Zelig/0000755000176000001440000000000012217234503011353 5ustar ripleyusersZelig/po/0000755000176000001440000000000012061700507011770 5ustar ripleyusersZelig/po/R-en.po0000644000176000001440000003722412061700507013141 0ustar ripleyusersmsgid "describe canned" msgstr "describe.<> <- function () {\n" "package <- list(name=\"stats\",\n" "version=\".9\"\n" ")\n" "\n" "# edit the below line to add a description\n" "# to this zelig model\n" "description <- \"a zelig model\"\n" "\n" "# edit the below with information about\n" "# the model that this zelig module is based on\n" "# the citation year may differ than the auto-generated one\n" "list(category = \"\",\n" "authors = \"<>\",\n" "year = <>,\n" "description = description,\n" "package = package,\n" "parameters=list(list())\n" ")\n" "}" msgid "describe how-to" msgstr "# HOW-TO WRITE A DESCRIBE FUNCTION\n" "# ================================\n" "# 1. Fill in the \"description\" variable with a *short*\n" "# description of the model. e.g. \"multinomial probit model\"\n" "# 2. Fill in the return-value for \"category\"\n" "# 3. Fill in the return-value for \"authors\" with either:\n" "# i. <>, or\n" "# ii. c(<>, >, <>, ...)\n" "# 4. Fill in the return-value for \"year\"\n" "# 5. Fill in the package variable" msgid "describe credit" msgstr "# @author: <>\n" "# @date: <>\n" "# .<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: describe.<> generates citation information\n" "# for the zelig model <>" msgid "zelig2 canned" msgstr "zelig2<> <- function (formula, model, data, M, ...) {\n" "# this file acts as an interface to the original model\n" "# the return (\"mf\") is a function call that zelig will later invoke\n" "# in order to process the specified data set\n" "#\n" "# any parameters unnecessary to the model\n" "# should be set to NULL (e.g. mf$M, mf$robust, etc...)\n" "\n" "mf <- match.call(expand.dots=T)\n" "mf$M <- mf$robust <- NULL\n" "mf[[1]] <- <>\n" "mf$model <- NULL\n" "mf$data <- data\n" "mf$formula <- formula\n" "as.call(mf)\n" "}" msgid "zelig2 how-to" msgstr "# 0. the zelig2 function acts as an interface between\n" "# the existing model and the zelig module that is\n" "# being created.\n" "# 1. construct a call object containing all the parameters\n" "# passed into \"zelig2<>\"\n" "# 2. remove all parameters that will not make sense within\n" "# the original model (in the demo code, e.g. robust, M, etc.)\n" "# 3. re-assign the call object's first entry to the name of the\n" "# model that must be called.\n" "# This step is crucial, as it is how your model invokes the\n" "# pre-existing model's name\n" "# 4. attach the data frame (mf$data <- data)\n" "# 5. return the call (as a call)\n" "#\n" "# NOTE: the returned value is going to be evaluated, and -\n" "# as a result - call the pre-existing model. Any\n" "# parameters passed to the original zelig function\n" "# will be forwarded to the model unless set to NULL\n" "#\n" "#\n" "# NOTE: THIS FUNCTION IS INVOKED BY THE ZELIG FUNCTION\n" "# (NOT THE USER)\n" "#\n" "# call order: zelig -> zelig2<>\"" msgid "zelig2 credit" msgstr "# @author: <>\n" "# @date: <>\n" "# zelig2<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: re-interprets parameters passed into zelig as\n" "# legal parameters to pass into <>" msgid "zelig3 canned" msgstr "zelig3<> <- function (res, ...) {\n" "class(res) <- c(\"<>\", class(res))\n" "\n" "# give it a terms object\n" "if (is.null(res$terms))\n" "res$terms <- terms(res$call$formula)\n" "\n" "# return\n" "res\n" "}" msgid "zelig3 how-to" msgstr "# HOW-TO WRITE A ZELIG3 FUNCTION\n" "# ==============================\n" "# 0. zelig3 functions act as in-betweens between\n" "# the zelig2 function and the zelig and sim function.\n" "# That is, after the model runs its initial computations,\n" "# it is often important to cast the result as an object of\n" "# the class which it is named after. This is crucial to\n" "# ensure that setx invokes the correct method\n" "# 1. set the class of the res passed into with the line:\n" "# class(res) <- class(\"<>\", class(res))\n" "# 2. return the obj (with class newly extended)\n" "#\n" "# NOTE: if the class \"<>\" is not added, the function\n" "# setx.default will be used, which may have unexpected\n" "# results" msgid "zelig3 credit" msgstr "# @author: <>\n" "# @date: <>\n" "# zelig3<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: not always necessary, but is useful to cast data\n" "# the result of a zelig2* call into whatever data-type\n" "# we want our setx function to work with" msgid "setx canned" msgstr "setx.<> <- function(obj, data=NULL, ...) {\n" "# send to default\n" "res <- setx.default(obj, ...)\n" "\n" "# cast as appropriate data-type, then return\n" "class(res) <- \"setx.<>\"\n" "\n" "# attach data frame here, if the model\n" "# requires sophisticated number-crunching\n" "# after setx is called\n" "# if not, remove the below line\n" "if (!is.null(data)) {\n" "res$data <- data\n" "}\n" "\n" "res\n" "}" msgid "setx how-to" msgstr "# HOW-TO WRITE A SETX FUNCTION\n" "# ============================\n" "# 0. For most purposes setx.default will compute\n" "# correctly values of interest, which are needed\n" "# to compute the quantities of interest. However,\n" "# some models will not provide data in the correct\n" "# fashion, etc. (e.g. computing a covariance matrix\n" "# of the explanatory variables may not make sense\n" "# or be relevant for certain models)\n" "# 1. parameters are passed in as a zelig model and potentially\n" "# a new data-set. The new data-set is used in place of the\n" "# original one that was passed into zelig. This\n" "# 2. the result of the setx function should be of class\n" "# \"setx.<>\"\n" "# this is important, because it ensures that the correct\n" "# qi function and sim function are called\n" "# 3. the data frame used to compute this setx may\n" "# be attached with the line\n" "# res$data <- data\n" "# if the sim function needs to make further computations\n" "# if this is not the case, please omit that line" msgid "setx credit" msgstr "# @author: <>\n" "# @date: <>\n" "# setx.<>\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: produces data based on the explanatory variables\n" "# in the model (set by user, not developer)" msgid "sim canned" msgstr "sim.<> <- function(obj,\n" "x=NULL,\n" "x1=NULL,\n" "num=c(1000, 100),\n" "prev = NULL,\n" "cond.data = NULL, ...\n" ") {\n" "# error-catching\n" "if (is.null(x))\n" "stop(\"Error: x cannot be NULL\")\n" "\n" "# simulate qi's for x\n" "# invoke qi.\n" "res <- qi.<>(obj, x=x, x1=x1)\n" "\n" "# change call name\n" "obj$call[[1]] <- as.name(\"sim\")\n" "\n" "\n" "# append\n" "res$call <- match.call(expand.dots=T)\n" "res$zelig.call <- obj$call\n" "res$par <- NA\n" "res$obj <- obj\n" "\n" "# change class so correct summary/print function\n" "# can be called\n" "class(res) <- \"sim.<>\"\n" "\n" "res\n" "}" msgid "sim how-to" msgstr "# HOW-TO WRITE A SIM FUNCTION\n" "# ===========================\n" "# 0. The sim function invokes the qi function.\n" "# Then, returns the quantities of interests\n" "# alongside a host of other relevant data, that\n" "# is presented along with summary and print\n" "# 1. importantly, sim should always have the line:\n" "# res <- qi(obj, x=x, x1=x1)\n" "# this enesure that the qi's are computed with the\n" "# exact parameters that enter the sim function itself\n" "# 2. the call to sim and the call should be returned along\n" "# with the quantities of interest (qi.stat) and their\n" "# titles (qi.name)\n" "# 3. the returned object should have class type:\n" "# \"sim.<>:" msgid "sim credit" msgstr "# @author: <>\n" "# @date: <>\n" "# sim.<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: simulates quantities of interest, then arranges\n" "# the data in an easily interprettable manner.\n" "# invokes qi.<>.R" msgid "sim.setx canned" msgstr "sim.setx.<> <- function (obj, x, ...) {\n" "# this function exists so that if\n" "# sim(obj) and sim(obj, x) should have radically\n" "# different behavior, we will be able to place them\n" "# in seperate files easily\n" "sim.<>(obj, x, ...)\n" "}" msgid "sim.setx how-to" msgstr "# HOW-TO WRITE A SIM.SETX FILE\n" "# ============================\n" "# 0. sim.setx functions offer alternative ways\n" "# to simulate quantities of interest. That is,\n" "# sim functions are called in this fashion\n" "# sim(zelig.out)\n" "#\n" "# while sim.setx functs are called as:\n" "# sim(zelig.out, x)\n" "# or\n" "# sim(zelig.out, x, x1)\n" "#\n" "# this allows the developer to separate\n" "# the different types of simulation algorithms\n" "# that may exist for his or her model\n" "#\n" "# if the model simulates quantities of interest\n" "# identically to that of the standard sim function\n" "# it should then only contain the line:\n" "# sim.<>(obj, x, ...)\n" "#\n" "# 1. invoke qi with\n" "# qi(obj, x=x, x=x1)\n" "#\n" "# do relevant computation on the return quantities\n" "# of interest, and attach relevant data that needs\n" "# to be passed to print and summary functions" msgid "sim.setx credit" msgstr "# @author: <>\n" "# @date: <>\n" "# sim.setx.<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: simulates qi's when additional explanatory\n" "# information is provided. usually simply\n" "# invokes the method specified in sim.<>.R" msgid "summary.sim canned" msgstr "summary.sim.<> <- function(obj, ...) {\n" "# set important summary objects\n" "# zelig models always have qi.stat, and qi.name\n" "# elements\n" "res <- list(model=\"mprobit\",\n" "qi.stat = obj$qi.stat,\n" "qi.name = obj$qi.name,\n" "original = obj$obj,\n" "call = obj$call,\n" "zelig.call = obj$zelig.call\n" ")\n" "\n" "# cast as class\n" "class(res) <- \"summary.sim.<>\"\n" "\n" "res\n" "}" msgid "summary.sim how-to" msgstr "# HOW TO WRITE A SUMMARY.SIM FUNCTION\n" "# ===================================\n" "# 0. summary.sim functions exclusively return\n" "# a list of important data, *summarizing*\n" "# important features of the result of the sim\n" "# function\n" "# 1. like summaries of most objects, the result should\n" "# contain a reference to the call that created it,\n" "# information on the class-type, etc.:\n" "# list(call = obj$call,\n" "# zelig.call = obj$zelig.call,\n" "# ...)\n" "# 2. importantly, summary.sim must return a qi.stat\n" "# and a qi.name data object. the indices of these\n" "# objects must have the same values for consistency\n" "# that is, names(qi.stat) == names(qi.name)\n" "# the return should resemble:\n" "# list(\n" "# qi.stat = obj$qi.stat,\n" "# qi.name = obj$qi.name,\n" "# call = obj$call,\n" "# zelig.call = obj$zelig.call\n" "# ...)" msgid "summary.sim credit" msgstr "# @author: <>\n" "# @date: <>\n" "# summary.sim.<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: returns a list of data summarizing the sim object\n" "# should always include qi.stat, qi.name entry" msgid "print.summary.sim canned" msgstr "print.summary.sim.<> <- function (obj, digits=F, print.x=F, ...) {\n" "# prints typically have qi, and qi.names defined as part of the summary object\n" "if (is.null(obj$qi.stat) || is.null(obj$qi.name)) {\n" "stop(\"Error: \")\n" "}\n" "\n" "# warn if name lists do not match\n" "if (any(sort(names(obj$qi.stat)) != sort(names(obj$qi.name)))) {\n" "warning(\"warning: quantities of interest do not match its name list\")\n" "}\n" "\n" "print(obj$original)\n" "\n" "for (key in names(obj$qi.stat)) {\n" "# value\n" "val <- obj$qi.stat[[key]]\n" "\n" "# pass-by conditions\n" "if (is.na(val) || (is.list(val) && !length(val)) || is.null(val))\n" "next\n" "\n" "# print the title of the qi\n" "s <- gsub(\"\\\\s+$\", \"\", obj$qi.name[[key]])\n" "message(s)\n" "message(rep(\"=\", min(nchar(s), 30)))\n" "\n" "# print the qi (should be a simple data-type, such as matrix or float)\n" "print(val)\n" "\n" "# line-feed\n" "message()\n" "}\n" "\n" "# return invisibly\n" "invisible(obj)\n" "}" msgid "print.summary.sim how-to" msgstr "# HOW TO WRITE A PRINT.SUMMARY.SIM FUNCTION\n" "# =========================================\n" "# 0. print.summary functions typically display the result\n" "# from a summary object (a list) in an organized fashion\n" "# with various text-formatting.\n" "# 1. for most purpose the default print function (below) should\n" "# work, however, various formatting, etc. can be added typically\n" "# without any impact on the operation of the program" msgid "print.summary.sim credit" msgstr "# @author: <>\n" "# @date: <>\n" "# .<>.R\n" "# auto-generated by zkeleton, written by Matt Owen\n" "# info: print.summary.sim.<>.R outputs summary\n" "# information from the zelig model <>" msgid "qi canned" msgstr "# @obj: zelig object\n" "# @simpar: parameters passed to the qi\n" "# return: qi list (qi.stat) and qi.names list (qi.name)\n" "\n" "# NOTE THIS FILE MUST ALWAYS BE EDITED!!!!\n" "# IT IS THE MOST IMPORTANT COMPONENT TO\n" "# ANY ZELIG MODULE\n" "qi.<> <- function(obj, simpar=NULL, x, x1=NULL, y=NULL) {\n" "# initialize values that necessarily must be\n" "# returned.\n" "qi.stat <- list()\n" "qi.name <- list()\n" "\n" "\n" "# add entries to qi.stat and qi.name\n" "# in the end, names(qi.stat) should == names(qi.name)\n" "# so that printing can be handled by the auto-generated\n" "# function\n" "\n" "# ...\n" "\n" "\n" "# qi computation must be written by the developer,\n" "# as it is impossible to tell automatically what is\n" "# the statistic of interest (or how to compute it)\n" "\n" "# ...\n" "\n" "\n" "# compute the quantities of interest\n" "# of this model\n" "list(qi.stat=qi.stat,\n" "qi.name=qi.name\n" ")\n" "}" msgid "qi how-to" msgstr "# HOW-TO WRITE A QI FILE\n" "# ======================\n" "# qi functions are the heart of any zelig module.\n" "# The qi function is passed information from the setx\n" "# function (via x, x1), parameters (simpar), and the\n" "# original zelig model (obj or object)\n" "# The developer (you) then writes the software that he/she\n" "# believes produces a significant quantity of interest.\n" "# The result should always be returned in the fashion\n" "# list(qi.stat=qi.stat\n" "# qi.name=qi.name\n" "# )\n" "# where qi.stat is a list of qi.stat and qi.name have the form\n" "# qi.stat <- list(qi.1 = <>,\n" "# qi.2 = <>,\n" "# qi.3 = <>,\n" "# ...\n" "# )\n" "#\n" "# qi.name <- list(qi.1 = <>,\n" "# qi.2 = <>,\n" "# qi.3 = <>,\n" "# ...\n" "# )\n" "#\n" "# qi.1, qi.2, etc. should be named in an easy to comprehend manner\n" "# the indices of qi.stat and qi.name (qi.1, qi.2, etc.) should match,\n" "# otherwise a warning will be displayed during the print stage" msgid "qi credit" msgstr "# @author: <>\n" "# @date: <>\n" "# qi.<>, auto-generated by zkeleton, written by Matt Owen\n" "# ===========================\n" "# info: produced quantities of interest for zelig model <>" Zelig/COPYING0000644000176000001440000003536412061700507012420 0ustar ripleyusers GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 675 Mass Ave, Cambridge, MA 02139, USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. 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If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. 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Zelig/inst/0000755000176000001440000000000012217153457012340 5ustar ripleyusersZelig/inst/templates/0000755000176000001440000000000012061700507014325 5ustar ripleyusersZelig/inst/templates/zelig2.R0000644000176000001440000000056712061700507015654 0ustar ripleyusers#' Interface between the Zelig Model \\model\\ and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2\\model\\ <- function (formula, ..., data) { list( .function = "", formula = formula, data = data ) } Zelig/inst/templates/param.R0000644000176000001440000000076612061700507015561 0ustar ripleyusers#' Extract Samples from a Distribution in Order to Pass Them to the \code{qi} Function #' (this is primarily a helper function for the \\model\\ model) #' @param obj a zelig object #' @param num an integer specifying the number of simulations to compute #' @param ... additional parameters #' @return a list specifying link, link-inverse, random samples, and ancillary parameters #' @export param.\\model\\ <- function(obj, num=1000, ...) { list( coef = NULL, linkinv = NULL ) } Zelig/inst/templates/ZELIG.README0000644000176000001440000000000012061700507016044 0ustar ripleyusersZelig/inst/templates/DESCRIPTION0000644000176000001440000000021312061700507016027 0ustar ripleyusersPackage: Version: .1 Date: Title: A Zelig Model Author: Maintainer: Depends: Description: A Zelig Model License: GPL (>=2) URL: Packaged: Zelig/inst/templates/PACKAGE.R0000644000176000001440000000063612061700507015510 0ustar ripleyusers#' \\package\\ #' #' \tabular{ll}{ #' Package: \tab \\package\\\cr #' Version: \tab 0.1\cr #' Date: \tab 2011-04-25\cr #' Depends: \\depends\\ #' License: \tab GPL version 2 or newer\cr #' } #' #' Edit this description #' #' @name \\package\\-package #' @aliases \\package\\-package \\package\\ #' @docType package #' @importFrom Zelig describe param qi #' @author \\author\\ #' @keywords package NULL Zelig/inst/templates/qi.R0000644000176000001440000000104712061700507015063 0ustar ripleyusers#' Compute Quantities of Interest for the Zelig Model \\model\\ #' @param obj a zelig object #' @param x a setx object #' @param x1 an optional setx object #' @param y ... #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of interest #' with their simulations #' @export qi.\\model\\ <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { list( "Expected Value: E(Y|X)" = NA ) } Zelig/inst/templates/describe.R0000644000176000001440000000035612061700507016234 0ustar ripleyusers#' Describe the \\model\\ Zelig Model #' @param ... ignored parameters #' @return a list specifying author, title, etc. information #' @export describe.\\model\\ <- function(...) { list( authors = "", text = "" ) } Zelig/inst/doc/0000755000176000001440000000000012217153457013105 5ustar ripleyusersZelig/inst/doc/gamma.pdf0000644000176000001440000057251712217234502014672 0ustar ripleyusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4748 /Filter /FlateDecode /N 97 /First 779 >> stream x[[s8~?okqښ*ljLΌy%FI JHʑ3Sul @_,I(Df61ICb,ġ'>Q^%!1OdXJ8cT&OSbpO:Mգ5C$#p=2Rd EZ\є)TbD{Š7&J(TЎ D+bSpI!K4T'(IL4@WСQuq$a+KC8]UAB/ td ƦdC1p4vG5u&N:I_Æg@[(M!Š.KD*SQ)"Q.$4 l"íHTJXԟp`fKiWhY?7=?eoȼw!ޑu3NͶꞰ=,݇o姭dwv|GtR>g ȫr:yWErX__n^MG;JGaAI6j<糛rH^/ٲ:Y*y#ҵ:~o黳#>z<v{wJa^#L8[#[ }L&r𤼺*ʎ,\Nb\\!.h423%$-|_Qn|:I @̇C 0bTۢ\$b0V*s&d4:/oI p9Â"WÚ|Z_uȼ \'lֆ>b%+GCপfK(c9h)UC|YY1xL+v ( 3hGY͌riMfc 5kGD^Mj/\k0įeh;prP+!$xKhN ONjܖDz[FѢmH.i5HԮ bā=t8HLdd.F4Ek3YéɍBv撇ՠU7arB(9D"NK%c4tjL-Ir;K6&H"*EOLF*}AZ0#WѬgR\GXQ#s Do9R%BhXEIrfbjH/-t 岽Bx'e%񸠭/b^.>M|vt74~8`8t{',u=,`|$}hI@ x|y'I$Nr~[cU-u}ߙT }G]"9GXKk KYb "PTPMRAz/91&뺷-/aOb=uƐԻu22%@vs Lj|OV*nNe뿰ۤ@ͦքj d~ {Өf2HCK㲄[bT1q\N4$VAlѼkML5zZ"nm"jm4 qN*6tP<[Vڒ{N3{@TFE\xꏖԔzmJjJ VF=z9| yq~dl((i[|~R"sq-b݋*#NUvUtc㕰Ei~ͱ̎L<ވw,ۥt Q QWZ܈b ~jɐdVAJ{ HhZZFDG3+ 66s9χqyoDw7y{9""8i/US #Kmin:/+ ) %e-)dzXHQq01'o/GfJU4k&վkR%:Ԕ#'Qˠ܋jn 57mv0gfS\5'{Rm0ˇSq,'+}x)NO{_Eh~**:_8Ee>BUX{I g;ΐq-"[ŢY13&]r*nH =b=> ֝u!o{{9Yt˙isu*1=ѡc|f,bX| pmz]nƂm6wl\M`vKF EѾlK--ŐFw[e>߄FLr\1xy[@-8Z}T<󸧟7`3Ѱh_lL?c[70kГN&:W'43 f8g.QHチяÁMWOg;!pu~Pc̒nHl?$m6o1޾oCџ'muJ%~Pަ:9R@1 ˪Orh<.+)Gmv ꐾi:!! 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################################################### ### code chunk number 3: Examples.data ################################################### data(klein) ################################################### ### code chunk number 4: Examples.list ################################################### formula <- list(mu1=C~Wtot + P + P1, mu2=I~P + P1 + K1, mu3=Wp~ X + X1 + Tm, inst= ~ P1 + K1 + X1 + Tm + Wg + G) ################################################### ### code chunk number 5: Examples.zelig ################################################### z.out<-zelig(formula=formula, model="twosls",data=klein) summary(z.out) ################################################### ### code chunk number 6: Examples.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 7: Examples.sim ################################################### s.out <-sim(z.out,x=x.out) summary(s.out) ################################################### ### code chunk number 8: Examplestwosls ################################################### plot(s.out) Zelig/inst/doc/negbinom.pdf0000644000176000001440000061530312061700507015375 0ustar ripleyusers%PDF-1.5 % 37 0 obj << /Length 2290 /Filter /FlateDecode >> stream xڽYm۸_'y)RK7+Z$ ֞dgwS~{%kE)K2 0N$}IIL!FJxyT[tbWccU֚gT+dɃ"f1Wbe:Wo<5۵)xZWf`y&YՂF!WĜhc{R`8SERo9EҩY07+ds\ʎktEv=\HmSGk!C[\d0㚁#7{$7zi!;5J#x;H<^MPS,P3}S*Y"p)%Qs?Jr \$_`iPw{pqAƞݬOgcW( (9: k_[`uPM]/w>)]/ౢ[MxfnCUDY좿cYRs?SY_.C#ٜ)3oKiEfpPpU4㩿+\pELF"4Qc.,Gf9n2+Odʶ*b0h |yG<J|Q=sPڿ[RRTN.I:d7<bʻyq;=Y{9jn+}+OoUdg.M^Qbv0}Pܦ)xm T.  ]@xgJ@ťt '1vQ&9^&zx jVɥ ]'T 폢ozDg e,y<}qy? endstream endobj 54 0 obj << /Length 1083 /Filter /FlateDecode >> stream xڝVYoF~ׯ# w!hShKi[(:C$MR \M RhM„ FD+CPɲJ3] ӇLq ޏ;߹m7E{ܕe!~A>PTbߔp*Xnd+lo_>b0Mr3\}劋ĉSg=4.a WTQgrm1>ci2nӣ TGpE PnfܻEa={~=>hg`vb1ït9F;j$]ۋCZ\0 K"ZiXn-(B+DcO.~G|P2 -`rƤM?a}дBlYfC edj+I| 4Ѥ0¢"J"^j3elD3pI,"e܄| ^{};\{t@E3XuMגט:9u\B="t#rS;^+H.rhpocCI0 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################################################### ### code chunk number 3: BasicExample.zelig ################################################### z.out <- zelig(vote ~ race + educate, model = "logit.bayes", data = turnout, verbose = FALSE) ################################################### ### code chunk number 4: BasicExample.geweke ################################################### geweke.diag(z.out$result$coefficients) ################################################### ### code chunk number 5: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 6: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 7: BasicExample.summary.zout ################################################### summary(z.out) ################################################### ### code chunk number 8: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 9: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) ################################################### ### code chunk number 10: BasicExample.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 11: FirstDifferences.setx.high ################################################### x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75)) ################################################### ### code chunk number 12: FirstDifferences.setx.low ################################################### x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25)) ################################################### ### code chunk number 13: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.high, x1 = x.low) ################################################### ### code chunk number 14: FirstDifferences.summary ################################################### summary(s.out2) ################################################### ### code chunk number 15: BasicExample.data ################################################### data(mexico) ################################################### ### code chunk number 16: BasicExample.zelig ################################################### z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes", data = mexico) ################################################### ### code chunk number 17: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 18: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 19: BasicExample.summary ################################################### summary(z.out) ################################################### ### code chunk number 20: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 21: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) ################################################### ### code chunk number 22: BasicExample.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 23: FirstDifferences.setx ################################################### x.weak <- setx(z.out, pristr = 1) x.strong <- setx(z.out, pristr = 3) ################################################### ### code chunk number 24: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.strong, x1 = x.weak) ################################################### ### code chunk number 25: FirstDifferences.summary ################################################### summary(s.out2) ################################################### ### code chunk number 26: BasicExample.data ################################################### data(macro) ################################################### ### code chunk number 27: BasicExample.zelig ################################################### z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes", data = macro, verbose = FALSE) ################################################### ### code chunk number 28: BasicExample.geweke ################################################### geweke.diag(z.out$result$coefficients) ################################################### ### code chunk number 29: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 30: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 31: BasicExample.summary ################################################### summary(z.out) ################################################### ### code chunk number 32: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 33: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) ################################################### ### code chunk number 34: BasicExample.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 35: FirstDifferences.setx ################################################### x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8)) x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2)) ################################################### ### code chunk number 36: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.high, x1 = x.low) ################################################### ### code chunk number 37: FirstDifferences.summary.sim ################################################### summary(s.out2) ################################################### ### code chunk number 38: BasicExample.data ################################################### data(sanction) ################################################### ### code chunk number 39: BasicExample.zelig ################################################### z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes", data = sanction, verbose = FALSE) ################################################### ### code chunk number 40: BasicExample.factor ################################################### sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain", "little effect", "modest loss", "major loss")) ################################################### ### code chunk number 41: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 42: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 43: BasicExample.summary ################################################### summary(z.out) ################################################### ### code chunk number 44: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 45: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) summary(s.out1) ################################################### ### code chunk number 46: FirstDifferences.setx ################################################### x.high <- setx(z.out, mil=0) x.low <- setx(z.out, mil=1) ################################################### ### code chunk number 47: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.high, x1 = x.low) summary(s.out2) ################################################### ### code chunk number 48: BasicExample.data ################################################### data(sanction) ################################################### ### code chunk number 49: BasicExample.zelig ################################################### z.out <- zelig(num ~ target + coop, model = "poisson.bayes", data = sanction, verbose = FALSE) ################################################### ### code chunk number 50: BasicExample.geweke ################################################### geweke.diag(z.out$result$coefficients) ################################################### ### code chunk number 51: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 52: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 53: BasicExample.summary ################################################### summary(z.out) ################################################### ### code chunk number 54: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 55: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) ################################################### ### code chunk number 56: BasicExample.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 57: FirstDifferences.setx ################################################### x.max <- setx(z.out, target = max(sanction$target)) x.min <- setx(z.out, target = min(sanction$target)) ################################################### ### code chunk number 58: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.max, x1 = x.min) summary(s.out2) ################################################### ### code chunk number 59: BasicExample.data ################################################### data(turnout) ################################################### ### code chunk number 60: BasicExample.zelig ################################################### z.out <- zelig(vote ~ race + educate, model = "probit.bayes", data = turnout, verbose = FALSE) ################################################### ### code chunk number 61: BasicExample.geweke ################################################### geweke.diag(z.out$result$coefficients) ################################################### ### code chunk number 62: BasicExample.heidel ################################################### heidel.diag(z.out$result$coefficients) ################################################### ### code chunk number 63: BasicExample.raftery ################################################### raftery.diag(z.out$result$coefficients) ################################################### ### code chunk number 64: BasicExample.summary ################################################### summary(z.out) ################################################### ### code chunk number 65: BasicExample.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 66: BasicExample.sim ################################################### s.out1 <- sim(z.out, x = x.out) ################################################### ### code chunk number 67: BasicExample.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 68: FirstDifferences.setx ################################################### x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25)) ################################################### ### code chunk number 69: FirstDifferences.sim ################################################### s.out2 <- sim(z.out, x = x.high, x1 = x.low) ################################################### ### code chunk number 70: FirstDifferences.summary ################################################### summary(s.out2) Zelig/tests/0000755000176000001440000000000012217153457012525 5ustar ripleyusersZelig/tests/lognorm.R0000644000176000001440000000124112061700507014312 0ustar ripleyuserslibrary(Zelig) # Load the sample data: data(coalition) # Estimate the model: user.prompt() z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm", data = coalition) user.prompt() # View the regression output: summary(z.out) # Set the baseline values (with the ruling coalition in the minority) # and the alternative values (with the ruling coalition in the majority) # for X: user.prompt() x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) # Simulate expected values qi$ev and first differences qi$fd: user.prompt() s.out <- sim(z.out, x = x.low, x1 = x.high) user.prompt() summary(s.out) user.prompt() plot(s.out) Zelig/tests/models-gee.R0000644000176000001440000000733612110047000014654 0ustar ripleyuserslibrary(Zelig) data(coalition) data(turnout) data(macro) data(sanction) cluster <- c(rep(c(1:62),5), rep(c(63),4)) coalition$cluster <- cluster z.out <- zelig(duration ~ fract + numst2, id = "cluster", model = "gamma.gee", data = coalition, corstr="exchangeable" ) summary(z.out) # Setting the explanatory variables at their default values # (mode for factor variables and mean for non-factor variables), # with numst2 set to the vector 0 = no crisis, 1 = crisis. x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) # Simulate quantities of interest s.out <- sim(z.out, x = x.low, x1 = x.high) summary(s.out) # Generate a plot of quantities of interest: plot(s.out) ## Attaching the sample turnout dataset: turnout$cluster <- rep(c(1:200),10) sorted.turnout <- turnout[order(turnout$cluster),] z.out1 <- zelig( vote ~ race + educate, model = "logit.gee", id = "cluster", data = turnout, corstr = "stat_M_dep", Mv=3 ) summary(z.out1) x.out1 <- setx(z.out1) s.out1 <- sim(z.out1, x = x.out1) summary(s.out1) plot(s.out1) x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) s.out2 <- sim(z.out1, x = x.high, x1 = x.low) summary(s.out2) plot(s.out2) ##### Example 3: Example with Fixed Correlation Structure ## User-defined correlation structure corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ## Generating empirical estimates: z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat) ## Viewing the regression output: summary(z.out2) # NORMAL.GEE # NORMAL.GEE # NORMAL.GEE z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1) summary(z.out) # Set explanatory variables to their default (mean/mode) values, with # high (80th percentile) and low (20th percentile) values: x.high <- setx(z.out, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out, trade = quantile(macro$trade, 0.2)) # Generate first differences for the effect of high versus low trade on # GDP: s.out <- sim(z.out, x = x.high, x1 = x.low) summary(s.out) # Generate a plot of quantities of interest: plot(s.out) # # # sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3)) z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sanction, robust=TRUE, corstr="exchangeable") summary(z.out) x.out <- setx(z.out) s.out <- sim(z.out, x = x.out) summary(s.out) plot(s.out) turnout$cluster <- rep(c(1:200),10) z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = turnout, robust = T, corstr = "stat_M_dep", Mv=3) summary(z.out1) x.out1 <- setx(z.out1) s.out1 <- sim(z.out1, x = x.out1) plot(s.out1) x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) s.out2 <- sim(z.out1, x = x.high, x1 = x.low) summary(s.out2) plot(s.out2) ##### Example 3: Example with Fixed Correlation Structure ## User-defined correlation structure corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ## Generating empirical estimates: z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = turnout, robust = T, corstr = "fixed", R=corr.mat) ## Viewing the regression output: summary(z.out2) Zelig/tests/MatchIt.R0000644000176000001440000000063512110047000014157 0ustar ripleyuserslibrary(MatchIt) library(Zelig) data(lalonde) m <- matchit( treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = lalonde, method = "subclass", subclass = 4 ) z <- zelig(re78 ~ re74 + re75 + distance, data = match.data(m, "control"), model = "ls", by = "subclass" ) # Fin. Zelig/tests/mi.R0000644000176000001440000000025612061700507013247 0ustar ripleyuserslibrary(Zelig) data(turnout) z <- zelig(vote ~ age, model = "logit", data = mi(turnout[1:10, ], turnout[100:110, ])) x <- setx(z, age = 90) s.out1 <- sim(z, x=x, num=20) Zelig/tests/plot-ci.R0000644000176000001440000000117312117373223014213 0ustar ripleyuserslibrary(Zelig) data(turnout) par(mfrow=c(2, 2)) z <- zelig(vote ~ income + educate, model="relogit", data=turnout) x <- setx(z, educate=2:8) x1 <- setx(z, educate=2:8, income = 10) s <- sim(z, x, x1) plot.ci(s, var="educate") z <- zelig(vote ~ income + educate, model="logit", data=turnout) x <- setx(z, educate=-5:5) s <- sim(z, x) plot.ci(s, var="educate", ylim = c(-2, 1)) z <- zelig(vote ~ income + educate, model="logit", data=turnout) x <- setx(z, educate=-5:5) s <- sim(z, x) plot.ci(s, var="educate") z <- zelig(vote ~ income + educate, model="logit", data=turnout) x <- setx(z, educate=12) s <- sim(z, x) plot.ci(s) Zelig/tests/relogit.R0000644000176000001440000000072712110047000014275 0ustar ripleyuserslibrary(Zelig) data(mid) z <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, model = "relogit", tau = 1042/303772, data = mid) x <- setx(z) s <- sim(z, x) summary(s) plot(s) ## weighting + bias correction + robust s.e. z <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772, case.control = "weighting", robust = TRUE) x <- setx(z) s <- sim(z, x) summary(s) Zelig/tests/models-bayes.R0000644000176000001440000000535312110047000015214 0ustar ripleyuserslibrary(Zelig) data(turnout) data(mexico) data(macro) data(sanction) # mlogit.bayes # mlogit.bayes # mlogit.bayes z.out <- zelig( vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes", data = mexico ) x.out <- setx(z.out) s.out <- sim(z.out, x = x.out) summary(z.out) summary(s.out) # logit.bayes # logit.bayes # logit.bayes names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort") z.out <- zelig( vote ~ race + educate, model = "logit.bayes", verbose = FALSE, data = turnout ) summary(z.out) x.out <- setx(z.out, age=65) x1.out <- setx(z.out, age=10, educate=5) s.out <- sim(z.out, x.out, x1.out) summary(s.out) # normal.bayes # normal.bayes # normal.bayes z.out <- zelig( unem ~ gdp + capmob + trade, model = "normal.bayes", data = macro, verbose=TRUE ) x.out <- setx(z.out) x1.out <- setx(z.out, gdp = 10) s.out <- sim(z.out, x.out, x1.out) summary(z.out) summary(s.out) sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain", "little effect", "modest loss", "major loss")) z.out <- zelig( ncost ~ mil + coop, model = "oprobit.bayes", data = sanction, verbose=FALSE ) x.out <- setx(z.out) x1.out <- setx(z.out, coop=3) s.out <- sim(z.out, x = x.out, num=10000) summary(z.out) summary(s.out) z.out <- zelig( num ~ target + coop, model = "poisson.bayes", data = sanction, verbose=TRUE ) x.out <- setx(z.out) x1.out <- setx(z.out, coop=3) s.out <- sim(z.out, x.out, x1.out) summary(z.out) summary(s.out) z.out <- zelig( vote ~ race + educate, model = "probit.bayes", verbose = FALSE, data = turnout ) x.out <- setx(z.out, age=65) x1.out <- setx(z.out, age=10, educate=5) s.out <- sim(z.out, x.out, x1.out) summary(s.out) z.out <- zelig(cbind(Agr,Exam,Educ,Cath,InfMort)~NULL, model="factor.bayes", data=swiss, factors=2, lambda.constraints=list(Exam=list(1,"+"), Exam=list(2,"-"), Educ=c(2,0), InfMort=c(1,0)), verbose=TRUE, a0=1, b0=0.15, burnin=5000, mcmc=10000) ## Checking for convergence before summarizing the estimates: geweke.diag(coef(z.out)) heidel.diag(coef(z.out)) raftery.diag(coef(z.out)) ## summarizing the output summary(z.out) # These methods should not work. #setx(z.out) #sim(z.out) Zelig/tests/twosls.R0000644000176000001440000000057012061700507014174 0ustar ripleyuserslibrary(Zelig) data(klein) formula <- list( mu1 = C ~ Wtot + P1, mu2 = I ~ P + P1 + K1, mu3 = Wp ~ X + X1 + Tm, inst = ~ P1 + K1 + X1 + Tm + Wg + G ) z <- zelig(formula, model="twosls", data=klein, cite=F) x <-setx(z) x1 <-setx(z, Wtot = 60) s <-sim(z, x, x1) summary(s) # Plot plot(s) Zelig/tests/mix.R0000644000176000001440000000106112071063367013441 0ustar ripleyuserslibrary(Zelig) # mix(list('a')) # mix(list('a', 'b', 'c'), list(1, 2, 3, 4)) # # # data(turnout) z1 <- zelig(vote ~ race, model = "logit", data = turnout) x1 <- setx(z1, race = "others") summary(x1) z2 <- zelig(vote ~ race, model = "logit", data = turnout) x2 <- setx(z1, race = c("white", "others")) summary(x2) z3 <- zelig(vote ~ race + educate, model = "logit", data = turnout) x3 <- setx(z2, race = "others", educate = 10:15) class(x3) summary(x3) z4 <- zelig(vote ~ race + educate, model = "logit", data = turnout) x4 <- setx(z3) summary(x4) # Fin. Zelig/tests/models-core.R0000644000176000001440000000524012071063367015060 0ustar ripleyuserslibrary(Zelig) data(coalition) data(macro) data(mid) data(tobin) data(turnout) data(sanction) # exp # exp # exp z.out <- zelig(Surv(duration, ciep12) ~ invest + polar + numst2 + crisis, model = "exp", data = coalition[1:100,]) x.low<- setx(z.out, numst1 = 0) x.high <- setx(z.out, numst2 = 1) s.out <- sim(z.out, x = x.low, x1 = x.high, num = 10) plot(s.out) # gamma # gamma # gamma z <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition) x.low <- setx(z, numst2 = 0) x.high <- setx(z, numst2 = 1) s <- sim(z, x = x.low, x1 = x.high, num = 10) plot(s) # logit # logit # logit z <- zelig(vote ~ age*educate + race, model = "logit", data = turnout) x.high <- setx(z, educate = quantile(turnout$educate, probs = 0.75)) x.low <- setx(z, educate = quantile(turnout$educate, probs = 0.25)) s <- sim(z, x = x.low, x1 = x.high, num = 10) plot(s) # ls # ls # ls z <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro) x.high <- setx(z, trade = quantile(trade, 0.8)) x.low <- setx(z, trade = quantile(trade, 0.2)) s <- sim(z, x = x.high, x1 = x.low, num = 10) plot(s) # negbinom # negbinom # negbinom z <- zelig(num ~ target + coop, model = "negbinom", data = sanction) x <- setx(z) s <- sim(z, x = x, num = 10) plot(s) # normal # normal # normal z <- zelig(unem ~ gdp + capmob + trade, model = "normal", data = macro) x.high <- setx(z, trade = quantile(trade, 0.8)) x.low <- setx(z, trade = quantile(trade, 0.2)) s <- sim(z, x = x.high, x1 = x.low) plot(s) # poisson # poisson # poisson z <- zelig(num ~ target + coop, model = "poisson", data = sanction) x <- setx(z) s <- sim(z, x = x, num = 10) plot(s) # probit # probit # probit z <- zelig(vote ~ race + educate, model = "probit", data = turnout) x.low <- setx(z, educate = quantile(turnout$educate, probs = 0.75)) x.high <- setx(z, educate = quantile(turnout$educate, probs = 0.25)) s <- sim(z, x = x.low, x1 = x.high, num = 10) plot(s) # relogit # relogit # relogit z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772) z.out2 <- zelig( conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772, case.control = "weighting", robust = TRUE ) x.out1 <- setx(z.out1) x.out2 <- setx(z.out2) s.out1 <- sim(z.out1, x = x.out1, num=10) s.out2 <- sim(z.out2, x = x.out2, num=10) plot(s.out1) plot(s.out2) # tobit # tobit # tobit z <- zelig(durable ~ age + quant, data = tobin, model = "tobit") x <- setx(z) s <- sim(z, x = x, num = 10) Zelig/tests/by.R0000644000176000001440000000026512110047000013237 0ustar ripleyuserslibrary(Amelia) library(Zelig) data(turnout) z <- zelig(vote ~ educate + income, model = "logit", by = "race", data = turnout) x <- setx(z, educate = 4) s <- sim(z, x) summary(s) Zelig/tests/summary.MI.R0000644000176000001440000000034612142745743014656 0ustar ripleyuserslibrary(Zelig) data(turnout) d1 <- turnout[1:500, ] d2 <- turnout[501:1000, ] d3 <- turnout[1001:2000, ] z <- zelig(vote ~ I(educate*income) + educate, model = "logit", data = mi(d1, d2, d3)) summary(z, subset = c(1, 3)) # F Zelig/tests/models-survey.R0000644000176000001440000001552612071063367015475 0ustar ripleyuserslibrary(Zelig) data(api, package = 'survey') data(scd, package = 'survey') # gamma.survey (1 of 3) # gamma.survey (1 of 3) # gamma.survey (1 of 3) # TEST 1 z.out1 <- zelig( api00 ~ meals + yr.rnd, model = 'gamma.survey', weights = ~ pw, data = apistrat ) summary(z.out1) x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) x.low x.high s.out1 <- sim(z.out1, x=x.high, x1=x.low) plot(s.out1) # gamma.survey (2 of 3) # gamma.survey (2 of 3) # gamma.survey (2 of 3) z.out2 <- zelig( api00 ~ meals + yr.rnd, model = "gamma.survey", strata=~stype, fpc=~fpc, data = apistrat ) summary(z.out2) jk1reps <- jk1weights(psu=apistrat$dnum) # gamma.survey (2 of 3) # gamma.survey (2 of 3) # gamma.survey (2 of 3) z.out3 <- zelig( api00 ~ meals + yr.rnd, model = "gamma.survey", data = apistrat, repweights=jk1reps$weights, type="JK1" ) summary(z.out3) x.low <- setx(z.out3, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out3, meals= quantile(apistrat$meals, 0.8)) x.low x.high s.out3 <- sim(z.out3, x=x.high, x1=x.low) plot(s.out3) # logit.survey (1 of 3) # logit.survey (1 of 3) # logit.survey (1 of 3) data(api, package="survey") # TEST 1 z.out1 <- zelig( yr.rnd ~ meals + mobility, model = "logit.survey", weights=~pw, data = apistrat ) summary(z.out1) x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) # x.low x.high s.out1 <- sim(z.out1, x=x.low, x1=x.high) plot(s.out1) # logit.survey (2 of 3) # logit.survey (2 of 3) # logit.survey (2 of 3) z.out2 <- zelig( yr.rnd ~ meals + mobility, model = "logit.survey", strata=~stype, fpc=~fpc, data = apistrat ) summary(z.out2) # logit.survey (3 of 3) # logit.survey (3 of 3) # logit.survey (3 of 3) data(scd) scd$sued <- as.vector(c(0,0,0,1,1,1)) BRRrep<-2 * cbind( c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0) ) z.out3 <- zelig( formula=sued ~ arrests + alive, model = "logit.survey", repweights=BRRrep, type="BRR", data=scd ) summary(z.out3) x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) s.out3 <- sim(z.out3, x=x.high, x1=x.low) # normal.survey (1 of 3) # normal.survey (1 of 3) # normal.survey (1 of 3) z.out1 <- zelig( api00 ~ meals + yr.rnd, model = "normal.survey", weights=~pw, data = apistrat ) summary(z.out1) x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) x.low x.high s.out1 <- sim(z.out1, x=x.high, x1=x.low) plot(s.out1) z.out2 <- zelig( api00 ~ meals + yr.rnd, model = "normal.survey", strata=~stype, fpc=~fpc, data = apistrat ) summary(z.out2) # normal.survey (2 of 3) # normal.survey (2 of 3) # normal.survey (2 of 3) BRRrep<-2 * cbind( c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0) ) z.out3 <- zelig( formula=alive ~ arrests, model = "normal.survey", repweights=BRRrep, type="BRR", data=scd, na.action=NULL ) summary(z.out3) x.min <- setx(z.out3, arrests = min(scd$alive)) x.max <- setx(z.out3, arrests = max(scd$alive)) x.min x.max s.out3 <- sim(z.out3, x=x.max, x1=x.min) plot(s.out3) data(api, package="survey") # TEST 1 z.out1 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat) summary(z.out1) x.low <- setx(z.out1, api00= quantile(apistrat$api00, 0.2)) x.high <- setx(z.out1, api00= quantile(apistrat$api00, 0.8)) x.low x.high s.out1 <- sim(z.out1, x=x.low, x1=x.high) plot(s.out1) # TEST 2 z.out2 <- zelig( enroll ~ api99 + yr.rnd, model = "poisson.survey", data = apistrat, strata=~stype, fpc=~fpc ) summary(z.out2) data(scd, package="survey") BRRrep<-2*cbind( c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0) ) z.out3 <- zelig( alive ~ arrests, model = "poisson.survey", repweights=BRRrep, type="BRR", data=scd ) summary(z.out3) x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) x.low x.high s.out3 <- sim(z.out3, x=x.high, x1=x.low) plot(s.out3) data(api, package="survey") z.out1 <- zelig( yr.rnd ~ meals + mobility, model = "probit.survey", weights=~pw, data = apistrat ) summary(z.out1) x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) x.low x.high s.out1 <- sim(z.out1, x=x.low, x1=x.high) plot(s.out1) # TEST 2 z.out2 <- zelig( yr.rnd ~ meals + mobility, model = "probit.survey", strata=~stype, fpc=~fpc, data = apistrat ) summary(z.out2) data(scd) scd$sued <- as.vector(c(0,0,0,1,1,1)) BRRrep<-2*cbind( c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0) ) z.out3 <- zelig( formula=sued ~ arrests + alive, model = "probit.survey", repweights=BRRrep, type="BRR", data=scd ) summary(z.out3) x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) x.low x.high s.out3 <- sim(z.out3, x=x.high, x1=x.low) Zelig/tests/pooled.R0000644000176000001440000000027512061700507014125 0ustar ripleyuserslibrary(Zelig) data(turnout) z <- zelig(vote ~ race + educate + age, model = "logit", data = turnout) x <- setx(z, educate = 6:7, age = 17) s <- sim(z, x, num = 200) summary(s) plot(s) Zelig/tests/amelia.R0000644000176000001440000000144112110047000014052 0ustar ripleyuserslibrary(Zelig) library(Amelia) # Create data set beta <- c(.3, -10) .x1 <- runif(1000, -5, 5) .x2 <- runif(1000, -2, 2) .x3 <- sample(1:4, 1000, TRUE) .y <- t(beta %*% rbind(.x1 + rnorm(1000, 0, 1.2), .x2 + rnorm(1000, 0, .1))) + 3 + rnorm(1000, 0, .3) data.set <- data.frame(y = .y, x1 = .x1, x2 = .x2, x3 = .x3) # Add missing data missing.data.percent <- .3 missing.data.column <- "x1" missing.data.rows <- sample(1:nrow(data.set), round(missing.data.percent * nrow(data.set))) data.set[missing.data.rows, missing.data.column] <- NA # Impute imputed.data <- amelia(data.set) # Remove unused data sets rm(.y, .x1, .x2) # Print amelia obj imputed.data # Fit statistical model z <- zelig(y ~ x1 + x2, model = "ls", data = imputed.data) x <- setx(z) s <- sim(z, x) # summary(s) # Fin. Zelig/NAMESPACE0000644000176000001440000001503312152222105012565 0ustar ripleyusersexport(.getRandAndFixedTerms) export(.reduceFurther) export(.ZeligModelCategories) export(alpha) export(as.description) export(as.qi.default) export(as.qi.list) export(as.qi.qi) export(as.summarized.list) export(as.summarized.summarized.qi) export(as.summarized) export(bootstrap) export(cite) export(cmvglm) export(coef.parameters) export(combine) export(depends.on.zelig) export(describe.default) export(describe.gamma) export(describe.logit) export(describe.ls) export(describe.negbinom) export(describe.normal) export(describe.poisson) export(describe.probit) export(describe) export(description) export(GetObject) export(getPredictorTerms) export(getResponseTerms) export(GetSlot.zelig) export(GetSlot) export(has.zelig2) export(help.zelig) export(ignore) export(link) export(linkinv) export(loadDependencies) export(make.parameters) export(Max) export(MCMChook) export(McmcHookFactor) export(Median) export(mi) export(Min) export(mix) export(Mode) export(param.default) export(param) export(parameters) export(parse.formula) export(parseFormula) export(plot.ci) export(print.setx) export(print.zelig) export(qi) export(reduceMI) export(relogit) export(repl) export(robust.glm.hook) export(robust.gee.hook) export(rocplot) export(setx) export(sim) export(simulation.matrix) export(simulations.parameters) export(splitUp) export(structuralToReduced) export(summarize) export(summary.MI.sim) export(summary.sim) export(summary.zelig) export(termsFromFormula) export(TexCite) export(tolmerFormat) export(user.prompt) export(z) export(zelig.call) export(zelig.skeleton) export(zelig) export(zelig2exp) export(zelig2factor.bayes) export(zelig2gamma.gee) export(zelig2gamma.survey) export(zelig2gamma) export(zelig2logit.bayes) export(zelig2logit.gee) export(zelig2logit.survey) export(zelig2logit) export(zelig2lognorm) export(zelig2ls) export(zelig2mlogit.bayes) export(zelig2negbinom) export(zelig2normal.bayes) export(zelig2normal.gee) export(zelig2normal.survey) export(zelig2normal) export(zelig2oprobit.bayes) export(zelig2poisson.bayes) export(zelig2poisson.gee) export(zelig2poisson.survey) export(zelig2poisson) export(zelig2probit.bayes) export(zelig2probit.gee) export(zelig2probit.survey) export(zelig2probit) export(zelig2relogit) export(zelig2tobit) export(zelig2twosls) export(ZeligDescribeModel) export(ZeligListModels) export(ZeligListTitles) export(zeligBuildWeights) S3method("[[",qi) S3method("[[",zelig) S3method(as.data.frame,setx) S3method(as.description,description) S3method(as.description,list) S3method(as.matrix,pooled.setx) S3method(as.matrix,setx) S3method(bootstrap,default) S3method(bootstrap,gamma) S3method(bootstrap,negbinom) S3method(bootstrap,normal) S3method(coef,parameters) S3method(coef,zelig) S3method(describe,default) S3method(describe,exp) S3method(describe,factor.bayes) S3method(describe,gamma.gee) S3method(describe,gamma.survey) S3method(describe,logit.bayes) S3method(describe,logit.gee) S3method(describe,logit.survey) S3method(describe,logit) S3method(describe,lognorm) S3method(describe,ls) S3method(describe,mlogit.bayes) S3method(describe,negbinom) S3method(describe,normal.bayes) S3method(describe,normal.gee) S3method(describe,normal.survey) S3method(describe,normal) S3method(describe,oprobit.bayes) S3method(describe,poisson.bayes) S3method(describe,poisson.gee) S3method(describe,poisson.survey) S3method(describe,poisson) S3method(describe,probit.bayes) S3method(describe,probit.gee) S3method(describe,probit.survey) S3method(describe,relogit) S3method(describe,tobit) S3method(describe,twosls) S3method(describe,zelig) S3method(getResponseTerms,formula) S3method(getResponseTerms,Formula) S3method(getResponseTerms,list) S3method(logLik,zelig) S3method(model.frame,multiple) S3method(model.matrix,multiple) S3method(model.matrix,parseFormula) S3method(names,qi) S3method(param,default) S3method(param,exp) S3method(param,factor.bayes) S3method(param,gamma.gee) S3method(param,gamma.survey) S3method(param,gamma) S3method(param,logit.bayes) S3method(param,logit.gee) S3method(param,logit.survey) S3method(param,logit) S3method(param,lognorm) S3method(param,ls) S3method(param,mlogit.bayes) S3method(param,negbinom) S3method(param,normal.bayes) S3method(param,normal.gee) S3method(param,normal.survey) S3method(param,oprobit.bayes) S3method(param,poisson.bayes) S3method(param,poisson.gee) S3method(param,poisson.survey) S3method(param,probit.bayes) S3method(param,probit.gee) S3method(param,probit.survey) S3method(param,relogit) S3method(param,relogit2) S3method(param,tobit) S3method(param,twosls) S3method(parseFormula,formula) S3method(parseFormula,Formula) S3method(parseFormula,list) S3method(plot,MI.sim) S3method(plot,pooled.sim) S3method(plot,sim.cloglog.net) S3method(plot,sim.gamma.gee) S3method(plot,sim.logit.gee) S3method(plot,sim.normal.gee) S3method(plot,sim.poisson.gee) S3method(plot,sim.probit.gee) S3method(plot,sim.twosls) S3method(plot,sim) S3method(plot,zelig) S3method(print,qi.summarized) S3method(print,qi) S3method(print,setx.mi) S3method(print,setx) S3method(print,sim) S3method(print,summary.MCMCZelig) S3method(print,summary.pooled.sim) S3method(print,summary.relogit) S3method(print,summary.relogit2) S3method(print,summary.setx) S3method(print,summary.sim) S3method(print,summary.MCMCZelig) S3method(print,summaryMI) S3method(print,summarySim.MI) S3method(print,zelig) S3method(qi,exp) S3method(qi,gamma.gee) S3method(qi,gamma.survey) S3method(qi,gamma) S3method(qi,logit.bayes) S3method(qi,logit.gee) S3method(qi,logit.survey) S3method(qi,logit) S3method(qi,lognorm) S3method(qi,ls) S3method(qi,mlogit.bayes) S3method(qi,negbinom) S3method(qi,normal.bayes) S3method(qi,normal.gee) S3method(qi,normal.survey) S3method(qi,normal) S3method(qi,oprobit.bayes) S3method(qi,poisson.gee) S3method(qi,poisson.survey) S3method(qi,poisson) S3method(qi,probit.bayes) S3method(qi,probit.gee) S3method(qi,probit.survey) S3method(qi,probit) S3method(qi,relogit) S3method(qi,relogit2) S3method(qi,tobit) S3method(qi,twosls) S3method(repl,default) S3method(repl,sim) S3method(setx,default) S3method(setx,MI) S3method(sim,default) S3method(sim,MI) S3method(simulation.matrix,pooled.sim) S3method(simulation.matrix,sim) S3method(summarize,default) S3method(summary,glm.robust) S3method(summary,MCMCZelig) S3method(summary,MI.sim) S3method(summary,MI) S3method(summary,pooled.sim) S3method(summary,Relogit) S3method(summary,Relogit2) S3method(summary,setx) S3method(summary,sim) S3method(summary,zelig) S3method(t,setx) S3method(terms,multiple) S3method(terms,vglm) S3method(terms,zelig) S3method(vcov,gee.naive) S3method(vcov,gee.robust) S3method(vcov,glm.robust) S3method(vcov,Relogit) S3method(vcov,zelig) Zelig/demo/0000755000176000001440000000000012215712501012274 5ustar ripleyusersZelig/demo/vertci.R0000644000176000001440000000162612151655275013735 0ustar ripleyusers ## Attaching the sample turnout dataset: data(turnout) ## Estimate the model: user.prompt() z.out <- zelig(vote ~ race + educate + age + I(age^2) + income, model = "logit", data = turnout) user.prompt() summary(z.out) ## Creating setx structures with education set to high school and ## post-college levels, for the whole range of the age variable. user.prompt() x.low <- setx(z.out, educate = 12, age = 18:95) x.high <- setx(z.out, educate = 16, age = 18:95) ## Using sim to generate the simulated predicted probabilites: user.prompt() s.out <- sim(z.out, x = x.low, x1 = x.high) user.prompt() plot.ci(s.out, xlab = "Age in Years", ylab = "Predicted Probability of Voting", main = "Effect of Education and Age on Voting Behavior") text(x=50,y=.95,labels="College Education (16 years)",cex=0.6) text(x=60,y=.8,labels="High School Education (12 years)",cex=0.6) Zelig/demo/lognorm.R0000644000176000001440000000124712061700507014102 0ustar ripleyuserslibrary(ZeligCommon) # Load the sample data: data(coalition) # Estimate the model: user.prompt() z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm", data = coalition) user.prompt() # View the regression output: summary(z.out) # Set the baseline values (with the ruling coalition in the minority) # and the alternative values (with the ruling coalition in the majority) # for X: user.prompt() x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) # Simulate expected values qi$ev and first differences qi$fd: user.prompt() s.out <- sim(z.out, x = x.low, x1 = x.high) user.prompt() summary(s.out) user.prompt() plot(s.out) Zelig/demo/logit.survey.R0000644000176000001440000000734612061700507015105 0ustar ripleyusers##### Example 1: User has Existing Sample Weights ##### # Attach sample data: data(api, package="survey") # In this example, we will estimate a model using # the percentages of students who receive subsidized # lunch and the percentage who are new to a school # to predict whether each California public school # attends classes year round. z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", weights=~pw, data = apistrat) summary(z.out1) # Set explanatory variables to their default (mean/mode) values, and set # a high (80th percentile) and low (20th percentile) value for "meals," # the percentage of students who receive subsidized meals: x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) # Generate first differences for the effect of high versus low "meals" # on the probability that a school will hold classes year round: s.out1 <- sim(z.out1, x=x.low, x1=x.high) summary(s.out1) # Generate a second set of fitted values and a plot: plot(s.out1) #### Example 2: User has Details about Complex Survey Design #### #### (but not sample weights) #### # Suppose that the survey house that provided # the dataset excluded probability weights # but made other details about the survey # design available. We can still estimate # a model without probability weights that takes # instead variables that identify each the stratum # and/or cluster from which each observation was # selected and the size of the finite sample from # which each observation was selected. z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", strata=~stype, fpc=~fpc, data = apistrat) summary(z.out2) # The coefficient estimates from this model are identical to # point estimates in the previous example, but the standard errors # are smaller. When sampling weights are omitted, Zelig estimates # them automatically for "normal.survey" models based on the # user-defined description of sampling designs. In addition, # when user-defined descriptions of the sampling design are # entered as inputs, variance estimates are better and standard # errors are consequently smaller. # # setx() and sim() can then be run on z.out2 in the same fashion # described in Example 1. ##### Example 3: User has Replicate Weights ##### # Load data for a model using the number of out-of-hospital # cardiac arrests and the number of patients who arrive # alive in hospitals to predict whether each hospital # has been sued (an indicator variable artificially created # here for the purpose of illustration). data(scd) scd$sued <- as.vector(c(0,0,0,1,1,1)) # Again, for the purpose of illustration, create four Balanced # Repeated Replicate (BRR) weights: BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) # Estimate the model using Zelig: z.out3 <- zelig(formula=sued ~ arrests + alive , model = "logit.survey", repweights=BRRrep, type="BRR", data=scd) summary(z.out3) # Set the explanatory variables at their means and set # arrests at its 20th and 80th quartiles x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) # Generate first differences for the effect of the minimum # versus the maximum number of individuals who arrive # alive on the probability that a hospital will be sued: s.out3 <- sim(z.out3, x=x.high, x1=x.low) summary(s.out3) # Generate a second set of fitted values and a plot: plot(s.out3) #### The user should also refer to the logit model demo, since #### #### logit.survey models can take many of the same options as #### #### logit models. #### Zelig/demo/ls.R0000644000176000001440000000321412061700507013037 0ustar ripleyusers##### Example 1: Basic Example with First Differences ##### # Attach sample data and variable names: data(macro) # Estimate model and present a summary: z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro) # Set explanatory variables to their default (mean/mode) values, with # high (80th percentile) and low (20th percentile) values: x.high<- setx(z.out1, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2)) x.high x.low # Generate first differences for the effect of high versus low trade on # GDP: s.out1 <- sim(z.out1, x = x.high, x1 = x.low) # Summary of fitted statistical model summary(z.out1) # Summary of simualted quantities of interest summary(s.out1) # Plot of simulated quantities of interest plot(s.out1) ##### Example 2: Using Dummy Variables ##### # Estimate a model with a dummy variable for each year and country. # Note that you do not need to create dummy variables, as the program # will automatically parse the unique values in the selected variables # into dummy variables. z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country), model = "ls", data = macro) # Set values for the explanatory variables, using the default mean/mode # values, with country set to the United States and Japan, respectively: x.US <- setx(z.out2, country = "United States") x.Japan <- setx(z.out2, country = "Japan") # Simulate quantities of interest: s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan) # Summary of fitted statistical model summary(z.out2) # Summary of simulated quantities of interest summary(s.out2) # Plot differences: plot(s.out2) Zelig/demo/factor.bayes.R0000644000176000001440000000150312061700507015000 0ustar ripleyusers## Attaching the example dataset: data(swiss) names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort") user.prompt() ## Estimating the model using MCMCfactanal: z.out <- zelig(cbind(Agr,Exam,Educ,Cath,InfMort)~NULL, model="factor.bayes", data=swiss, factors=2, lambda.constraints=list(Exam=list(1,"+"), Exam=list(2,"-"), Educ=c(2,0), InfMort=c(1,0)), verbose=TRUE, a0=1, b0=0.15, burnin=5000, mcmc=50000) user.prompt() ## Checking for convergence before summarizing the estimates: geweke.diag(z.out$result$coefficients) user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) Zelig/demo/logit.R0000644000176000001440000000220112061700507013532 0ustar ripleyusers# Attach the data frame data(turnout) ## Generating empirical estimates: z.out1 <- zelig(vote ~ age + race, model = "logit", data = turnout) ## Using setx to generate baseline and alternative velus for the ## explanatory variables. x.out1 <- setx(z.out1, age = 36, race = "white") x.out1 ## Simulating quantities of interest (predicted probabilites, risk ## ratios, and risk differences): s.out1 <- sim(z.out1, x = x.out1) # Summary of fitted statistical model summary(z.out1) # Summary of simulations of quantities of interest summary(s.out1) # Plot simulations of quantities of interest plot(s.out1) ## Example 2: First Differences # Fit the statistical model z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout) # Set alternate values x.high <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.25)) s.out2 <- sim(z.out2, x = x.high, x1 = x.low) # Summary of the fitted model summary(z.out2) # Summary of the simulated quantities of interest summary(s.out2) # Plot of the simulated quantities of interest plot(s.out2) Zelig/demo/mi.R0000644000176000001440000000025112061700507013024 0ustar ripleyuserslibrary(Zelig) data(turnout) z <- zelig(vote ~ age, model = "logit", data = mi(turnout[1:10, ], turnout[100:110, ])) x <- setx(z, age = 90) s <- sim(z, x=x, num=20) Zelig/demo/gamma.survey.R0000644000176000001440000000647412061700507015052 0ustar ripleyusers##### Example 1: User has Existing Sample Weights ##### # Attach sample data and variable names: data(api) # In this example, we will estimate a model using # the percentages of students who receive subsidized # lunch and an indicator for whether schooling is # year-round to predict California public schools' # academic performance index scores: z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey", weights=~pw, data = apistrat) summary(z.out1) # Set explanatory variables to their default (mean/mode) values, and set # a high (80th percentile) and low (20th percentile) value for "meals," # the percentage of students who receive subsidized meals: x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) # Generate first differences for the effect of high versus low "meals" # on academic performance: s.out1 <- sim(z.out1, x=x.high, x1=x.low) summary(s.out1) # Generate a second set of fitted values and a plot: plot(s.out1) #### Example 2: User has Details about Complex Survey Design #### #### (but not sample weights) #### # Suppose that the survey house that provided # the dataset excluded probability weights # but made other details about the survey # design available. We can still estimate # a model without probability weights that takes # instead variables that identify each the stratum # and/or cluster from which each observation was # selected and the size of the finite sample from # which each observation was selected. z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey", strata=~stype, fpc=~fpc, data = apistrat) summary(z.out2) # Note that these results are identical to the results obtained # when pre-existing sampling weights were used. When sampling # weights are omitted, Zelig estimates them automatically for # "gamma.survey" models based on the user-defined description # of sampling designs. If no description is present, the default # assumption is equal probability sampling. # # setx() and sim() can then be run on z.out2 in the same fashion # described in Example 1. ##### Example 3: User has Replicate Weights ##### # Suppose that the survey house that published # these data withheld details about the survey # design and instead published replication weights # For the purpose of illustration, create a set of # jk1 replicate weights jk1reps <- jk1weights(psu=apistrat$dnum) # Estimate the model regressing api00 on the "meals" # "yr.rnd" variables. z.out3 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey", data = apistrat, repweights=jk1reps$weights, type="JK1") summary(z.out3) # Set the explanatory variable "meals" at high and low values x.low <- setx(z.out3, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out3, meals= quantile(apistrat$meals, 0.8)) # Generate first differences for the effect of the high # versus low concentrations of poverty on school performance s.out3 <- sim(z.out3, x=x.high, x1=x.low) summary(s.out3) # Generate a second set of fitted values and a plot: plot(s.out3) #### The user should also refer to the gamma model demo, since #### #### gamma.survey models can take many of the same options as #### #### gamma models. ####Zelig/demo/mlogit.bayes.R0000644000176000001440000000237212061700507015022 0ustar ripleyusers## Attaching the example dataset: data(mexico) ## Estimating the model using mlogit.bayes: z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes", data = mexico) user.prompt() ## Checking for convergence before summarizing the estimates: heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## Summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences: ## Setting explanatory variables to their default(mean/mode) ## values, with pristr(the strength of PRI) equal to 1(weak) or ## 3(strong) x.weak <- setx(z.out, pristr = 1) x.strong <- setx(z.out, pristr = 3) user.prompt() ## Estimating the first difference for the effect of ## military action on the probabilities of ## incurring differnt level of cost: s.out2 <- sim(z.out, x = x.strong, x1 = x.weak) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/relogit.R0000644000176000001440000000137512063675316014107 0ustar ripleyusersdata(mid) user.prompt() ## prior correction + bias correction z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772) user.prompt() summary(z.out1) user.prompt() x.out1 <- setx(z.out1) user.prompt() s.out1 <- sim(z.out1, x = x.out1) user.prompt() summary(s.out1) user.prompt() plot(s.out1) ## weighting + bias correction + robust s.e. z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772, case.control = "weighting", robust = TRUE) user.prompt() summary(z.out2) user.prompt() x.out2 <- setx(z.out2) user.prompt() s.out2 <- sim(z.out2, x = x.out2) user.prompt() Zelig/demo/normal.bayes.R0000644000176000001440000000252112061700507015013 0ustar ripleyusers## Attaching the example dataset: data(macro) ## Estimating the model using normal.bayes: z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes", data = macro, verbose=TRUE) user.prompt() ## Checking for convergence before summarizing the estimates: geweke.diag(z.out$result$coefficients) user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences ## Set explanatory variables to their default(mean/mode) values, ## with high (80th percentile) and low (20th percentile) trade on GDP: x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8)) x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2)) user.prompt() ## Estimating the first difference for the effect of ## high versus low trade on unemployment rate: s.out2 <- sim(z.out, x = x.high, x1 = x.low) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/exp.R0000644000176000001440000000071112061700507013214 0ustar ripleyusers# exp # exp # exp # Fit the statistical model data(coalition) z.out <- zelig(Surv(duration, ciep12) ~ invest + polar + numst2 + crisis, model = "exp", data = coalition) user.prompt() # Set explanatory variables x.low<- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) user.prompt() # Simulate quantities of interest s.out <- sim(z.out, x = x.low, x1 = x.high, num = 10) summary(s.out) user.prompt() # Plot simualted results plot(s.out) Zelig/demo/poisson.gee.R0000644000176000001440000000143312061700507014653 0ustar ripleyusers##### Example 1: Basic Example ##### # Attach sample data and variable names: data(sanction) # Variable identifying clusters sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3)) # Sorting by cluster sorted.sanction <- sanction[order(sanction$cluster),] # Estimate model and present a summary: user.prompt() z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust=TRUE, corstr="exchangeable") user.prompt() summary(z.out) # Set explanatory variables to their default values: user.prompt() x.out <- setx(z.out) # Simulate quantities of interest user.prompt() s.out <- sim(z.out, x = x.out) user.prompt() summary(s.out) # Generate a plot of quantities of interest: user.prompt() plot(s.out) Zelig/demo/logit.gee.R0000644000176000001440000000342012061700507014275 0ustar ripleyusers## Attaching the sample turnout dataset: data(turnout) ## Variable identifying clusters turnout$cluster <- rep(c(1:200),10) ## Sorting by cluster sorted.turnout <- turnout[order(turnout$cluster),] ##### Example 1: Simple Example with Stationary 3 Dependence ## Generating empirical estimates: user.prompt() z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = T, corstr = "stat_M_dep", Mv=3) user.prompt() ## Viewing the regression output: summary(z.out1) ## Using setx to generate baseline and alternative values for the ## explanatory variables. user.prompt() x.out1 <- setx(z.out1) ## Simulating quantities of interest: user.prompt() s.out1 <- sim(z.out1, x = x.out1) user.prompt() ## Summarizing the simulated quantities of interest: summary(s.out1) ## Diagnostic plot of the s.out: user.prompt() plot(s.out1) ## Example 2: First Differences user.prompt() x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) user.prompt() s.out2 <- sim(z.out1, x = x.high, x1 = x.low) user.prompt() summary(s.out2) user.prompt() plot(s.out2) ##### Example 3: Example with Fixed Correlation Structure ## User-defined correlation structure user.prompt() corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ## Generating empirical estimates: user.prompt() z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat) user.prompt() ## Viewing the regression output: summary(z.out2) Zelig/demo/twosls.R0000644000176000001440000000060012061700507013750 0ustar ripleyusersdata(klein) formula <- list( mu1 = C ~ Wtot + P1, mu2 = I ~ P + P1 + K1, mu3 = Wp ~ X + X1 + Tm, inst= ~ P1 + K1 + X1 + Tm + Wg + G ) z.out<-zelig(formula=formula, model="twosls",data=klein, cite=F) x.out <-setx(z.out) s.out <-sim(z.out,x=x.out) summary(s.out) # Plot user.prompt() plot(s.out) Zelig/demo/normal.R0000644000176000001440000000144212061700507013712 0ustar ripleyuserslibrary(Zelig) ##### Example 1: Basic Example with First Differences ##### # Attach sample data and variable names: data(macro) # Estimate model and present a summary: z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal", data = macro) # Set explanatory variables to their default (mean/mode) values, with # high (80th percentile) and low (20th percentile) values: x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2)) # Generate first differences for the effect of high versus low trade on # GDP: s.out1 <- sim(z.out1, x = x.high, x1 = x.low) # Summarize the fitted model summary(z.out1) # Summarize the simulated quantities of interest summary(s.out1) # Plot the simulated quantities of interest plot(s.out1) Zelig/demo/normal.gee.R0000644000176000001440000000155312061700507014454 0ustar ripleyusers##### Example 1: Basic Example with First Differences ##### # Attach sample data and variable names: data(macro) # Estimate model and present a summary: user.prompt() z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1) user.prompt() summary(z.out) # Set explanatory variables to their default (mean/mode) values, with # high (80th percentile) and low (20th percentile) values: user.prompt() x.high <- setx(z.out, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out, trade = quantile(macro$trade, 0.2)) # Generate first differences for the effect of high versus low trade on # GDP: user.prompt() s.out <- sim(z.out, x = x.high, x1 = x.low) user.prompt() summary(s.out) # Generate a plot of quantities of interest: user.prompt() plot(s.out) Zelig/demo/oprobit.bayes.R0000644000176000001440000000277712061700507015216 0ustar ripleyusers## Attaching the example dataset: data(sanction) # Create an ordered dependent variable: user.prompt() sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain", "little effect", "modest loss", "major loss")) ## Estimating the model using oprobit.bayes: z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes", data = sanction, verbose=FALSE, tune=0.3) user.prompt() ## Checking for convergence before summarizing the estimates: #geweke.diag(z.out$coefficients) #user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences: ## Setting explanatory variables to their default(mean/mode) ## values, with military action to be yes(1) or no(0) x.high <- setx(z.out, mil=0) x.low <- setx(z.out, mil=1) user.prompt() ## Estimating the first difference for the effect of ## military action on the probabilities of ## incurring differnt level of cost: s.out2 <- sim(z.out, x = x.high, x1 = x.low) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/00Index0000644000176000001440000000356412215712501013436 0ustar ripleyusersexp Exponential regression and simulation gamma Gamma regression and simulation logit Logit regression and simulation lognorm Lognormal regression and simulation ls Least Squares regression and simulation mi Multiply imputed regressions and simulations negbinom Negative Binomial regression and simulation normal Normal (Gaussian) regression and simulation poisson Poisson regression and simulation probit Probit regression and simulation relogit Rare events logit regression and simulation twosls Two Stage Least Squares factor.bayes MCMC factor analysis logit.bayes MCMC logistic regression model and simulation normal.bayes MCMC regression model and simulation probit.bayes MCMC probit regression model and simulation poisson.bayes MCMC poisson regression model and simulation mlogit.bayes MCMC multinomial regression model and simulation oprobit.bayes MCMC ordered probit regression model and simulation logit.gee GEE logistic regression gamma.gee GEE gamma regression normal.gee GEE normal regression poisson.gee GEE poisson regression probit.gee GEE probit regression normal.survey Survey-Weighted Normal Regression for Continuous Dependent Variables logit.survey Survey-Weighted Logistic Regression for Dichotomous Dependent Variables probit.survey Survey-Weighted Probit Regression for Dichotomous Dependent Variables poisson.survey Survey-Weighted Poisson Regression for Event-count Dependent Variables gamma.survey Survey-Weighted Poisson Regression for Positive Continuous Dependent Variables vertci Confidence intervals across the range of a continuous covariate Zelig.HelloWorld Step-by-step demo on creating Zelig packages Zelig/demo/probit.gee.R0000644000176000001440000000342112061700507014457 0ustar ripleyusers## Attaching the sample turnout dataset: data(turnout) ## Variable identifying clusters turnout$cluster <- rep(c(1:200),10) ## Sorting by cluster sorted.turnout <- turnout[order(turnout$cluster),] ##### Example 1: Simple Example with Stationary 3 Dependence ## Generating empirical estimates: user.prompt() z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = T, corstr = "stat_M_dep", Mv=3) user.prompt() ## Viewing the regression output: summary(z.out1) ## Using setx to generate baseline and alternative values for the ## explanatory variables. user.prompt() x.out1 <- setx(z.out1) ## Simulating quantities of interest: user.prompt() s.out1 <- sim(z.out1, x = x.out1) user.prompt() ## Summarizing the simulated quantities of interest: summary(s.out1) ## Diagnostic plot of the s.out: user.prompt() plot(s.out1) ## Example 2: First Differences user.prompt() x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) user.prompt() s.out2 <- sim(z.out1, x = x.high, x1 = x.low) user.prompt() summary(s.out2) user.prompt() plot(s.out2) ##### Example 3: Example with Fixed Correlation Structure ## User-defined correlation structure user.prompt() corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ## Generating empirical estimates: user.prompt() z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat) user.prompt() ## Viewing the regression output: summary(z.out2) Zelig/demo/poisson.bayes.R0000644000176000001440000000246312061700507015222 0ustar ripleyusers## Attaching the example dataset: data(sanction) ## Estimating the model using poisson.bayes: z.out <- zelig(num ~ target + coop, model = "poisson.bayes", data = sanction, verbose=TRUE) user.prompt() ## Checking for convergence before summarizing the estimates: geweke.diag(z.out$result$coefficients) user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences: ## Setting explanatory variables to their default(mean/mode) ## values, with the number of targets to be its maximum ## versus its minimum: x.max <- setx(z.out, target = max(sanction$target)) x.min <- setx(z.out, target = min(sanction$target)) user.prompt() ## Estimating the first difference for the effect of ## maximum versus minimum number of targets: s.out2 <- sim(z.out, x = x.max, x1 = x.min) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/probit.R0000644000176000001440000000213412061700507013720 0ustar ripleyusers## Attaching the sample turnout dataset: data(turnout) ##### Example 1: Simple Example ## Generating empirical estimates: z.out1 <- zelig(vote ~ race + educate, model = "probit", data = turnout) ## Viewing the regression output: ## Using setx to generate baseline and alternative velus for the ## explanatory variables. x.out1 <- setx(z.out1) x.out1 ## Simulating quantities of interest (predicted probabilites, risk ## ratios, and risk differences): s.out1 <- sim(z.out1, x = x.out1) # Summary of fitted the statistical model summary(z.out1) # Summary of the simulated quantities of interest summary(s.out1) ## Diagnostic plot of the s.out: plot(s.out1) ## Example 2: First Differences x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) # Simulate quantities of interest (include first-differences, etc.) s.out2 <- sim(z.out1, x = x.low, x1 = x.high) # Summary of quantities of interest (for difference in x.low and x.high summary(s.out2) # Plot of quantities of interest plot(s.out2) Zelig/demo/probit.survey.R0000644000176000001440000000734612061700507015266 0ustar ripleyusers##### Example 1: User has Existing Sample Weights ##### # Attach sample data: data(api, package="survey") # In this example, we will estimate a model using # the percentages of students who receive subsidized # lunch and the percentage who are new to a school # to predict whether each California public school # attends classes year round. z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", weights=~pw, data = apistrat) summary(z.out1) # Set explanatory variables to their default (mean/mode) values, and set # a high (80th percentile) and low (20th percentile) value for "meals," # the percentage of students who receive subsidized meals: x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) # Generate first differences for the effect of high versus low "meals" # on the probability that a school will hold classes year round: s.out1 <- sim(z.out1, x=x.low, x1=x.high) summary(s.out1) # Generate a second set of fitted values and a plot: plot(s.out1) #### Example 2: User has Details about Complex Survey Design #### #### (but not sample weights) #### # Suppose that the survey house that provided # the dataset excluded probability weights # but made other details about the survey # design available. We can still estimate # a model without probability weights that takes # instead variables that identify each the stratum # and/or cluster from which each observation was # selected and the size of the finite sample from # which each observation was selected. z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", strata=~stype, fpc=~fpc, data = apistrat) summary(z.out2) # The coefficient estimates from this model are identical to # point estimates in the previous example, but the standard errors # are smaller. When sampling weights are omitted, Zelig estimates # them automatically for "normal.survey" models based on the # user-defined description of sampling designs. In addition, # when user-defined descriptions of the sampling design are # entered as inputs, variance estimates are better and standard # errors are consequently smaller. # # setx() and sim() can then be run on z.out2 in the same fashion # described in Example 1. ##### Example 3: User has Replicate Weights ##### # Load data for a model using the number of out-of-hospital # cardiac arrests and the number of patients who arrive # alive in hospitals to predict whether each hospital # has been sued (an indicator variable artificially created # here for the purpose of illustration). data(scd) scd$sued <- as.vector(c(0,0,0,1,1,1)) # Again, for the purpose of illustration, create four Balanced # Repeated Replicate (BRR) weights: BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) # Estimate the model using Zelig: z.out3 <- zelig(formula=sued ~ arrests + alive , model = "probit.survey", repweights=BRRrep, type="BRR", data=scd) summary(z.out3) # Set the explanatory variables at their means and set # arrests at its 20th and 80th quartiles x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) # Generate first differences for the effect of the minimum # versus the maximum number of individuals who arrive # alive on the probability that a hospital will be sued: s.out3 <- sim(z.out3, x=x.high, x1=x.low) summary(s.out3) # Generate a second set of fitted values and a plot: plot(s.out3) #### The user should also refer to the probit model demo, since #### #### probit.survey models can take many of the same options as #### #### probit models. #### Zelig/demo/gamma.R0000644000176000001440000000121312061700507013500 0ustar ripleyusersdata(coalition) # Fit the statistical model z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition) ## Setting the explanatory variables at their default values ## (mode for factor variables and mean for non-factor variables), ## with numst2 set to the vector 0 = no crisis, 1 = crisis. x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) ## Simulating draws using the default bootstrap method. s.out <- sim(z.out, x = x.low, x1 = x.high) # Summary of fitted model summary(z.out) # Summary of simulated quantities of interest summary(s.out) # Plot of simulated quantities of interest plot(s.out) Zelig/demo/logit.bayes.R0000644000176000001440000000255012061700507014643 0ustar ripleyusers## Attaching the example dataset: data(turnout) ## Estimating the model using MCMClogit: z.out <- zelig(vote ~ race + educate, model = "logit.bayes", data = turnout, verbose=TRUE) user.prompt() ## Checking for convergence before summarizing the estimates: geweke.diag(z.out$result$coefficients) user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences: ## Setting education is set to be between low(25th percentile) ## versus high(75th percentile) while all the other variables ## held at their default values. x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25)) user.prompt() ## Estimating the first difference for the effect of ## high versus low trade on unemployment rate: s.out2 <- sim(z.out, x = x.high, x1 = x.low) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/Zelig.HelloWorld.R0000644000176000001440000001264212061700507015552 0ustar ripleyusers## Load data data(turnout) # The following demo is a step-by-step instruction guide on building a Zelig # model. For the most part, the steps have been simplified, and the model # itself is simply written to show broad ideas, rather than the specifics # of developing a fully functioning statistical model user.prompt("Press to Read about External Methods") # Step 1: Creating and Using External Methods (optional) # ====================================================== # Create a model to be used to be used by the Zelig function. This method # should be designed with the singular purpose of fitting a statistical model. # That is, it should analyze a data-set given several parameters # # For the most part, this step is optional, as quite often R contains builtin # functions for doing these kinds of analyses. Regardless, this step is kept # here for completeness. # # The foreign model, in its simplest form, has only one of two requirements, # either: # 1. The model contains a slot labeled "formula", or # 2. There is a "formula" method defined for objects of this class user.prompt("Press to Continue to Step 1") HelloWorldMethod <- function(formula, verbose=TRUE, data) { if (verbose) { print.form <- paste(as.character(formula), collapse=" ") print.data <- as.character(substitute(data)) cat("Hello, Zelig!