Use \code{link[base]{aperm}} or \code{\link[vcd]{structable}} to change this. } \item{expected}{ optionally, for contingency tables, an array of expected frequencies of the same dimension as \code{x}, or alternatively the corresponding loglinear model specification as used by \code{link[stats]{loglin}} or \code{link[MASS]{loglm}} (see \code{\link[vcd]{structable}} for details).} \item{residuals}{ optionally, an array of residuals of the same dimension as \code{x} (see details). } \item{type}{ a character string indicating whether the \code{"observed"} or the \code{"expected"} frequencies in the table should be visualized by the volume of the 3D tiles. } \item{residuals_type}{ a character string indicating the type of residuals to be computed when none are supplied. If residuals is \code{NULL}, \code{residuals_type} must be one of \code{"pearson"} (default; giving components of Pearson's chi-squared), \code{"deviance"} (giving components of the likelihood ratio chi-squared), or \code{"FT"} for the Freeman-Tukey residuals. The value of this argument can be abbreviated. } \item{shape}{ The initial 3D shape on which the mosaic is based. Typically this is a call to an rgl function, and must produce a \code{shape3d} object. The default is a "unit cube" on (-1, +1), with transparency specified by \code{alpha}. } \item{alpha}{ Specifies the transparency of the 3D tiles used to compose the 3D mosaic. } \item{spacing}{ A number or vector giving the total amount of space used to separate the 3D tiles along each of the dimensions of the table. The values specified are re-cycled to the number of table dimensions. } \item{split_dir}{ A numeric vector composed of the integers \code{1:3} or a character vector composed of \code{c("x", "y", "z")}, where \code{split_dir[i]} specifies the axis along which the tiles should be split for dimension \code{i} of the table. The values specified are re-cycled to the number of table dimensions. } \item{shading}{ A function, taking an array or vector of residuals for the given model, returning a vector of colors. At present, only the default \code{shading=shading_basic} is provided. This is roughly equivalent to the use of the \code{shade} argument in \code{\link[graphics]{mosaicplot}} or to the use of \code{gp=shading_Friendly} in \code{\link[vcd]{mosaic}}. } \item{interpolate}{a vector of interpolation values for the \code{shading} function. } \item{zero_size}{ The radius of a small sphere used to mark zero cells in the display. } \item{label_edge}{ A character vector composed of \code{c("-", "+")} indicating whether the labels for a given table dimension are to be written at the minima (\code{"-"}) or maxima (\code{"+"}) of the \emph{other} dimensions in the plot. The default is \code{rep( c('-', '+'), each=3, length=ndim)}, meaning that the first three table variables are labeled at the minima, and successive ones at the maxima. } \item{labeling_args}{ This argument is intended to be used to specify details of the rendering of labels for the table dimensions, but at present has no effect. } \item{newpage}{ logical indicating whether a new page should be created for the plot or not. } \item{box}{ logical indicating whether a bounding box should be drawn around the plot. } \item{\dots}{ Other arguments passed down to \code{mosaic.default} or 3D functions. } } \details{ Friendly (1995), Friendly [Sect. 4.5](2000) and Theus and Lauer (1999) have all used the idea of 3D mosaic displays to explain various aspects of loglinear models (the iterative proportional fitting algorithm, the structure of various models for 3-way and n-way tables, etc.), but no implementation of 3D mosaics was previously available. For the default method, residuals, used to color and shade the 3D tiles, can be passed explicitly, or, more typically, are