\n") } x <- list(formula = formula) class(x) <- "HelloWorld" x } user.prompt("Press to Read about Describing Zelig Models") # Step 2: Describing Zelig Models (optional) # ========================================== # Describing the model is an optional, though important step if the developer # would like to be correctly cited in scholarly documents. In its most basic # form, it is simply a list specifying "authors", "text" as the title-text, # and and publication year. user.prompt("Press to Continute to Step 2") describe.hello <- function (...) { list(authors = "You", text='A "Hello, World!" Model') } user.prompt("Press to Read about zelig2 Functions") # Step 3: Interfacing between the External Model and Zelig (crucial) # ================================================================== # The 'zegli2' function of a model is named in the style the model's name # appended to "zelig2". This informs Zelig that a model by the appropriate # name exists. In this demo, "hello" is the model's name, and, as such, # the zelig2 function is named "zelig2hello". # # In the upcoming example, please note that the parameters of the external # method "HelloWorldMethod" are all included within the list that is being # returned from the "zelig2hello" function. # # In general, all "zelig2" functions follow this format. For more detailed # information concerning "zelig2" functions, type: # ?zelig2 # # within an R session. user.prompt("Press to See an Example of a \"zelig2\" Method") zelig2hello <- function (formula, ..., data) { list( .function = "HelloWorldMethod", formula = formula, data = data ) } user.prompt('Press to Read about the "param" Functions') # Step 4: Simulating Parameters # ============================= # The "param" function of a Zelig model is written by concatenating "param." # with the model's name. In the ongoing example, the "hello" model will have # a param function named "param.hello". # # The retun value of a "param" function is a list optionally containing the # values: simulations, alpha, link, linkinv, and family. For more detailed # concerning writing "param" functions, type: # ?param # # within an R session. user.prompt('Press to See an Example "param" Function') param.hello <- function(obj, num=1000, ...) { list( simulations = rbinom(n=num, 1, .5), alpha = .5, linkinv = NULL ) } user.prompt('Press to Read about "qi" Methods') # Step 5: Simulating Quantities of Interest # ========================================= # The "qi" method of a Zelig model is written by concatentating "qi." with the # model's name. In the ongoing example, the "hello" model will have a qi method # named "qi.hello". # # The return-value of a qi method is a list pairing titles of quantities of # interest and their simulations. For example, a model that computes # "Expected Values" will have a return value: # list("Expected Values" = ev) # # where 'ev' is a variable containing the simulated expected value. For more # detailed information concerning writing 'qi' methods, type: # ?qi # # within an R session. user.prompt('Press to See and Example "qi" Method') qi.hello <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { possibilities <- c('Hello', 'World') success.prob <- alpha(param) sims <- rbinom(n=num, 1, success.prob) + 1 pv <- possibilities[sims] list( "Predicted Value: E(Y|X)" = pv ) } user.prompt('Press to Read More about Zelig') # More Information about Zelig # ============================ # That's it! Now that the zelig2, qi, and param methods are defined, Zelig can # run the "hello" model. For more detailed information concerning the Zelig # package, visit: # http://gking.harvard.edu/zelig # # or type: # ?Zelig # # within an R-session user.prompt('Press to see the results of the "hello" model') ## Run Zelig Functions z <- zelig(~ 1, model="hello", data=turnout) x <- setx(z) s <- sim(z) ## Display Fictional Summary summary(s) Zelig/demo/negbinom.R0000644000176000001440000000063512061700507014223 0ustar ripleyusers# Attach the data-frame data(sanction) # Fit the statistical model z <- zelig(num ~ target + coop, model = "negbinom", data = sanction) # Set explanatory variables (in this case, nothing is explicitly set) x <- setx(z) # Simulate Quantities of Interest s <- sim(z, x) # Summarize the statistical model summary(z) # Summarize the simulated quantities of interest summary (s) # Plot the results plot(s) Zelig/demo/poisson.survey.R0000644000176000001440000000701312061700507015450 0ustar ripleyusers##### Example 1: User has Existing Sample Weights ##### # Attach sample data: data(api, package="survey") # In this example, we will estimate a model using # each school's academic performance in 2000 and an # indicator for year-round schools to predict the # number of students who enrolled in each California school. z.out1 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat) summary(z.out1) # Set explanatory variables to their default (mean/mode) values, and set # a high (80th percentile) and low (20th percentile) value for the # measure of academic performance, "api00": x.low <- setx(z.out1, api00= quantile(apistrat$api00, 0.2)) x.high <- setx(z.out1, api00= quantile(apistrat$api00, 0.8)) # Generate first differences for the effect of high versus low "meals" # on the probability that a school will hold classes year round: s.out1 <- sim(z.out1, x=x.low, x1=x.high) summary(s.out1) # Generate a second set of fitted values and a plot: plot(s.out1) #### Example 2: User has Details about Complex Survey Design #### #### (but not sample weights) #### # Suppose that the survey house that provided # the dataset excluded probability weights # but made other details about the survey # design available. We can still estimate # a model without probability weights that takes # instead variables that identify each the stratum # and/or cluster from which each observation was # selected and the size of the finite sample from # which each observation was selected. z.out2 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat, strata=~stype, fpc=~fpc) summary(z.out2) # The coefficient estimates from this model are identical to # point estimates in the previous example, but the standard errors # are smaller. When sampling weights are omitted, Zelig estimates # them automatically for "normal.survey" models based on the # user-defined description of sampling designs. In addition, # when user-defined descriptions of the sampling design are # entered as inputs, variance estimates are better and standard # errors are consequently smaller. # # setx() and sim() can then be run on z.out2 in the same fashion # described in Example 1. ##### Example 3: User has Replicate Weights ##### # Load data for a model using the number of out-of-hospital # cardiac arrests to predict the number of patients who arrive # alive in hospitals. data(scd, package="survey") # For the purpose of illustration, create four Balanced # Repeated Replicate (BRR) weights: BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) # Estimate the model using Zelig: z.out3 <- zelig(alive ~ arrests , model = "poisson.survey", repweights=BRRrep, type="BRR", data=scd) summary(z.out3) # Set the explanatory variables at their means and set # arrests at its 20th and 80th quartiles x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2)) x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8)) # Generate first differences for the effect of the minimum # versus the maximum number of individuals who arrive # alive on the probability that a hospital will be sued: s.out3 <- sim(z.out3, x=x.high, x1=x.low) summary(s.out3) # Generate a second set of fitted values and a plot: plot(s.out3) #### The user should also refer to the poisson model demo, since #### #### poisson.survey models can take many of the same options as #### #### poisson models. #### Zelig/demo/probit.bayes.R0000644000176000001440000000255612061700507015032 0ustar ripleyusers## Attaching the example dataset: data(turnout) ## Estimating the model using probit.bayes: z.out <- zelig(vote ~ race + educate, model = "probit.bayes", data = turnout, verbose=TRUE) user.prompt() ## Checking for convergence before summarizing the estimates: geweke.diag(z.out$result$coefficients) user.prompt() heidel.diag(z.out$result$coefficients) user.prompt() raftery.diag(z.out$result$coefficients) user.prompt() ## summarizing the output summary(z.out) user.prompt() ## Setting values for the explanatory variables to ## their sample averages: x.out <- setx(z.out) user.prompt() ## Simulating quantities of interest from the posterior ## distribution given x.out: s.out1 <- sim(z.out, x = x.out) user.prompt() ## Summarizing the simulation results: summary(s.out1) user.prompt() ## Simulating First Differences: ## Setting education is set to be between low(25th percentile) ## versus high(75th percentile) while all the other variables ## held at their default values. x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25)) user.prompt() ## Estimating the first difference for the effect of ## high versus low trade on unemployment rate: s.out2 <- sim(z.out, x = x.high, x1 = x.low) user.prompt() ## Summarizing the simulation results: summary(s.out2) Zelig/demo/poisson.R0000644000176000001440000000073612061700507014121 0ustar ripleyusers# Attach the data frame data(sanction) # Fit the statistical model z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction) # Set explanatory variables (in this case non are explicitly set) x.out <- setx(z.out) # Simulate the quantities of interest s.out <- sim(z.out, x = x.out) # Summary of the statistical model summary(z.out) # Summary of the simulated quantities of interest summary(s.out) # Plot the simulated quantities of interest plot(s.out) Zelig/demo/gamma.gee.R0000644000176000001440000000175412061700507014251 0ustar ripleyusers##### Example 1: Basic Example ##### # Attach sample data and variable names: data(coalition) # Variable identifying clusters coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4)) # Sorting by cluster sorted.coalition <- coalition[order(coalition$cluster),] # Estimate model and present a summary: user.prompt() z.out <- zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition, robust=TRUE, corstr="exchangeable") user.prompt() summary(z.out) # Setting the explanatory variables at their default values # (mode for factor variables and mean for non-factor variables), # with numst2 set to the vector 0 = no crisis, 1 = crisis. user.prompt() x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) # Simulate quantities of interest user.prompt() s.out <- sim(z.out, x = x.low, x1 = x.high) user.prompt() summary(s.out) # Generate a plot of quantities of interest: user.prompt() plot(s.out) Zelig/demo/normal.survey.R0000644000176000001440000000647512061700507015261 0ustar ripleyusers##### Example 1: User has Existing Sample Weights ##### # Attach sample data and variable names: data(api) # In this example, we will estimate a model using # the percentages of students who receive subsidized # lunch and an indicator for whether schooling is # year-round to predict California public schools' # academic performance index scores: z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey", weights=~pw, data = apistrat) summary(z.out1) # Set explanatory variables to their default (mean/mode) values, and set # a high (80th percentile) and low (20th percentile) value for "meals," # the percentage of students who receive subsidized meals: x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2)) x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8)) # Generate first differences for the effect of high versus low "meals" # on academic performance: s.out1 <- sim(z.out1, x=x.high, x1=x.low) summary(s.out1) # Generate a second set of fitted values and a plot: plot(s.out1) #### Example 2: User has Details about Complex Survey Design #### #### (but not sample weights) #### # Suppose that the survey house that provided # the dataset excluded probability weights # but made other details about the survey # design available. We can still estimate # a model without probability weights that takes # instead variables that identify each the stratum # and/or cluster from which each observation was # selected and the size of the finite sample from # which each observation was selected. z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey", strata=~stype, fpc=~fpc, data = apistrat) summary(z.out2) # Note that these results are identical to the results obtained # when pre-existing sampling weights were used. When sampling # weights are omitted, Zelig estimates them automatically for # "normal.survey" models based on the user-defined description # of sampling designs. If no description is present, the default # assumption is equal probability sampling. # # setx() and sim() can then be run on z.out2 in the same fashion # described in Example 1. ##### Example 3: User has Replicate Weights ##### # Load data for a model using the number of out-of-hospital # cardiac arrests to predict the number of patients who arrive # alive in hospitals: data(scd) # Create four Balanced Repeated Replicate (BRR) weights: BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) # Estimate the model using Zelig: z.out3 <- zelig(formula=alive ~ arrests , model = "normal.survey", repweights=BRRrep, type="BRR", data=scd, na.action=NULL) summary(z.out3) # Set the explanatory variable at its minimum and maximum x.min <- setx(z.out3, arrests = min(scd$alive)) x.max <- setx(z.out3, arrests = max(scd$alive)) # Generate first differences for the effect of the minimum # versus the maximum number of cardiac arrests on the number # of people who arrive alive: s.out3 <- sim(z.out3, x=x.max, x1=x.min) summary(s.out3) # Generate a second set of fitted values and a plot: plot(s.out3) #### The user should also refer to the normal model demo, since #### #### normal.survey models can take many of the same options as #### #### normal models. #### Zelig/NEWS0000644000176000001440000001770112216140074012056 0ustar ripleyusersThe Zelig core team is pleased to announce the alpha release of Zelig 4. Designated as the "Developer Update", Zelig 4 offers a wide-range of improvements to ease the process of adding new statistical models to the already extensive Zelig software suite. Significantly, this release is packaged with a brand-new API, geared towards reducing the complexity and length of Zelig's development functions - the zelig2, param and qi methods. In addition to this, Zelig now brandishes a package-creator (zelig.skeleton) that operates in the same vein as R's core function "package.skeleton". In addition to changes in the development toolkit, Zelig has now been split across 13 distinct packages. This change has been made to refine the scope of Zelig and its add-ons. In particular, this restructuring of Zelig into a full software suite allows developers to contribute, develop and repair add-on packages without tinkering with the Zelig API and core functionality. While this release's prime focus has been improving the developer toolkit and restructuring the software suite, Zelig 4 offers an end-user experience completely identical to previous versions. That is, zelig's basic functions - zelig, setx and sim - ostensibly remain unchanged in functionality for available statistical models. For full details concerning changes between Zelig 3.5 and Zelig 4, please refer to: http://zeligdev.github.com/ New Features ------------ Some of the new available features are: A revised developer API. The primary developer methods - zelig2, param and sim - have been reimplemented to use a sleeker, simpler API. For information, please read the Zelig developer's manual found here: http://zeligdev.github.com/files/booklet.pdf The core package has been restructured and minimized. In particular, Zelig core now contains only code essential to its operation, while all non-essential tasks have been made into specific R-packages. For a complete list of official Zelig packages, please refer to: https://github.com/zeligdev Development tools for contributors have been added to the core package. In particular, the "zelig.skeleton" function is packaged within Zelig-core in order to facilitate the rapid development of new Zelig packages. The Zelig software suite has grown to include a total of 7 R-packages. This change offers a simple and easy method for ensuring that development and bug-fixing within any particular Zelig add-on will leave the remainder of the Zelig software suite unchanged. A hook API has been integrated into the core package, in order to reduce the necessity to directly alter the zelig, setx and sim methods. Roxygen-compliant documentation has become standard in all Zelig packages. This offers an easy way to manage Rd documentation, dependencies and exports from within the R code itself. That is, documentation is more tightly paired with the actual R code. For more information about Roxygen, please refer to: http://roxygen.org/ GitHub ------ Zelig is now on GitHub! Fork an add-on package or contribute bug-finds today! For a full listing of official packages and their repositories, please see: https://github.com/zeligdev Links ----- The following comprises a list of relevant information for Zelig 4: * Website: http://zeligdev.github.com/ * Package Repositories: https://github.com/zeligdev/ * Installation Guide: http://zeligdev.github.com/files/zelig.pdf * Zelig Manual: http://zeligdev.github.com/#install * Available and Missing Add-on Packages: http://zeligdev.github.com/#models Questions --------- For any particular questions on developing new Zelig models, please send all mail to: zelig@lists.gking.harvard.edu Zelig v4.0-4 Release Notes (May 16, 2011) Introduction ================================================================================ This document is a brief overview of the current state of the Zelig project as of the 4.0-3 release. This release hopes to maintain the canonical Zelig syntax and interface for end-users, while supplying developers with tools to aid in the development of effective statistical modeling techniques. Emphasis has been placed on readability and modularity. As a result of this gargantuan change, a plethora of features, API functionality, and documentation has been added to the Zelig R-package. Several previously existing models, however, have been removed temporarily or moved from the Zelig core package to more-specific Zelig extensions. Project Information ================================================================================ The Zelig software suite is an easy-to-use R-package geared towards making complex statistical techniques available to end users, particularly those researching the quantitative social sciences. In particular, it offers unifying syntax and programming-style between seemingly disparate and unrelated statistical mdoels. To facilitate this purpose, Zelig (as of May 16th, 2011) includes an array of programming tools, geared towards allowing the rapid development, debugging, and inclusion of new statistical models. That is, Zelig now facilitates and encourages collaboration between novel and pre-existing statistical packages. Author Information ================================================================================ Zelig is a collaborative effort by Harvard's Institute for Quantitive Social Sciences (Harvard IQSS). Publications, software releases, and additional information can be found at: http://gking.harvard.edu/ http://iq.harvard.edu/ Licensing ================================================================================ Zelig is licensed under the GNU General Public License version 2, and as such can be freely used and edited given proper attribution to Harvard's IQSS Department. What's New in this Release? ================================================================================ This release offers a large variety of coding style changes, as well as core functionality. Please carefully read the following: Major Changes (from version 3.5) -------------------------------------------------------------------------------- - Models are now added to Zelig as separate extensions. The method 'zelig.skeleton' has been added to the R-package to facilitate this change. - The main Zelig package now contains a mere 8 models. 22 additional models are available via extensions, geared towards adding specific functionality to the Zelig software suite. - zelig.skeleton: a method used to create blank zelig packages. This follows the style and format of the R-core method 'package.skeleton' - Simplified zelig2-function API. See "?zelig2" within an R-session for help - Enhanced API for 'param' and 'qi' functions. See develoepr documentation for more information Minor Changes (from version 3.5) -------------------------------------------------------------------------------- - Slight changes to the plotting of simulated quantities of interest. Most changes are stylistic - Quantities of interest using two different sets of explanatory variables now output information containing concerning the simulations of the second 'setx' object's Predicted Values and Expected Values. This was previuosly not the case - ZeligListModels: a method used to list available models, installed on the current operating system. - More robust support for various ways of describing 'terms' of a statistical model. This is essentially light error-detection Missing Features -------------------------------------------------------------------------------- - The 'setx' method currently doesn not support setting multiple counterfactuals in a single call to 'setx'. This feature is under current development, and should soon be admitted into the main branch. - "ternaryplot" plotting style is ommitted from the core package, and will be instead moved to the 'bivariate' and 'multinomial' Zelig modules. - "Average Treatment Effect" quantities of interest are not being included in zelig models temporarily. Simulation of these qi's will return pending a minor update to the 'setx' function. 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QGz#9ҪLdmxsrݘDhƔ7tg{~7 Fw&R쮸7%Jè1})O5=w]𢰧;G?QD5nErq4.ן{;qxg݁rpRfr5eϾGfI#& ׆-Qa FI[Ο6QW5favg 'BޛP"6ʘgμgu9}Ǝ lP =.K {ބ[.ǀkAnsn`2ze,{l3P+U@Ŝ/CVjCj-dS<-NG\!wU1]C=ᡣ<-$}իEGP:G̤:WDd`جEUǫսAyqMNX0tCrC& ^[w!q@:'QU>qǶŴ#JyV>ֆ}y HPIP+Ldl60j'wCw6^,݇op-9\ {Riln\=e%ְŨUq*7 ЉMJ#[`+eB5dEIz|0HB$N"cO~y&՚SQ~85%@(ÜnVOsLVe붣 Cn&v#D3wNZ# T$#nؖMc͍ ji=OiY kBg/0fAOX}*,r:ݝʆ3F:gG\xXz' X VTgR jLJ=sA%/i5 Q2k3x1<yg*z'&̩9[ UO3Bѻ oP y;;cUrJr;ٹ~1s%U+2 soqvj#/)Rz}d[֕#u2"6-oK2:Jw5*۪y$6LPvS Hdu݂7H 6RuGI=n9<g ͮlC~gSM=NzsH~]KJ}%&eU H6k0#4S,F? 0FO]Zh6 Cwx5qd}tHI η&KbrvS1%k6Am1FEv-8HtWeoC}YHUJ=. ]M=0{]s=k;h|?_EP& {o6c8UI2At%BGBCIdMs_6o*>{C-r r9ie#]oBNMv!HW V?k>)H@DA'~vJe3՛g$ClaĊF= n a/ũ C {35rkm9Z2ԽQ)r4:uB1V`Xߓ7r _L7*e|@[z{n}'`ٹ,&b<Չsj[Y܍m"R[ 3{p 3Q6|UEߴB ^LoH,-7[ )xLvYd/oPl|[AGq6\^%z=nO_zz1#)5x\xǕQbOm\͵;T7ݝf޺\em$%$Yν(sSӺj'6fD"Ofw9{n+ Q?AדF|lvkY8@I3O[뜾~vqvY 4I=Hc 3i6Gitq ʑnjH.c['ܟW=}ܻUu=GEIm w l@G;1* b+P{qcA?θSk5=P33 A7TJ~F@z/{wRf0|` %V~}^2.< ra^}n\>r.x~܁Аd]UDz_wuwN]z!/ê|~&/=U F7wrݪbnOÞ xW52e_8U?I߄vshK 5' =̝Ϧz~dmP^=Mllw6udeꢗj<=s0o09y~Dlu"_0o꼷8>oqX$\͜>mۃe F-T9/++f Q3ﾝnv9nk<(։vUiwcB}\P+jDa GZB<Ѓd{9sNdvđ~%'1ZO[{oVZ;Wo{[|\ > v-$e!@z{{,^]c6~ďnDC!o6>=x|i!.n"eY{|@rn?|^AHEV"/I螮]S+u!}H=sBGg>QI BB}IБ}Σ@i IxIJnP}䑍ΤR$OI3)„Zelig/data/tobin.txt.gz0000644000176000001440000000031712217153460014562 0ustar ripleyusers%IjA E:Pq:$d64?_s<.q?=ݎԢ5NfԌtGa3l.J4Q4^ҴȚEcaX4;(QQ5Hºɠ/V.̘z5&ExPZ)Qzv6.M¡֝&AIClYCxNbTӃ^dW!nNJCE3EM̗8E;iAEbc [5f"Q)ᑧfi4*uì|,*6B;T'-♈:Cة#狲 7R _aœr5SR+`Hиe/t3]\cdZ(mCK~`Ju̜dJ=#)JM-e|5tyҜ>>)~W DQQ~bZelig/data/bivariate.tab.bz20000644000176000001440000000050112217153457015422 0ustar ripleyusersBZh91AY&SYH؀@P`@fh 24J&ILP%ET&y|[wwEGj5jh"5c14PW-U /9*"șIHRD";<.9mFs! hb*;DWIi e!Bˬb@̺>υlS-jRԮu{đtwSY[wkg3 *e {T@~>^D=mۈUM+ $ +6IR+a?ܑN$1zZelig/data/newpainters.txt.bz20000644000176000001440000000100712217153460016060 0ustar ripleyusersBZh91AY&SY@-_P|??@\&224 y@iI4dC ij*z@dC 7[ 0"1PXDDIAa#AE$DF4""+k3ݼ0eɮȱ/ QYw^8cȘ^%!VH;s!3aM5MV(j6Z %"p`:)4#ht&䎊p!$*pqJcڍ1buHFPUՕtRcBaP`QHHESnǁ>,fϢjVZ"_ 1Ǣ@ubr}IdFI QoL-᫨d}JJ H⶿)D1ؕS-5QڲߥG5KR'eLjr娆 #By3<գ5۶׌]c'2s7JĒ78tq\5+*0N dgZ\ r%kFl{\¢EK|y!] >Vw$S Zelig/data/approval.tab.bz20000644000176000001440000000156512217153457015313 0ustar ripleyusersBZh91AY&SYIXoـP.P18\M<2iPh5hh4@HLL HH h@{J@PAhPO܌X2ݕ㒹Ts|.lq{ͮ<i`e7k@_QmhZjijMJ0B%f' #V JOy81JUր% BwIZn+/ƚTC/-MfuieuSViЛԏQٜ o*+q+fRos}q]5IJ$)$$I$%u`ª1DZ_hV̽QOH-a daRUb{'.^V6&dgrܲ1%so ix<2 X MBFlFlb޻*bҏ/i݊H1S$)6$Tq)D,RQYU9瑨JURP2FrkǢy֪βI"c:ibgX sŹbM޼@#lvF *AX荳ws;͵:{8lN , < ) lhs <- call("Surv", lhs, call("<", below, lhs), type="left") # Place back within formula formula[[2]] <- lhs # Return formula } #' Param Method for the \code{tobit} Zelig Model #' @note This method is used by the \code{tobit} Zelig model #' @usage \method{param}{tobit}(obj, num, ...) #' @S3method param tobit #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored parameters #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.tobit <- function(obj, num=1000, ...) { cov <- vcov(.fitted) mu <- c(coef(.fitted), log(.fitted$scale)) # Return list( coef = mvrnorm(num, mu=mu, Sigma=cov), linkinv = NULL ) } #' Compute quantities of interest for 'tobit' Zelig models #' @usage \method{qi}{tobit}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi tobit #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.tobit <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # This needs to be fixed. ev1 <- ev2 <- pr1 <- pr2 <- fd <- NA # return list("Expected Values: E(Y|X)" = ev1, "Expected Values: E(Y|X1)" = ev2, "Predicted Values: Y|X" = pr1, "Predicted Values: Y|X1" = pr2, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe a ``tobit'' model to Zelig #' @usage \method{describe}{tobit}(...) #' @S3method describe tobit #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} describe.tobit <- function(...) { list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2011, category = "continuous", text = "Linear regression for Left-Censored Dependent Variable" ) } Zelig/R/parseFormula.R0000644000176000001440000000630012061700507014335 0ustar ripleyusers#' Parse Zelig-style Formulae #' #' Zelig uses three distinct types of formulae. This method is a re-design #' of the Zelig function \code{parse.formula}. #' @param obj a list or formula #' @param data the data set associated with the formula object #' @return an object of type "parseFormula". This object has slots specifying: #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export parseFormula <- function (obj, data=NULL) { UseMethod("parseFormula") } #' Parse Standard Formulae #' #' This method parses a formula-style Zelig formula #' @usage \method{parseFormula}{formula}(obj, data=NULL) #' @param obj a formula #' @param data a data frame #' @return an object of type "parseFormula" #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @S3method parseFormula formula parseFormula.formula <- function (obj, data=NULL) { # Extract terms TERMS <- terms(obj) # MODEL.MATRIX <- tryCatch(model.matrix(obj, data), error = function (e) NULL) # Build the object res <- list( formula = obj, terms = TERMS, response = getResponseTerms(obj), predictor = getPredictorTerms(obj), model.matrix = MODEL.MATRIX ) # Return class(res) <- "parseFormula" res } #' Parse List-Style Zelig Formulae #' #' This method parses a list-style Zelig formula. #' @usage \method{parseFormula}{list}(obj, data=NULL) #' @param obj a list of formulae #' @param data a data frame #' @return an object of type "parseFormula" #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @S3method parseFormula list parseFormula.list <- function (obj, data=NULL) { # Extract terms (and place in a list) TERMS <- Map(terms, obj) # MODEL.MATRIX <- makeModelMatrix(obj, data) # Build the object res <- list( formula = obj, terms = TERMS, response = getResponseTerms(obj), predictor = getPredictorTerms(obj), model.matrix = MODEL.MATRIX ) # Return class(res) <- "parseFormula" res } #' Parse ``Formula''-style Zelig Formulae #' #' This method parses a ``Formula''-style Zelig formula. This is to support the #' ``Formula'' object. It seems like it has the right idea when it comes to #' expressing multiple responses. #' @usage \method{parseFormula}{Formula}(obj, data=NULL) #' @param obj a list of formulae #' @param data a data frame #' @return an object of type ``parseFormula'' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @S3method parseFormula Formula parseFormula.Formula <- function (obj, data=NULL) { message("parseFormula.Formula") # Create the actual object res <- list( # Remember the source class source.class = class(obj), # Store the original copy of the formula formula = obj, # Store the terms terms = terms(obj), # Use Zelig-style methods to get the responseTerms response = getResponseTerms(obj), predictor = getPredictorTerms(obj), # Create the design matrix from the ``Formula'' package model.matrix = NULL ) # Return class(res) <- "parseFormula" res } Zelig/R/GetSlot.zelig.R0000644000176000001440000000200012061700507014360 0ustar ripleyusers#' Return a Value from a \code{zelig} Fitted Model #' #' Returns a value from the result of a model fitting function #' @usage \method{GetSlot}{zelig}(obj, key, ...) #' @note This function is primarily used by Zelig developers within \code{qi} #' functions #' @param obj a \code{zelig} object #' @param key a character-string specifying the which value to extract from #' the fitted model object #' @param ... subsequent values to extract from the fitted model object #' @return values of the specified keys #' @export #' @author Matt Owen \emph{mowen@@iq.harvard.edu} GetSlot.zelig <- function(obj, key, ...) { # expand dots dots <- list(...) # error-catching if (!all(sapply(dots, is.character))) stop("all dot parameters must be characters") # get result of zelig object obj <- obj$result # res <- obj[[key]] for (key in dots) { # res <- try(res[[key]], silent=T) # if it doesn't exist, then NULL if (inherits(res, "try-error")) return(NULL) } # return res } Zelig/R/zelig2.R0000644000176000001440000000415112061700507013073 0ustar ripleyusers#' The \code{zelig2} function acts as a simple interface between a user's call #' to the \code{zelig} function and the zelig functions subsequent call to the #' pre-existing external model. The external model varies based on which model #' is being called. #' #' @title Interface Between Zelig Models and External Functions #' @note Writing \code{zelig2} functions is required of Zelig developers. In #' particular, \code{zelig2} functions act as an interface between external #' models (models not included in the Zelig package) and the \code{zelig} #' function which must use that model. #' #' \code{zelig2} is not an actual function. Rather, #' #' @name zelig2 #' @return #' The main purpose of the \code{zelig2} function is to return a list of #' key-value pairs, specifying how Zelig should interface with the external #' method. This list has the following format: #' #' \item{\code{.function}}{specifies the name of the external method to be #' called by \code{zelig} function. Subsequent parameters, are called and #' evaluated as a function call to the function of the named string.} #' \item{\code{.hook}}{specifies the name of a hook function as a string. The #' hook function is only evaluated on zelig object once the external method #' fits the statistical model} #' \item{...}{any parameters aside from \code{.function} and \code{.hook} is #' as part of the function call to the external model} #' #' @examples #' zelig2some.model <- function (formula, weights, verbose, ..., data) { #' list( #' .function = 'some.other.method', #' .hook = NULL, #' formula = formula, #' weights = 2 * weights, #' data = data #' ) #' } #' #' ## This \code{zelig2} function equates the following function call: #' ## zelig(formula, weights = weights, verbose = TRUE, data = data, model="some.model") #' ## #' ## with: #' ## some.other.method(formula = formula, weights = 2 * weights, data=data) #' #' ## Note that the 'verbose' parameter is ignored, since the #' ## 'zelig2some.model' does not include the 'verbose' parameter in its return #' ## value. NULL Zelig/R/bootstrap.negbinom.R0000644000176000001440000000125712061700507015515 0ustar ripleyusers#' Bootstrap Parameters for Zelig ``negbinom'' GLM #' #' Returns bootstrapped parameter estimates for a negative-binomial GLM. #' @usage \method{bootstrap}{negbinom}(obj, ...) #' @S3method bootstrap negbinom #' @param obj a ``zelig'' object that will be used to produce boot-strapped #' parameters #' @param ... extra parameters to be passed to the ``boot'' method. These are #' typically ignored, but is included for further expansion. #' @return a list containing information concerning link, link-inverses, etc. #' @author Matt Owen \email{mowen@@iq.harvard.edu} bootstrap.negbinom <- function (obj, ...) { list( alpha = .fitted$theta, beta = coef(.fitted) ) } Zelig/R/lognorm.R0000644000176000001440000000525712061700507013364 0ustar ripleyusers#' Interface between the Zelig Model lognorm and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2lognorm <- function (formula, ..., robust = FALSE, cluster = NULL, data) { loadDependencies("survival") if (!(is.null(cluster) || robust)) stop("If cluster is specified, then `robust` must be TRUE") # Add cluster term if (robust || !is.null(cluster)) formula <- cluster.formula(formula, cluster) # Return list( .function = "survreg", formula = formula, dist = "lognormal", robust = robust, data = data, ... ) } #' @S3method param lognorm param.lognorm <- function(obj, num=1000, ...) { # These are the fitted parameters coef <- coef(obj) # Append the log-scale mu <- c(coef, log(obj$result$scale)) # These are their correlations cov <- vcov(obj) # Simulate the results simulations <- mvrnorm(num, mu, cov) # Return list( coef = as.matrix(simulations[, 1:length(coef)]), alpha = as.matrix(simulations[, -(1:length(coef))]), linkinv = survreg.distributions[["lognormal"]]$itrans ) } #' @S3method qi lognorm qi.lognorm <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { linkinv <- linkinv(param) alpha <- alpha(param) beta <- coef(param) # Compute expected values for "lognorm" regression # # This function is nested within qi.lognorm for code-clarity and because it # will not be used by any other function # @param coef # @param alpha sim.scale # @param x # @return a matrix compute.ev <- function (coef, alpha, x) { if (is.null(x) || is.na(x)) # If there are missing explanatory variables, ignore them return(NA) # Compute eta # This value must be *inverted* to be restored to the true "observed" value eta <- coef %*% t(x) # Apply inverse link function theta <- as.matrix(apply(eta, 2, linkinv)) # Copied from qi.survreg in Zelig v3.5 ev <- exp(log(theta) + 0.5*(exp(alpha))^2) dimnames(ev) <- dimnames(theta) # Return as.matrix(ev) } # Compute expected values for X and X1 ev1 <- compute.ev(beta, alpha, x) ev2 <- compute.ev(beta, alpha, x1) list( "Expected Value: E(Y|X)" = ev1, "Expected Value: E(Y|X1)" = ev2, "First Differences: E(Y|X1) - E(Y|X)" = ev2 - ev1 ) } #' @S3method describe lognorm describe.lognorm <- function(...) { list( authors = c("Matthew Owen", "Olivia Lau", "Kosuke Imai", "Gary King"), text = "Log-Normal Regression for Duration Dependent Variables", year = 2007 ) } Zelig/R/bootfn.default.R0000644000176000001440000002144512216401247014617 0ustar ripleyusers#' Default Boot-strapping procedure #' #' The default procedure for extracting bootstrap information. Note that this #' method re-fits the data and resamples the data frequently. This is a good #' candidate for fixing-up. #' #' @param data a data.frame #' @param i an integer or chacter-string specifying the index of the row to #' be used in the bootstrapping procedure. #' @param object the fitted model object #' @param bootstrapfn a function used to bootstrap the object #' @param num an integer specifying the number of samples to simulate #' @param ... unspecified parameters #' @return a list of paramters bootfn.default <- function(data, i, object, bootstrapfn=NULL, num, ...) { # This is mostly here to squelch R-check notes, however sloppy programming # can potentially prevent the ".model" variable from being defined in the # attached environment. To make sense of this line, see the "sim.default" # function where an environment (containing the variable ".model" is # explicity attached to the boot function if (!exists(".model")) .model <- "default" # Get a random sample of the data set d <- data[i,] # Extract the call object # Naming it "jeez" because it's really hard to find names for call objects # that are meaningful and not reserved for other functions jeez <- .call # Replace the data frame with an appropriate one jeez$data <- d .env <- if (exists('.env')) .env else NULL # Fit the model fit <- eval(jeez) # If "bootstrapfn" is unspecified, then we try to search its appropriate value # down if (is.null(bootstrapfn)) bootstrapfn <- getS3method("bootstrap", .model, TRUE) # If bootstrap search came up sour, get default ## CRAN is opposed to ::: within same package, ## but I'm opposed to S4 environment artifacts ## if (is.null(bootstrapfn)) ## bootstrapfn <- Zelig:::bootstrap.default ## So this obviously makes my code better: if (is.null(bootstrapfn)){ localbootstrap.default <- function (obj, ...) list( alpha = NULL, beta = coef(obj) ) bootstrapfn <- localbootstrap.default } # Attach the ".num" private variable bootstrapfn <- attach.env(bootstrapfn, NULL, .num = num, .fitted = object) # Get a result res <- bootstrapfn(fit) # Return vectorized bootstrap simulation to "boot" function as.bootvector(res)$vector } #' Convert Boot Object to a Vector #' #' Receives a list with 2 slots as its input, and returns a vector of the two #' smashed together alongwith the offsets used to reverse-construct the object. #' #' @note This method is used internally by Zelig to allow an intuitive, #' ``param''-like API for bootstrapping. #' #' @param obj a list with two slots: ``alpha'' and ``beta''. Respectively, these #' represent bootstrap samples for ancillary parameters and systematic #' component of the bootstrapped GLM. #' @return a list containing the resulting vector, as well as an object used to #' reverse-build the list (``obj'') from the resulting call to ``bootstrap''. #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.bootvector <- function (obj) { # If this fails, something is really wrong. a <- obj$alpha b <- obj$beta # Error-checking if (!(is.vector(a) || is.null(a))) stop('The "alpha" slot of "obj" must be a vector or NULL.') if (!(is.vector(b))) stop('The "beta" slot of "obj" must be a vector') # Return list( # For antiquity, beta should be placed before alpha. This is partially # because alpha is not always specified. vector = c(b, a), # The respective lengths of each vector lengths = c(beta = length(b), alpha = length(a)), # Names names = list(beta = names(b), alpha = names(a)) ) } #' Convert of Vector of Bootstrapped Parameters to a List-style Boot Object #' #' This inverts the ``as.bootvector'' function, and returns a list containing #' the slots ``alpha'' and ``beta''. #' #' @param bootstraps ... #' @param lengths ... #' @param names a character-vector specifying the names of the boot terms #' @return ... #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.bootlist <- function (bootstraps, lengths, names) { # Error-checking. "bootstraps" and "lengths" must: # 1. "bootstraps" must be a matrix && have at least 1 value # 2. "lengths" must be a vector # 3. The sum of all the lengths must perfectly add up to the number of # columns in bootstraps if (!is.matrix(bootstraps) && ncol(bootstraps) > 0 && nrow(bootstraps) > 0) stop('The parameter "bootstraps" must be a matrix') if (!is.vector(lengths)) stop('The parameter "lengths" must be a vector.') if (sum(lengths) != ncol(bootstraps)) stop('The parameters "bootstraps" and "lengths" must be ', 'the same length.' ) # Actual work begins here. This could be made more general, but if there's # more info besides "alpha" and "beta", it's not very much like a bootstrap... # In the future, we might need to add support for "link", "inverse link" and # "family" slots, but there is overlap here with the "param" method. # Note that to make sense of the below, it has to be understood that the # canonical form of these bootstrapped values is: # (beta, alpha) # where "beta" is several columns of systematic parameters and # "alpha" is several columns of ancillary parameters a <- b <- NULL # If beta is 0-sized, then we should ignore it if (lengths[["beta"]] > 0) { # Extract the "beta" portion of "bootstraps". These values should represent # the systematic parameters b <- bootstraps[ , 1:lengths[["beta"]] ] # Change the column names of the system's parameter (beta) simulations b <- name.object(b, names$beta) } # Note that 1 + 1:2 is 2:3, so that this statement offsets subsetting by the # length of "a". if (lengths[["alpha"]] > 0) { # Extract several columns from "bootstraps". These values should represent # the model's ancillary parameters a <- bootstraps[ , lengths[["beta"]] + 1:lengths[["alpha"]] ] # Change the column names of the ancillary parameter (alpha) simulations a <- name.object(a, names$alpha) } # Return the appropriate list(alpha = a, beta = b) } #' Name Elements of an Object #' #' Returns an object #' @note This method is used internally by Zelig to name the columns and #' elements of matrices and vectors for simulations and bootstrapped parameters. #' @param obj a vector or matrix #' @param names a character-vector specifying names #' @return the original object, with a "colnames" or "names" equal to the #' parameter "names". If "names" is larger than "obj", the "names" parameter #' is truncated appropriately. If it is smaller, then the latter part of "obj" #' is replaced with a numbered generic column name #' @author Matt Owen \email{mowen@@iq.harvard.edu} name.object <- function (obj, names) { # Handle the special case, which shouldn't really happen... if (is.null(names)) { if (is.matrix(obj)) colnames(obj) <- NULL else if (is.vector(obj)) names(obj) <- NULL return(obj) } # Get the length of names names.len <- length(names) # Get the 'length' of the object, regardless of whether it is a vector or # matrix. Note that in our case, length is equivalient to "ncol" if the # object is a matrix obj.len <- if (is.matrix(obj)) ncol(obj) else if (is.vector(obj)) length(obj) else { # Warn the user. This might be necessary, but it helps debug for # developers. Ideally this case never crops up in well-made Zelig models warning('"name.object" ignores objects that are not matrices or vectors') # Bail out of the function return(obj) } # Ensure that names is the exact length of "obj" by if (names.len < obj.len) { # Create vector equal in size to the length of the object being named temp <- paste(rep("col", obj.len), 1:obj.len, sep = "") # Replace default values (col1, col2, ... colN) with the value that # *should* there in a perfect world, where there is never any glitchy code temp[1:names.len] <- names # Replace "names" with the newly constructed, appropriately size, vector # of names names <- temp } # Truncate the "names" parameter if it is too largit is too large else if (names.len > obj.len) { # Warn the user. This is probably only useful/meaningful to developers. # This case should not crop up in well made Zelig models. warning('"names.object" is truncating the names parameter, because it ', 'is larger than "obj" the object of the function.') # Truncate "names" names <- names[1:obj.len] } # After all the prep work, finally name the object if (is.matrix(obj)) colnames(obj) <- names else if (is.vector(obj)) names(obj) <- names else warning('"obj" must be a matrix or a vector. ', 'Returning the "obj" untouched.') # Return modified object obj } Zelig/R/parse.formula.R0000644000176000001440000004664512061700507014473 0ustar ripleyusers#' Parse Formulas for Zelig Models #' @note This is used typically in multinomial and multivariate Zelig models #' @param formula a formula #' @param model a Zelid model #' @param data a data-frame #' @export #' @author Kosuke Imai and Olivia Lau parse.formula<-function( formula, model,data=NULL){ if(class(formula)[[1]]=="multiple") return(formula) nrUserOpt<-nrUserReq<-nrUserFixed<-nrUserSubreq<-0 userOpt<-userReq<-userFixed<-userSubreq<-list() fc <- paste("describe.", model, sep = "") if (!exists(fc)) stop("describe.",model," does not exsist") modelReq<-do.call(fc,list()) modelReq <-modelReq$parameters checkNrReq<-function(modelNumEqn,nrUserReq,modelParsReq){ if(length(modelNumEqn)==1){ if(nrUserReq != modelNumEqn) stop("The parameter \"",modelParsReq,"\" requires ",modelNumEqn, " equation(s). You have provided ", nrUserReq, " See model doc. for more details") }else{ if(!(betweenf(nrUserReq,modelNumEqn))) stop("The parameter \"",modelParsReq,"\" requires between ",modelNumEqn[[1]]," and ",modelNumEqn[[2]], " equation(s). You have provided ", nrUserReq, " See model doc. for more details") } } checkNrOpt<-function(modelNumEqn,nrUserOpt,modelParsOpt,userOpt){ if(!(betweenf(nrUserOpt,modelNumEqn))) if(nrUserOpt < modelNumEqn[[1]]){ if(modelNumEqn[[1]]==1) userOpt[[modelParsOpt]]<- as.formula("~1") else for(i in (nrUserOpt+1):modelNumEqn[[1]]){ userOpt[[i]]<-as.formula("~1") names(userOpt)[[i]]<-paste(modelParsOpt,i,sep="") } }else stop("The parameter \"",modelParsOpt,"\" requires between ",modelNumEqn[[1]]," and ",modelNumEqn[[2]], " equation(s). You have provided ", nrUserOpt, " See model doc. for more details") return(userOpt) } betweenf<-function(a,range){ if (is.finite(range[[2]])) return(a >= range[[1]] && a<=range[[2]]) else return(a>=range[[1]]) } "%w/o%" <- function(x,y) x[!x %in% y] matchPars<-function(parName,userNames){ res<-c() for(i in 1:length(userNames)){ a<-substr(userNames[[i]],nchar(parName)+1,nchar(userNames[[i]])) b<-substr(userNames[[i]],1,nchar(parName)) if(b==parName && (!is.na(suppressWarnings(as.numeric(a))) || userNames[[i]]==parName)) res<-c(res,userNames[[i]]) } return (res) } fMode<-function(b){ if(b$depVar == TRUE && b$expVar == TRUE) return (1) if(b$depVar == FALSE && b$expVar == TRUE) return (2) if(b$depVar == FALSE && b$expVar == FALSE) return (3) if(b$depVar == TRUE && b$expVar == FALSE) return (4) stop("some error occurred ... please contact the Zelig team") } parsType<-lapply(modelReq,fMode) modelParsReq<-names(parsType[parsType==1]) modelParsOpt<-names(parsType[parsType==2]) modelParsFixed<-names(parsType[parsType==3]) modelParsSubreq<-names(parsType[parsType==4]) modelNrParsReq<-length(modelParsReq) modelNrParsOpt<-length(modelParsOpt) modelNrParsFixed<-length(modelParsFixed) modelNrParsSubreq<-length(modelParsSubreq) userNrLevels<-0 dataNrLevels<-0 userLevels<-c() if(class(formula)[[1]]=="formula") formula<-list(formula) nreqns <-length(formula) if(is.null(names(formula))){ if(modelNrParsReq >1) stop("You should name the equations. The model requires more than 1 systematic component. Please see model documentation for more details") for (i in 1:nreqns){ eqni<-formula[[i]] if (length(eqni)==3){ rootNames<-modelParsReq lhs<-eqni[[2]] rhs<-deparse(eqni[[3]],width.cutoff=500) if(length(lhs)>1 && (lhs[[1]]=="cbind" || lhs[[1]]=="as.factor" || lhs[[1]]=="id")){ if( lhs[[1]]=="cbind"){ #rhs=deparse(rhs) g<- as.list(lhs)[-1] for (j in 1:length(g)){ e<-paste(g[[j]],"~",sep="") if(rhs!="1"){ nrUserReq=nrUserReq+1 userReq[[nrUserReq]]<-as.formula(paste(e,rhs,sep="")) }else{ nrUserSubreq=nrUserSubreq+1 userSubreq[[nrUserSubreq]]<-as.formula(paste(e,rhs,sep="")) } } }else{ if(is.null(data)) stop("Data argument is required when you use as.factor() or id() as a dependent variable\n") if(lhs[[1]]=="as.factor"){ varname<-as.character(lhs[[2]]) userLevels<-levels(as.factor(data[[varname]]))[-1] userNrLevels<-length(userLevels) for (j in 1:userNrLevels){ e<-paste("id(",lhs[[2]],",\"",userLevels[[j]],"\")","~",sep="") if(rhs!="1"){ nrUserReq=nrUserReq+1 userReq[[nrUserReq]]<-as.formula(paste(e,rhs,sep="")) }else{ nrUserSubreq=nrUserSubreq+1 userSubreq[[nrUserSubreq]]<-as.formula(paste(e,rhs,sep="")) } } }else{ varname<-as.character(lhs[[2]]) userLevels<-c(userLevels,lhs[[3]]) userNrLevels<-length(userLevels) levels<-levels(data[[varname]]) lhs<-deparse(lhs) # rhs<-deparse(rhs) e<-paste(lhs,"~",sep="") if(rhs !="1"){ nrUserReq=nrUserReq+1 userReq[[nrUserReq]]<-as.formula(paste(e,rhs,sep="")) }else{ nrUserSubreq<-nrUserSubreq+1 userSubreq[[nrUserSubreq]]<-as.formula(paste(e,rhs,sep="")) } } } }else{ lhs<-deparse(lhs) # rhs<-deparse(rhs) e<-paste(lhs,"~",sep="") if(rhs !="1"){ nrUserReq=nrUserReq+1 userReq[[nrUserReq]]<-as.formula(paste(e,rhs,sep="")) }else{ nrUserSubreq<-nrUserSubreq+1 userSubreq[[nrUserSubreq]]<-as.formula(paste(e,rhs,sep="")) } } }else{ rhs<-deparse(eqni[[2]]) if(rhs !="1"){ nrUserOpt=nrUserOpt+1 userOpt[[nrUserOpt]]<-as.formula(paste("~",rhs,sep="")) }else{ nrUserFixed=nrUserFixed+1 userFixed[[nrUserFixed]]<-as.formula(paste("~",rhs,sep="")) } } } if (modelNrParsOpt==0){ if (nrUserOpt !=0){ stop("the equation(s) ",userOpt," does not match model requirements!")} }else{ modelNumEqn<-modelReq[[modelParsOpt]]$equations userOpt<-checkNrOpt(modelNumEqn,nrUserOpt,modelParsOpt,userOpt) if(length(userOpt)==1) names(userOpt)<-modelParsOpt else names(userOpt)<-paste(modelParsOpt,1:length(userOpt),sep="") } if(length(modelParsFixed)>0){ modelNumFixedEqns<-modelReq[[modelParsFixed]]$equations for(i in 1:modelNumFixedEqns) userFixed[[i]]<-as.formula("~1") if(modelNumFixedEqns==1) names(userFixed)<-modelParsFixed else names(userFixed)<-paste(modelParsFixed,1:modelNumFixedEqns,sep="") } if (modelNrParsReq==0){ if (nrUserReq !=0){ stop("the equation(s) ",userReq," does not match model requirements!")} }else{ modelNumEqn<-modelReq[[modelParsReq]]$equations checkNrReq(modelNumEqn,nrUserReq,modelParsReq) if(userNrLevels>0){ if(userNrLevels !=nrUserReq) stop("The number of equation for the systematic component should be equal to the number of levels -1\n") names(userReq)<-userLevels }else{ if(nrUserReq==1) names(userReq)<-modelParsReq else names(userReq)<-paste(modelParsReq,1:length(userReq),sep="") } } if (modelNrParsSubreq==0){ if (nrUserSubreq !=0){ stop("the equation(s) ",userSubreq," does not match model requirements!")} }else{ modelNumEqn<-modelReq[[modelParsSubreq]]$equations checkNrReq(modelNumEqn,nrUserSubreq,modelParsSubreq) if(nrUserSubreq==1) names(userSubreq)<-modelParsSubreq else names(userSubreq)<-paste(modelParsSubreq,1:length(userSubreq),sep="") } result<-c(userReq,userOpt,userFixed,userSubreq) }else{ ##user provides names for formulas modelPars<-names(modelReq) parsS<-names(sort(sapply(modelPars,nchar),decreasing=TRUE)) userNames<-names(formula) userEqnNamesByPars<-list() tmpUserNames<-userNames for (i in 1:length(parsS)){ userEqnNamesByPars[[parsS[[i]]]]<-matchPars(parsS[[i]],tmpUserNames) tmpUserNames<-"%w/o%"(tmpUserNames,userEqnNamesByPars[[parsS[[i]]]]) } tmp<-"%w/o%"(userNames,unlist(userEqnNamesByPars)) if (length(tmp)>0) stop("Ambigous equation name ","\"",tmp,"\"") res<-list() userPars<-names(userEqnNamesByPars) for (i in 1:length(modelPars)){ modelPar<-modelPars[[i]] userNumEqn<-length(userEqnNamesByPars[[modelPar]]) modelNumEqn<-modelReq[[modelPar]]$equations mode<-fMode(modelReq[[modelPar]]) tmplst<-formula[userEqnNamesByPars[[modelPar]]] if(modelNumEqn[[1]]==1 && modelNumEqn[[2]]==1 ) tmpNames<-modelPar else tmpNames<-paste(modelPar,1:userNumEqn,sep="") if(mode==1){ whiche<-which(lapply(formula[(userEqnNamesByPars[[modelPar]])],length)!=3) if(length(whiche)!=0) stop("The equation ",formula[[names(whiche)]]," is not conform model requirements or its name is ambigous . DepVar/ExpVar is missing.\n") checkNrReq(modelNumEqn,userNumEqn,modelPar) whiche<-which((names(tmplst) %in% tmpNames)==FALSE) if(length(whiche)!=0){ warning("The name \"",names(tmplst)[whiche],"\" is ambigous. The equations of the paramter \"",modelPar,"\" are renamed\n") names(tmplst)<-tmpNames } }else{ if(mode==2){ whiche<-which(lapply(formula[(userEqnNamesByPars[[modelPar]])],length)!=2) if(length(whiche)!=0) stop("The equation ",formula[names(whiche)]," is not conform model requirements or its name is ambigous A .\n") whiche<-which((names(tmplst) %in% tmpNames)==FALSE) if(length(whiche)!=0){ warning("The name \"",names(tmplst)[whiche],"\" is ambigous. The equations of the paramter \"",modelPar,"\" are renamed\n") names(tmplst)<-tmpNames } tmplst<- checkNrOpt(modelNumEqn,userNumEqn,modelPar,tmplst) }else{ if (mode==3){ whiche<-which(tmplst !="~1") if(length(whiche)>0) warning("You cannot specify a formula for the parameter \"",modelPar,"\" . All your equation for this parameter are set to their default value.For example your equation:\n",deparse(formula[names(whiche)]),"\n") if(userNumEqn !=modelNumEqn) warning("The parameter \"",modelPar,"\" requires ",modelNumEqn, "equation(s). You are providing ",userNumEqn, " equation(s) for this parameter. This problem is fixed. All the equations for this parameter are set to the default value \n") tmplst<-list() if(modelNumEqn==1) tmpname<-modelPar else tmpname<-paste(modelPar,1:modelNumEqn,sep="") for(i in 1:modelNumEqn) tmplst[[tmpname[[i]]]]<-as.formula("~1") }else{ if(mode==4) { whiche<-which(lapply(formula[(userEqnNamesByPars[[modelPar]])],length)!=3) whicha<-which(lapply(formula[(userEqnNamesByPars[[modelPar]])],FUN=function(a){if (a[[3]]=="1") return (TRUE) else return(FALSE)})==FALSE) if(length(whiche)!=0 ) stop("The equation ",formula[names(whiche)]," is not conform model requirements or its name is ambigous . DepVar/ExpVar is missing.\n") else{ if (length(whicha)!=0) stop("The equation ",formula[names(whicha)]," is not conform model requirements or its name is ambigous . Its right hand side shoule be \"1\".\n") } checkNrReq(modelNumEqn, userNumEqn, modelPar) whiche<-which((names(tmplst) %in% tmpNames)==FALSE) if(length(whiche)!=0){ warning("The name \"",names(tmplst)[whiche],"\" is ambigous. The equations of the paramter \"",modelPar,"\" are renamed\n") names(tmplst)<-tmpNames } } } } } res[[modelPar]]<-tmplst } result<-c() for(i in 1:length(res)) result<-c(result,res[[i]]) } class(result)<-c("multiple","list") return(result) } Zelig/R/bootstrap.R0000644000176000001440000000307512217150360013717 0ustar ripleyusers#' Generic Method for ``bootstrap'' #' #' This method is intended to be overried by statistical models that would like #' to support statistical bootstrapping. #' @note This method has private memory storage and can reference the objects: #' ``.fitted'', ``.data'', ``.call'', ``.env'', despite having no declaration in #' the argument list. #' @param obj a fitted model object that will be used to produce boot-strapped #' parameters. This object usually inherits the class ``glm'' or ``lm'' object #' @param ... unspecified parameters #' @return a list with the ``alpha'' and ``beta'' slots set. Note that ``alpha'' #' corresponds to ancillary parameters and ``beta'' corresponds to systematic #' components of the model #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export bootstrap <- function (obj, ...) UseMethod("bootstrap") #' Produce Boot-strapped Parameters for a Statistical Model #' #' This method is a fallback for bootstrapping models that do not have a defined #' ``bootstrap'' method. For most models, this default is sufficient, so long as #' the model follows the usual convention that ``coef(obj)'' returns the #' systematic parameters of a fitted model. #' @usage \method{bootstrap}{default}(obj, ...) #' @S3method bootstrap default #' @param obj a fitted model object. This is typically of type ``glm'' or ``lm'' #' @param ... unspecified parameters #' @return a list with the ``alpha'' and ``beta'' slots set #' @author Matt Owen \email{mowen@@iq.harvard.edu} bootstrap.default <- function (obj, ...) list( alpha = NULL, beta = coef(obj) ) Zelig/R/logit.survey.R0000644000176000001440000001222012071063367014354 0ustar ripleyusers#' @export zelig2logit.survey <- function( formula, weights=NULL, ids=NULL, probs=NULL, strata = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL, fpctype="fraction", return.replicates=FALSE, na.action="na.omit", start=NULL, etastart=NULL, mustart=NULL, offset=NULL, model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL, design=NULL, data ) { loadDependencies("survey") if (is.null(ids)) ids <- ~1 # the following lines designate the design # NOTE: nothing truly special goes on here; # the below just makes sure the design is created correctly # for whether or not the replication weights are set design <- if (is.null(repweights)) svydesign( data=data, ids=ids, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights ) else { .survey.prob.weights <- weights svrepdesign( data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc ) } # we cannot plug in family=Gamma yet because of weird issues # with glm. Uncomment the below lines for an explanation: ## fails: # test <- Gauss # svyglm(formula=formula, design=design, family=test) ## works: # svyglm(formula=formula, design=design, family=Gauss) # this is because of how glm is written (it evaluates the # family variable as a function in the parent.frame) z(.function = svyglm, formula = formula, design = design, family = quasibinomial(link="logit") ) } #' @S3method param logit.survey param.logit.survey <- function(obj, num=1000, ...) { list( simulations = mvrnorm(num, coef(obj), vcov(obj)), alpha = NULL, fam = binomial(link="logit") ) } #' @S3method qi logit.survey qi.logit.survey <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { model <- GetObject(obj) coef <- coef(param) alpha <- alpha(param) eta <- coef %*% t(x) link.inverse <- linkinv(param) theta <- matrix(link.inverse(eta), nrow=nrow(coef)) pr <- ev <- matrix(NA, nrow=nrow(theta), ncol(theta)) dimnames(pr) <- dimnames(ev) <- dimnames(theta) ev <- theta for (k in 1:ncol(theta)) { pr[,k] <- rbinom(length(ev[,k]), 1, ev[,k]) pr[,k] <- as.character(pr[,k]) } levels(pr) <- c("0", "1") if (!is.null(y) && NCOL(y)) y <- y[,1] # invisiblify pr1 <- ev1 <- fd <- rr <- NA if (!is.null(x1)) { ev1 <- theta1 <- matrix(link.inverse(coef %*% t(x1)), nrow = nrow(coef) ) pr1 <- matrix(NA, nrow=nrow(theta), ncol(theta)) for (k in 1:ncol(theta)) { pr1[,k] <- rbinom(length(ev1[,k]), 1, ev1[,k]) pr1[,k] <- as.character(pr1[,k]) } levels(pr1) <- c("0", "1") fd <- ev1-ev rr <- ev1/ev } att.ev <- att.pr <- NA if (!is.null(y)) { yvar <- matrix(rep(y, nrow(coef)), nrow = nrow(coef) ) tmp.ev <- yvar - ev tmp.pr <- yvar - as.integer(pr) att.ev <- matrix(apply(tmp.ev, 1, mean), nrow=nrow(coef)) att.pr <- matrix(apply(tmp.pr, 1, mean), nrow=nrow(coef)) } list( "Expected Values: E(Y|X)" = ev, "Expected Values (for X1): E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values (for X1): Y|X1" = pr1, "First Differences: E(Y|X1) - E(Y|X)" = fd, "Risk Ratios: P(Y=1|X1)/P(Y=0|X)" = rr, "Average Treatment Effect: Y - EV" = att.ev, "Average Treatment Effect: Y - PR" = att.pr ) } #' @S3method describe logit.survey describe.logit.survey <- function(...) { list( authors = "Nicholas Carnes", year = 2008, description = "Survey-Weighted Logitistic Regression for Continuous, Positive Dependent Variables" ) } Zelig/R/ls.R0000644000176000001440000000611412214411116012311 0ustar ripleyusers#' Interface between ls model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2ls <- function(formula, ..., data, weights=NULL) z( lm, formula = formula, weights = weights, model = F, data = data ) #' Param Method for the 'ls' Zelig Model #' @note This method currently returns via a deprectated style #' @usage \method{param}{ls}(obj, num, \dots) #' @S3method param ls #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored parameters #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.ls <- function(obj, num, ...) { mvrnorm(n=num, mu=coef(.object), Sigma=vcov(.object)) } #' Compute quantities of interest for 'ls' Zelig models #' @usage \method{qi}{ls}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi ls #' @param obj a \code{zelig} object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.ls <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { # error-catching if (missing(x)) stop("x cannot be missing while computing the `ls' model") # Get coefficients of the linear model coefs <- coef(param) # compute expected value ev <- coefs %*% t(x) ev1 <- NA fd <- NA if (!is.null(x1)) { ev1 <- coefs %*% t(x1) fd <- ev1 - ev } # return list("Expected Values: E(Y|X)" = ev, "Expected Values: E(Y|X1)" = ev1, "Predicted Values: Y|X" = ev, "Predicted Values: Y|X1" = ev1, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe a \code{ls} model to Zelig #' @note \code{ls} stands for "least squares fit" #' @usage \method{describe}{ls}(...) #' @S3method describe ls #' @param ... ignored parameters #' @return a list to be processed by \code{as.description} #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.ls <- function(...){ parameters <-list(mu = list( equations = c(1,1), tags.allowed = FALSE, dep.vars = TRUE, exp.vars = TRUE ) ) # return list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2007, category = "continuous", parameters = parameters, text = "Least Squares Regression for Continuous Dependent Variables" ) } Zelig/R/model.matrix.parseFormula.R0000644000176000001440000000227212061700507016743 0ustar ripleyusers#' Construct Design Matrix from a Parsed, Zelig-style Formula #' #' This method constructs a design matrix from a Zelig-style formula. This #' matrix is commonly used in statistical simulation, and will likely be #' relevent as the relevant form of a \code{setx} object. #' @usage \method{model.matrix}{parseFormula}(object, data = NULL, ...) #' @note This method is primarily used by the \code{setx} function. #' @param object a "parseFormula" object #' @param data a "data.frame" #' @param ... ignored parameters #' @return a "model.matrix" specifying information relevant to a statistical #' model #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @S3method model.matrix parseFormula model.matrix.parseFormula <- function (object, data = NULL, ...) { # if (is.null(object$model.matrix)) # # Note that if data is NULL, then "makeModelMatrix" will return NULL # makeModelMatrix(formula(object), data) # # else if (!missing(data)) # # If data is supplied, recompute the model matrix # makeModelMatrix(formula(object), data) # # else # # Otherwise use the previous stored value (which still might be NULL) # object$model.matrix makeModelMatrix(formula(object), data) } Zelig/R/factor.bayes.R0000644000176000001440000000254512110047000014247 0ustar ripleyusers#' @export zelig2factor.bayes <- function ( formula, factors = 2, burnin = 1000, mcmc = 20000, verbose=0, ..., data ) { loadDependencies("MCMCpack", "coda") if (missing(verbose)) verbose <- round((mcmc + burnin)/10) if (factors < 2) stop("Number of factors needs to be at least 2") x <- as.matrix(model.response(model.frame(formula, data=data, na.action=NULL))) list( .function = "MCMCfactanal", .hook = "McmcHookFactor", formula = formula, x = x, burnin = burnin, mcmc = mcmc, verbose= verbose, data = data, factors = factors, ... ) } #' @S3method param factor.bayes param.factor.bayes <- function (...) { } #' @S3method param factor.bayes qi.factor.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { stop('There is no qi function for the "factor.bayes" model') list( "Expected Value: E(Y|X)" = NA ) } #' @S3method describe factor.bayes describe.factor.bayes <- function(...) { list( authors = c("Ben Goodrich", "Ying Lu"), text = "Bayesian Factor Analysis", year = 2013 ) } Zelig/R/as.dataframe.setx.R0000644000176000001440000000132512061700507015207 0ustar ripleyusers#' Coerce a \code{setx} Object into a \code{data.frame} #' @usage \method{as.data.frame}{setx}(x, row.names=NULL, optional=FALSE, ...) #' @note In subsequent versions of Zelig, this version is expected to undergo #' minor modifications. #' @param x a \code{setx} object #' @param row.names ignored parameter #' @param optional ignored parameter #' @param ... ignored parameters #' @return the \code{setx} object interpretted as a \code{data.frame}. The #' column-names of the resulting \code{data.frame} are specified by the names #' of the \code{setx} object. The row-names are typically unlabeled. #' @S3method as.data.frame setx as.data.frame.setx <- function (x, row.names=NULL, optional=FALSE, ...) { x$matrix } Zelig/R/names.relogit.R0000644000176000001440000000000012110047000014416 0ustar ripleyusersZelig/R/logit.R0000644000176000001440000001100012214411116012777 0ustar ripleyusers#' Interface between logit model and Zelig #' #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param robust a boolean (logical) specifying whether robust error estimates #' should be used #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2logit <- function(formula, weights=NULL, robust = F, ..., data) { w <- weights z( glm, formula = formula, weights = w, family = binomial(link="logit"), model = F, data = data ) } #' Param Method for the \code{logit} Zelig Model #' @note This method is used by the \code{logit} Zelig model #' @usage \method{param}{logit}(obj, num, ...) #' @S3method param logit #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored parameters #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.logit <- function(obj, num, ...) { list( simulations = mvrnorm(n=num, mu=coef(.object), Sigma=vcov(.object)), alpha = NULL, fam = binomial(link="logit") ) } #' Compute quantities of interest for 'logit' Zelig models #' @usage \method{qi}{logit}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi logit #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.logit <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # Compute expected values compute.ev <- function(obj, x=NULL, num=1000, param=NULL) { if (is.null(x)) return(NA) coef <- coef(param) link.inverse <- linkinv(param) eta <- coef %*% t(x) eta <- Filter(function (y) !is.na(y), eta) theta <- matrix(link.inverse(eta), nrow = nrow(coef)) ev <- matrix(link.inverse(eta), ncol=ncol(theta)) ev } # Simulate quantities of interest for "x" ev1 <- compute.ev(obj, x, num, param) pr1 <- matrix(nrow=nrow(ev1), ncol=ncol(ev1)) # Simulate the quantities of interest for "x1" ev2 <- compute.ev(obj, x1, num, param) pr2 <- fd <- NA # Produce 0 or 1 (FALSE/TRUE) results for "x" for (i in 1:ncol(ev1)) pr1[,i] <- as.character(rbinom(length(ev1[,i]), 1, ev1[,i])) # Produce 0 or 1 (FALSE/TRUE) results for "x1" and comppute first-differences if (!is.null(x1)) { pr2 <- matrix(nrow=nrow(ev2), ncol=ncol(ev2)) for (i in 1:ncol(ev2)) pr2[,i] <- as.character(rbinom(length(ev2[,i]), 1, ev2[,i])) # This is the computation of the first difference... fd <- ev2 - ev1 } # Ensure that the correct levels are passed along. levels(pr1) <- levels(pr2) <- c('0', '1') # return list("Expected Values: E(Y|X)" = ev1, "Expected Values: E(Y|X1)" = ev2, "Predicted Values: Y|X" = pr1, "Predicted Values: Y|X1" = pr2, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } .compute.ev <- function(obj, x=NULL, num=1000, param=NULL) { if (is.null(x)) return(NA) coef <- coef(param) link.inverse <- linkinv(param) eta <- coef %*% t(x) theta <- matrix(link.inverse(eta), nrow = nrow(coef)) ev <- matrix(link.inverse(eta), ncol=ncol(theta)) ev } #' Describe a `logit' model to Zelig #' @usage \method{describe}{logit}(...) #' @S3method describe logit #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.logit <- function(...) { # parameters object parameters <- list(pi = list( equations = c(1, 1), tags.allowed = FALSE, dep.var = TRUE, exp.var = TRUE ) ) # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2008, category = "dichotomous", parameters = parameters, text = "Logistic Regression for Dichotomous Dependent Variables" ) } Zelig/R/simulation.matrix.R0000644000176000001440000001037712061700507015375 0ustar ripleyusers#' Get Simulations as a Matrix #' #' Returns a MxN matrix where N is the number of simulations and M is the number #' of predicted values. Additionally, a ``labels'' attribute is attached that #' produces a human-readable identifier for each column. #' @param obj an object, typically a ``sim'' or ``pooled.sim'' object. #' @param which a character-vector specifying the \emph{titles} of quantities of #' interest to extract #' @param ... additional parameters #' @return a simulation matrix #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export simulation.matrix <- function (obj, which = NULL, ...) { UseMethod("simulation.matrix") } #' @S3method simulation.matrix sim simulation.matrix.sim <- function (obj, which, ...) { which <- find.match(which, attr(obj, "titles")) if (is.na(which)) { warning( 'The "which" parameter does not exist. Valid titles are:\n ', paste('"', names(obj$qi), '"', sep="", collapse=", ") ) # Return a matrix containing the single entry NA return(matrix(NA)) } # Store the little matrix (probably a column-vector) lil.matrix <- as.matrix(obj$qi[[which]]) # Specify what quantities of interest this matrix represents attr(lil.matrix, "qi") <- which # Return the little, modified matrix lil.matrix } #' @S3method simulation.matrix pooled.sim simulation.matrix.pooled.sim <- function (obj, which, ...) { # Get the best match for the value "which" which <- find.match(which, attr(obj, "titles")) # This will become the matrix that is returned big.matrix <- NULL # Iterate through all the results for (label in names(obj)) { # Get the matrix for the single quantity of interest small.matrix <- simulation.matrix(obj[[label]], which = which, exact.match = FALSE) # Column-bind this result with the total matrix. # This might want to be wrapped by a tryCatch in case weird things happen big.matrix <- cbind(big.matrix, small.matrix) } # Column-wise specification attr(big.matrix, "labels") <- names(obj) attr(big.matrix, "which") <- 1:ncol(big.matrix) names(attr(big.matrix, "which")) <- names(obj) # Specify what quantities of interest this matrix represents attr(big.matrix, "qi") <- which # Return the big matrix big.matrix } #' Find a Partial or Exact Match from a Vector of Strings #' Searches a vector of character-string, and returns the best match. #' @param needle a character-string to search for in the #' @param haystack a vector of character-strings #' @param fail the value to return in case no match is found. Defaults to NA #' @return the best-matched string or NA #' @details ``find.match'' attempts to use several common matching functions in #' an order that sequentially prefers less strict matching, until a suitable #' match is found. If none is found, then return the value of the ``fail'' #' parameter (defaults to NA). The functions used for matching are: ``match'', #' ``charmatch'', and finally ``grep''. #' @author Matt Owen \email{mowen@@iq.harvard.edu} find.match <- function (needle, haystack, fail = NA) { # Having multiple approximate hits is bad form, since the string "x" can match # "xe", "xen", "xs", etc. If it allows this possibility, we'll be constructing # matrices out of potentially disparate quantities of interest. That is, it # obviously would not be good to match the string "Average" with # "Averge Treatment Effect" and "Average Value". # That is, we want our matrices to be constructed consistently if (length(needle) != 1) return(NA) # Search the strings all at once for code clarity. We can write this smoother, # but then it sacrifices readability for nested if clauses. exact.match <- match(needle, haystack, nomatch = 0) partial.match <- charmatch(needle, haystack, nomatch = 0) grep.match <- grep(needle, haystack)[1] # If we found an exact match, then we go with it. if (exact.match != 0) return(haystack[exact.match]) # If there is a unique partial match, then that will work too. else if (partial.match != 0) return(haystack[partial.match]) # If there are non-unique partial matches, then we take the first incidence else if (!is.na(grep.match)) return(haystack[grep.match]) # If nothing else is good, then return whatever value a failure should be. NA by default return(fail) } Zelig/R/GetSlot.R0000644000176000001440000000067012061700507013262 0ustar ripleyusers#' Generic method for extracting variables from both #' S3 and S4 fitted model object #' #' @param obj an object of type `zelig' #' @param key a character-string specifying the name #' of the variable to extract #' @param ... typically ignored parameters #' @return the value of that extracted object or NULL #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} GetSlot <- function(obj, key, ...) UseMethod("GetSlot") Zelig/R/mi.R0000644000176000001440000000144212110047000012267 0ustar ripleyusers#' Bundle Data-sets for Multiple Imputation #' #' This object prepares data-sets for processing with multiple imputation. #' @note This function is largely identical to simply creating a list object, #' with the exception that any unnamed data-sets are automatically labeled #' via the \code{substitute} function #' @param ... a set of \code{data.frame}'s #' @return an \code{almost.mi} object, which contains the important internals #' of a valid, useful \code{mi} object #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export mi <- function (...) { # Get arguments as list data.frames <- list(...) # Ensure that everything is data.fram for (k in length(data.frames):1) { if (!is.data.frame(data.frames[[k]])) data.frames[[k]] <- NULL } # Return data.frames } Zelig/R/zelig.skeleton.R0000644000176000001440000001103112216141336014630 0ustar ripleyusers#' 'zelig.skeleton' generates the necessary files used to create a Zelig #' package. Based on (and using) R's 'package.skeleton' it removes some of the #' monotony of building statistical packages. In particular, 'zelig.skeleton' #' produces templates for the \code{zelig2}, \code{describe}, \code{param}, and #' \code{qi} methods. For more information about creating these files on an #' individual basis, please refer to the tech manuals, which are available #' by typing: \code{?zelig2}, \code{?param}, or \code{?qi}. #' @title Creates a Skeleton for a New Zelig package #' @param pkg a character-string specifying the name of the Zelig package #' @param models a vector of strings specifying models to be included in the #' package #' @param author a vector of strings specifying contributors to the package #' @param path a character-string specifying the path to the package #' @param force a logical specifying whether to overwrite files and create #' necessary directories #' @param email a string specifying the email address of the package's #' maintainer #' @param depends a vector of strings specifying package dependencies #' @param ... ignored parameters #' @param .gitignore a logical specifying whether to include a copy of a #' simple \code{.gitignore} in the appropriate folders (\code{inst/doc} and #' the package root #' @param .Rbuildignore a logical specifying whether to include a copy of a #' simple \code{.Rbuildignore} in the appropriate folders (\code{inst/doc} #' and the package root #' @return nothing #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig.skeleton <- function ( pkg, models=c(), author="UNKNOWN AUTHOR", path = ".", force = FALSE, email = "maintainer@software-project.org", depends = c(), ..., .gitignore = TRUE, .Rbuildignore = TRUE ) { # WARNING BLOCK # so that developers are aware of potential pitfalls that will prevent # installation of their packages if (!is.character(pkg)) { warning("invalid 'pkg' parameter; should be a character string") pkg <- as.character(pkg) } if (length(pkg) > 1) { warning("invalid 'pkg' parameter; length cannot be greater than one") pkg <- pkg[1] } if (!is.character(models)) { warning("invalid 'models' parameter; should be a character vector") models <- as.character(models) } if (!length(models)) warning("invalid 'models' parameter; should contain at least one model-name") if (missing(author)) warning("missing 'author' parameter; please change the value in the", "'DESCRIPTION' file's 'Author' field") if (missing(email)) warning("Missing 'email' parameter; please change the value in the ", "'DESCRIPTION' file's 'Maintainer' field") if (missing(depends)) warning("Missing 'depends' parameter") # new environment e <- new.env() for (m in models) { # Place proper functions in # correct environment (out of global) # this technically doesn't work # (bug in package.skeleton) describe <- function (...) list() zelig2 <- function (formula, ..., data) list(.function = "") param <- function (obj, num, ...) list(coef=NULL) qi <- function (obj, x, x1, y, param, num) list() assign(paste("describe", m, sep="."), describe, e) assign(paste("zelig2", m, sep=""), describe, e) assign(paste("param", m, sep="."), describe, e) assign(paste("qi", m, sep="."), describe, e) } # Invoke package.skeleton package.skeleton( name = pkg, environment = e, path = path, force = force ) # Copy files over - as of 3/11 these files are blank for (m in models) { .copy.templates(m, pkg, path) } .make.description(pkg, author, email, depends, url, path) .make.package.R(pkg, author, email, depends, url, path) # copy .gitignore and .Rbuildignore if (.gitignore) { src <- system.file('hidden', 'gitignore', package='Zelig') dest <- file.path(path, pkg, '.gitignore') file.copy(src, dest) dest <- file.path(path, pkg, 'man', '.gitignore') file.copy(src, dest) } if (.Rbuildignore) { src <- system.file('hidden', 'Rbuildignore', package='Zelig') dest <- file.path(path, pkg, '.Rbuildignore') file.copy(src, dest) dest <- file.path(path, pkg, 'inst', 'doc', '.Rbuildignore') dir.create(file.path(path, pkg, 'inst', 'doc'), recursive=TRUE) file.copy(src, dest) } # Why zero? Eh, maybe a return code thing. This function is really just used # for side-effects invisible(0) } Zelig/R/describe.zelig.R0000644000176000001440000000116312061700507014570 0ustar ripleyusers#' Get Description Object Used to Cite this Zelig Model #' @note This function should be reevaluated in design, since 'description' #' objects are exclusively used internally. In particular, this method would #' be more useful to users as a 'cite' method. #' @usage \method{describe}{zelig}(object, ...) #' @S3method describe zelig #' @param object a 'zelig' object #' @param ... ignored parameters #' @return a 'description' object used internally to produce citation text #' @author Matt Owen \email{mowen@@iq.harvard.edu} describe.zelig <- function(object, ...) { append(list(model=object$name), NextMethod("describe")) } Zelig/R/as.summarized.R0000644000176000001440000000177012061700507014465 0ustar ripleyusers#' Generic Method for Casting Objectst as 'summarized' Objects #' #' This function is particularly for use by the 'summarize' method, which #' summarizes the simulations taken from the 'qi' method. The generic function #' 'summary' when applied to a Zelig Simulation implicitly uses this function. #' #' @note This is made available on the Global namespace as a matter of potential #' future compliancy. #' @param x an object #' @param ... unspecified parameters #' @return a 'summarized.qi' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.summarized <- function(x, ...) { UseMethod("as.summarized") } #' summarized.qi -> summarized.qi #' #' Identity operation on ``summarized.qi'' objects #' @usage \method{as.summarized}{summarized.qi}(x, ...) #' @param x an object of type 'summarized.qi' #' @param ... ignored parameters #' @return the same 'summarized.qi' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.summarized.summarized.qi <- function(x, ...) { x } Zelig/R/callToString.R0000644000176000001440000000060512061700507014304 0ustar ripleyusers#' Convert \code{call} Object to a String #' #' This method concerts \code{call} objects into a simple, intuitive #' human-readable form. #' @param x a \code{call} object #' @param ... ignored parameters #' @return a character-string representing the \code{call} object #' @author Matt Owen \email{mowen@@iq.harvard.edu} callToString <- function (x, ...) as.character(as.expression(x)) Zelig/R/makeModelMatrix.R0000644000176000001440000000144612061700507014766 0ustar ripleyusers#' Make a Model Matrix from a Zelig-Style Formula #' #' This is a helper function that creates a \code{model.matrix} like object #' of Zelig-style formulae. #' @param formula a Zelig-style formula #' @param data a \code{data.frame} #' @return a design (or model) matrix #' @author Matt Owen \email{mowen@@iq.harvard.edu} makeModelMatrix <- function (formula, data) { if (missing(data) || is.null(data)) return(NULL) # This is kludge and should be generalized if (inherits(formula, "Formula")) { } if (is.list(formula)) { m <- NULL for (form in formula) { m <- cbind(m, model.matrix(form, data)) } t(as.matrix(m[, unique(colnames(m))])) } else { return(model.matrix(formula, data)) } } # # # makeModelMatrixFromFormula <- function (formula, data) { } Zelig/R/vcov.R0000644000176000001440000000071112061700507012652 0ustar ripleyusers#' @S3method vcov gee.naive vcov.gee.naive <- function(object, ...) object$naive.variance #' @S3method vcov gee.robust vcov.gee.robust <- function(object, ...) object$robust.variance #' @S3method vcov glm.robust vcov.glm.robust <- function(object, ...) { so <- summary.glm.robust(object, corr=FALSE, ...) so$dispersion * so$cov.unscaled } #' @S3method vcov Relogit vcov.Relogit <- function(object, ...) summary.Relogit(object, ...)