computed as needed from observed and expected frequencies. In this case, the expected frequencies are optionally computed for a specified loglinear model given by the \code{expected} argument. For the loglm method, residuals and observed frequencies are calculated from the model object. } \value{ Invisibly, the list of \code{shape3d} objects used to draw the 3D mosaic, with names corresponding to the concatenation of the level labels, separated by ":". } \references{ Friendly, M. (1995). Conceptual and Visual Models for Categorical Data, \emph{The American Statistician}, \bold{49}, 153-160. Friendly, M. \emph{Visualizing Categorical Data}, Cary NC: SAS Insitute, 2000. Web materials: \url{http://www.datavis.ca/books/vcd/}. Theus, M. & Lauer, S. R. W. (1999) Visualizing Loglinear Models. \emph{Journal of Computational and Graphical Statistics}, \bold{8}, 396-412. } \author{ Michael Friendly, with the help of Duncan Murdoch and Achim Zeileis } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[vcd]{strucplot}}, \code{\link[vcd]{mosaic}}, \code{\link[graphics]{mosaicplot}} \code{\link[stats]{loglin}}, \code{\link[MASS]{loglm}} for details on fitting loglinear models } \examples{ # 2 x 2 x 2 mosaic3d(Bartlett, box=TRUE) # compare with expected frequencies under model of mutual independence mosaic3d(Bartlett, type="expected", box=TRUE) # 2 x 2 x 3 mosaic3d(Heart, box=TRUE) \dontrun{ # 2 x 2 x 2 x 3 # illustrates a 4D table mosaic3d(Detergent) # compare 2D and 3D mosaics demo("mosaic-hec") } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{hplot } %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/logseries.Rd0000644000175100001440000000670512576352702015127 0ustar hornikusers\name{logseries} \alias{Logseries} \alias{dlogseries} \alias{plogseries} \alias{qlogseries} \alias{rlogseries} %- Also NEED an '\alias' for EACH other topic documented here. \title{ The Logarithmic Series Distribution } \description{ The logarithmic series distribution is a long-tailed distribution introduced by Fisher etal. (1943) in connection with data on the abundance of individuals classified by species. These functions provide the density, distribution function, quantile function and random generation for the logarithmic series distribution with parameter \code{prob}. } \usage{ dlogseries(x, prob = 0.5, log = FALSE) plogseries(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE) qlogseries(p, prob = 0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000) rlogseries(n, prob = 0.5) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x, q}{ vector of quantiles representing the number of events. } \item{prob}{ parameter for the distribution, \code{0 < prob < 1} } \item{log, log.p}{ ogical; if TRUE, probabilities \code{p} are given as \code{log(p)} } \item{lower.tail}{ logical; if TRUE (default), probabilities are \eqn{P[X \le x]}{P[X <= x]}, otherwise, \eqn{P[X > x]}{P[X > x]}. } \item{p}{ vector of probabilities } \item{max.value}{ maximum value returned by \code{qlogseries} } \item{n}{ number of observations for \code{rlogseries} } } \details{ The logarithmic series distribution with \code{prob} = \eqn{p} has density \deqn{ p ( x ) = \alpha p^x / x } for \eqn{x = 1, 2, \dots}, where \eqn{\alpha= -1 / \log(1 - p)} and \eqn{0 < p <1}. Note that counts \code{x==2} cannot occur. } \value{ \code{dlogseries} gives the density, \code{plogseries} gives the distribution function, \code{qlogseries} gives the quantile function, and \code{rlogseries} generates random deviates. %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ \url{http://en.wikipedia.org/wiki/Logarithmic_distribution} Fisher, R. A. and Corbet, A. S. and Williams, C. B. (1943). The relation between the number of species and the number of individuals \emph{Journal of Animal Ecology}, 12, 42-58. } \author{ Michael Friendly, using original code modified from the \code{gmlss.dist} package by Mikis Stasinopoulos. } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[stats]{Distributions}}, ~~~ } \examples{ XL <-expand.grid(x=1:5, p=c(0.33, 0.66, 0.99)) lgs.df <- data.frame(XL, prob=dlogseries(XL[,"x"], XL[,"p"])) lgs.df$p = factor(lgs.df$p) str(lgs.df) require(lattice) mycol <- palette()[2:4] xyplot( prob ~ x, data=lgs.df, groups=p, xlab=list('Number of events (k)', cex=1.25), ylab=list('Probability', cex=1.25), type='b', pch=15:17, lwd=2, cex=1.25, col=mycol, key = list( title = 'p', points = list(pch=15:17, col=mycol, cex=1.25), lines = list(lwd=2, col=mycol), text = list(levels(lgs.df$p)), x=0.9, y=0.98, corner=c(x=1, y=1) ) ) # random numbers hist(rlogseries(200, prob=.4), xlab='x') hist(rlogseries(200, prob=.8), xlab='x') } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{distribution} %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Mental.Rd0000644000175100001440000000317412576352702014350 0ustar hornikusers\name{Mental} \Rdversion{1.1} \alias{Mental} \docType{data} \title{ Mental impariment and parents SES} \description{ A 6 x 4 contingency table representing the cross-classification of mental health status (\code{mental}) of 1660 young New York residents by their parents' socioeconomic status (\code{ses}). } \usage{data(Mental)} \format{ A data frame frequency table with 24 observations on the following 3 variables. \describe{ \item{\code{ses}}{an ordered factor with levels \code{1} < \code{2} < \code{3} < \code{4} < \code{5} < \code{6}} \item{\code{mental}}{an ordered factor with levels \code{Well} < \code{Mild} < \code{Moderate} < \code{Impaired}} \item{\code{Freq}}{cell frequency: a numeric vector} } } \details{ Both \code{ses} and \code{mental} can be treated as ordered factors or integer scores. For \code{ses}, 1="High" and 6="Low". } \source{ Haberman, S. J. \emph{The Analysis of Qualitative Data: New Developments}, Academic Press, 1979, Vol. II, p. 375. Srole, L.; Langner, T. S.; Michael, S. T.; Kirkpatrick, P.; Opler, M. K. & Rennie, T. A. C. \emph{Mental Health in the Metropolis: The Midtown Manhattan Study}, NYU Press, 1978, p. 289 } \references{ Friendly, M. \emph{Visualizing Categorical Data}, Cary, NC: SAS Institute, 2000, Appendix B.7. } \examples{ data(Mental) str(Mental) (Mental.tab <- xtabs(Freq ~ ses+mental, data=Mental)) # mosaic and sieve plots mosaic(Mental.tab, gp=shading_Friendly) sieve(Mental.tab, gp=shading_Friendly) library(ca) plot(ca(Mental.tab), main="Mental impairment & SES") title(xlab="Dim 1", ylab="Dim 2") } \keyword{datasets} vcdExtra/man/Mice.Rd0000644000175100001440000000303412576352702014000 0ustar hornikusers\name{Mice} \alias{Mice} \docType{data} \title{ Mice Depletion Data } \description{ Data from Kastenbaum and Lamphiear (1959). The table gives the number of depletions (deaths) in 657 litters of mice, classified by litter size and treatment. This data set has become a classic in the analysis of contingency tables, yet unfortunately little information on the details of the experiment has been published. } \usage{data("Mice")} \format{ A freqency data frame with 30 observations on the following 4 variables, representing a 5 x 2 x 3 contingency table. \describe{ \item{\code{litter}}{litter size, a numeric vector} \item{\code{treatment}}{treatment, a factor with levels \code{A} \code{B}} \item{\code{deaths}}{number of depletions, a factor with levels \code{0} \code{1} \code{2+}} \item{\code{Freq}}{cell frequency, a numeric vector} } } %\details{ %%% ~~ If necessary, more details than the __description__ above ~~ %} \source{ Goodman, L. A. (1983) The analysis of dependence in cross-classifications having ordered categories, using log-linear models for frequencies and