$cov.scaled Zelig/R/gamma.survey.R0000644000176000001440000001152412071063367014326 0ustar ripleyusers#' @export zelig2gamma.survey <- function( formula, weights=NULL, ids=NULL, probs=NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates=FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, link = "inverse", data, ... ) { loadDependencies("survey") if (is.null(ids)) ids <- ~1 # the following lines designate the design # NOTE: nothing truly special goes on here; # the below just makes sure the design is created correctly # for whether or not the replication weights are set design <- if (is.null(repweights)) { svydesign( data=data, ids=ids, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights ) } else { # Using the "z" function stores this implicitly in a namespace .survey.prob.weights <- weights # svrepdesign( data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc ) } z(.function = svyglm, formula = formula, design = design, family = Gamma() ) } #' @S3method param gamma.survey param.gamma.survey <- function(obj, num=1000, ...) { shape <- gamma.shape(.fitted) list( # .fitted is the fitted model object simulations = mvrnorm(num, coef(.fitted), vcov(.fitted)), alpha = rnorm(num, shape$alpha, shape$SE), fam = Gamma() ) } #' @S3method qi gamma.survey qi.gamma.survey <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { model <- GetObject(obj) coef <- coef(param) alpha <- alpha(param) eta <- coef %*% t(x) link.inverse <- linkinv(param) theta <- matrix(link.inverse(eta), nrow=nrow(coef)) pr <- ev <- matrix(NA, nrow=nrow(theta), ncol(theta)) dimnames(pr) <- dimnames(ev) <- dimnames(theta) ev <- theta for (i in 1:nrow(ev)) { pr[i,] <- rgamma( n = length(ev[i,]), shape = alpha[i], scale = theta[i,]/alpha[i] ) } # ensure these are no-show pr1 <- ev1 <- fd <- NA # if x1 is available if (!is.null(x1)) { ev1 <- theta1 <- matrix(link.inverse(coef %*% t(x1)), nrow(coef)) fd <- ev1-ev } # ensure these are no-show att.pr <- att.ev <- NA # I have no clue if this even works if (!is.null(y)) { yvar <- matrix( rep(y, nrow(param)), nrow = nrow(param), byrow = TRUE ) tmp.ev <- yvar - ev tmp.pr <- yvar - pr att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(param)) att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(param)) } list( "Expected Values: E(Y|X)" = ev, "Expected Values for (X1): E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values (for X1): Y|X1" = pr1, "First Differences E(Y|X1)-E(Y|X)" = fd, "Average Treatment Effect: Y-EV" = att.ev, "Average Treatment Effect: Y-PR" = att.pr ) } #' @S3method describe gamma.survey describe.gamma.survey <- function(...) { list( authors = "Nicholas Carnes", year = 2008, description = "Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables" ) } Zelig/R/cluster.formula.R0000644000176000001440000000115012061700507015020 0ustar ripleyusers#' Generate Formulae that Consider Clustering #' #' This method is used internally by the "Zelig" Package to interpret #' clustering. #' @param formula a formula object #' @param cluster a vector #' @return a formula object describing clustering cluster.formula <- function (formula, cluster) { # Convert LHS of formula to a string lhs <- deparse(formula[[2]]) cluster.part <- if (is.null(cluster)) # NULL values require sprintf("cluster(1:nrow(%s))", lhs) else # Otherwise we trust user input sprintf("cluster(%s)", cluster) update(formula, paste(". ~ .", cluster.part, sep=" + ")) } Zelig/R/MLutils.R0000644000176000001440000002371712061700507013301 0ustar ripleyusers#' Reduce MI Formulas #' Take a formula in any of the reduced form or in a structural form and return #' the most reduced form of that formula #' @note This formula is used primarily by 'zelig2' functions of multivariate #' Zelig models #' @param f a formula #' @export #' @author Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau reduceMI <-function(f){ if(class(f)=="list") f <- structuralToReduced(f) return(.reduceFurther(f)) } #' Transform the Multilevel's Structural Formulas Into Reduced Form #' @param f a list of formulas #' @return a formula in reduced form #' @export #' @author Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau # possible bug: what if class(f) is 'multiple' and not a list? structuralToReduced <- function(f){ ## input should be a list if(class(f) != "list" || (class(f)=="list" && length(f)<2)) stop("the input should be a list of formulas") ## take the first formula; It should be of length 3 main.fml <- f[[1]] if(length(main.fml)!=3) stop("the main formula in the extended form should be of length 3 !") lhs<- main.fml[[2]] TT <- terms(main.fml,specials="tag") TT.labels <- attr(TT,"term.labels") TT.vars <- attr(TT,"variables") tagattr<-attr(TT,"specials")$tag hastag<-!(is.null(tagattr)) if (hastag){ for(j in tagattr){ lind<-j-1 vind<-j+1 tg<- .deparseTag(TT.vars[[vind]]) whicheq<-which(names(f) %in% tg$label) if (length(whicheq)!=0) tg$label<-deparse(f[[whicheq]][[2]]) else stop("one of the equation's name is expected to be ",tg$label) TT.labels[[lind]]<-.newTag(tg) res<-(as.formula(paste(lhs,"~",paste(TT.labels,collapse="+")))) } } else stop("tag is missing in the first equation\n") return(res) } #' Convert a Formula into 'lmer' Representation from Reduced Form #' Take a formula in its reducd from and return it as a 'lmer' representation #' (from the lme4 package). This is basically removing the starting 'tag' from #' each term. #' @param f a formula in reduced form #' @return the 'lmer' representation of 'f' #' @export #' @author Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau tolmerFormat<-function(f){ lhs <- f[[2]] tt <- terms(f, specials="tag") tt.labels<-attr(tt,"term.labels") for (i in 1:length(tt.labels)){ tt.labels[[i]]<-.trim(tt.labels[[i]]) tt.labels[[i]]<-gsub('^tag',"",tt.labels[[i]]) } rhs <- paste(tt.labels,collapse="+") res <- as.formula(paste(lhs,"~",rhs,sep="")) return(res) } #' Further Reduce Formulas in Reduced Form #' Given a formula in a reduced form, output the most reduced one. #' @param f a formula in reduced form #' @return an even-more reduced formula #' @export #' @author Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau .reduceFurther <- function(f){ if(length(f)!=3) stop("the main formula in the extended form should be of length 3 !") lhs<- f[[2]] TT <- terms(f,specials="tag") TT.labels <- attr(TT,"term.labels") TT.vars <- attr(TT,"variables") tagattr<-attr(TT,"specials")$tag hastag<-!(is.null(tagattr)) lstOfTags<-c() if (hastag){ for(j in tagattr){ vind<-j+1 lstOfTags<-c(lstOfTags,.expandTag(.deparseTag(TT.vars[[vind]]))) } } else stop("tag is missing in the first equation\n") tmp <- paste(lstOfTags,collapse="+") rhs<-paste(.replace(TT.labels,tagattr-1,tmp),collapse="+" ) return(as.formula(paste(lhs,"~",rhs,sep=""))) } ### ## Helper function which takes the term with tag and return ## all its parts ## ## input: a tag like call/list i.e. tag(z1,w1+w2 | state) ## output: list(var= "z1", label="w1 + w2", id="state") .deparseTag <- function(f){ f <- as.character(f) res<-list() if(length(f) == 3){ ## tag(var,label|id) or tag(var,label) res$var <- f[[2]] tmp <- .trim(unlist(strsplit(f[[3]],"|",fixed=TRUE))) if(length(tmp) == 2){ ## tag(var,label|id) res$label <- tmp[[1]] res$id <- tmp[[2]] }else{ ## tag(var,label) if(length(tmp)==1){ res$label <-tmp[[1]] res$id <- "none" }else stop("wrong use of tag function!!") } } else { ## tag(var|id) tmp <- .trim(unlist(strsplit(f[[2]],"|",fixed=TRUE))) res$var <- tmp[[1]] res$id <- tmp[[2]] res$label="none" } return(res) } ### ## takes the output from .deparseTag (a list) and construct a new tag ## as a string ## i.e. takes list(var="z",label="w1",id="state") and output "tag(z,w1|state) .newTag <- function(lst){ res <- "tag(" if (lst$var != "none") # must have var res <- paste(res,lst$var,sep="") else stop("wrong use of tag(); variable is missing") if (lst$label !="none"){ ## tag(z,gamma??) res <-paste(res,",",sep="") res <- paste(res,lst$label,sep="") if(lst$id != "none") ## tag(z,gamma|state) res <- paste(res,"|",lst$id,sep="") }else{ ## tag(z|state) res <-paste(res,"|",sep="") if(lst$id !="none") res <- paste(res,lst$id,sep="") else stop("wrong use of tab") # tag(x |) } res <- paste(res,")",sep = "") return(res) } ### ## expands tag. tag(1,w1+w2 | state) => tag(w1|state) + tag(w2|state) ## tag(z,w1+w2 | state) => tag(z:w1|state)+ tag(z:w2|state) ## input tag as a list; i.e the output from .deparseTag .expandTag <- function(l){ if(l$var == "1" && l$label!="none"){ ## tag(1,z1 | state) == tag (z1|state) l$var <- l$label l$label <- "none" } if(l$label =="none"){ ## tag(1+z1|state) vars<-unlist(strsplit(l$var,"+", fixed=TRUE)) }else{ ## tag(z1,w1+w2|state) vars<-unlist(strsplit(l$label,"+", fixed=TRUE)) } if(length(vars) == 1){ ## nothing to expand return (.newTag(l)) }else{ alltgs<-c() for(i in 1:length(vars)){ if(l$label == "none") alltgs <- c(alltgs,.newTag(list(label="none",var=vars[[i]],id=l$id))) else alltgs <- c(alltgs,.newTag(list(label="none",var=paste(l$var,":",vars[[i]],sep=""),id=l$id))) } } return (paste(alltgs,collapse="+")) } ### ## In the vector 'src' replace the element in the position ## 'index' with elementSSS in vector 'dest' .replace<-function(src,index,dest){ "%w/o%" <- function(x,y) x[!x %in% y] return (c(src %w/o% src[index],dest)) if(1==2){ if(index <1 || index > length(src)) stop("wrong index arguemnt in function .replace") if(index==1) beforeEls<-c() else beforeEls<-src[1:(index-1)] if(index == length(src)) afterEls<-c() else afterEls<-src[(index+1):length(src)] return(c(beforeEls,dest,afterEls)) } } ## # Trim the word's white spaces # input : one word string s # output: trimed version of s .trim <-function(v){ for(i in 1:length(v)){ v[[i]] <- gsub('^[[:space:]]+', '', v[[i]]) v[[i]]<- gsub('[[:space:]]+$', '', v[[i]]) } return(v) } ## # Reaction ~ Days + tag(1 + Days | subject) ==> # list (fixed = ~ Days, # random = ~ 1 + Days) # #' @export .getRandAndFixedTerms <- function (fml){ f <- function(x){ as.formula(paste("~",paste(x, collapse = "+"))) } res <- list() if(length(fml)!=3) stop("the main formula in the extended form should be of length 3 !") lhs <- fml[[2]] rhs <- fml[[3]] TT <- terms(fml,specials="tag") TT.labels <- attr(TT,"term.labels") TT.vars <- attr(TT,"variables") tagattr<-attr(TT,"specials")$tag hastag<-!(is.null(tagattr)) if (hastag){ ## fixed F.labels <- TT.labels[-(tagattr-1)] if (!length(F.labels)) F.labels <- 1 res$fixed <- as.formula(paste("~",paste(F.labels,collapse="+"))) ## random random <- list() idx = 1 for (j in tagattr){ vind <- j + 1 tmp <- .deparseTag(TT.vars[[vind]]) idx <- idx + 1 ## if tags have the same id, merge them together if (tmp$id %in% names(random)){ random[[tmp$id]] <- c(random[[tmp$id]], tmp$var) } else { random[[tmp$id]] <- tmp$var } } res$random <- lapply(random,f) } else { res$fixed <- fml } return(res) } Zelig/R/describe.default.R0000644000176000001440000000134112061700507015100 0ustar ripleyusers#' Default describe function for an arbitrary model #' This method exists solely as a backup when an author does not contribute a #' 'describe' function for their model #' @usage \method{describe}{default}(...) #' @S3method describe default #' @param ... dummy parameters purely to cast the correct object. That is, the #' parameters of the function should not #' BE referenced specifically #' @return a list to be processed by \code{as.description} #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.default <- function(...) { warning("The 'describe' method for this function is unspecified") list( authors = "Unknown Author", year = as.numeric(format(Sys.Date(), "%Y")) ) } Zelig/R/mlogit.bayes.R0000644000176000001440000000520512110047000014260 0ustar ripleyusers#' @export zelig2mlogit.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose=0, ..., data ) { loadDependencies("MCMCpack", "coda") list( .function = "MCMCmnl", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param mlogit.bayes param.mlogit.bayes <- function(obj, num=1000, ...) { list( coef = coef(obj), linkinv = NULL ) } #' @S3method qi mlogit.bayes qi.mlogit.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { res1 <- compute.mlogit.bayes(.fitted, x, y, num, param) res2 <- compute.mlogit.bayes(.fitted, x1, y, num, param) list( "Expected Value: E(Y|X)" = res1$ev, "Predicted Value: Y|X" = res1$pv, "Expected Value (for X1): E(Y|X1)" = res2$ev, "Predicted Value (for X1): Y|X1" = res2$pv, "First Differences" = res2$ev - res1$ev ) } compute.mlogit.bayes <- function (obj, x, y, num, param) { # If either of the parameters are invalid, # Then return NA for both qi's if (is.null(x) || is.na(x) || is.null(param)) return(list(ev=NA, pv=NA)) # resp <- model.response(model.frame(obj)) level <- length(table(resp)) p <- dim(model.matrix(eval(obj),data=obj$data))[2] coef <- coef(obj) eta <- array(NA, c(nrow(coef),level, nrow(x$matrix))) eta[, 1, ] <- matrix(0, nrow(coef), nrow(x$matrix)) for (j in 2:level) { ind <- (1:p)*(level-1)-(level-j) eta[,j,]<- coef[,ind]%*%t(x) } eta<-exp(eta) ev <- array(NA, c(nrow(coef), level, nrow(x$matrix))) pr <- matrix(NA, nrow(coef), nrow(x$matrix)) colnames(ev) <- rep(NA, level) for (k in 1:nrow(x$matrix)) { for (j in 1:level) ev[,j,k] <- eta[,j,k]/rowSums(eta[,,k]) } for (j in 1:level) { colnames(ev)[j] <- paste("P(Y=", j, ")", sep="") } for (k in 1:nrow(x$matrix)) { probs <- as.matrix(ev[,,k]) temp <- apply(probs, 1, FUN=rmultinom, n=1, size=1) temp <- as.matrix(t(temp)%*%(1:nrow(temp))) pr <- apply(temp,2,as.character) } list(ev = ev, pv = pr) } #' @S3method describe mlogit.bayes describe.mlogit.bayes <- function(...) { list( authors = c("Ben Goodrich", "Ying Lu"), text = "Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values", year = 2013 ) } Zelig/R/GetObject.R0000644000176000001440000000042112061700507013541 0ustar ripleyusers#' Extract the fitted model object from the Zelig object #' #' @param obj an object of type `zelig' #' @return the fitted model object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} GetObject <- function(obj) { if (inherits(obj, 'zelig')) obj$result } Zelig/R/as.matrix.pooled.setx.R0000644000176000001440000000236712061700507016057 0ustar ripleyusers#' Convert a ``pooled.setx'' Object to a Matrix #' #' The setx object is, in its most basic form, a list of column names and values #' specified for each of these column names. This function simply converts the #' key-value pairs of column-name and specified value into a matrix. #' #' @note This method allows basic matrix arithmetic operations on data objects, #' which mirror values stored within setx objects. In many scenarios, #' simulations require matrix-multiplication, etc. to be performed on a #' data-set. This function faciliates that need. #' #' @usage \method{as.matrix}{pooled.setx}(x, ...) #' @S3method as.matrix pooled.setx #' @param x a setx object #' @param ... ignored parameters #' @return a matrix containing columns and rows corrseponding to the explanatory #' variables specified in the call to the 'setx' function #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.matrix.pooled.setx <- function(x, ...) { big.matrix <- NULL for (label in names(x)) { small.matrix <- as.matrix(x[[label]]) big.matrix <- rbind(big.matrix, small.matrix) } rownames(big.matrix) <- names(x) attr(big.matrix, "labels") <- names(x) attr(big.matrix, "which") <- 1:nrow(big.matrix) names(attr(big.matrix, "which")) <- names(x) big.matrix } Zelig/R/param.R0000644000176000001440000000505012061700507012776 0ustar ripleyusers#' The \code{param} method is used by developers to specify simulated and fixed #' ancillary parameters of the Zelig statistical model. That is, this method #' is used between the \link{zelig2} function and the \link{qi} #' as a helper function that specifies all the necessary details needed to #' simulate quantities of interest, given the fitted statistical model produced #' by the \code{zelig2} function. #' #' @title Generic Method for Simulating Ancillary/Auxillary Parameters of Zelig #' Models #' @note The 'param' function is a method meant to be overloaded by Zelig #' Developers #' @param obj a \code{zelig} object #' @param num an integer specifying the number of simulations to sample #' @param ... optional parameters which will likely be ignored #' @return #' The main purpose of the \code{param} function is to return a list of #' key-value pairs, specifuing information that should be shared between #' the \code{qi} function and the fitted statistical model (produced by the #' \code{zelig2} function. This list can contain the following entries: #' #' \item{\code{simulations}}{specifies a set of simulated parameters used to #' describe the statistical model's underlying distribution} #' \item{\code{alpha}}{specifies the fixed (non-simulated) ancillary #' parameters used by the statistical model's underlying distribution} #' \item{\code{family}}{specifies a family object used to implicitly define #' the \code{link} and \code{linkinv} functions. That is, this specifies #' the "link" and "inverse link" functions of generalized linear models} #' \item{\code{link}}{specifies the \code{link} function to be used. This #' parameter is largely unimportant compared to the "inverse link" #' function} #' \item{\code{linkinv}}{specifies the \code{linkinv} function to be used.} #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @examples #' param.some.model <- function (obj, num, ...) { #' list( #' simulations = NULL, #' alpha = NULL, #' link = NULL, #' linkinv = NULL, #' fam = NULL #' ) #' } param <- function (obj, num, ...) UseMethod("param") #' Default Method for ``param'' #' #' If no \code{param} function is set for a Zelig model, then this function will #' return NULL. #' @usage \method{param}{default}(obj, num, ...) #' @S3method param default #' @param obj ignored parameter #' @param num ignored parameter #' @param ... ignored parameters #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.default <- function (obj, num, ...) list() Zelig/R/simulations.plot.R0000644000176000001440000001362512157603402015233 0ustar ripleyusers#' Plot Quantities of Interest in a Zelig-fashion #' #' Various graph generation for different common types of simulated results from #' Zelig #' @usage simulations.plot(y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, axisnames=TRUE) #' @param y A matrix or vector of simulated results generated by Zelig, to be #' graphed. #' @param y1 For comparison of two sets of simulated results at different #' choices of covariates, this should be an object of the same type and #' dimension as y. If no comparison is to be made, this should be NULL. #' @param xlab Label for the x-axis. #' @param ylab Label for the y-axis. #' @param main Main plot title. #' @param col A vector of colors. Colors will be used in turn as the graph is #' built for main plot objects. For nominal/categorical data, this colors #' renders as the bar color, while for numeric data it renders as the background #' color. #' @param line.col A vector of colors. Colors will be used in turn as the graph is #' built for line color shading of plot objects. #' @param axisnames a character-vector, specifying the names of the axes #' @return nothing #' @author James Honaker simulations.plot <-function( y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, axisnames=TRUE ) { ## Univariate Plots ## if(is.null(y1)){ if (is.null(col)) col <- rgb(100,149,237,maxColorValue=255) if (is.null(line.col)) line.col <- "black" # Character if (is.character(y)) { # Try to cast y as integers, note that this is not always possible for the # general case of characters newy <- tryCatch( as.numeric(y), warning = function (w) NULL, error = function (e) NULL ) # If: # newy is not NULL (can be cast as a numeric) AND # newy is actually a collection of integers (not just numeric) # Then: # we can tabulate (so sick) if (!is.null(newy) && all(as.integer(y) == y)) { # Create a sequence of names nameseq <- paste("Y=", min(newy):max(newy), sep="") # Set the heights of the barplots. # Note that tablar requires that all out values are greater than zero. # So, we subtract the min value (ensuring everything is at least zero) # then add 1 bar.heights <- tabulate(newy - min(newy) + 1) / length(y) # Barplot with (potentially) some zero columns output <- barplot( bar.heights, xlab=xlab, ylab=ylab, main=main, col=col[1], axisnames=axisnames, names.arg=nameseq ) } # Otherwise, we stick with old-style tables else { y <- if (is.null(levels(y))) factor(y) else factor(y, levels = levels(y)) bar.heights <- table(y)/length(y) bar.names <- paste("Y=", names(bar.heights), sep="") output <- barplot( bar.heights, xlab=xlab, ylab=ylab, main=main, col=col[1], axisnames=axisnames, names.arg=bar.names ) } } ## Numeric else if(is.numeric(y)){ den.y <- density(y) output <- plot(den.y, xlab=xlab, ylab=ylab, main=main, col=line.col[1]) if(!identical(col[1],"n")){ polygon(den.y$x, den.y$y, col=col[1]) } } ## Comparison Plots ## } else{ ## Character - Plot and shade a matrix if(is.character(y) & is.character(y1) & length(y)==length(y1) ){ newy<-trunc(as.numeric(y)) newy1<-trunc(as.numeric(y1)) yseq<-min(c(newy,newy1)):max(c(newy,newy1)) nameseq<- paste("Y=",yseq,sep="") n.y<-length(yseq) colors<-rev(heat.colors(n.y^2)) lab.colors<-c("black","white") comp<-matrix(NA,nrow=n.y,ncol=n.y) for(i in 1:n.y){ for(j in 1:n.y){ flag<- newy==yseq[i] & newy1==yseq[j] comp[i,j]<-mean(flag) } } old.pty<-par()$pty old.mai<-par()$mai par(pty="s") par(mai=c(0.3,0.3,0.3,0.1)) image(z=comp, axes=FALSE, col=colors, zlim=c(min(comp),max(comp)),main=main ) locations.x<-seq(from=0,to=1,length=nrow(comp)) locations.y<-locations.x for(m in 1:n.y){ for(n in 1:n.y){ text(x=locations.x[m],y=locations.y[n],labels=paste(round(100*comp[m,n])), col=lab.colors[(comp[m,n]> ((max(comp)-min(comp))/2) )+1]) } } axis(side=1,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=1) axis(side=2,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=3) box() par(pty=old.pty,mai=old.mai) ## Numeric - Plot two densities on top of each other }else if(is.numeric(y) & is.numeric(y1)){ if(is.null(col)){ col<-c("blue","red") }else if(length(col)<2){ col<-c(col,col) } if(is.null(col)){ semi.col.x <-rgb(142,229,238,150,maxColorValue=255) semi.col.x1<-rgb(255,114,86,150,maxColorValue=255) col<-c(semi.col.x,semi.col.x1) }else if(length(col)<2){ col<-c(col,col) } den.y<-density(y) den.y1<-density(y1,bw=den.y$bw) all.xlim<-c(min(c(den.y$x,den.y1$x)),max(c(den.y$x,den.y1$x))) all.ylim<-c(min(c(den.y$y,den.y1$y)),max(c(den.y$y,den.y1$y))) output<-plot(den.y,xlab=xlab,ylab=ylab,main=main,col=col[1],xlim=all.xlim,ylim=all.ylim) par(new=TRUE) output<-plot(den.y1,xlab=xlab,ylab=ylab,main="",col=col[2],xlim=all.xlim,ylim=all.ylim) if(!identical(col[1],"n")){ polygon(den.y$x,den.y$y,col=col[1]) } if(!identical(col[1],"n")){ polygon(den.y1$x,den.y1$y,col=col[2]) } } } } Zelig/R/zeligBuildWeights.R0000644000176000001440000001633012215715475015341 0ustar ripleyusers#' Utility to build a vector (or sometimes matrix) of weights for analysis model. #' #' This takes standardized Zelig user input about weights, and tailors it #' via developer defined settings, to correspond with the format of #' weights acceptable by the model Zelig bridges to. It also runs a #' set of checks to uncover any potential errors in the specified weights. #' @param weights A set of non-negative value weights. Overrides repweights #' if defined. #' @param repweights A set of whole number (non-negative integer) weights. #' useful if weights are just for making copies or deleting certain #' or frequency weights. #' @param zeros An option on how to deal with zero valued user supplied weights. #' Default of "zero" allows zero weights, "epsilon" changes zeroes to 1e-08, #' "remove" removes those observations from the dataset. #' @param rebuild An option to allow specified repweights to reconfigure the #' rows of the dataset to rebuild a corresponding dataset where every row is #' of weight 1. Useful if analysis model does not accept weights. #' @param allowweights Defines if weights are allowed in model. #' @param allowrepweights Defines if repweights are allowed in model. Overridden if #' \code{useweights=TRUE}. #' @param data Dataset, required if weights are defined by variable name, or if #' dataset is to be reconfigured (by \code{rebuild} or \code{zeros} options) #' @return weights A vector of weights of the structure defined by the #' developer and required by the analysis model. Or NULL if certain checks #' are failed. #' @return data A reconfigured dataset, if modified. #' @author James Honaker \email{zelig-zee@@iq.harvard.edu} #' @export zeligBuildWeights <- function (weights=NULL, repweights=NULL, zeros="zeros", rebuild=FALSE, allowweights=TRUE, allowrepweights=TRUE, data=NULL) { ## Developer can turn off certain types of weights ## NOTE: Can't currently turn off "repweights", if "weights" allowable. if(!allowweights & !allowrepweights & !is.null(weights)){ warning("You have specified weights, but weighting is not available for this model. Ignoring weights. ") return(list(weights=NULL, data=data)) } if(!allowweights & !allowrepweights & !is.null(repweights)){ warning("You have specified repweights, but weighting is not available for this model. Ignoring weights. ") return(list(weights=NULL, data=data)) } if(!allowweights & !is.null(weights)){ warning("You have specified weights, but weights are not an option in this model. Ignoring weights. repweights may be an available option.") weights=NULL } ## Override repweights with weights when in conflict. if(!is.null(weights) & !is.null(repweights)){ warning("You have specified both weights and repweights. The repweights will be ignored.") repweights<-NULL } ## Turn weights as variable name into vector, with checking. if(is.character(weights)){ if(is.null(data)){ warning("ZELIG DEVELOPER WARNING: You have named a weight variable in the dataset, but not supplied dataset to zeligBuildWeights. Weights will be ignored in your model until amended.") return(list(weights=NULL, data=data)) }else if (!(weights %in% names(data))){ warning("The variable name supplied for the weights is not present in the dataset. Ignoring weights.") return(list(weights=NULL, data=data)) }else if ( !is.numeric(data[,weights])){ warning("The variable supplied for the weights is not numeric. Ignoring weights.") return(list(weights=NULL, data=data)) }else{ weights<-data[,weights] } } ## Turn repweights as variable name into vector, with checking. if(is.character(repweights)){ if(is.null(data)){ warning("ZELIG DEVELOPER WARNING: You have named a repweight variable in the dataset, but not supplied dataset to zeligBuildWeights. repweights will be ignored in your model until amended.") return(list(weights=NULL, data=data)) }else if (!(repweights %in% names(data))){ warning("The variable name supplied for the repweights is not present in the dataset. Ignoring weights.") return(list(weights=NULL, data=data)) }else if ( !is.numeric(data[,repweights])){ warning("The variable supplied for the repweights is not numeric. Ignoring weights.") return(list(weights=NULL, data=data)) }else{ repweights<-data[,repweights] } } ## Some checking/transforming on repweights if(!is.null(repweights)){ if(!all(floor(repweights)==repweights)){ warning("Defined repweights are not integer, so will be rounded.") repweights=round(repweights) # Maybe allow floor/ceiling as other options? } if(sum(is.na(repweights))>0){ # any(is.na()) sometimes has issues warning("Some defined repweights are missing values, so will be treated as zeros") flag<-is.na(repweights) repweights[flag]<-0; } if(min(repweights)<0){ warning("Some defined repweights are negative, so will be treated as zeros") flag<-repweights<0 repweights[flag]<-0; } if(sum(repweights)==0){ warning("Defined repweights give no weight to any observation. Ignoring weights.") return(list(weights=NULL, data=data)) } } ## Some checking/transforming on weights if(!is.null(weights)){ if(sum(is.na(weights))>0){ # any(is.na()) sometimes has issues warning("Some defined weights are missing values, so will be treated as zeros") flag<-is.na(weights) weights[flag]<-0; } if(min(weights)<0){ warning("Some defined weights are negative, so will be treated as zeros") flag<-weights<0 weights[flag]<-0; } if(sum(weights)==0){ warning("Defined weights give no weight to any observation. Ignoring weights.") return(list(weights=NULL, data=data)) } } ## If repweights not available to function, reconstruct a dataset ## NOTE: "rebuild" overrides any setting of "zeros" if( is.null(weights) & !is.null(repweights) & rebuild){ if(is.null(data)){ warning("ZELIG DEVELOPER WARNING: You have set zeligBuildWeights to rebuild dataset, but not supplied dataset to zeligBuildWeights function. Weights will be ignored in your model until amended.") return(list(weights=NULL, data=data)) }else{ ## Rebuild dataset according to replication weights newobs<-rep(1:nrow(data), repweights) # Index of rows to use data<-data[newobs,] # Copy relevant rows } weights<-NULL # Or, could be weights<-rep(1,nrow(data)) ## when repweights are correct, but need to be transfered to final output }else if (is.null(weights) & !is.null(repweights) ){ weights<-repweights } ## From this point, only "weights" exists in a meaningful way. if(!is.null(weights)){ ## Implement zeros option. if(zeros=="epsilon"){ flag<-weights==0 weights[flag]<- .00000001 }else if (zeros=="remove"){ flag<-weights==0 weights<-weights[!flag] data<-data[!flag,] } } ## NOTE: Ideally, we could just pass back a vector of indexes to reformat the ## data, rather than passing back and forwards the data. ## But simpler for developer this way. built <- list(weights=weights, data=data) # Return return(built) } Zelig/R/as.summarized.list.R0000644000176000001440000000053712061700507015437 0ustar ripleyusers#' list -> summarized.qi #' Convert a list into a ``summarized.qi'' object #' @usage \method{as.summarized}{list}(x, ...) #' @param x a list #' @param ... ignored parameters #' @return a ``summarized.qi'' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.summarized.list <- function(x, ...) { class(x) <- "summarized.qi" x } Zelig/R/model.frame.multiple.R0000644000176000001440000000620512061700507015724 0ustar ripleyusers#' Create Model Frame from \code{multiple} Object #' #' This method creates a \code{model.frame} from a \code{multiple} object. This #' method will be deprecated as the development of Zelig 4 progresses. #' @usage \method{model.frame}{multiple}(formula,data,eqn=NULL,...) #' @S3method model.frame multiple #' @param formula an object of both type \code{formula} and \code{multiple} #' @param data a \code{data.frame} #' @param eqn the number of equations in the formula #' @param ... ignored parameters #' @return a \code{model.frame} object #' @author Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi model.frame.multiple <- function (formula,data,eqn=NULL,...){ if(class(formula)[[1]]=="terms"){ terms <-formula }else{ terms<-terms(formula) } #is multilevel? if(!(is.logical(attr(terms,"subs")))) return(multilevel(tt=terms,data=data,eqn=eqn,mode=2)) "%w/o%" <- function(x,y) x[!x %in% y] eqns<-names(formula) eqns<-attr(terms,"systEqns") nrEquations<-length(eqns) termlabels<-attr(terms,"term.labels") depVars<-attr(terms,"depVars") Xs<-Ys<-tlNew<-dvNew<-list() for (i in 1:nrEquations){ rhs<-toBuildFormula(termlabels[[eqns[[i]]]]) if(!(is.null(rhs))){ rhs<-paste(rhs,"-1") rhs<-as.formula(paste("~",rhs)) Xs[[eqns[[i]]]]<-model.matrix.default(rhs,data=data) tlNew[[eqns[[i]]]]<-colnames(Xs[[eqns[[i]]]]) tlNew[[eqns[[i]]]]<-gsub("as.factor\\(.*\\)","",tlNew[[eqns[[i]]]]) colnames(Xs[[eqns[[i]]]])<-tlNew[[eqns[[i]]]] } } depFactors<-attr(terms,"depFactors") if(!(is.logical(depFactors))) depVars<- paste("as.factor(",depFactors[[1]],")",sep="") #print(depVars) lhs<-toBuildFormula(unique(unlist(depVars))) if(!(is.null(lhs))){ lhs<-paste(lhs,"-1") lhs<-as.formula(paste("~",lhs)) Ys<-model.matrix.default(lhs,data=data) dvNew<-colnames(Ys) dvNew<-gsub("as.factor\\(.*\\)","",dvNew) colnames(Ys)<-dvNew } attr(terms,"term.labels")[names(tlNew)]<-tlNew attr(terms,"depVars")[names(dvNew)]<-dvNew ronames<-rownames(data) ronr<-nrow(data) Xnames<-unique(unlist(tlNew)) Ynames<-unique(unlist(dvNew)) if(!(is.logical(depFactors))) Ynames<-c(depFactors[[2]],Ynames %w/o% depFactors[[2]]) X<-matrix(0,nrow=ronr,ncol=length(Xnames),dimnames=list(ronames,Xnames)) Y<-matrix(0,nrow=ronr,ncol=length(Ynames),dimnames=list(ronames,Ynames)) if(length(tlNew)>0) for(i in 1:length(tlNew)){ xtmp<-intersect(tlNew[[i]],Xnames) X[,xtmp]<-Xs[[i]][,xtmp] } Y<-Ys my.data.frame<-as.data.frame(cbind(Y,X)) rhs<-toBuildFormula(Xnames) if(!(is.null(rhs))) rhs<-(paste("~",rhs)) else rhs<-"~1" cb<-FALSE if(length(Ynames)>1){ lhs<-toBuildFormula(Ynames,",") if (!