log-linear models for odds. \emph{Biometrics}, 39, 149-160. } \references{ Kastenbaum, M. A. & Lamphiear, D. E. (1959) Calculation of chi-square to calculate the no three-factor interaction hypothesis. \emph{Biometrics}, 15, 107-115. } \examples{ data(Mice) # make a table ftable(mice.tab <- xtabs(Freq ~ litter + treatment + deaths, data=Mice)) library(vcd) mosaic(mice.tab, shade=TRUE) } \keyword{datasets} vcdExtra/man/vcdExtra-deprecated.Rd0000644000175100001440000000126012576352702017000 0ustar hornikusers\name{vcdExtra-deprecated} \alias{vcdExtra-deprecated} \alias{summarise} %\alias{summarise.glm} %\alias{summarise.glmlist} %\alias{summarise.loglm} %\alias{summarise.loglmlist} % \title{Deprecated Functions in vcdExtra Package} % \description{ These functions are provided for compatibility with older versions of the \pkg{vcdExtra} package only. They are replaced by \code{\link{LRstats}}. } % \usage{ summarise(...) %summarise.glm(...) %summarise.glmlist(...) %summarise.loglm(...) %summarise.loglmlist(...) } % \arguments{ \item{\dots}{pass arguments down.} } % \details{ \code{summarise.*} have been replaced by \code{\link{LRstats}} functions. } vcdExtra/man/seq_mosaic.Rd0000644000175100001440000000647612576352702015263 0ustar hornikusers\name{seq_mosaic} \alias{seq_mosaic} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Sequential Mosaics and Strucplots for an N-way Table } \description{ This function takes an n-way contingency table and plots mosaics for series of sequential models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of types of loglinear models. } \usage{ seq_mosaic(x, panel = mosaic, type = c("joint", "conditional", "mutual", "markov", "saturated"), plots = 1:nf, vorder = 1:nf, k = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ a contingency table in array form, with optional category labels specified in the dimnames(x) attribute, or else a data.frame in frequency form, with the frequency variable named \code{"Freq"}. } \item{panel}{ a \code{\link[vcd]{strucplot}} panel function, typicially \code{\link[vcd]{mosaic}} or \code{\link[vcd]{sieve}. NOT yet implemented.} } \item{type}{ type of sequential model to fit, a character string. One of \code{"joint"}, \code{"conditional"}, \code{"mutual"}, \code{"markov"}, or \code{"saturated"}. } \item{plots}{ which marginal sub-tables to plot? A vector of a (sub)set of the integers, \code{1:nf} where \code{nf} is the number of factors in the full n-way table. } \item{vorder}{ order of variables, a permutation of the integers \code{1:nf}, used to reorder the variables in the original table for the purpose of fitting sequential marginal models. } \item{k}{ conditioning variable(s) for \code{type} = \code{"joint"}, \code{"conditional"} or Markov chain order for \code{type} = \code{"markov"} } \item{\dots}{ other arguments passed to \code{\link[vcd]{mosaic}}. } } \details{ This function produces similar plots to the use of \code{\link{mosaic.loglmlist}}, called with the result of \code{\link{seq_loglm}}. } \value{ None. Used for its side-effect of producing plots %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ These functions were inspired by the original SAS implementation of mosaic displays, described in the \emph{User's Guide}, \url{http://www.datavis.ca/mosaics/mosaics.pdf} } \author{ Michael Friendly } %\note{ %%% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{loglin-utilities}} for descriptions of sequential models, \code{\link{conditional}}, \code{\link{joint}}, \code{\link{mutual}}, \dots \code{\link{loglmlist}}, \code{\link{mosaic.loglmlist}}, \code{\link{seq_loglm}} \code{\link{mosaic.glm}}, \code{\link[vcd]{mosaic}}, \code{\link[vcd]{strucplot}}, for the many parameters that control the details of mosaic plots. } \examples{ data(Titanic, package="datasets") seq_mosaic(Titanic) # models