(is.null(lhs))){ lhs<-paste("cbind(",lhs) lhs<-paste(lhs,")") cb<-TRUE } }else{ lhs=Ynames } lhs<-as.formula(paste(lhs,rhs)) Y<-model.frame.default(lhs,data=my.data.frame) result=Y if(cb) names(result)[[1]]<-"response" new.response<-attr(attr(result,"terms"),"response") attr(terms,"response")<-new.response attr(result,"terms")<-terms class(result)<-c(class(result),"multiple") return(result) } Zelig/R/getResponseTerms.list.R0000644000176000001440000000221412061700507016160 0ustar ripleyusers#' Get Response Terms from a List-style Formula #' #' This method gets the response terms from a standard formula #' @usage \method{getResponseTerms}{list}(x, ...) #' @param x a list of formulae #' @param ... ignored parameters #' @return a character-vector specifying the response terms of the formula #' @S3method getResponseTerms list #' @author Matt Owen getResponseTerms.list <- function (x, ...) { if (! all(unlist(Map(is.formula, x)))) { # If not all the elements are formulae, then we should strip them from 'x' warning("All non-formula will be removed from this list.") x <- Filter(is.formula, x) } if (length(x) == 0) # Zero-sized lists will have no available response terms, and should thus # return a zero-length character vector. Note this is intended to ensure # the result of 'getResponseTerms' is always a character-string. vector("character", 0) else # Get response terms of each element of 'x', # then transform the list into a vector, which should always be flat, since # getResponseTerms should always return a character-string unique(unlist(Map(getResponseTerms, x, single.only=TRUE))) } Zelig/R/setx.R0000644000176000001440000002403112217150360012660 0ustar ripleyusers#' Setting Explanatory Variable Values #' #' The \code{setx} command uses the variables identified in #' the \code{formula} generated by \code{zelig} and sets the values of #' the explanatory variables to the selected values. Use \code{setx} #' after \code{zelig} and before \code{sim} to simulate quantities of #' interest. #' @param obj the saved output from zelig #' @param fn a list of functions to apply to the data frame #' @param data a new data frame used to set the values of #' explanatory variables. If data = NULL (the default), the #' data frame called in zelig is used #' @param cond a logical value indicating whether unconditional #' (default) or conditional (choose \code{cond = TRUE}) prediction #' should be performed. If you choose \code{cond = TRUE}, \code{setx} #' will coerce \code{fn = NULL} and ignore the additional arguments in #' \code{\dots}. If \code{cond = TRUE} and \code{data = NULL}, #' \code{setx} will prompt you for a data frame. #' @param ... user-defined values of specific variables for overwriting the #' default values set by the function \code{fn}. For example, adding #' \code{var1 = mean(data\$var1)} or \code{x1 = 12} explicitly sets the value #' of \code{x1} to 12. In addition, you may specify one explanatory variable #' as a range of values, creating one observation for every unique value in #' the range of values #' @return For unconditional prediction, \code{x.out} is a model matrix based #' on the specified values for the explanatory variables. For multiple #' analyses (i.e., when choosing the \code{by} option in \code{\link{zelig}}, #' \code{setx} returns the selected values calculated over the entire #' data frame. If you wish to calculate values over just one subset of #' the data frame, the 5th subset for example, you may use: #' \code{x.out <- setx(z.out[[5]])} #' @export #' @examples #' #' # Unconditional prediction: #' data(turnout) #' z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout) #' x.out <- setx(z.out) #' s.out <- sim(z.out, x = x.out) #' #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Olivia Lau and Kosuke Imai #' @seealso The full Zelig manual may be accessed online at #' \url{http://gking.harvard.edu/zelig} #' @keywords file setx <- function(obj, fn=NULL, data=NULL, cond=FALSE, ...) UseMethod("setx") #' Set explanatory variables #' #' Set explanatory variables #' @usage \method{setx}{default}(obj, fn=NULL, data=NULL, cond=FALSE, ...) #' @S3method setx default #' @param obj a 'zelig' object #' @param fn a list of key-value pairs specifying which function apply to #' columns of the keys data-types #' @param data a data.frame #' @param cond ignored #' @param ... parameters specifying what to explicitly set each column as. This #' is used to produce counterfactuals #' @return a 'setx' object #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Kosuke Imai, and Olivia Lau setx.default <- function(obj, fn=NULL, data=NULL, cond=FALSE, ...) { # Warnings and errors if (!missing(cond)) warning('"cond" is not currently supported by this version of Zelig') # Get formula used for the call to the model form <- formula(obj) # Parsed formula. This is an intermediate for used for processin design # matrices, etc. parsed.formula <- parseFormula(form, data) # If data.frame is not explicitly set, use the one from the Zelig call if (is.null(data)) data <- obj$data # Create a variable to hold the values of the dot parameters dots <- list() # Get the dots as a set of expressions symbolic.dots <- match.call(expand.dots = FALSE)[["..."]] # Assign values to the dot parameters for (key in names(symbolic.dots)) { result <- with(data, eval(symbolic.dots[[key]])) dots[[key]] <- result } # Extract information about terms # Note: the functions 'getPredictorTerms' and 'getOutcomeTerms' are in need # of a rewrite. At the moment, they are pretty kludgey (written by Matt O.). vars.obj <- getPredictorTerms(form) not.vars <- getResponseTerms(form) # Default the environment to the parent env.obj <- parent.frame() # explanatory variables explan.obj <- Filter(function (x) x %in% vars.obj, names(dots)) # defaults for fn if (missing(fn) || !is.list(fn)) # set fn to appropriate values, if NULL fn <- list(numeric = mean, ordered = Median, other = Mode ) # res res <- list() # compute values # if fn[[mode(data(, key))]] exists, # then use that function to compute result for (key in all.vars(form[[3]])) { # skip values that are explicitly set if (key %in% names(dots) || key %in% not.vars) next m <- class(data[,key])[[1]] # Match the class-type with the correct function to call if (m %in% names(fn)) res[[key]] <- fn[[m]](data[ ,key]) # If it is a numeric, then we just evaluate it like a numeric else if (is.numeric(data[,key])) res[[key]] <- fn$numeric(data[ ,key]) # If it's ordered, then we take the median, because that's the best we got else if (is.ordered(data[,key])) res[[key]] <- fn$ordered(data[ ,key]) # Otherwise we take the mode, because that always kinda makes sense. else res[[key]] <- fn$other(data[ ,key]) } # Add explicitly set values for (key in names(symbolic.dots)) { if (! key %in% colnames(data)) { warning("`", key, "` is not an column in the data-set, and will be ignored") next } res[[key]] <- if (is.factor(data[,key])) { factor(dots[[key]], levels=levels(data[,key])) } else dots[[key]] } # Convert "res" into a list of lists. This makes atomic entries into lists. for (k in 1:length(res)) { if (!is.factor(res[[k]])) res[[k]] <- as.list(res[[k]]) } # Combine all the sublists res <- do.call("mix", res) # A list containing paired design matrices and their corresponding data.frame's frames.and.designs <- list() # Iterate through all the results for (k in 1:length(res)) { # label <- paste(names(res[[k]]), "=", res[[k]], sep="", collapse=", ") # Get specified explanatory variables specified <- res[[k]] # Construct data-frame d <- constructDataFrame(data, specified) # Construct model/design matrix # NOTE: THIS NEEDS TO BE MORE ROBUST m <- constructDesignMatrix(d, parsed.formula) # Model matrix, as a data.frame dat <- tryCatch(as.data.frame(m), error = function (e) NA) # Specify information frames.and.designs[[label]] <- list( label = label, data.frame = d, model.matrix = m, as.data.frame = dat ) } # Phonetically... setx's setexes <- list() for (key in names(frames.and.designs)) { mod <- frames.and.designs[[key]]$model.matrix d <- frames.and.designs[[key]]$data.frame dat <- frames.and.designs[[key]]$as.data.frame specified <- res[[k]] setexes[[key]] <- list( name = obj$name, call = match.call(), formula= form, matrix = mod, updated = d, data = dat, values = specified, fn = fn, cond = cond, new.data = data, special.parameters = dots, symbolic.parameters = symbolic.dots, label = obj$label, explan = vars.obj, pred = not.vars, package.name = obj$package.name ) attr(setexes[[key]], "pooled") <- F class(setexes[[key]]) <- c(obj$name, "setx") } if (length(setexes) == 1) { attr(setexes, "pooled") <- FALSE setexes <- setexes[[1]] class(setexes) <- c(obj$name, "setx") } else { attr(setexes, "pooled") <- TRUE class(setexes) <- c(obj$name, "pooled.setx", "setx") } # Return setexes } #' Construct Data Frame #' Construct and return a tiny (single-row) data-frame from a larger data-frame, #' a list of specified values, and a formula #' @param data a ``data.frame'' that will be used to create a small design matrix #' @param specified a list with key-value pairs that will be used to explicitly #' set several values #' @return a ``data.frame'' containing a single row constructDataFrame <- function (data, specified) { # Make a tiny data-frame with all the necessary columns d <- data[1,] # Give the computed values to those entries for (key in names(specified)) { val <- specified[[key]] if (is.factor(val) || !(is.numeric(val) || is.ordered(val))) val <- factor(val, levels=levels(data[,key])) d[, key] <- val } # Return tiny data-frame d } #' Construct Design Matrix from #' Construct and return a design matrix based on a tiny data-frame (single-row). #' @param data a ``data.frame'' (preferably single-rowed) that will be used to #' create a small design matrix #' @param formula a formula, whose predictor variables will be used to create a #' design matrix #' @return a design (model) matrix constructDesignMatrix <- function (data, formula) { tryCatch( # Attempt to generate the design matrix of the formula model.matrix(formula, data), # If there is a warning... probably do nothing # warning = function (w) w, # If there is an error, warn the user and specify the design # matrix as NA error = function (e) { NA } ) } #' Set Explanatory Variables for Multiply Imputed Data-sets #' This function simply calls setx.default once for every fitted model #' within the 'zelig.MI' object #' @usage \method{setx}{MI}(obj, ..., data=NULL) #' @S3method setx MI #' @param obj a 'zelig' object #' @param ... user-defined values of specific variables for overwriting the #' default values set by the function \code{fn} #' @param data a new data-frame #' @return a 'setx.mi' object used for computing Quantities of Interest by the #' 'sim' method #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @seealso \link{setx} setx.MI <- function(obj, ..., data = NULL) { results.list <- list() for (key in names(obj)) { object <- obj[[key]] results.list[[key]] <- setx(object, ..., data = data) } class(results.list) <- c("setx.mi", "setx") results.list } Zelig/R/relogit.R0000644000176000001440000003044512214411116013344 0ustar ripleyusers#' Fit a rare-event logistic model in Zelig #' #' Fits a rare-event (``relogit'') model. #' @param formula a formula object #' @param data ... #' @param tau ... #' @param bias.correct ... #' @param case.control ... #' @param ... ??? #' @return a ``relogit'' ``glm'' object #' @export relogit <- function( formula, data = sys.parent(), tau = NULL, bias.correct = TRUE, case.control = "prior", ... ){ mf <- match.call() mf$tau <- mf$bias.correct <- mf$case.control <- NULL if (!is.null(tau)) { tau <- unique(tau) if (length(case.control) > 1) stop("You can only choose one option for case control correction.") ck1 <- grep("p", case.control) ck2 <- grep("w", case.control) if (length(ck1) == 0 & length(ck2) == 0) stop("choose either case.control = \"prior\" ", "or case.control = \"weighting\"") if (length(ck2) == 0) weighting <- FALSE else weighting <- TRUE } else weighting <- FALSE if (length(tau) > 2) stop("tau must be a vector of length less than or equal to 2") else if (length(tau)==2) { mf[[1]] <- relogit res <- list() mf$tau <- min(tau) res$lower.estimate <- eval(as.call(mf)) mf$tau <- max(tau) res$upper.estimate <- eval(as.call(mf)) res$formula <- formula class(res) <- c("Relogit2", "Relogit") return(res) } else { mf[[1]] <- glm mf$family <- binomial(link="logit") y2 <- model.response(model.frame(mf$formula, data)) if (is.matrix(y2)) y <- y2[,1] else y <- y2 ybar <- mean(y) if (weighting) { w1 <- tau/ybar w0 <- (1-tau)/(1-ybar) wi <- w1*y + w0*(1-y) mf$weights <- wi } res <- eval(as.call(mf)) res$call <- match.call(expand.dots = TRUE) res$tau <- tau X <- model.matrix(res) ## bias correction if (bias.correct){ pihat <- fitted(res) if (is.null(tau)) # w_i = 1 wi <- rep(1, length(y)) else if (weighting) res$weighting <- TRUE else { w1 <- tau/ybar w0 <- (1-tau)/(1-ybar) wi <- w1*y + w0*(1-y) res$weighting <- FALSE } W <- pihat * (1 - pihat) * wi ##Qdiag <- diag(X%*%solve(t(X)%*%diag(W)%*%X)%*%t(X)) Qdiag <- lm.influence(lm(y ~ X-1, weights=W))$hat/W if (is.null(tau)) # w_1=1 since tau=ybar xi <- 0.5 * Qdiag * (2*pihat - 1) else xi <- 0.5 * Qdiag * ((1+w0)*pihat-w0) res$coefficients <- res$coefficients - lm(xi ~ X - 1, weights=W)$coefficients res$bias.correct <- TRUE } else res$bias.correct <- FALSE ## prior correction if (!is.null(tau) & !weighting){ if (tau <= 0 || tau >= 1) stop("\ntau needs to be between 0 and 1.\n") res$coefficients["(Intercept)"] <- res$coefficients["(Intercept)"] - log(((1-tau)/tau) * (ybar/(1-ybar))) res$prior.correct <- TRUE res$weighting <- FALSE } else res$prior.correct <- FALSE if (is.null(res$weighting)) res$weighting <- FALSE res$linear.predictors <- t(res$coefficients) %*% t(X) res$fitted.values <- 1/(1+exp(-res$linear.predictors)) res$zelig <- "Relogit" class(res) <- c("Relogit", "glm") return(res) } } #' Zelig2 bridge function #' #' ... #' @note T #' @param formula a formula object #' @param ... ignored parameters #' @param tau ... #' @param bias.correct ... #' @param case.control ... #' @param data a data.frame that will be used to fit the model #' @return a list used internally by zelig #' @export zelig2relogit <- function( formula, ..., tau = NULL, bias.correct = NULL, case.control = NULL, data ) { # Catch NULL case.control if (is.null(case.control)) case.control <- "prior" # Catch NULL bias.correct if (is.null(bias.correct)) bias.correct = TRUE # Construct formula. Relogit models have the structure: # cbind(y, 1-y) ~ x1 + x2 + x3 + ... + xN # Where y is the response. form <- update(formula, cbind(., 1 - .) ~ .) # Set the environment to be this function's environment(form) <- environment() # Return the obvious answer z( .function = relogit, formula = form, bias.correct = bias.correct, case.control = case.control, tau = tau, data = data ) } #' Estimate Parameters for the ``relogit'' Zelig Mdoel #' #' Returns estimates on parameters, as well as, specifying link and #' inverse-link functions. #' @note This method merely calls ``param.logit''. #' @usage \method{param}{relogit}(obj, num, ...) #' @S3method param relogit #' @param obj a zelig object containing the fitted model #' @param num an integer specifying the number of simulations to compute #' @param ... unspecified parameters #' @return a list specifying important parameters for the ``relogit'' model param.relogit <- param.logit #' Estimate Parameters for the ``relogit'' Zelig Mdoel #' #' Returns estimates on parameters, as well as, specifying link and inverse-link #' functions. #' @usage \method{param}{relogit2}(obj, num, x, ...) #' @S3method param relogit2 #' @param obj a zelig object containing the fitted model #' @param num an integer specifying the number of simulations to compute #' @param x ideally we should be able to remove this parameter #' @param ... unspecified parameters #' @return a list specifying important parameters for the ``relogit'' model param.relogit2 <- function (obj, num, x, ...) { object <- obj stop("Currently zelig does not support relogit models containing 2 ", "tau parameters") pping <- function(tmp0, tmp1, num, bootstrap, x) { par0 <- param.relogit(tmp0, num=num, x=x, bootstrap=bootstrap) par1 <- param.relogit(tmp1, num=num, x=x, bootstrap=bootstrap) P00 <- qi.relogit(tmp0, par0, x=x) P00 <- as.matrix(qi.relogit(tmp0, param = par0, x=x)$qi$ev) message("P01") P10 <- as.matrix(qi.relogit(tmp1, param = par1, x=x)$qi$ev) test <- P00[,1] < P10[,1] par0 <- as.matrix(par0[test,]) par1 <- as.matrix(par1[test,]) list(par0 = par0, par1 = par1) } tmp0 <- tmp1 <- object tmp0$result <- object$result$lower.estimate tmp1$result <- object$result$upper.estimate tmp <- pping(tmp0, tmp1, num = num, bootstrap=bootstrap, x=x) par0 <- tmp$par0 par1 <- tmp$par1 while (nrow(par0) < num) { tmp <- pping(tmp0, tmp1, num=num, bootstrap=bootstrap, x=x) par0 <- rbind(par0, tmp$par0) par1 <- rbind(par1, tmp$par1) } if (nrow(par0) > num) { par0 <- par0[1:num,] par1 <- par1[1:num,] } par0 <- as.matrix(par0) par1 <- as.matrix(par1) rownames(par0) <- 1:nrow(par0) rownames(par1) <- 1:nrow(par1) return(list(par0 = par0, par1 = par1)) } #' simulate quantities of interest for the zelig ``relogit'' model #' #' ... #' @usage #' \method{qi}{relogit}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi relogit #' @param obj a zelig object, containing the fitted ``relogit'' model #' @param x a ``setx'' object #' @param x1 a ``setx'' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a ``parameter'' obejct containing information about the link, #' inverse-link, and simulated parameters #' @return a param qi.relogit <- qi.logit #' simulate quantities of interest for the zelig ``relogit'' model #' #' ... #' @usage #' \method{qi}{relogit2}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi relogit2 #' @param obj a zelig object, containing the fitted ``relogit'' model #' @param x a ``setx'' object #' @param x1 a ``setx'' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a ``parameter'' obejct containing information about the link, #' inverse-link, and simulated parameters #' @return a param qi.relogit2 <- function (obj, x = NULL, x1 = NULL, y = NULL, num=1000, param = NULL) { simpar <- param # Aliased, because object <- obj # This model needs work, so it will be discontinued for now stop("Relogit 2 is not currently supported") num <- nrow(simpar$par0) tmp0 <- object$result$lower.estimate tmp1 <- object$result$upper.estimate low <- qi.relogit(tmp0, simpar$par0, x, x1) up <- qi.relogit(tmp1, simpar$par1, x, x1) PP <- PR <- array(NA, dim = c(num, 2, nrow(x)), dimnames = list(NULL, c("Lower Bound", "Upper Bound"), rownames(x))) PP[,1,] <- P00 <- low$qi$ev PP[,2,] <- P10 <- up$qi$ev qi <- list(ev = PP) qi.name <- list(ev = "Expected Values: E(Y|X)") if (!is.null(x1)) { FD <- RR <- array(NA, dim = c(num, 2, nrow(x)), dimnames = list(NULL, d2 = c("Lower Bound", "Upper Bound"), rownames(x) )) sim01 <- qi.relogit(tmp0, simpar$par0, x = x1, x1 = NULL) sim11 <- qi.relogit(tmp1, simpar$par1, x = x1, x1 = NULL) tau0 <- object$result$lower.estimate$tau tau1 <- object$result$upper.estimate$tau P01 <- as.matrix(sim01$qi$ev) P11 <- as.matrix(sim11$qi$ev) OR <- (P10/(1-P10)) / (P00/(1-P00)) RR[,1,] <- pmin(as.matrix(P01/P00), as.matrix(P11/P10)) RR[,2,] <- pmax(as.matrix(P01/P00), as.matrix(P11/P10)) RD0 <- as.matrix(P01-P00) RD1 <- as.matrix(P11-P10) RD <- as.matrix((sqrt(OR)-1) / (sqrt(OR)+1)) ## checking monotonicity y.bar <- mean(object$y) beta0.e <- coef(tmp0) beta1.e <- coef(tmp1) ## evaluating RD at tau0 and tau1 RD0.p <- 1/(1+exp(-t(beta0.e) %*% t(x1))) - 1/(1+exp(-t(beta0.e) %*% t(x))) RD1.p <- 1/(1+exp(-t(beta1.e) %*% t(x1))) - 1/(1+exp(-t(beta1.e) %*% t(x))) ## evaluating RD at tau0+e and tau1+e e <- 0.001 beta0.e["(Intercept)"] <- beta0.e["(Intercept)"]+log(1-tau0)-log(tau0) - log(1-tau0-0.001)+log(tau0+0.001) beta1.e["(Intercept)"] <- beta1.e["(Intercept)"]+log(1-tau1)-log(tau1) - log(1-tau1-e)+log(tau1+e) RD0.e <- 1/(1+exp(-t(beta0.e) %*% t(x1))) - 1/(1+exp(-t(beta0.e) %*% t(x))) RD1.e <- 1/(1+exp(-t(beta1.e) %*% t(x1))) - 1/(1+exp(-t(beta1.e) %*% t(x))) ## checking the sign and computing the bounds check <- sum((RD1.e-RD1.p) * (RD0.e-RD0.p)) if (check > 0) { FD[,1,] <- pmin(RD0, RD1) FD[,2,] <- pmax(RD0, RD1) } else { FD[,1,] <- pmin(RD0, RD1, RD) FD[,2,] <- pmax(RD0, RD1, RD) } qi$fd <- FD qi$rr <- RR qi.name$fd <- "First Differences: P(Y=1|X1) - P(Y=1|X)" qi.name$rr <- "Risk Ratios: P(Y=1|X1) / P(Y=1|X)" } if (!is.null(y)) { yvar <- matrix(rep(y, num), nrow = num, byrow = TRUE) # tmp.ev <- qi$tt.ev <- yvar - qi$ev # tmp.pr <- qi$tt.pr <- yvar - as.integer(qi$pr) # qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV" # qi.name$tt.pr <- "Unit Treatment Effect for the Treated: Y - PR" tmp.ev <- yvar - qi$ev tmp.pr <- yvar - as.integer(qi$pr) qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = num) qi$att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = num) qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV" qi.name$att.pr <- "Average Treatment Effect for the Treated: Y - PR" } return(list(qi = qi, qi.name = qi.name)) } #' Describe a `logit' model to Zelig #' @usage \method{describe}{relogit}(...) #' @S3method describe relogit #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} describe.relogit <- function(...) { # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2007, category = "dichotomous", text = "Rare Events Logistic Regression for Dichotomous Dependent Variables" ) } # Return Names of Relogit Model # names.Relogit <- function(x){ res <- list(default=names(unclass(x)), estimate = names(x$lower.estimate), tau = x$tau) class(res) <- "names.relogit" res } Zelig/R/normal.bayes.R0000644000176000001440000000445712110047000014265 0ustar ripleyusers#' Interface between the Zelig Model normal.bayes and the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2normal.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data ) { loadDependencies("MCMCpack", "coda") list( .function = "MCMCregress", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param normal.bayes param.normal.bayes <- function(obj, num=1000, ...) { list( coef = coef(obj), linkinv = gaussian() ) } #' @S3method qi normal.bayes qi.normal.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { normal.ev <- function (x, param) { # If either of the parameters are invalid, # Then return NA for both qi's if (is.null(x) || is.na(x) || is.null(param)) return(list(ev=NA, pv=NA)) # Extract simulated parameters and get column names coef <- coef(param) cols <- colnames(coef) # Place the simulated variances in their own vector sigma2 <- coef[, ncol(coef)] # Remove the "sigma2" (variance) parameter which should already be placed # in the simulated parameters cols <- cols[ ! "sigma2" == cols ] # coef <- coef[, cols] # ev <- coef %*% t(x) pv <- rnorm(nrow(ev), ev, sqrt(sigma2)) # list(ev = ev, pv = pv) } res1 <- normal.ev(x, param) res2 <- normal.ev(x1, param) list( "Expected Value: E(Y|X)" = res1$ev, "Predicted Value: Y|X" = res1$pv, "Expected Value (for X1): E(Y|X1)" = res2$ev, "Predicted Value (for X1): Y|X1" = res2$pv, "First Differences: E(Y|X1) - E(Y|X)" = res2$ev - res1$ev ) } #' @S3method describe normal.bayes describe.normal.bayes <- function(...) { list( authors = c("Ben Goodrich", "Ying Lu"), text = "Bayesian Normal Linear Regression", year = 2013 ) } Zelig/R/exp.R0000644000176000001440000001014212110047000012453 0ustar ripleyusers#' Interface between the Zelig Model exp and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param robust a boolean specifying whether to use robust error estimates #' @param cluster a vector describing the clustering of the data #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2exp <- function (formula, ..., robust = FALSE, cluster = NULL, data) { loadDependencies("survival") if (!(is.null(cluster) || robust)) stop("If cluster is specified, then `robust` must be TRUE") # Add cluster term if (robust || !is.null(cluster)) formula <- cluster.formula(formula, cluster) # Return z( .function = "survreg", formula = formula, dist = "exponential", robust = robust, data = data, ... ) } stratify.rqs <- function (obj) { x <- vector("list", length(obj$tau)) for(i in 1:length(obj$tau)) { xi <- obj xi$coefficients <- xi$coefficients[, i] xi$residuals <- xi$residuals[, i] xi$tau <- xi$tau[i] class(xi) <- "rq" x[[i]] <- xi } names(x) <- obj$tau x } #' Param Method for the \code{exp} Zelig Model #' @note This method is used by the \code{param} Zelig model #' @usage \method{param}{exp}(obj, num, ...) #' @S3method param exp #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored parameters #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.exp <- function(obj, num=1000, ...) { cov <- vcov(.object) mu <- coef(.object) # Return list( coef = mvrnorm(num, mu=mu, Sigma=cov), linkinv = survreg.distributions[["exponential"]]$itrans ) } #' Compute quantities of interest for 'exp' Zelig models #' @usage \method{qi}{exp}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi exp #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.exp <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { linkinv <- linkinv(param) # Compute Expected Values for the "exp" Regression # @param simulations # @param x # @return a matrix compute.ev <- function (simulations, x) { if (is.null(x) || is.na(x)) # If there are missing explanatory variables, ignore them return(NA) # Compute eta, which is the "flattened" prediction. # This value must be *inverted* to be restored to the true "observed" value eta <- simulations %*% t(x) # Return as a matrix, since this should be a vector at this point. as.matrix(apply(eta, 2, linkinv)) } # Compute Predicted Values compute.pv <- function (ev, param) { rexp(length(ev), rate = 1/ev) } # Compute expected values for X and X1 ev1 <- compute.ev(coef(param), x) ev2 <- compute.ev(coef(param), x1) # Compute Predicted values for X and X1 pr1 <- compute.pv(ev1, x) pr2 <- compute.pv(ev2, x1) # Return quantities of Interest list("Expected Values: E(Y|X)" = ev1, "Expected Values: E(Y|X1)" = ev2, "Predicted Values: Y|X" = pr1, "Predicted Values: Y|X1" = pr2, "First Differences: E(Y|X1) - E(Y|X)" = ev2 - ev1 ) } #' Describe a ``exp'' model to Zelig #' @usage \method{describe}{exp}(...) #' @S3method describe exp #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} describe.exp <- function(...) { list( authors = c("Olivia Lau", "Kosuke Imai", "Gary King"), year = 2011, category = "bounded", text = "Exponential Regression for Duration Dependent Variables" ) } Zelig/R/poisson.gee.R0000644000176000001440000000373612110047000014123 0ustar ripleyusers#' Interface between the Zelig Model poisson.gee and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param id a character-string specifying the column of the data-set to use #' for clustering #' @param robust a logical specifying whether to robustly or naively compute #' the covariance matrix. This parameter is ignore in the \code{zelig2} #' method, and instead used in the \code{robust.hook} function, which #' executes after the call to the \code{gee} function #' @param ... ignored parameters #' @param R a square-matrix specifying the correlation #' @param corstr a character-string specifying the correlation structure #' @param data a data.frame #' @return a list specifying the call to the external model #' @export zelig2poisson.gee <- function (formula, id, robust, ..., R = NULL, corstr = "independence", data) { loadDependencies("gee") if (corstr == "fixed" && is.null(R)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } z( .function = gee, .hook = robust.gee.hook, formula = formula, id = id, corstr = corstr, family = poisson(), data = data, R = R, ... ) } #' @S3method param poisson.gee param.poisson.gee <- function(obj, num=1000, ...) { # Extract means to compute maximum likelihood mu <- coef(obj) # Extract covariance matrix to compute maximum likelihood Sigma <- vcov(obj) # list( coef = mvrnorm(num, mu, Sigma), fam = Gamma() ) } #' @S3method qi poisson.gee qi.poisson.gee <- qi.gamma.gee #' @S3method describe poisson.gee describe.poisson.gee <- function(...) { list( authors = "Patrick Lam", text = "General Estimating Equation for Poisson Regression", year = 2011 ) } Zelig/R/MCMChook.R0000644000176000001440000000426112110047000013264 0ustar ripleyusers#' Hook to Clean-up MCMC Objects #' #' This method gives valid methods to the resulting MCMC object so that it can #' be used with Zelig. #' @note This function is used internally by the ZeligBayesian package. #' @param obj the fitted model object (in this case a \code{mcmc} object. #' @param model.call the call made to the external model #' @param zelig.call the actual call to zelig itself #' @param seed a seed for the MCMC algorithm #' @param ... ignored parameters #' @return an object useable by Zelig #' @author Olivia Lau, Kosuke Imai, Gary King and Matt Owen #' @export MCMChook <- function (obj, model.call, zelig.call, seed=NULL, ..., data = NULL) { # Create a new object res <- list() attr(obj, "call") <- NULL # Add the bare necessities for a zelig object res$coefficients <- obj res$formula <- zelig.call$formula res$data <- data res$model <- model.frame(eval(res$formula), data = data) res$terms <- attr(res$model, "terms") res$call <- model.call # Ensure that a "seed" element exists res$seed <- if (is.null(seed)) NA else seed class(res) <- "MCMCZelig" res } #' Hook to Clean-up MCMC Factor Object #' #' This method gives valid methods to the resulting MCMC object so that it can #' be used with Zelig. #' @note This function is used internally by the ZeligBayesian package. #' @param obj the fitted model object (in this case a \code{mcmc} object. #' @param model.call the call made to the external model #' @param zelig.call the actual call to zelig itself #' @param seed a seed for the MCMC algorithm #' @param ... ignored parameters #' @return an object useable by Zelig #' @author Olivia Lau, Kosuke Imai, Gary King and Matt Owen #' @export McmcHookFactor <- function (obj, model.call, zelig.call, seed = NULL, ...) { out <- list() out$coefficients <- obj out$formula <- zelig.call$formula out$data <- zelig.call$data out$model <- model.frame(eval(out$formula), eval(out$data)) out$terms <- attr(out$model, "terms") out$call <- model.call # Factors have no intercept term? attr(out$terms,"intercept") <- 0 if (is.null(zelig.call$seed)) out$seed <- NA else out$seed <- zelig.call$seed class(out) <- "MCMCZelig" out } Zelig/R/logit.gee.R0000644000176000001440000000507212110047000013542 0ustar ripleyusers#' General Estimating Equation for Logit Regression #' @param formula a formula #' @param id a character-string specifying the column of the data-set to use #' for clustering #' @param robust a logical specifying whether to robustly or naively compute #' the covariance matrix. This parameter is ignore in the \code{zelig2} #' method, and instead used in the \code{robust.hook} function, which #' executes after the call to the \code{gee} function #' @param ... ignored parameters #' @param R a square-matrix specifying the correlation #' @param corstr a character-string specifying the correlation structure #' @param data a data.frame #' @return a list specifying the call to the external model #' @export zelig2logit.gee #' @name logit.gee #' @aliases zelig2logit.gee zelig2logit.gee <- function (formula, id, robust, ..., R = NULL, corstr = "independence", data) { loadDependencies("gee") if (corstr == "fixed" && is.null(R)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } z( .function = gee, .hook = robust.gee.hook, formula = formula, id = id, corstr = corstr, family = binomial(link="logit"), data = data, R = R, ... ) } #' @S3method param logit.gee param.logit.gee <- function(obj, num=1000, ...) { # Extract means to compute maximum likelihood mu <- coef(obj) # Extract covariance matrix to compute maximum likelihood Sigma <- vcov(obj) list( coef = mvrnorm(num, mu, Sigma), fam = binomial(link="logit") ) } #' @S3method qi logit.gee qi.logit.gee <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { coef <- coef(param) inverse <- linkinv(param) eta1 <- coef %*% t(x) ev1 <- theta1 <- matrix(inverse(eta1), nrow=num) # default to NA rr <- ev2 <- fd <- NA if (!is.null(x1)) { eta2 <- coef %*% t(x1) ev2 <- theta1 <- matrix(inverse(eta2), nrow=num) fd <- ev2 - ev1 rr <- ev2/ev1 } list( "Expected Values (for x): E(Y|X)" = ev1, "Expected Values (for x1): E(Y|X1)" = ev2, "First Differences: E(Y|X1) - E(Y|X)" = fd, "Risk Ratios: E(Y|X1)/E(Y|X)" = rr ) } #' @S3method describe logit.gee describe.logit.gee <- function(...) { list( authors = "Patrick Lam", text = "General Estimating Equation for Logistic Regression", year = 2011 ) } Zelig/R/z.R0000644000176000001440000000264212110047000012136 0ustar ripleyusers#' Return value for a zelig2-function #' #' This is an API-function that bridges a model-fitting function with a zelig #' interface. #' @note This is used internally by Zelig-dependent packages to instruct Zelig #' how to evaluate the function call to a particular statistical model. #' @param .function a function #' @param ... a set of parameters to be evaluated symbolically #' @return a ``z'' object which specifies how to evaluate the fitted model #' @export z <- function (.function, ..., .hook = NULL) { # Construct the function call .call <- as.call(as.list(match.call())[-1]) .function.name <- as.character(.call[[1]]) .parent <- parent.frame() .dots <- list(...) # Ensure that hook works appropriately if(!missing(.hook)) { if (!is.function(.hook)) { warning(".hook parameter must be a function. ignoring.") .hook <- NULL } } s <- append(list(as.name(.function.name)), list(...)) literal.call <- as.call(s) # Construct the object s <- list( "function" = .function, "hook" = .hook, "call" = .call, "env" = .parent, "function.name" = .function.name, "dots" = .dots, "literal.call" = literal.call ) # Set attributes attr(s, 'baseenv') <- baseenv() attr(s, 'call') <- match.call() attr(s, 'function') <- substitute(.function) # Set the class class(s) <- 'z' # Return s } Zelig/R/Zelig-package.R0000644000176000001440000000303012110047000014320 0ustar ripleyusers#' Zelig Everyone's Statistical Software #' #' Zelig is an easy-to-use program that can estimate, and #' help interpret the results of, an enormous range of statistical models. It #' literally is ``everyone's statistical software'' because Zelig's simple #' unified framework incorporates everyone else's (R) code. We also hope it will #' become ``everyone's statistical software'' for applications and teaching, #' and so have designed Zelig so that anyone can easily use it or add their #' programs to it. Zelig also comes with infrastructure that facilitates the #' use of any existing method, such as by allowing multiply imputed data for #' any model, and mimicking the program Clarify (for Stata) that takes the raw #' output of existing statistical procedures and translates them into #' quantities of direct interest. #' #' \tabular{ll}{ #' Package: \tab Zelig\cr #' Version: \tab 4.1-2\cr #' Date: \tab 2013-01-11\cr #' Depends: \tab R (>= 2.14), boot, MASS, methods, sandwich, survival\cr #' Suggests: \tab mvtnorm, Formula \cr #' License: \tab GPL version 2 or newer\cr #' URL: \tab http://gking.harvard.edu/zelig\cr #' } #' #' @name Zelig-package #' @aliases Zelig #' @docType package #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Kosuke Imai, Olivia Lau, #' and Gary King #' @keywords package #' @seealso zelig setx sim NULL # SUPER SECRET VARIABLES... # These squelch "R CMD CHECK" issues for dynamically (though constantly added) # local variables to the "bootstrap", "param" and "qi" functions. .call <- .fitted <- .object <- NULL Zelig/R/common-methods.R0000644000176000001440000000075012061700507014631 0ustar ripleyusers# This file is a quick-hack to fix a mistake placed in Zelig Core on Oct. 1st. # The issue in Zelig should be fixed by November `12. :( #' @S3method coef zelig coef.zelig <- function (object, ...) coef(object$result, ...) #' @S3method logLik zelig logLik.zelig <- function (object, ...) logLik(object$result, ...) #' @S3method plot zelig plot.zelig <- function (x, ...) plot(x$result, ...) #' @S3method vcov zelig vcov.zelig <- function (object, ...) vcov(object$result, ...) Zelig/R/model.matrix.multiple.R0000644000176000001440000000603012061700507016132 0ustar ripleyusers#' Create Design Matrix of a \code{multiple} Object #' #' This method is used to generate a \code{model.matrix} adhering to the #' specifications in the help document "model.matrix". #' @usage #' \method{model.matrix}{multiple}(object,data,shape="compact",eqn=NULL,...) #' @note This method is scheduled to be deprecated. #' @param object an object of type \code{multiple}. This represents a Zelig 3.5 #' formula #' @param data a \code{data.frame} #' @param shape a character-string specifying the shape of the matrix #' @param eqn an integer specifying the number of equations #' @param ... ignored parameters #' @S3method model.matrix multiple model.matrix.multiple <- function (object,data,shape="compact",eqn=NULL,...){ intersect <- function(x, y) y[match(x, y, nomatch = 0)] #olny for multilevel if(class(formula)[[1]]=="terms"){ terms <-object }else{ terms<-terms(object) } if(!(is.logical(attr(terms,"subs")))) return(multilevel(tt=terms,data=data,eqn=eqn,mode=1)) ## if((shape != "compact") && (shape != "array") && (shape !="stacked")) stop("wrong shape argument! Choose from \"compact\", \"array\" or \"stacked\" \n") if(!(any(class(object)=="multiple"))) stop("Please run first parse.formula() on your formula ...\n") if(!(any(class(data)=="multiple"))) data<-model.frame(object,data) terms<-attr(data,"terms") whiche<-which(eqn %in% names(terms)==FALSE) if (length(whiche)!=0) stop("Unknown eqn name \"",eqn[whiche],"\"\n") intercAttr<-attr(terms,"intercept") systEqns<-attr(terms,"systEqns") ancilEqns<-attr(terms,"ancilEqns") if (is.null(eqn)) eqn=systEqns # if (!(all(eqn %in% systEqns))) # stop("all eqn names should be from systematic parameters") termlabels<-attr(terms,"term.labels")[eqn] nrEquations<-length(eqn) if (length(eqn)==1) shape="compact" Xnames<-unique(unlist(termlabels)) rhs<-toBuildFormula(Xnames) if(!(is.null(rhs))) rhs<-as.formula(paste("~",rhs)) else rhs<-as.formula("~1") rawX<-model.matrix.default(rhs,data=data) if (shape=="compact"){ result<-rawX if(all(intercAttr==0)){ result<-result[,colnames(result)!="(Intercept)"] } attr(terms,"response")<-0 attr(result,"terms")<-terms return(result) } ronames<-rownames(data) ronr<-nrow(data) parsMat<-make.parameters(terms, shape = "matrix", ancillary = FALSE,eqns=eqn) parsVec <- unique(na.omit(c(t(parsMat)))) result<-list() result<-array(0,dim=c(ronr,length(parsVec),length(eqn)),dimnames=list(ronames,parsVec,eqn)) for(i in 1:nrEquations){ eqni<-eqn[[i]] whiche<-which(is.na(parsMat[,eqni])==FALSE) result[,,eqni][,parsMat[names(whiche),eqni]]<-rawX[,names(whiche)] } if(shape=="array"){ res<-result } if(shape=="stacked"){ res<-result[,,eqn[[1]]] if(length(eqn)>1) for(i in 2:length(eqn)) res<-rbind(res,result[,,eqn[[i]]]) rownames(res)<-c(1:nrow(res)) } attr(terms,"response")<-0 attr(res,"terms")<-terms return(res) } Zelig/R/model.warnings.R0000644000176000001440000000275412061700507014635 0ustar ripleyusers# This code is rough looking. It needs to be made more elegant # but R doesn't really support block quotes model.warnings <- function (model) { # Get appropriate Zelig package pkg <- get.package(model) # Get zelig2 <- paste("zelig2", as.character(model), sep="") zelig2 <- tryCatch( { get(zelig2, mode="function"); 1 }, error = function (e) NA ) # # # if (is.na(zelig2) && is.na(pkg)) { msg <- ' ** The model "%s" is not available with the currently loaded packages, ** and is not an official Zelig package. ** The model\'s name may be a typo. ' message(sprintf(msg, model)) } else if (is.na(zelig2) && !is.na(pkg)) { if (pkg %in% .packages(TRUE)) { # The package is available on the system msg <- ' ** The model "%s" is not available with the currently loaded packages, ** however it *is* installed on your system. ** ** To load this model\'s package, please type: library("%s") ' message(sprintf(msg, model, pkg)) } # # # else { # Else... the package is not available on the system repos <- "http://r.iq.harvard.edu/" msg <- ' ** The model "%s" is not installed on your system, ** however it *is* available for download from Harvard. ** ** To install and load this model\'s package, please type: install.packages("%s", repos="%s", type="source") library("%s") ' message(sprintf(msg, model, pkg, repos, pkg)) } } invisible() } Zelig/R/twosls.R0000644000176000001440000001541112061700507013233 0ustar ripleyusers#' Interface between the Zelig Model twosls and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2twosls <- function (formula, ..., data) { loadDependencies("systemfit") # Helper function to perform set-difference "%w/o%" <- function(x, y) x[!x %in% y] formula<-parse.formula(formula, "twosls") tt<-terms(formula) ins<-names(tt) %w/o% names(attr(tt,"depVars")) if(length(ins)!=0) if(length(ins)==1) inst <- formula[[ins]] else inst <- formula[ins] else stop("twosls model requires instrument!!