of joint independence, Survived last seq_mosaic(Titanic, type="condit") seq_mosaic(Titanic, type="mutual") # other panel functions and options: presently BUGGED \dontrun{ seq_mosaic(Titanic, type="mutual", panel=sieve, gp=shading_Friendly, labeling=labeling_values) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{hplots} \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line vcdExtra/man/Draft1970.Rd0000644000175100001440000000446612576352702014516 0ustar hornikusers\name{Draft1970} \alias{Draft1970} \docType{data} \title{ USA 1970 Draft Lottery Data } \description{ This data set gives the results of the 1970 US draft lottery, in the form of a data frame. } \usage{data(Draft1970)} \format{ A data frame with 366 observations on the following 3 variables. \describe{ \item{\code{Day}}{day of the year, 1:366} \item{\code{Rank}}{draft priority rank of people born on that day} \item{\code{Month}}{an ordered factor with levels \code{Jan} < \code{Feb} \dots < \code{Dec}} } } \details{ The draft lottery was used to determine the order in which elligible men would be called to the Selective Service draft. The days of the year (including February 29) were represented by the numbers 1 through 366 written on slips of paper. The slips were placed in separate plastic capsules that were mixed in a shoebox and then dumped into a deep glass jar. Capsules were drawn from the jar one at a time. The first number drawn was 258 (September 14), so all registrants with that birthday were assigned lottery number \code{Rank} 1. The second number drawn corresponded to April 24, and so forth. All men of draft age (born 1944 to 1950) who shared a birthdate would be called to serve at once. The first 195 birthdates drawn were later called to serve in the order they were drawn; the last of these was September 24. } \source{ Starr, N. (1997). Nonrandom Risk: The 1970 Draft Lottery, \emph{Journal of Statistics Education}, v.5, n.2 \url{http://www.amstat.org/publications/jse/v5n2/datasets.starr.html} } \references{ Fienberg, S. E. (1971), "Randomization and Social Affairs: The 1970 Draft Lottery," \emph{Science}, 171, 255-261. \url{http://en.wikipedia.org/wiki/Draft_lottery_(1969)} } \seealso{\code{\link{Draft1970table}} } \examples{ data(Draft1970) # scatterplot plot(Rank ~ Day, data=Draft1970) with(Draft1970, lines(lowess(Day, Rank), col="red", lwd=2)) abline(lm(Rank ~ Day, data=Draft1970), col="blue") # boxplots plot(Rank ~ Month, data=Draft1970, col="bisque") lm(Rank ~ Month, data=Draft1970) anova(lm(Rank ~ Month, data=Draft1970)) # make the table version Draft1970$Risk <- cut(Draft1970$Rank, breaks=3, labels=c("High", "Med", "Low")) with(Draft1970, table(Month, Risk)) } \keyword{datasets} vcdExtra/man/Dyke.Rd0000644000175100001440000000471612576352702014027 0ustar hornikusers\name{Dyke} \Rdversion{1.1} \alias{Dyke} \docType{data} \title{Sources of Knowledge of Cancer} \description{Observational data on a sample of 1729 individuals, cross-classified in a 2^5 table according to their sources of information (read newspapers, listen to the radio, do 'solid' reading, attend lectures) and whether they have good or poor knowledge regarding cancer. Knowledge of cancer is often treated as the response.} \usage{ data(Dyke) } \format{ A 5-dimensional array resulting from cross-tabulating 5 variables for 1729 observations. The variable names and their levels are: \tabular{rll}{ No \tab Name \tab Levels \cr 1\tab \code{Knowledge}\tab \code{"Good", "Poor"}\cr 2\tab \code{Reading}\tab \code{"No", "Yes"}\cr 3\tab \code{Radio}\tab \code{"No", "Yes"}\cr 4\tab \code{Lectures}\tab \code{"No", "Yes"}\cr 5\tab \code{Newspaper}\tab \code{"No", "Yes"}\cr } } %\details{ } \source{ % \cite{Fienberg:80 [Table 5-6]} Fienberg, S. E. (1980). \emph{The Analysis of Cross-Classified Categorical Data} Cambridge, MA: MIT Press, p. 85, Table 5-6. } \references{ Dyke, G. V. and Patterson, H. D. (1952). Analysis of factorial arrangements when the data are proportions. \emph{Biometrics}, 8, 1-12. Lindsey, J. K. (1993). \emph{Models for Repeated Measurements} Oxford, UK: Oxford University Press, p. 57. } %\seealso{ } \examples{ data(Dyke) # independence model mosaic(Dyke, shade=TRUE) # null model, Knowledge as response, independent of others require(MASS) dyke.mod0 <- loglm(~ Knowledge + (Reading * Radio * Lectures * Newspaper), data=Dyke) dyke.mod0 mosaic(dyke.mod0) # view as doubledecker plot Dyke <- Dyke[2:1,,,,] # make Good the highlighted value of Knowledge doubledecker(Knowledge ~ ., data=Dyke) # better version, with some options doubledecker(Knowledge ~ Lectures + Reading + Newspaper + Radio, data=Dyke, margins = c(1,6, length(dim(Dyke)) + 1, 1), fill_boxes=list(rep(c("white", gray(.90)),4)) ) # separate (conditional) plots for those who attend lectures and those who do not doubledecker(Knowledge ~ Reading + Newspaper + Radio, data=Dyke[,,,1,], main="Do not attend lectures", margins = c(1,6, length(dim(Dyke)) + 1, 1), fill_boxes=list(rep(c("white", gray(.90)),3)) ) doubledecker(Knowledge ~ Reading + Newspaper + Radio, data=Dyke[,,,2,], main="Attend lectures", margins = c(1,6, length(dim(Dyke)) + 1, 1), fill_boxes=list(rep(c("white", gray(.90)),3)) ) drop1(dyke.mod0, test="Chisq") } \keyword{datasets} vcdExtra/man/TV.Rd0000644000175100001440000000447712576352702013470 0ustar hornikusers\name{TV} \Rdversion{1.1} \alias{TV} \title{TV Viewing Data} \description{ This data set \code{TV} comprises a 5 x 11 x 3 contingency table based on audience viewing data from Neilsen Media Research for the week starting November 6, 1995. } \usage{data(TV)} \format{ A 5 x 11 x 3 array of cell frequencies with the following structure: \preformatted{ int [1:5, 1:11, 1:3] 146 244 233 174 294 151 181 161 183 281 ... - attr(*, "dimnames")=List of 3 ..$ Day : chr [1:5] "Monday" "Tuesday" "Wednesday" "Thursday" ... ..$ Time : chr [1:11] "8:00" "8:15" "8:30" "8:45" ... ..$ Network: chr [1:3] "ABC" "CBS" "NBC" } } \details{ The original data, \code{tv.dat}, contains two additional networks: "Fox" and "Other", with small frequencies. These levels were removed in the current version. There is also a fourth factor, transition State transition (turn the television Off, Switch channels, or Persist in viewing the current channel). The \code{TV} data here includes only the Persist observations. } \source{ The original data, \code{tv.dat}, came from the initial implementation of mosaic displays in R by Jay Emerson (1998). Similar data had been used by Hartigan and Kleiner (1984) as an illustration. } \references{ Emerson, John W. Mosaic Displays in S-PLUS: A General Implementation and a Case Study. \emph{Statistical Graphics and Computing Newsletter}, 1998, 9(1), 17--23, \url{http://www.stat.yale.edu/~jay/R/mosaic/v91.pdf} Hartigan, J. A. & Kleiner, B. A Mosaic of Television Ratings. \emph{The American Statistician}, 1984, 38, 32-35. } \examples{ data(TV) structable(TV) doubledecker(TV) # reduce number of levels of Time TV.df <- as.data.frame.table(TV) levels(TV.df$Time) <- rep(c("8:00-8:59", "9:00-9:59", "10:00-10:44"), c(4, 4, 3)) TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df) # re-label for mosaic display levels(TV.df$Time) <- c("8", "9", "10") # fit mode of joint independence, showing association of Network with Day*Time mosaic(~ Day + Network + Time, data = TV.df, expected = ~ Day:Time + Network, legend = FALSE) # with doubledecker arrangement mosaic(~ Day + Network + Time, data = TV.df, expected = ~ Day:Time + Network, split = c(TRUE, TRUE, FALSE), spacing = spacing_highlighting, legend = FALSE) } \keyword{datasets}