\n") class(formula) <- c("multiple", "list") # Return list( .function = "callsystemfit", formula = formula[names(attr(tt,"depVars"))], method = "2SLS", inst = inst, data = data, ... ) } #' @S3method param twosls param.twosls <- function(obj, num=1000, ...) { # Produce a vector of all terms big.coef <- coef(obj) # Produce a pretty sparse matrix containing 3 vcov matrices. # # Note that this matrix will give a value of zero to any invalid row-column # combination. # In particular, any terms that do not belong to the same equation will have # a zero value. big.vcov <- vcov(obj) # This is a complete list of the terms. This is largely ignored, aside from # the fact that we need a list of the formulae. In general, terms.multiple # produced a pretty unwieldy list of items. all.terms <- terms(obj) # This list stores the results simulations.list <- list() # Iterate through the set of terms, and simulate each list separately. for (key in names(all.terms)) { # Extract the terms for an individual model. eq.terms <- terms(all.terms[[key]]) # Extract the labels for the terms eq.term.labels <- attr(eq.terms, "term.labels") # Add the labeled for the intercept column, if it should exist if (attr(eq.terms, "intercept")) eq.term.labels <- c("(Intercept)", eq.term.labels) # Format the title, this should look like: # _ # # So for the list: list(mu1 = y ~ x + sin(x)) # We get: # "mu1_(Intercept)" "mu1_x" "mu1_sin(x)" entries <- paste(key, eq.term.labels, sep = "_") # Extract the mean-value of this term (from the lumped-toegether vector) eq.coef <- big.coef[entries] # Extract the vcov matrix of this term (from the lumped-together matrix) eq.vcov <- big.vcov[entries, entries] # Simulate the parameters eq.simulations <- mvrnorm(num, eq.coef, eq.vcov) # Name the columns colnames(eq.simulations) <- eq.term.labels # Add to the list simulations.list[[key]] <- eq.simulations } # Return. list( coef = simulations.list, linkinv = NULL ) } #' @S3method qi twosls qi.twosls <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # Compute the expected value of multistage LS methods compute.ev <- function (obj, x, param) { # if (is.null(x) || is.na(x)) { return(NA) } # If 'x' has too many rows, there will currently be errors. This is an issue # in Zelig-core if (nrow(x$matrix) > 1) { warning("This package does not currently support pooled results.") x <- x[1, ] } # Begin regular function terms <- terms(obj) # :q coef.list <- coef(param) # Hold Results eta <- list() # for (key in names(coef.list)) { # coef <- coef.list[[key]] # print(colnames(coef)) small.x <- as.matrix(x$matrix[, colnames(coef)]) # eta[[key]] <- coef %*% (small.x) } # Convert list into a matrix eta <- Reduce(function (x, y) cbind(x, y), eta) colnames(eta) <- names(terms) eta } ev1 <- compute.ev(obj, x, param) ev2 <- compute.ev(obj, x1, param) fd <- ev2 - ev1 # Name each column after the associated equation # Return the results list( "Expected Value: E(Y|X)" = ev1, "Expected Value (for X1): E(Y|X1)" = ev2, "First Differences: E(Y|X1)-E(Y|X)" = ev2 - ev1 ) } #' @S3method describe twosls describe.twosls <- function (...) { category <- "continuous" description <- "Two Stage Least Squares" authors <- c("Ferdinand Alimadhi", "Ying Lu", "Elena Villalon") year <- 2007 package <-list( name = "systemfit", version = "0.8" ) parameters <- list() parameters$mu <-list( equations=c(2,Inf), tagsAllowed=TRUE, depVar=TRUE, expVar=TRUE ) parameters$inst<-list( equations=c(1,1), tagsAllowed=FALSE, depVar=FALSE, expVar=TRUE ) list(category = category, authors = authors, year = year, description = description, package = package, parameters = parameters) } #' @S3method plot sim.twosls plot.sim.twosls <- function (x, ...) { # Define locak function to plot a set of quantities of interest plotSet <- function (title) { for (col in colnames(qis[[title]])) { q <- qis[[title]][, col] plot(density(q), main = paste(col, title, sep=": ")) } } # Code begins here cols <- c( rep(), rep(), rep() ) qis <- as.list.qi(x$qi) qis <- Filter(function (y) any(!is.na(y)), qis) qis <- Filter(is.matrix, qis) max.cols <- max(unlist(Map(ncol, qis))) layout.matrix <- matrix(0, length(qis), max.cols) rownames(layout.matrix) <- names(qis) count <- 1 for (title in names(qis)) { for (k in 1:ncol(qis[[title]])) { layout.matrix[title, k] <- count count <- count + 1 } } layout(layout.matrix) for (key in names(qis)) { plotSet(key) } } callsystemfit<-function(formula,data,method,inst=NULL,...){ # Call systemfit.. out <- systemfit( data = data, formula = formula, method = method, inst = inst, ... ) # Assign class to formula, so that it is correctly parsed class(formula) <- c("multiple", "list") # Set the terms explicitly attr(out,"terms") <- terms(formula) # Set the class explicitly class(out) <- c("multiple", class(out)) # Fin. Return the modified object return(out) } as.list.qi <- function (x, names = "") { class(x) <- "list" indices <- attr(x, ".index") attr(x, ".index") <- NULL rename.keys(x, indices, names(indices)) } rename.keys <- function (x, keys, to, warn = TRUE) { all.names <- names(x) indices <- match(keys, all.names) if (any(is.na(indices))) stop("Keys contains values that are not in `x`") all.names[indices] <- to names(x) <- all.names x } Zelig/R/summarize.R0000644000176000001440000000725712061700507013725 0ustar ripleyusers#' Generic methonf for summarizing simualted quantities of interest #' #' @S3method summarize default #' #' @param obj a \code{qi} object, storing simulations of quantities of interest #' @return a \code{summarized.qi} object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} summarize <- function(obj) UseMethod("summarize") #' Summarize Simualted Quantities of Interest #' #' @usage \method{summarize}{default}(obj) #' @S3method summarize default #' @param obj a \code{qi} object, storing simulations of quantities of interest #' @return a 'summarized.qi' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} summarize.default <- function(obj) { res <- list() titles <- names(attr(obj, ".index")) for (key in titles) { val <- obj[[key]] if (!is.qi(val)) next if (!is.matrix(val)) val <- matrix(val, ncol=1, nrow=length(val)) # make a matrix that is data-friendly m <- if (is.numeric(val)) { matrix(NA, nrow=ncol(val), ncol=5) } else if (is.character(val) || is.factor(val)) { levels <- levels(val) if (is.null(levels)) { #warning("Indeterminate number of levels for qi: ", key) levels <- unique(c(val)) } levels <- sort(levels) matrix(NA, nrow=ncol(val), ncol=length(levels), dimnames=list(NULL, levels)) } # for (k in 1:ncol(val)) { if (is.numeric(val[,k])) { row <-c( mean(val[,k], na.rm = TRUE), sd(val[,k], na.rm = TRUE), quantile(val[,k], c(.5, .025, .975), na.rm=TRUE) ) m[k,] <- row # colnames(m) <- c("mean", "sd", "50%", "2.5%", "97.5%") } else if (is.character(val[,k]) || is.factor(val[,k])) { # A table specifying the _percentage_ of simulations matching # each particular level of the factor qi's result.table <- table.levels(val[,k], levels = levels) result.table <- result.table/length(val[,k]) # A character-vector specifying the factors found in the qi factor.names <- sort(names(result.table)) # This should prevent size errors for qi's with # a NULL levels attribute # in particular, it resolves issues m[k, ] <- 0 m[k, factor.names] <- result.table[factor.names] m[k,] <- result.table colnames(m) <- names(result.table) } else m[k,] <- NA col.names <- colnames(val) rownames(m) <- if (is.null(col.names)) "" else col.names } # add to list res[[key]] <- m } # cast as class - for some reason - then return class(res) <- "summarized.qi" res } #' Test If Value is Interpretable as a QI #' @param qi a potential quantity of interest #' @return a logical specifying whether this value should or should-not #' be output #' @author Matt Owen \email{mowen@@iq.harvard.edu} is.qi <- function(qi) { if (is.null(qi)) return(FALSE) else if (!length(qi)) return(FALSE) else if (all(is.na(qi))) return(FALSE) TRUE } #' Create a table, but ensure that the correct #' columns exist. In particular, this allows for #' entires with zero as a value, which is not #' the default for standard tables #' @param x a vector #' @param levels a vector of levels #' @param ... parameters for table #' @return a table #' @author Matt Owen \email{mowen@@iq.harvard.edu} table.levels <- function (x, levels, ...) { # if levels are not explicitly set, then # search inside of x if (missing(levels)) { levels <- attr(x, 'levels') table(factor(x, levels=levels), ...) } # otherwise just do the normal thing else { table(factor(x, levels=levels), ...) } } Zelig/R/user.prompt.R0000644000176000001440000000064012061700507014174 0ustar ripleyusers#' Prompts user to hit enter #' @title Prompt User #' @param msg a character-string, specifying a message to be displayed #' @return This function is used for its side effects #' @export #' @note This function is primarily used by Zelig demo scripts user.prompt <- function (msg = NULL) { if (is.null(msg)) msg <- "Press to continue: " msg <- paste("\n", msg, sep="") invisible(readline(msg)) } Zelig/R/as.parameters.R0000644000176000001440000000635612061700507014455 0ustar ripleyusers#' Generic Method for Converting Objects into 'parameters' #' #' Converts list-style objects into Parameter lists primarily used by the 'qi' #' methods. These list-style objects may contain keys specifying: 'link' (the #' link function of a statistical model), 'linkinv' (the inverse-link #'function), 'family' (a object of 'family' class used to specify the model's #' classification), 'alpha' (a vector of ancillary parameters, and 'simulations' #' (a vector of simulated draws from the model's underlying distribution. #' #' @note Only three scenarios may exist - converting 'parameters' to #' 'parameters', 'list' to 'parameters', and vectors to 'parameters'. The #' third in particular is needed only for backwards compatibility, and support #' will likely be deprecated. #' #' Furthermore, this function should be exlusively used implicitly and #' by Zelig. #' #' @param params the object to be casted #' @param ... parameters reserved for future revisions #' @return an object of type `parameters' #' @seealso as.parameters.list as.parameters.parameters, as.parameters.default #' @author Matt Owen \email{mowen@@ig.harvard.edu} as.parameters <- function(params, ...) UseMethod("as.parameters") #' list -> parameters #' #' The list may contain: 'link', 'linkinv', 'family', 'alpha', and #' 'simulations' keys. #' #' @param params a list object #' @param num an integer specifying the number of simulations #' to be taken #' @param ... ignored parameters #' @return an object of type `parameters' #' @seealso as.parameters #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.parameters.list <- function(params, num=NULL, ...) { # coefficients <- if ("simulations" %in% names(params)) params$simulations else if (num < length(params)) params[1:num] else params[[1]] # Extract alpha parameters from Zelig alpha <- if ("alpha" %in% names(params)) params$alpha else if (num < length(params)) tail(params, -num) # link function if ("link" %in% names(params)) link <- params$link # link-inverse function if ("linkinv" %in% names(params)) linkinv <- params$linkinv # family object, has both a link and link-inverse fam <- if ("family" %in% names(params)) params$family else if ("fam" %in% names(params)) params$fam else NULL # Return parameters(coefficients, alpha, fam=fam, link=link, linkinv=linkinv) } #' parameters -> parameters #' This is merely an identity function when casting 'parameters' objects into #' 'parameters'. #' @param params a parameters object #' @param ... ignored parameters #' @return the same parameter object #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.parameters.parameters <- function(params, ...) params #' ??? -> parameters #' @note This function should be deprecated. #' @param params any non-supported data-type #' @param num an integer specifying the number of simulations to compute #' @param ... ignored #' @return the object passed in #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.parameters.default <- function(params, num=NULL, ...) { if (!missing(num)) { alpha <- if (num < nrow(params)) tail(params, -num) # parameters(simulations=head(params, num), alpha=alpha) } else parameters(simulations=params, alpha=NULL) } Zelig/R/plots.R0000644000176000001440000005503312157627053013057 0ustar ripleyusers#' @S3method plot sim.gamma.gee plot.sim.gamma.gee <- function (x, ...) { # store device settings original.par <- par(no.readonly=TRUE) if (is.null(x$x)) return() panels <- if (is.null(x$x1)) { palette <- rep("black", 3) matrix(1, nrow=1, ncol=1) # How the layout window will look: # +---+ # | 1 | # +---+ } else { palette <- c('red', 'navy', 'black') matrix(c(1, 2, 3, 3), nrow=2, ncol=2, byrow=TRUE) # How the layout window will look: # +-------+ # | 1 | 2 | # +-------+ # | 3 | # +-------+ } layout(panels) # extract quantities of interest ev1 <- x$qi$ev1 ev2 <- x$qi$ev2 fd <- x$qi$fd # Plot ev1 .plot.density(ev1, "Expected Values (for X): E(Y|X)", palette[1]) if (!is.null(x$x1)) { .plot.density(ev2, "Expected Values (for X1): E(Y|X1)", palette[2]) .plot.density(fd, "First Differences: E(Y|X1) - E(Y|X)", palette[3]) } # return plotting device par(original.par) } #' @S3method plot sim.normal.gee plot.sim.normal.gee <- plot.sim.gamma.gee #' @S3method plot sim.poisson.gee plot.sim.poisson.gee <- plot.sim.gamma.gee #' @S3method plot sim.logit.gee plot.sim.logit.gee <- function (x, ...) { # store device settings original.par <- par(no.readonly=TRUE) if (is.null(x$x)) return() panels <- if (is.null(x$x1)) { palette <- rep("black", 4) matrix(1, nrow=1, ncol=1) # How the layout window will look: # +---+ # | 1 | # +---+ } else { palette <- c('red', 'navy', 'black', 'black') matrix(c(1, 2, 3, 3, 4, 4), nrow=3, ncol=2, byrow=TRUE) # How the layout window will look: # +-------+ # | 1 | 2 | # +-------+ # | 3 | # +-------+ # | 4 | # +-------+ } layout(panels) # extract quantities of interest ev1 <- x$qi$ev1 ev2 <- x$qi$ev2 fd <- x$qi$fd rr <- x$qi$rr # Plot ev1 .plot.density(ev1, "Expected Values (for X): E(Y|X)", palette[1]) .plot.density(ev2, "Expected Values (for X1): E(Y|X1)", palette[2]) .plot.density(fd, "First Differences: E(Y|X1) - E(Y|X)", palette[3]) .plot.density(rr, "Risk Ratios: E(Y|X1)/E(Y|X)", palette[4]) # return plotting device par(original.par) } #' @S3method plot sim.probit.gee plot.sim.probit.gee <- plot.sim.logit.gee # Plot Density Graphs for GEE Quantities of Interest # @param x a vector containing quantities of interest # @param main the main title of the plot # @param col the color of the line-plot .plot.density <- function (x, main, col) { if (all(is.na(x))) return() density <- density(x) plot(density(x), main = main, col = col) } #' Plot graphs of simulated multiply-imputed data #' #' This function combines results across multiply imputed results #' and then calls the appropriate plot for that class. #' #' @usage \method{plot}{MI.sim}(...) #' @S3method plot MI.sim #' @param ... ignored parameters #' #' @return the return of the appropriate plot method #' @author James Honaker \email{jhonaker@@iq.harvard.edu} plot.MI.sim <- function(x, ...) { m<-length(x) # The number of imputed datasets reformed<-x[[1]] # Simplified object of the original class all.qi<-attributes(x[[1]]$qi)$names # Convoluted given the current structure of objects ## Currently, we're appending all the qi's together into one object ## Note - everything that is not a qi, will just come from the first imputed dataset if(m>1){ for(i in 2:m){ for(j in all.qi){ # Could do this by position number as "in 1:length(all.qi)" ## The $qi's are themselves lists, so this is difficult: reformed$qi[j][[1]]<-rbind(reformed$qi[j][[1]],x[[i]]$qi[j][[1]]) } } } output<-plot(reformed) ## Return any plot returns invisibly invisible(output) } #' Method for plotting pooled simulations by confidence intervals #' #' Plot confidence intervals of pooled simulated values. #' #' @param x A `sim' object #' @param qi a character-string specifying the quantity of interest to plot #' @param var The variable to be used on the x-axis. Default is the variable #' across all the chosen values with smallest nonzero variance #' @param ... Parameters to be passed to the `truehist' function which is #' implicitly called for numeric simulations #' @param main a character-string specifying the main heading of the plot #' @param sub a character-string specifying the sub heading of the plot #' @param xlab a character-string specifying the label for the x-axis #' @param ylab a character-string specifying the label for the y-axis #' @param legcol ``legend color'', an valid color used for plotting the line #' colors in the legend #' @param col a valid vector of colors of at least length 3 to use to color the #' confidence intervals #' @param leg ``legend position'', an integer from 1 to 4, specifying the #' position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and #' ``NE'' respectively #' @param legpos ``legend type'', exact coordinates and sizes for legend. #' Overrides argment ``leg.type'' #' @return the current graphical parameters. This is subject to change in future #' implementations of Zelig #' @author James Honaker, adapted by Matt Owen \email{mowen@@iq.harvard.edu} #' @export plot.ci #' @usage \method{plot}{ci}(x, qi="ev", var=NULL, ..., legcol="gray20", col=NULL, leg=1, legpos=NULL) plot.ci <- function(x, qi="ev", var=NULL, ..., main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, legcol="gray20", col=NULL, leg=1, legpos=NULL, ci=c(80,95,99.9)) { if(length(ci)<3){ ci<-rep(ci,3) } if(length(ci)>3){ ci<-ci[1:3] } ci<-sort(ci) if (! "pooled.sim" %in% class(x)) { something <- list(x=x) class(something) <- "pooled.sim" attr(something, "titles") <- x$titles x <- something } xmatrix<-matrix(NA,nrow=length(x),ncol=length(x[[1]]$x$data)) for(i in 1:length(x)){ xmatrix[i,]<-as.matrix(x[[i]]$x$data) } if (length(x) == 1 && is.null(var)) { warning("Must specify the `var` parameter when plotting the confidence interval of an unvarying model. Plotting nothing.") return(invisible(FALSE)) } if(is.character(var)){ if( !(var %in% names(x[[1]]$x$data) ) ){ warning("Specified variable for confidence interval plot is not in estimated model. Plotting nothing.") return(invisible(FALSE)) } } if (is.null(var)) { each.var <- apply(xmatrix,2,sd) flag <- each.var>0 min.var<-min(each.var[flag]) var.seq<-1:ncol(xmatrix) position<-var.seq[each.var==min.var] } else { if(is.numeric(var)){ position<-var }else if(is.character(var)){ position<-grep(var,names(x[[1]]$x$data)) } } position<-min(position) xseq<-xmatrix[,position] xname<-names(x[[1]]$x$data[position]) # Use "qi" argument to select quantities of interest and set labels ev1<-NULL if(qi=="pv"){ request<-"Predicted Values: Y|X" if(!is.null(x[[1]]$x1)){ ev1<-simulation.matrix(x, "Predicted Values: Y|X1") } } else if(qi=="fd") { request<-"First Differences: E(Y|X1) - E(Y|X)" } else { request<-"Expected Values: E(Y|X)" if(!is.null(x[[1]]$x1)){ ev1<-simulation.matrix(x, "Expected Values: E(Y|X1)") } } ev<-simulation.matrix(x, request) if (is.null(ylab)){ ylab <- request } # Define functions to compute confidence intervals ci.upper <- function (x, alpha) { pos <- max(round((1-(alpha/100))*length(x)), 1) return(sort(x)[pos]) } ci.lower <- function (x, alpha) { pos<-max(round((alpha/100)*length(x)), 1) return(sort(x)[pos]) } # k<-ncol(ev) n<-nrow(ev) # if(is.null(col)){ myblue1<-rgb( 100, 149, 237, alpha=50, maxColorValue=255) myblue2<-rgb( 152, 245, 255, alpha=50, maxColorValue=255) myblue3<-rgb( 191, 239, 255, alpha=70, maxColorValue=255) myred1 <-rgb( 237, 149, 100, alpha=50, maxColorValue=255) myred2 <-rgb( 255, 245, 152, alpha=50, maxColorValue=255) myred3 <-rgb( 255, 239, 191, alpha=70, maxColorValue=255) col<-c(myblue1,myblue2,myblue3,myred1,myred2,myred3) }else{ if(length(col)<6){ col<-rep(col,6)[1:6] } } form.history <- function (k,xseq,results,ci=c(80,95,99.9)){ history<-matrix(NA, nrow=k,ncol=8) for (i in 1:k) { v <- c( xseq[i], median(results[,i]), ci.upper(results[,i],ci[1]), ci.lower(results[,i],ci[1]), ci.upper(results[,i],ci[2]), ci.lower(results[,i],ci[2]), ci.upper(results[,i],ci[3]), ci.lower(results[,i],ci[3]) ) history[i, ] <- v } if (k == 1) { left <- c( xseq[1]-.5, median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) right <- c( xseq[1]+.5, median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) v <- c( xseq[1], median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) history <- rbind(left, v, right) } return(history) } history<- form.history(k,xseq,ev,ci) if(!is.null(ev1)){ history1<- form.history(k,xseq,ev1,ci) }else{ history1<-NULL } # This is for small sets that have been duplicated so as to have observable volume if(k==1){ k<-3 } # Specify x-axis length all.xlim <- if (is.null(xlim)) c(min(c(history[, 1],history1[, 1])),max(c(history[, 1],history1[, 1]))) else xlim # Specify y-axis length all.ylim <-if (is.null(ylim)) c(min(c(history[, -1],history1[, -1])),max(c(history[, -1],history1[, -1]))) else ylim # Define xlabel if (is.null(xlab)) xlab <- paste("Range of",xname) if (is.null(ylab)) ylab <- "Expected Values: E(Y|X)" ## This is the plot par(bty="n") plot(x=history[, 1], y=history[, 2], type="l", xlim=all.xlim, ylim=all.ylim, main = main, sub = sub, xlab=xlab, ylab=ylab) polygon(c(history[,1],history[k:1,1]),c(history[,7],history[k:1,8]),col=col[3],border="white") polygon(c(history[,1],history[k:1,1]),c(history[,5],history[k:1,6]),col=col[2],border="gray90") polygon(c(history[,1],history[k:1,1]),c(history[,3],history[k:1,4]),col=col[1],border="gray60") polygon(c(history[,1],history[k:1,1]),c(history[,7],history[k:1,8]),col=NA,border="white") if(!is.null(ev1)){ lines(x=history1[, 1], y=history1[, 2], type="l") polygon(c(history1[,1],history1[k:1,1]),c(history1[,7],history1[k:1,8]),col=col[6],border="white") polygon(c(history1[,1],history1[k:1,1]),c(history1[,5],history1[k:1,6]),col=col[5],border="gray90") polygon(c(history1[,1],history1[k:1,1]),c(history1[,3],history1[k:1,4]),col=col[4],border="gray60") polygon(c(history1[,1],history1[k:1,1]),c(history1[,7],history1[k:1,8]),col=NA,border="white") } ## This is the legend if(is.null(legpos)){ if(leg==1){ legpos<-c(.91,.04,.2,.05) }else if(leg==2){ legpos<-c(.09,.04,.2,.05) }else if(leg==3){ legpos<-c(.09,.04,.8,.05) }else{ legpos<-c(.91,.04,.8,.05) } } lx<-min(all.xlim)+ legpos[1]*(max(all.xlim)- min(all.xlim)) hx<-min(all.xlim)+ (legpos[1]+legpos[2])*(max(all.xlim)- min(all.xlim)) deltax<-(hx-lx)*.1 my<-min(all.ylim) +legpos[3]*min(max(all.ylim) - min(all.ylim)) dy<-legpos[4]*(max(all.ylim) - min(all.ylim)) lines(c(hx+deltax,hx+2*deltax,hx+2*deltax,hx+deltax),c(my+3*dy,my+3*dy,my-3*dy,my-3*dy),col=legcol) lines(c(hx+3*deltax,hx+4*deltax,hx+4*deltax,hx+3*deltax),c(my+1*dy,my+1*dy,my-1*dy,my-1*dy),col=legcol) lines(c(lx-deltax,lx-2*deltax,lx-2*deltax,lx-deltax),c(my+2*dy,my+2*dy,my-2*dy,my-2*dy),col=legcol) lines(c(lx-5*deltax,lx),c(my,my),col="white",lwd=3) lines(c(lx-5*deltax,lx),c(my,my),col=legcol) lines(c(lx,hx),c(my,my)) polygon(c(lx,lx,hx,hx),c(my-3*dy,my+3*dy,my+3*dy,my-3*dy),col=col[3],border="white") polygon(c(lx,lx,hx,hx),c(my-2*dy,my+2*dy,my+2*dy,my-2*dy),col=col[2],border="gray90") polygon(c(lx,lx,hx,hx),c(my-1*dy,my+1*dy,my+1*dy,my-1*dy),col=col[1],border="gray60") polygon(c(lx,lx,hx,hx),c(my-3*dy,my+3*dy,my+3*dy,my-3*dy),col=NA,border="white") text(lx,my,labels="median",pos=2,cex=0.5,col=legcol) text(lx,my+2*dy,labels=paste("ci",ci[2],sep=""),pos=2,cex=0.5,col=legcol) text(hx,my+1*dy,labels=paste("ci",ci[1],sep=""),pos=4,cex=0.5,col=legcol) text(hx,my+3*dy,labels=paste("ci",ci[3],sep=""),pos=4,cex=0.5,col=legcol) } #' Method for plotting pooled simulations by confidence intervals #' #' Plot pooled simulated quantities of interest. #' @usage \method{plot}{pooled.sim}(x, qi="ev", var=NULL, ..., legcol="gray20", col=NULL, leg=1, legpos=NULL) #' @S3method plot pooled.sim #' @param x A `sim' object #' @param qi a character-string specifying the quantity of interest to plot #' @param var The variable to be used on the x-axis. Default is the variable #' across all the chosen values with smallest nonzero variance #' @param ... Parameters to be passed to the `truehist' function which is #' implicitly called for numeric simulations #' @param legcol ``legend color'', an valid color used for plotting the line #' colors in the legend #' @param col a valid vector of colors of at least length 3 to use to color the #' confidence intervals #' @param leg ``legend position'', an integer from 1 to 4, specifying the #' position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and #' ``NE'' respectively #' @param legpos ``legend type'', exact coordinates and sizes for legend. #' Overrides argment ``leg.type'' #' @return the current graphical parameters. This is subject to change in future #' implementations of Zelig #' @author James Honaker, adapted by Matt Owen \email{mowen@@iq.harvard.edu} plot.pooled.sim <- plot.ci #' Method for plotting simulations #' #' Plot simulated quantities of interest. #' @usage \method{plot}{sim}(x, ...) #' @S3method plot sim #' @param x a `sim' object #' @param ... parameters to be passed to the `truehist' function which is #' implicitly called for numeric simulations #' @return nothing #' @author Matt Owen \email{mowen@@iq.harvard.edu} plot.sim <- function (x, ...) { env <- tryCatch( asNamespace(x$package.name), # error = function (e) { warning("") globalenv() } ) # If plotPackageName if (exists("plot.simulations", envir = env, mode="function")) { # Get the simulation, because we know it exists .plotter <- get("plot.simulations", envir = env, mode="function") # Pass to a temporary variable to improve the visibility of the traceback # if there is an error res <- .plotter(x, ...) # Return object (whatever it is) return(invisible(res)) } # Otherwise we just use this fall-back old.par <- par(no.readonly = T) # Some numbers we use to make things count<- 0 for(i in 1:length(names(x$qi))){ if (!all( is.na(x$qi[[i]]) ) & !is.null(x$qi[[i]]) ) count<-count+1 } total.qis<-max(count,1) #total.qis <- length(names(x$qi)) palette <- rainbow(total.qis) total.cols <- 2 total.rows <- ceiling(total.qis/total.cols) if(total.rows==1){ total.rows<-2 total.cols<-1 } vals <- ifelse(total.qis %% 2, c(1:total.qis, total.qis), 1:total.qis) # Colors! color.blue <- rgb(100, 149, 237, maxColorValue=255) # vals <- if (total.qis %% 2) { c(1:total.qis, total.qis) } else { 1:total.qis } # Construct layout layout(matrix(vals, total.rows, total.cols, byrow=TRUE)) k <- 1 for (title in names(x$qi)) { simulations.plot(x$qi[[title]], main = title, col = palette[k], line.col = "black") k <- k + 1 } # return(par(old.par)) } #' @S3method plot sim.cloglog.net plot.sim.cloglog.net <- function (x, ...) { env <- tryCatch( asNamespace(x$package.name), error = function (e) { warning("") globalenv() } ) # If plotPackageName if (exists("plot.simulations", envir = env, mode="function")) { # Get the simulation, because we know it exists .plotter <- get("plot.simulations", envir = env, mode="function") # Pass to a temporary variable to improve the visibility of the traceback # if there is an error res <- .plotter(x, ...) # Return object (whatever it is) return(invisible(res)) } # Otherwise we just use this fall-back old.par <- par(no.readonly = T) # Some numbers we use to make things total.qis <- length(names(x$qi)) palette <- rainbow(total.qis) total.cols <- 2 total.rows <- ceiling(total.qis/total.cols) vals <- ifelse(total.qis %% 2, c(1:total.qis, total.qis), 1:total.qis) # Colors! color.blue <- rgb(100, 149, 237, maxColorValue=255) # vals <- if (total.qis %% 2) { c(1:total.qis, total.qis) } else { 1:total.qis } # Construct layout layout(matrix(vals, total.rows, total.cols, byrow=TRUE)) k <- 1 for (title in names(x$qi)) { simulations.plot(x$qi[[title]], main = title, col = palette[k], line.col = "black") k <- k + 1 } # return(par(old.par)) } #' Plot Any Simulation from the Zelig Core Package #' #' Plots any simulation from the core package. In general, this function can #' \emph{neatly} plot simulations containing five of the popular ``quantities #' of interest'' - ``Expected Values: E(Y|X)'', ``Predicted Values: Y|X'', #' ``Expected Values (for X1): E(Y|X1)'', ``Predicted Values (for X1): Y|X1'' #' and ``First Differences: E(Y|X1) - E(Y|X)''. #' @param x an object #' @param ... parameters passed to the ``plot'' and ``barplot'' functions #' @return the original graphical parameters #' @author Matt Owen \email{mowen@@iq.harvard.edu} plot.simulations <- function (x, ...) { # Save old state old.par <- par(no.readonly=T) # Quantities of Interest qi <- x$qi # Define Relevant quantity of interest titles that have special properties ev.titles <- c('Expected Values: E(Y|X)', 'Expected Values: E(Y|X1)') pv.titles <- c('Predicted Values: Y|X', 'Predicted Values: Y|X1') # Determine whether two "Expected Values" qi's exist both.ev.exist <- all(ev.titles %in% names(qi)) # Determine whether two "Predicted Values" qi's exist both.pv.exist <- all(pv.titles %in% names(qi)) # Color of x should always be this pertty blue color.x <- rgb(242, 122, 94, maxColorValue=255) color.x1 <- rgb(100, 149, 237, maxColorValue=255) # This mixes the above two colors, and converts the result into hexadecimal color.mixed <- rgb(t(round((col2rgb(color.x) + col2rgb(color.x1))/2)), maxColorValue=255) if (is.null(x$x)) { return(par(old.par)) } else if (is.null(x$x1) || is.na(x$x1)) { panels <- matrix(1:2, 2, 1) # The plotting device: # +--------+ # | 1 | # +--------+ # | 2 | # +--------+ } else { panels <- matrix(c(1:5, 5), ncol=2, nrow=3, byrow = TRUE) panels <- if (xor(both.ev.exist, both.pv.exist)) rbind(panels, c(6, 6)) else if (both.ev.exist && both.pv.exist) rbind(panels, c(6, 7)) else panels # the plotting device: # # +-----------+ +-----------+ # | 1 | 2 | | 1 | 2 | # +-----+-----+ +-----+-----+ # | 3 | 4 | | 3 | 4 | # +-----+-----+ OR +-----+-----+ # | 5 | | 5 | # +-----------+ +-----------+ # | 6 | 7 | | 6 | # +-----+-----+ +-----+-----+ } # layout(panels) titles <- list( ev = "Expected Values: E(Y|X)", ev1 = "Expected Values: E(Y|X1)", pv = "Predicted Values: Y|X", pv1 = "Predicted Values: Y|X1", fd = "First Differences: E(Y|X1) - E(Y|X)" ) # Plot each simulation simulations.plot(qi[[titles$pv]], main = titles$pv, col = color.x, line.col = "black") simulations.plot(qi[[titles$pv1]], main = titles$pv1, col = color.x1, line.col = "black") simulations.plot(qi[[titles$ev]], main = titles$ev, col = color.x, line.col = "black") simulations.plot(qi[[titles$ev1]], main = titles$ev1, col = color.x1, line.col = "black") simulations.plot(qi[[titles$fd]], main = titles$fd, col = color.mixed, line.col = "black") if (both.pv.exist) { simulations.plot( qi[["Predicted Values: Y|X"]], qi[["Predicted Values: Y|X1"]], main = "Comparison of Y|X and Y|X1", # Note that we are adding transparency to this col = paste(c(color.x, color.x1), "80", sep=""), line.col = "black") } if (both.ev.exist) { simulations.plot( qi[["Expected Values: E(Y|X)"]], qi[["Expected Values: E(Y|X1)"]], main = "Comparison of E(Y|X) and E(Y|X1)", # Note that we are adding transparency to this col = paste(c(color.x, color.x1), "80", sep=""), line.col = "black") } # Restore old state par(old.par) # Return old parameter invisibly invisible(old.par) } plot.zelig.relogit <- function(x, xlab ="", user.par = FALSE, alt.col = "red", ylab = NULL, samples = 100, ...){ k <- length(x$qi) op <- par(no.readonly = TRUE) if (!user.par) par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0)) par(mfrow = c(k, 1)) if (dim(x$qi[[1]])[2] == 1) { pr <- x$qi$pr y0 <- 100 * sum(pr == 0)/length(pr) y1 <- 100 * sum(pr == 1)/length(pr) barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1, names.arg = c("Y = 0", "Y = 1"), xlab = "Percentage of Simulations", main = x$qi.name$pr, xlim = c(0, 100)) x$qi$pr <- x$qi.name$pr <- NULL for (i in 1:(k-1)) { qi <- as.vector(x$qi[[i]]) plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...) } } else { for (i in 1:k) { qi <- x$qi[[i]] main <- as.character(x$qi.name[i]) if (is.null(rownames(qi))) rownames(qi) <- 1:dim(qi)[1] idx <- as.integer(sample(rownames(qi), 100)) tmp <- qi[idx,,1] xlim <- c(min(qi[,1,1]), max(qi[,2,1])) if (is.null(ylab)) ylab <- paste("Observations (n = ", samples, ")", sep = "") plot(xlim, type = "n", xlab = xlab, ylab = ylab, main = main, ylim = c(0, 100), xlim = xlim, ...) for (j in 1:nrow(tmp)) lines(c(tmp[j,1], tmp[j,2]), c(j,j), col = alt.col) abline(v = mean(qi[,1,1])) abline(v = mean(qi[,2,1])) } } par(op) } Zelig/R/help.zelig.R0000644000176000001440000000505112061700507013740 0ustar ripleyusers#' Help system for Zelig models #' @param ... the help files to look-up #' @return results of calling the specific help function #' @export #' @author Matt Owen \emph{mowen@@iq.harvard.edu} help.zelig <- function (...) { driver <- match.call() driver <- as.character(driver) name <- NULL if(length(driver) > 1){ name <- driver[2] } else { print(do.call("help",list(package="Zelig"), envir=parent.frame())) return(invisible(NULL)) } if (name == "models"){ print(do.call("vignette", list(package="Zelig"))) return(invisible(NULL)) } filesPDf <- NULL helpfile <- try(system.file("Meta", "vignette.rds", package="Zelig")) if(helpfile!=""){ helpMtrx <- readRDS(helpfile) ix <- grep("[pP][dD][fF]", colnames(helpMtrx)) if(length(ix)) filesPDF <- helpMtrx[,ix] if(length(filesPDF) && length(name)) { fl <- paste("^",name,".pdf$",sep="") ix <- grep(fl,filesPDF) if(length(ix)){ file <- filesPDF[ix] print(do.call("vignette", c(list(topic=name),list(package="Zelig")))) return(invisible(list())) } } } helpfile <- try(system.file("Meta", "hsearch.rds", package="Zelig")) fileshtml <- NULL if(helpfile != "") { helpMtrx <- readRDS(helpfile) fileshtml <- helpMtrx[[2]][,"Aliases"] ix <- grep("url$", fileshtml) if(length(ix)) fileshtml <- fileshtml[-ix] if(length(fileshtml) && length(name)) { ix <- grep(name, fileshtml) if(length(ix)){ print(do.call("help", c(list(as.name(name)), list(package="Zelig")), envir=parent.frame())) return(invisible(NULL)) } } } ##message("Not valid input...Showing package description") do.call("help", c(list("Zelig"), list(package="Zelig")), envir=parent.frame()) } Zelig/R/list.depth.R0000644000176000001440000000175512061700507013764 0ustar ripleyusers#' Count the Depth of a List Object #' #' This function recursively computes the depth of a list object. That is, it #' determines how many layers or levels exist within the object. #' @note This function is used internally by Zelig. #' @param obj a vector or list object #' @author Matt Owen \email{mowen@@iq.harvard.edu} list.depth <- function (obj) { # Stop-recursing conditions if (length(obj) == 0) return(0) else if (is.atomic(obj)) # Atomic vectors can only have one level return(1) else if (!is.list(obj)) # If the object is not a list, then we have the option whether to compute # the depth of its elements. return(1) # Produce a list of integers, specifying the depth of each element results <- Map(list.depth, obj) # Ensure that the result is a non-list results <- unlist(results) # Find the maximum, ensuring that the value is neither negative nor -Inf max.depth <- max(results, 0) # Add one for the level that we are on 1 + max.depth } Zelig/R/normal.R0000644000176000001440000000743712071063367013210 0ustar ripleyusers#' Interface between normal model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2normal <- function(formula, weights=NULL, ..., data) z( glm, # .hook = "robust.glm.hook", formula = formula, weights = weights, family = gaussian, model = F, data = data ) #' Param Method for the 'normal' Zelig Model #' @note This method is used by the 'normal' Zelig model #' @usage \method{param}{normal}(obj, num=1000, ...) #' @S3method param negbinom #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.normal <- function(obj, num=1000, ...) { degrees.freedom <- .fitted$df.residual sig2 <- summary(.fitted)$dispersion list( simulations = mvrnorm(n=num, mu=coef(.fitted), Sigma=vcov(.fitted)), alpha = sqrt(degrees.freedom * sig2 / rchisq(num, degrees.freedom)), link = function (x) x, linkinv = function (x) x ) } #' Compute quantities of interest for 'normal' Zelig models #' @usage \method{qi}{normal}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi normal #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.normal <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { # get `num` samples from the underlying distribution coef <- coef(param) alpha <- alpha(param) # theta = eta, because inverse of # normal models' link function is # the identity theta <- matrix(coef %*% t(x), nrow=nrow(coef)) # pr <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta)) # ev <- theta ev1 <- pr1 <- fd <- NA for (i in 1:nrow(ev)) pr[i,] <- rnorm(ncol(ev), mean = ev[i,], sd = alpha[i]) # if x1 is not NULL, run more simultations # ... if (!is.null(x1)) { # quantities of interest lis1 <- qi(obj, x1, num=num, param=param) # pass values over ev1 <- lis1[[1]] pr1 <- lis1[[3]] # compute first differences fd <- ev1 - ev } # return list("Expected Values: E(Y|X)" = ev, "Expected Values: E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values: Y|X1" = pr1, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe the \code{normal} model to Zelig #' @usage \method{describe}{normal}(...) #' @S3method describe normal #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.normal <- function(...) { # parameters object parameters <- list(pi = list( equations = c(1, 1), tags.allowed = FALSE, dep.var = TRUE, exp.var = TRUE ) ) # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2008, category = "continuous", parameters = parameters, text = "Normal Regression for Continuous Dependent Variables" ) } Zelig/R/normal.gee.R0000644000176000001440000000374112110047000013715 0ustar ripleyusers#' Interface between the Zelig Model normal.gee and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param id a character-string specifying the column of the data-set to use #' for clustering #' @param robust a logical specifying whether to robustly or naively compute #' the covariance matrix. This parameter is ignore in the \code{zelig2} #' method, and instead used in the \code{robust.hook} function, which #' executes after the call to the \code{gee} function #' @param ... ignored parameters #' @param R a square-matrix specifying the correlation #' @param corstr a character-string specifying the correlation structure #' @param data a data.frame #' @return a list specifying the call to the external model #' @export zelig2normal.gee <- function (formula, id, robust, ..., R = NULL, corstr = "independence", data) { loadDependencies("gee") if (corstr == "fixed" && is.null(R)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } z( .function = gee, .hook = robust.gee.hook, formula = formula, id = id, corstr = corstr, family = gaussian(), R = R, data = data, ... ) } #' @S3method param normal.gee param.normal.gee <- function(obj, num=1000, ...) { # Extract means to compute maximum likelihood mu <- coef(obj) # Extract covariance matrix to compute maximum likelihood Sigma <- vcov(obj) # list( coef = mvrnorm(num, mu, Sigma), linkinv = function (x) x ) } #' @S3method qi normal.gee qi.normal.gee <- qi.gamma.gee #' @S3method describe normal.gee describe.normal.gee <- function(...) { list( authors = "Patrick Lam", text = "General Estimating Equation for Normal Regression", year = 2011 ) } Zelig/R/as.qi.R0000644000176000001440000001607612061700507012723 0ustar ripleyusers#' Generic Method for Converting Various Objects into 'qi' Objects #' 'qi' objects are list-style objects used by the 'summarize' function to #' compute simple summaries about the simulated data. For readability and #' and simplicity purposes, the 'qi' function typically returns a list of #' named simulations. This list is converted internally by Zelig into a 'qi' #' object so that several methods can be easily applied to the Quantities of #' Interest: plot, summarize, and print #' @note These functions are primarily used internall by Zelig and should not #' be used in the Global namespace. #' @param s the object to be casted #' @return an object of type `qi' #' @seealso as.qi.default as.qi.qi as.qi.list #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.qi <- function(s) UseMethod("as.qi") #' ??? -> qi #' #' @param s any unsupported object #' @return an object of type `qi' #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.qi.default <- function(s) stop("as.qi does not yet support this data-type") #' qi -> qi #' #' @param s an object of type `qi' #' @return s an object of type `qi' #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.qi.qi <- function(s) s #' list -> qi #' This function has a lot of room to go wrong. It tries o detect whether the #' zelig model is old-style or new-style (as of 4/4/2011). Eventually this #' feature should be phased out. #' @note This method has peculiar behavior when the list contains only two #' elements. The crucial fix is to simply remove the portion of code which #' intentionally implements this perculiar behavior. #' @param s a list #' @return an object of type `qi' #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.qi.list <- function(s) { #q <- list(titles=list(), stats=list()) titles <- list() stats <- list() # divide the list into ones with/without keys keys <- splitUp(s) fail.names <- paste("qi", 1:length(s), sep="") success.names <- unlist(Map(.acronym, names(s), fail='')) success.names <- .number.list(success.names) # create new environment env <- new.env() # iterator k <- 1 long <- list() short <- list() stats <- list() # add the named entries for (title in names(keys$wordful)) { key <- if (regexpr("^[a-zA-Z]", success.names[k]) != -1) success.names[k] else '' stats[[key]] <- keys$wordful[[title]] long[[title]] <- key #attr(stats, title) <- key # increment counter k <- k + 1 } attr(stats, ".index") <- long q <- stats # cast as `qi' object, and return class(q) <- "qi" q } #' Print a Quantity of Interest in Human-Readable Form #' #' Print simulated quantities of interest in a human-readable form #' #' @usage \method{print}{qi}(x, ...) #' @S3method print qi #' @param x a qi object #' @param ... ignored parameters #' @return the object that was printed (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.qi <- function(x, ...) { self <- x # error-catching if (length(self$titles) != length(self$stats)) stop("corrupted object! titles and stats length mismatch") qi.length <- length(self) # iterate through for (k in 1:qi.length) { # output title message(self$titles[[k]]) # output qi print(self$stats[[k]]) # just to prevent extra end-line if (k != qi.length) message() } invisible(x) } #' The Names of a 'qi' Object #' #' Function to get the names of a 'qi' object. This function does not entirely #' parallel the functionality of traditional 'names' methods; this is because #' the \code{$} operator has been overloaded to support a unique style of value #' extraction. For technical details, please see the source code. #' @note No method exists to set the names of a 'qi' object, once it is #' constructed. This will be a feature added later. #' @usage \method{names}{qi}(x) #' @S3method names qi #' @param x a 'qi' object #' @return a character-vector containing the names of the Quantities of #' Interest #' @author Matt Owen \email{mowen@@iq.harvard.edu} names.qi <- function(x) { names(attr(x, ".index")) } #' Convert a Vector of Character Strings into Acronyms #' This function will convert a vector of character strings into their #' appropriately titled acronym forms. That is, the two Quantity of Interest #' titles: #' \begin{itemize} #' \item "Expected Values (for X): E(Y|X)" #' \item "Expected Values (for X1): E(Y|X1)" #' \end{itemize} #' The result will be: "ev1" and "ev2". That is, the acronym will not contain #' information kept in paranetheses or after a colon. #' @note This function currently includes preopositions as parts of acroynms #' @param str a vector of character strings to convert into acronymns #' @param fail a result to produce upon failure #' @return a vector of character-strings #' @author Matt Owen \email{mowen@@iq.harvard.edu} .acronym <- function(str, fail=str) { ignored.words <- c( "in", "for", "by", "the", "a", "an" ) # remove all text after colon # remove trailing whitespace # remove leading whitespace # remove paranthetical statements # remove non-alphanumerics reduced <- sub(':.*$', '', str) reduced <- sub('\\s+$', '', reduced, perl=TRUE) reduced <- sub('^\\s+', '', reduced, perl=TRUE) reduced <- gsub('\\(.*?\\)', '', reduced, perl=TRUE) # if we get an empty string, return whatever the fail value is if (nchar(reduced) < 1) return(fail) # splitted is not a word, I know # 1. split the reduced string into non-whitespace characters # 2. take the first letter of each # 3. put into lowercase splitted <- unlist(strsplit(reduced, '\\s+')) # remove ignored words ## splitted <- Filter( ## function (char) regexpr( ## splitted ## ) splitted <- substr(splitted, 1, 1) splitted <- tolower(splitted) # remove all non-letters acronym <- Filter( function (char) regexpr('^[a-zA-Z]$', char, perl=TRUE), splitted ) # paste together, and return paste(acronym, sep="", collapse="") } #' Append Numbers to Identically Valued Strings #' This function ensures that vectors of strings are uniquely named. #' @note This function is used in tandem with '.acronym' to correctly produce #' short-names for quantities of interest. #' @param vec a vector of character-string #' @return a vector of character-strings of shorter length. Duplicate hits on #' short-titled names append a number to the end. E.g.: the character vector #' if vec equals c('ev', 'ev', 'pr'), then the result will be: #' c('ev1', 'ev2', 'pr') #' @author Matt Owen \email{mowen@@iq.harvard.edu} .number.list <- function(vec) { if (!is.character(vec)) { warning() return(vec) } final.list <- c() unique.vec <- unique(vec) for (k in 1:length(vec)) { val <- vec[k] hits <- sum(val == vec[1:k]) total.hits <- sum(val == vec) final.list[names(vec)[k]] <- if (total.hits > 1) paste(val, hits, sep="") else val } # return final.list } Zelig/R/sim.MI.R0000644000176000001440000000251212151471374013001 0ustar ripleyusers#' Simulate Multiply Imputed Data #' @usage \method{sim}{MI}(obj, x=NULL, x1=NULL, y=NULL, num=1000, ...) #' @S3method sim MI #' @param obj a 'zelig.MI' object containing several fits for two or more #' subsetted data-frames #' @param x a 'setx.mi' object containing explanatory variables for each #' fitted model #' @param x1 a 'setx.mi' object containing explanatory variables for each #' fitted model #' @param y this feature is currently unimplemented #' @param num an integer specifying the number of simulations to compute #' @param ... ignored parameters #' @return a 'sim.MI' with simulated quantities of interest for each fitted #' contained by 'obj' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @seealso \link{sim} sim.MI <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, ...) { sim.results <- list() m<-length(obj) mi.num<-ceiling(num/m) for (key in names(obj)) { object <- obj[[key]] new.x <- x[[key]] new.x1 <- x1[[key]] new.y <- y[[key]] sim.results[[key]] <- sim(object, x=new.x, x1=new.x1, y=new.y, num=mi.num) } model <- get('model', attr(obj, 'state')) class(sim.results) <- c( 'MI.sim', paste(model, "mi-sim", sep="-"), paste(model, "mi.sim", sep=".") ) sim.results } Zelig/R/bootstrap.normal.R0000644000176000001440000000157112061700507015206 0ustar ripleyusers#' Bootstrap Parameters for Zelig ``normal'' GLM #' #' Returns bootstrapped parameter estimates for a Gaussian GLM. #' @usage \method{bootstrap}{normal}(obj, num, ...) #' @S3method bootstrap normal #' @param obj a ``zelig'' object that will be used to produce boot-strapped #' parameters #' @param num an integer specifying the number of simulations to produce #' @param ... extra parameters to be passed to the ``boot'' method. These are #' typically ignored, but is included for further expansion. #' @return a list containing information concerning link, link-inverses, etc. #' @author Matt Owen \email{mowen@@iq.harvard.edu} bootstrap.normal <- function (obj, num, ...) { degrees.freedom <- obj[["df.residual"]] sig2 <- summary(obj)$dispersion alpha <- sqrt(degrees.freedom * sig2 / rchisq(20, degrees.freedom)) list( alpha = alpha, beta = coef(obj) ) } Zelig/R/as.matrix.setx.R0000644000176000001440000000200112061700507014557 0ustar ripleyusers#' Convert a 'setx' Object to a Matrix #' #' The setx object is, in its most basic form, a list of column names and values #' specified for each of these column names. This function simply converts the #' key-value pairs of column-name and specified value into a matrix. #' #' @note This method allows basic matrix arithmetic operations on data objects, #' which mirror values stored within setx objects. In many scenarios, #' simulations require matrix-multiplication, etc. to be performed on a #' data-set. This function faciliates that need. #' #' @usage \method{as.matrix}{setx}(x, ...) #' @S3method as.matrix setx #' @param x a setx object #' @param ... ignored parameters #' @return a matrix containing columns and rows corrseponding to the explanatory #' variables specified in the call to the 'setx' function #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.matrix.setx <- function(x, ...) { if (!is.null(x$matrix)) #as.matrix(x$updated[, x$explan]) x$matrix else stop("unspecified error") } Zelig/R/description.R0000644000176000001440000001155612061700507014231 0ustar ripleyusers#' Constructor for the 'description' class #' #' @param authors a character-vector of author names #' @param year a numeric specifying the year #' @param model a character-string specifying model name #' @param text a character-string specifying the title of the model. This #' typically includes more exact information than 'model'. E.g., for the #' 'logit' the title 'Logistic Regression for Dichotomous Variables' would be #' a suitable text parameter. #' @param url a character-string specifying the model's software page #' @param category deprecated until data-verse bindings are reevaluated #' @return an object of type 'description' #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} description <- function(authors=c("Kosuke Imai", "Gary King", "Olivia Lau"), year=NULL, model="", text="", url="", category = NULL) { # error-catching if (!is.character(authors)) author <- "Kosuke Imai, Gary King, and Olivia Lau" else if (length(authors) > 1) { # collapse author names if it is a character-vector bigger than 1 authors <- paste(paste(head(authors, -1), collapse=", "), ", and ", tail(authors, 1), sep = "" ) } if (!is.numeric(year)) year <- as.numeric(format(Sys.Date(), "%Y")) if (!is.character(model) || length(model) != 1) { print(model) stop("model must be a character-string") } if (length(text) > 1) stop("text must be a character-vector of length 1") if (is.null(url)) url <- "http://gking.harvard.edu/zelig" if (!is.character(category)) category <- "" else if (length(url) > 1 || !is.character(url)) stop("url must be a character-vector of length 1") # double back-up, even though this should be impossible now authors <- ifelse(nchar(authors) > 0, authors, "NAMELESS AUTHOR") year <- ifelse(!is.null(year), year, "UNKNOWN YEAR") model <- ifelse(nchar(model) > 0, model, "UNNAMED MODEL") # construct object self <- list(authors = authors, year = year, model = model, text = text, url = url ) class(self) <- "description" self } #' Citation information for a 'description' object #' @param descr an object of type 'description' #' @return a character-string giving citation info #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} cite <- function(descr) { # if (inherits(descr, "list")) descr <- as.description(descr) else if (!inherits(descr, "description")) descr <- description() # url <- "http://gking.harvard.edu/zelig" title <- if (is.null(descr$text)) descr$model else paste(descr$model, ": ", descr$text, sep="") # quote title <- paste('"', title, '"', sep="") # construct string. This should be done much more elegantly # and with localization str <- "How to cite this model in Zelig:\n " str <- paste(str, descr$authors, ". ", descr$year, ".\n ", title, sep="") str <- paste(str, "\n in Kosuke Imai, Gary King, and Olivia Lau, ", sep="") str <- paste(str, "\"Zelig: Everyone's Statistical Software,\"", sep="") str <- paste(str, "\n ", url, "\n", sep="") str } #' Generic Method for Casting 'description' Objects #' #' Convert the result of a call to the 'describe' method into an object #' parseble by Zelig. Currently conversions only exist for lists and #' description objects. #' @param descr an object to cast an object of type 'description' #' @param ... parameters which are reserved for future Zelig revisions #' @return an object of type 'description' #' @export #' @seealso as.description.description as.description.list #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.description <- function(descr, ...) UseMethod("as.description") #' description -> description #' #' Identity operation on a description object. #' @S3method as.description description #' @usage \method{as.description}{description}(descr, ...) #' @param descr an object of type 'description' #' @param ... ignored #' @return the same object #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.description.description <- function(descr, ...) descr #' list -> description #' #' Convert list into a description object. #' @usage \method{as.description}{list}(descr, ...) #' @S3method as.description list #' @param descr a list #' @param ... ignored #' @return an object of type 'description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} as.description.list <- function(descr, ...) { text <- if (!is.null(descr$text)) descr$text else if (!is.null(descr$description)) descr$description else NULL description(authors = descr$authors, year = descr$year, model = descr$model, text = text, url = descr$url, category= descr$category ) } Zelig/R/is.formula.R0000644000176000001440000000051612061700507013757 0ustar ripleyusers#' Whether an Object is a Formula #' #' This is a boolean-check to see whether an object is a formula. #' @note This will not be shared in the Zelig/ZeligFormulae namespace. #' @param x an object #' @return a logical specifying whether an object is a formula #' @author Matt Owen is.formula <- function (x) "formula" %in% class(x) Zelig/R/oprobit.bayes.R0000644000176000001440000000703212110047000014443 0ustar ripleyusers#' @export zelig2oprobit.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose=0, ..., data ) { loadDependencies("MCMCpack", "coda") if (missing(verbose)) verbose <- round((mcmc + burnin)/10) list( .function = "MCMCoprobit", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param oprobit.bayes param.oprobit.bayes <- function(obj, num=1000, ...) { # Produce the model matrix in order to get all terms (explicit and implicit) # from the regression model. mat <- model.matrix(obj$result, data=obj$data) # Response Terms p <- ncol(mat) # All coefficients coefficients <- coef(obj) # Coefficients for predictor variables beta <- coefficients[, 1:p] # Middle values of "gamma" matrix mid.gamma <- coefficients[, -(1:p)] # ... level <- ncol(coefficients) - p + 2 # Initialize the "gamma" parameters gamma <- matrix(NA, nrow(coefficients), level + 1) # The first, second and last values are fixed gamma[, 1] <- -Inf gamma[, 2] <- 0 gamma[, ncol(gamma)] <- Inf # All others are determined by the coef-matrix (now stored in mid.gamma) if (ncol(gamma) > 3) gamma[, 3:(ncol(gamma)-1)] <- mid.gamma # return list( simulations = beta, alpha = gamma, linkinv = NULL ) } #' @S3method qi oprobit.bayes qi.oprobit.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { labels <- levels(model.response(model.frame(obj$result))) res1 <- compute.oprobit.bayes(x, param, labels) res2 <- compute.oprobit.bayes(x1, param, labels) # list( "Expected Value: E(Y|X)" = res1$ev, "Predicted Value: Y|X" = res1$pv, "Expected Value (for X1): E(Y|X1)" = res2$ev, "Predicted Value (for X1): Y|X1" = res2$pv, "First Differences: E(Y|X1) - E(Y|X)" = res2$ev - res1$ev ) } # Helper function used to generate expected values compute.oprobit.bayes <- function (x, param, labels) { # If either of the parameters are invalid, # Then return NA for both qi's if (is.null(x) || is.na(x) || is.null(param)) return(list(ev=NA, pv=NA)) # Extract simulated parameters beta <- coef(param) gamma <- alpha(param) # x is implicitly cast into a matrix eta <- beta %*% t(x) # **TODO: Sort out sizes of matrices for these things. ev <- array(NA, c(nrow(eta), ncol(gamma) - 1, ncol(eta))) pv <- matrix(NA, nrow(eta), ncol(eta)) # Compute Expected Values # *********************** # Note that the inverse link function is: # pnorm(gamma[, j+1]-eta) - pnorm(gamma[, j]-eta) for (j in 1:(ncol(gamma)-1)) { ev[, j, ] <- pnorm(gamma[, j+1]-eta) - pnorm(gamma[, j]-eta) } colnames(ev) <- labels # Compute Predicted Values # ************************ for (j in 1:nrow(pv)) { mu <- eta[j, ] pv[j, ] <- as.character(cut(mu, gamma[j, ], labels=labels)) } # **TODO: Update summarize to work with at most 3-dimensional arrays ev <- ev[, , 1] # Return list(ev = ev, pv = pv) } #' @S3method describe oprobit.bayes describe.oprobit.bayes <- function(...) { list( text = "Bayesian Probit Regression for Dichotomous Dependent Variables", authors = c("Ben Goodrich", "Ying Lu"), year = 2013 ) } Zelig/R/print.R0000644000176000001440000002765412216375154013060 0ustar ripleyusers#' Print a Zelig Object #' @S3method print zelig print.zelig <- function (x, ...) { name <- x$name package.name <- x$package.name call <- x$call cat("Model Name: ", name, "\n") cat("Package Name: ", package.name, "\n") cat("Call:\n") print(call) # message("\nFor information about the fitted model, use the summary() function.") # Return invisibly invisible(x) } #' Print a Bundle of Data-sets #' #' @S3method print setx.mi #' @usage \method{print}{setx.mi}(x, ...) #' @param x a \code{setx} object to print #' @param ... ignored parameters #' @return the \code{setx} object (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.setx.mi <- function(x, ...) { # Store size for readability size <- length(x) for (k in 1:size) { # Print object print(x[[k]]) # If this is not the last element, print a new-line if (k < size) cat("\n") } invisible(x) } #' Print values of `setx' objects #' #' Print a ``setx'' object in human-readable form. #' @usage \method{print}{setx}(x, ...) #' @S3method print setx #' @param x a `setx' object #' @param ... ignored parameters #' @return the value of x (invisibly) #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.setx <- function(x, ...) { model <- x$name formula <- x$formula label <- x$label cat("Call:\n") print(x$call) cat("Model name = ", model, "\n") cat("Formula = ") print(formula) cat("\nComplete data.frame:\n") print(x$updated) cat("\nModel Matrix (Design Matrix):\n") print(x$matrix) invisible() } #' @S3method print summary.setx print.summary.setx <- function (x, ...) { cat("\nModel name =", x$model.name, "\n") cat("Label =", x$label, "\n") cat("Formula = ") print(x$formula) cat("\nCall:\n") print(x$call) cat("\nModel Matrix (Design Matrix):\n") print(x$model.matrix) invisible(x) } #' Print values of `sim' objects #' #' This function is currently unimplemented, and included for future development #' @usage \method{print}{sim}(x, ...) #' @S3method print sim #' @param x a `sim' object (ignored) #' @param ... ignored parameters #' @return NULL (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.sim <- function(x, ...) { o <- x class(o) <- 'list' print(o) } #' Print a Summary MCMCZelig Object #' #' This method prints a summary object for \code{MCMCZelig} objects #' @param x an "MCMCZelig" object #' @param digits a numeric specifying the precision of the summary object #' @param ... ignored parameters #' @return a \code{summary.MCMCZelig} object #' @S3method print summary.MCMCZelig print.summary.MCMCZelig <- function(x, digits=max(3, getOption("digits") - 3), ...) { cat("\nCall: ") print(x$call) cat("\n", "Iterations = ", x$start, ":", x$end, "\n", sep = "") cat("Thinning interval =", x$thin, "\n") cat("Number of chains =", x$nchain, "\n") cat("Sample size per chain =", (x$end - x$start)/x$thin + 1, "\n") cat("\n", "Mean, standard deviation, and quantiles for marginal posterior distributions.", "\n") print(round(x$summary, digits=digits)) cat("\n") } print.summary.glm.robust <- function (x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...) { class(x) <- "summary.glm" print(x) cat("\nRobust standard errors computed using", x$robust) cat("\n") invisible(x) } #' Print a Summary of a Set of Pooled Simulated Interests #' #' Prints the summary information from a set of pooled simulated interests. This #' method assumes that quantities of interest are kept in a data type which can #' be used with ``rbind''. #' @usage \method{print}{summary.pooled.sim}(x, ...) #' @S3method print summary.pooled.sim #' @param x a ``summary.pooled.sim'' object, containing summarized information #' about simulated quantities of interest #' @param ... Optional parameters that will be passed onward to ``print.matrix'' #' (the matrix printing function) #' @return a ``summary.pooled.sim'' object storing the quantities of interest #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.summary.pooled.sim <- function (x, ...) { # los labels... kinda like spanish for "the labels" # labels is function name in base, so we needed a name that said "labels," # without using "labels". You know? los.labels <- x$labels los.titles <- x$titles # Pooled summarized data for (title in los.titles) { # This will implicity become a matrix m <- NULL for (label in los.labels) m <- rbind(m, x$stats[[label]][[title]]) rownames(m) <- paste("[", los.labels, "]", sep="") cat(title, "\n") print(m) cat("\n\n") } } #' Print Summary of a Rare-event Logistic Model #' #' Prints the #' @usage #' \method{print}{summary.relogit}(x, digits = max(3, getOption("digits") - 3), ...) #' @S3method print summary.relogit #' @param x an ``relogit.summary'' object produced by the ``summary'' method. #' @param digits an integer specifying the number of digits of precision to #' specify #' @param ... parameters passed forward to the ``print.glm'' function #' @return x (invisibly) print.summary.relogit <- function( x, digits = max(3, getOption("digits") - 3), ... ) { # Straight-forwardly print the model using glm's method ## Was: ##stats:::print.glm(x, digits = digits, ...) ## ":::" not allowed by CRAN ## Copied from Stats Internals ## Temporary Patch / Need to write print method now print.relogitglm<-function (x, digits = max(3L, getOption("digits") - 3L), ...) { cat("\nCall: ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") if (length(coef(x))) { cat("Coefficients") if (is.character(co <- x$contrasts)) cat(" [contrasts: ", apply(cbind(names(co), co), 1L, paste, collapse = "="), "]") cat(":\n") print.default(format(x$coefficients, digits = digits), print.gap = 2, quote = FALSE) } else cat("No coefficients\n\n") cat("\nDegrees of Freedom:", x$df.null, "Total (i.e. Null); ", x$df.residual, "Residual\n") if (nzchar(mess <- naprint(x$na.action))) cat(" (", mess, ")\n", sep = "") cat("Null Deviance:\t ", format(signif(x$null.deviance, digits)), "\nResidual Deviance:", format(signif(x$deviance, digits)), "\tAIC:", format(signif(x$aic, digits))) cat("\n") invisible(x) } print.relogitglm(x, digits = digits, ...) # Additional slots # Prior co if (x$prior.correct) cat("\nPrior correction performed with tau =", x$tau, "\n") # Weighting? Sure, if it exists, we'll print it. if (x$weighting) cat("\nWeighting performed with tau =", x$tau, "\n") # If there is bias-correction if (x$bias.correct) cat("Rare events bias correction performed\n") # If robust errors are computed... if (!is.null(x$robust)) cat("\nRobust standard errors computed using", x$robust, "\n") # This is not a mutator assignment! class(x) <- "summary.glm" # Return object to be printed invisibly invisible(x) } #' Print Summary of a Rare-event Logistic Model #' #' ... #' @usage #' \method{print}{summary.relogit2}(x, digits = max(3, getOption("digits") - 3), ...) #' @S3method print summary.relogit2 #' @param x the object to print #' @param digits an integer specifying the number of digits of precision #' @param ... ignored parameters #' @return x (invisibly) print.summary.relogit2 <- function(x, digits = max(3, getOption("digits") - 3), ... ) { cat("\nCall:\n", deparse(x$call), "\n\n", sep = "") print(x$lower.estimate) print(x$upper.estimate) } #' Print Values of a Summarized ``sim'' Object #' #' Print values of simulated quantities of interest (stored in a ``summary.sim'' #' object. #' @usage \method{print}{summary.sim}(x, ...) #' @S3method print summary.sim #' @param x a 'summary.sim' object #' @param ... ignored parameters #' @return the value of the `summary.sim' object (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.summary.sim <- function(x, ...) { # Rename 'x' 'summary' summary <- x obj <- summary$zeligcall model <- summary$model x <- summary$x x1 <- summary$x1 stats <- summary$stats num <- summary$num # Error if there are no statistics to display if (is.null(stats)) stop("stats object cannot be NULL") # new-line cat("\n") # Print model name cat("Model: ", model, "\n") # Print number of simulations cat("Number of simulations: ", num, "\n") # new-line cat("\n") # Display information about the X setx object # This should probably be reconsidered in the future if (!is.null(x$matrix)) { cat("Values of X\n") print(as.matrix(x$matrix)) # new-line cat("\n") } else if (is.list(x$s.x)) { # add special hooks here? } # Display information about the X1 setx object # This should probably be reconsidered in the future if (!is.null(x1$matrix)) { cat("Values of X1\n") print(as.matrix(x1$matrix)) # new-line cat("\n") } # Decrementing the size of the list will give us an easy way to print size <- length(stats) # Loop across all qi's for (key in names(stats)) { # Create variable for code clarity val <- stats[[key]] if (!is.qi(val)) next # Display Title cat(key, "\n") # Round value if numeric if (is.numeric(val)) print(round(val*(1000))/1000) # Simply print if anything else else print(val) # Print a new-line between qi's if (size <- size - 1) { cat("\n") } } # Return invisibly invisible(x) } #' Print Multiply Imputed Simulations Summary #' #' Prints summary information about Multiply Imputed Fits #' @usage \method{print}{summarySim.MI}(x, digits=3, ...) #' @S3method print summarySim.MI #' @param x a 'summarySim.MI' object #' @param digits an integer specifying the number of digits of precision to #' print #' @param ... ignored parameters #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.summarySim.MI <- function(x, digits=3, ...) { for (qi.name in names(x)) { if (!is.valid.qi.list(x[[qi.name]])) next summed.qi <- qi.summarize(qi.name, x[[qi.name]]) print(summed.qi) cat("\n") } invisible(x) } #' Row-bind Matrices and Lists #' @param x a list or a matrix #' @param y a list or a matrix #' @return a matrix #' @author Matt Owen \email{mowen@@iq.harvard.edu} .bind <- function (x, y) { # Get names for future columns if (!is.matrix(x)) x <- matrix(x, nrow=1, ncol=length(x), dimnames=list(NULL, names(x))) if (missing(y)) return(x) if (!is.matrix(y)) y <- matrix(y, nrow=1, ncol=length(y), dimnames-list(NULL, names(y))) names <- unique(c(colnames(x), colnames(y))) ncol <- length(names) X <- matrix(NA, nrow=nrow(x), ncol=ncol, dimnames=list(NULL, names)) Y <- matrix(NA, nrow=nrow(y), ncol=ncol, dimnames=list(NULL, names)) X[, colnames(x)] <- x Y[, colnames(y)] <- y rbind(X, Y) } #' Check If Object Is a List of Valid Quantities of Interest #' @param x an object to be tested #' @return TRUE or FALSE #' @author Matt Owen \email{mowen@@iq.harvard.edu} is.valid.qi.list <- function (x) { # if it is not a list or that list has no entries if (!(is.list(x) && length(x))) return(FALSE) # if any are not a matrix for (val in x) { if (is.matrix(val) && !(ncol(val) && ncol(val))) return(FALSE) else if (is.list(val) && !length(val)) return(FALSE) } TRUE } #' Print values of ``zelig'' objects #' #' Print the zelig object as a list #' @usage \method{print}{zelig}(x, ...) #' @S3method print zelig #' @param x a `zelig' object #' @param ... ignored parameters #' @return the `zelig' object (invisibly) #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} print.zelig <- function(x, ...) { class(x) <- "list" print(x) } Zelig/R/sim.default.R0000644000176000001440000002055512216401440014114 0ustar ripleyusers#' Method for Simulating Quantities of Interest wrom 'zelig' Objects #' #' Simulate quantities of interest #' @usage \method{sim}{default}(obj, #' x=NULL, x1=NULL, y=NULL, #' num=1000, bootstrap = FALSE, #' bootfn=NULL, #' cond.data = NULL, #' ...) #' @S3method sim default #' @param obj a 'zelig' object #' @param x a 'setx' object #' @param x1 a secondary 'setx' object used to perform particular computations #' of quantities of interest #' @param y a parameter reserved for the computation of particular quantities of #' interest (average treatment effects). Few models currently support this #' parameter #' @param num an integer specifying the number of simulations to compute #' @param bootstrap ignored #' @param bootfn ignored #' @param cond.data ignored #' @param ... parameters to be passed to the boot function, if one is supplied #' @return a 'sim' object storing the replicated quantities of interest #' @author Matt Owen \email{mowen@@iq.harvard.edu} sim.default <- function( obj, x = NULL, x1 = NULL, y = NULL, num = 1000, bootstrap = FALSE, bootfn = NULL, cond.data = NULL, ... ) { # Create environment of local variables model.env <- new.env() # Add local variables assign(".object", obj$result, model.env) assign(".fitted", obj$result, model.env) assign(".model", "model-name", model.env) # Get S3 methods paramfunction <- getS3method("param", obj$name, FALSE) qifunction <- getS3method("qi", obj$name, FALSE) bootfunction <- getS3method("bootstrap", obj$name, TRUE) parent.env(model.env) <- environment(paramfunction) environment(paramfunction) <- model.env environment(qifunction) <- model.env # Begin function if (length(attr(x, "pooled")) > 0 && attr(x, "pooled")) { xes <- list() titles <- NULL # for (key in names(x)) { for (k in 1:length(x)) { key <- names(x)[[k]] xes[[key]] <- sim(obj, x[[k]], x1[[k]], y, num, bootstrap, bootfn, cond.data, ...) attr(xes[[key]], "pooled") <- FALSE titles <- append(titles, xes[[key]]$titles) } attr(xes, "pooled") <- TRUE attr(xes, "pooled.setx") <- x attr(xes, "titles") <- unique(titles) class(xes) <- c("pooled.sim") return(xes) } # Stop on unimplemented features if (!is.null(cond.data)) warning("conditions are not yet supported") # Simulate Parameters # param <- param(obj, num=num) param <- paramfunction(obj, num=num) # Cast list into a "parameters" object param <- as.parameters(param, num) # Define the pre-sim hook name post.hook <- obj$zc$.post # apply the hook if it exists if (!is.null(post.hook)) { zelig2 <- get(paste("zelig2", obj$name, sep="")) envir <- environment(zelig2) # Produce a warning if the post-hook defined cannot be found if (!exists(post.hook, mode="function", envir=envir)) warning("the hook '", post.hook, "' cannot be found") # Otherwise, business as usual. Extract the hook and apply it to the zelig # object. Note that a post-hook always has the arguments: # obj, x, x1, bootstrap, bootfn, param else { # Retrieve the hook, since it exists hook <- get(post.hook, envir=envir) # Assign the param object. In the case of bootstrapping, the param object # might not have any meaning. param <- if (bootstrap) param # Otherwise apply the hook and return it as the parameters else hook(obj, x, x1, bootstrap, bootfn, param=param) } } # Get default boot-strapping function if boot is enabled and no boot-function # is specified if (bootstrap && missing(bootfn)) bootfn <- bootfn.default # Boot-strapping!! if (!missing(bootfn) && !is.null(bootfn)) { # Get the appropriate d <- obj$data d <- d[complete.cases(d), ] # Add several private variables to bootfn: # .fitted : a fitted model object # .data : the data-set used to fit the original model # .call : the call used to fit the original model # .env : the environment in which the .call variable should/can be # evaluated boot.env <- obj$method.env bootfn <- attach.env(bootfn, obj$method.env) # Bootstrapfn bootstrapfn <- getS3method("bootstrap", obj$name, TRUE) environment(bootstrapfn) <- model.env # If is.null then we just get the default bootstrap fn, which is merely to # simulate the systematic paramaters ## CRAN is opposed to ::: within same package, ## but I'm opposed to S4 environment artifacts ## if (is.null(bootstrapfn)) ## bootstrapfn <- Zelig:::bootstrap.default ## So this obviously makes my code better: if (is.null(bootstrapfn)){ localbootstrap.default <- function (obj, ...) list( alpha = NULL, beta = coef(obj) ) bootstrapfn <- localbootstrap.default } # Attach the appropriate environment to the function bootstrapfn <- attach.env(bootstrapfn, model.env) # Get a sample, so we know how to re-size the result. # Note: This "example" object will be used at the end of this if-clause to # build an object similar in structure to that of "bootstrapfn(obj)" example <- bootstrapfn(obj) example <- as.bootvector(example) # Bootstrap using a function with parameters: data, i, object # Where data is a data.frame, i is an vector of integers used to sample the # data.frame, and object is a fitted model object. res <- boot(d, bootfn, num, object = obj$result, bootstrapfn = bootstrapfn, num = num ) # Copy the param object that was made earlier via ``param'' method res.param <- param # Reverse-construct a bootlist object from this bl <- as.bootlist(res$t, example$lengths, example$names) # Replace slots corresponding to "alpha" and "beta" on the "param" object param$coefficients <- bl$beta param$alpha <- bl$alpha } # Compute quantities of interest res.qi <- qifunction(obj, x=x, x1=x1, y=y, param=param, num=num) # Cast as a "qi" object if it is not one res.qi <- as.qi(res.qi) # Assign class class(res.qi) <- c(obj$name, class(res.qi)) # This is kludge (for now) # This can be removed as of 4-27-2011 if (inherits(obj, "MI")) class(res.qi) <- c("MI", class(res.qi)) # build object s <- list( model = obj$name, x = x, x1 = x1, stats = summarize(res.qi), qi = res.qi, titles = names(res.qi), bootfn = bootfn, cond.data= cond.data, zelig = obj, call = match.call(), zcall = obj$call, result = obj$result, num = num, special.parameters = list(...), package.name = obj$package.name ) # cast class sim.class <- if (inherits(obj, "MI")) sim.class <- "MI.sim" attr(s, "titles") <- unique(names(res.qi)) class(s) <- c(sim.class, paste("sim", obj$name, sep="."), obj$name, "sim" ) # return s } create.pooled.sim <- function( obj, x = NULL, x1 = NULL, y = NULL, num = 1000, bootstrap = FALSE, bootfn = NULL, cond.data = NULL, ... ) { xes <- list() titles <- NULL for (key in names(x)) { xes[[key]] <- sim(obj, x[[key]], x1[[key]], y, num, bootstrap, bootfn, cond.data, ...) attr(xes[[key]], "pooled") <- FALSE titles <- append(titles, xes[[key]]$titles) } attr(xes, "pooled") <- TRUE attr(xes, "pooled.setx") <- x attr(xes, "titles") <- unique(titles) class(xes) <- c("pooled.sim") return(xes) } Zelig/R/bootstrap.gamma.R0000644000176000001440000000125012061700507014772 0ustar ripleyusers#' Bootstrap Parameters for Zelig ``gamma'' GLM #' #' Returns bootstrapped parameter estimates for a ``gamma'' GLM. #' @usage \method{bootstrap}{gamma}(obj, ...) #' @S3method bootstrap gamma #' @param obj a ``zelig'' object that will be used to produce boot-strapped #' parameters #' @param ... extra parameters to be passed to the ``boot'' method. These are #' typically ignored, but is included for further expansion. #' @return a list containing information concerning link, link-inverses, etc. #' @author Matt Owen \email{mowen@@iq.harvard.edu} bootstrap.gamma <- function (obj, ...) { list( alpha = gamma.shape(.fitted)$alpha, beta = coef(.fitted) ) } Zelig/R/cmvglm.R0000644000176000001440000000431012061700507013161 0ustar ripleyusers#' cmvglm #' @param formula a formula #' @param model the names of the Zelig model #' @param ndim the number of dimensions in the statistical model #' @param data a data-frame #' @param fact ??? #' @author Kosuke Imai and Olivia Lau #' @export cmvglm <- function(formula, model, ndim,data=NULL, fact=NULL){ toBuildFormula<-function(Xnames,sepp="+"){ lng<-length(Xnames) rhs<-NULL if (lng!=0){ if(lng==1){ rhs=Xnames }else{ for (j in 1:(lng-1)){ rhs<-paste(rhs,as.name(Xnames[[j]])) rhs<-paste(rhs,sepp) } rhs<-paste(rhs,Xnames[[lng]]) } } return (rhs) } tt<-terms(formula) attr(tt,"systEqns")<-names(formula) p<-make.parameters(tt,shape="matrix") vars<-rownames(p) cm<-vector("list", length(vars)) names(cm)<-vars for(i in 1:length(cm)) cm[[i]]<-diag(1, ndim) constrain<-attr(tt,"constraints") if(!is.logical(constrain)){ tmp <- sort(colnames(constrain)) for (i in 1:length(tmp)) { ci<-constrain[,i] if (is.null(na.omit(ci)) || length(unique(na.omit(ci)))!=1) stop("invalid input for constrain") minj <- match(FALSE, is.na(ci)) whatvar <- pmatch(unique(na.omit(ci)), names(cm)) for (j in 1:3) if (!is.na(ci[j])) { cm[[whatvar]][j,j]<-0 cm[[whatvar]][j,minj]<-1 } } } for(i in rownames(p)){ for(j in 1:ncol(p)){ if(is.na(p[i,j])) cm[[i]][j,j]<-0 } } # if(!is.null(constant)) # for(i in 1:length(constant)) # for(j in 1:length(cm)) # if(names(cm)[j]!="(Intercept)") # cm[[j]][constant[i],]<-matrix(0, ncol=ncol(cm[[j]])) for(i in 1:length(cm)) cm[[i]]<-as.matrix(cm[[i]][,apply(cm[[i]], 2, sum)!=0]) rhs<-toBuildFormula(attr(tt,"indVars")) if(!(is.null(rhs))) rhs<-(paste("~",rhs)) else rhs<-"~1" Ynames<-unlist(attr(tt,"depVars")) if(!is.null(fact)) lhs<-fact else{ if(length(Ynames)>1){ lhs<-toBuildFormula(Ynames,",") if (!(is.null(lhs))){ lhs<-paste("cbind(",lhs) lhs<-paste(lhs,")") } }else{ lhs=Ynames } } formula<-as.formula(paste(lhs,rhs)) list("formula"=formula, "constraints"=cm) } Zelig/R/probit.gee.R0000644000176000001440000000376412110047000013731 0ustar ripleyusers#' Interface between the Zelig Model probit.gee and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param id a character-string specifying the column of the data-set to use #' for clustering #' @param robust a logical specifying whether to robustly or naively compute #' the covariance matrix. This parameter is ignore in the \code{zelig2} #' method, and instead used in the \code{robust.hook} function, which #' executes after the call to the \code{gee} function #' @param ... ignored parameters #' @param R a square-matrix specifying the correlation #' @param corstr a character-string specifying the correlation structure #' @param data a data.frame #' @return a list specifying the call to the external model #' @export zelig2probit.gee <- function (formula, id, robust, ..., R = NULL, corstr = "independence", data) { loadDependencies("gee") if (corstr == "fixed" && is.null(R)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } z( .function = gee, .hook = robust.gee.hook, formula = formula, id = id, corstr = corstr, family = binomial(link="probit"), data = data, R = R, ... ) } #' @S3method param probit.gee param.probit.gee <- function(obj, num=1000, ...) { # Extract means to compute maximum likelihood mu <- coef(obj) # Extract covariance matrix to compute maximum likelihood Sigma <- vcov(obj) # list( coef = mvrnorm(num, mu, Sigma), fam = binomial(link="probit") ) } #' @S3method qi probit.gee qi.probit.gee <- qi.logit.gee #' @S3method describe probit.gee describe.probit.gee <- function(...) { list( authors = "Patrick Lam", text = "General Estimating Equation for Poisson Regression", year = 2011 ) } Zelig/R/repl.R0000644000176000001440000000472212061700507012645 0ustar ripleyusers#' Generic Method for Replicating Data #' @param object a 'zelig' object #' @param ... parameters #' @return a replicated object #' @export #' @author Kosuke Imai and Olivia Lau \email{mowen@@iq.harvard.edu} repl <- function(object, ...) UseMethod("repl") #' Default Method for Replicating Statistics #' #' Replicate a simulation #' @usage \method{repl}{default}(object, data=NULL, ...) #' @S3method repl default #' @param object an object to replicate #' @param data a data.frame #' @param ... ignored parameters #' @return a replicated object #' @author Kosuke Imai and Olivia Lau \email{mowen@@iq.harvard.edu} repl.default <- function(object, data=NULL, ...) { if (!is.null(data)) obectj$call$data <- data eval(object$call$data, sys.parent()) } #' Method for Replicating Simulated Quantities of Interest #' #' Replicate simulated quantities of interest #' @usage \method{repl}{sim}(object, x=NULL, x1=NULL, y=NULL, #' num=1000, #' prev = NULL, bootstrap = FALSE, #' boot.fn=NULL, #' cond.data = NULL, ...) #' @S3method repl sim #' @param object a 'zelig' object #' @param x a 'setx' object #' @param x1 a secondary 'setx' object used to perform particular computations #' of quantities of interest #' @param y a parameter reserved for the computation of particular quantities of #' interest (average treatment effects). Few models currently support this #' parameter #' @param num an integer specifying the number of simulations to compute #' @param prev ignored #' @param bootstrap ignored #' @param boot.fn ignored #' @param cond.data ignored #' @param ... special parameters which are reserved for future versions of Zelig #' @return a 'sim' object storing the replicated quantities of interest #' @author Matt Owen \email{mowen@@iq.harvard.edu} repl.sim <- function(object, x=NULL, x1=NULL, y=NULL, num=1000, prev = NULL, bootstrap = FALSE, boot.fn=NULL, cond.data = NULL, ...) { # would rather use a factory function new.call <- object$call # this should always give the same value... rep.zelig <- eval(object$zcall, sys.parent()) # new.call$z <- rep.zelig # x new.call$x <- if (is.null(x)) object$x else x # x1 new.call$x1 <- if (is.null(x1)) object$x1 else x1 # how is this EVER true? if (!is.null(object$seed)) set.seed(object$seed) eval(new.call, sys.parent()) } Zelig/R/robust.glm.hook.R0000644000176000001440000000206012061700507014727 0ustar ripleyusers#' Hook for ``glm'' Models in Zelig #' #' Adds support for robust error-estimates in the Zelig ``glm'' models. #' @param obj a zelig object #' @param zcall the original call to the zelig model #' @param call the call that will be evaluated for the #' @param robust a logical specifying whether or not to use robust error #' estimates #' @param ... ignored parameters #' @return the fitted model object #' @export robust.glm.hook <- function (obj, zcall, call, robust = FALSE, ...) { # If "robust" is a list, if (is.list(robust)) { # if none of the entries of robust belong to the vector below if (!any(robust$method %in% c("vcovHAC", "kernHAC", "weave"))) stop("robust contains elements that are not supported.") # Acquire the value of the robust parameter obj$robust <- robust } else if (!is.logical(robust)) stop("Invalid input for robust: choose either TRUE or a list of options.") # Set as a robust generalized linear model model (in addition to other types) class(obj) <- c("glm.robust", class(obj)) # Return... obj } Zelig/R/terms.R0000644000176000001440000002344212061700507013035 0ustar ripleyusers### ## terms ## -accept single and multiple equations: ## -in case of single equations, the equation is named "mu". is this right? ## -if mu=y~x:z then the attr(tt,"variable") gives list(y,x:z). Should it be list(y,x,z) ?? ## - #' Extract Terms from a \code{multiple} Object #' #' Extracts terms from Zelig-3.5-style formulae. This function is scheduled for #' removal. #' @usage \method{terms}{multiple}(x, data=NULL,...) #' @param x a Zelig v3.5 formula #' @param data a \code{data.frame} #' @param ... ignored parameters #' @author Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi #' @S3method terms multiple terms.multiple<-function(x, data=NULL,...){ object <- x termsexist<-attr(object,"terms") if(!(is.null(termsexist))) return (termsexist) nreq<-nrConstr<-nrEquationsNew<-0 constr<-XconstrEqn<-variables<-termlabels<-depVars<-objectNew<-intercAttr<-depFactors<-list() depFactorVar<-depLevels<-namesConstr<-c() if(!(any(class(object)=="list"))){ object<-list(object) names(object)<-"mu" } namesOfEquations<- names(object) nrEquations <-length(object) "%w/o%" <- function(x,y) x[!x %in% y] for (i in 1:nrEquations){ TT<-terms.formula(object[[i]], specials=c("id","tag")) attrTTvars<-attr(TT,"variables") attrTTlabels<-attr(TT,"term.labels") eqni<-object[[i]] namei<-namesOfEquations[[i]] tagattr<-attr(TT,"specials")$tag hastag<-!(is.null(tagattr)) if (hastag){ ## has tag so make a new list of variables and term.labels newVars<-list() newLabels<-c() indxV<-indxL<-1 constrTmp<-c() for(j in 1:length(tagattr)){ taglabels<-c() if(length(eqni)==3) lind<-tagattr[[j]]-1 else lind<-tagattr[[j]] vind<-tagattr[[j]]+1 ## add all vars/terms prior to tag into new list of ## newVars and newLabels for(v in indxV:(vind)) newVars<-c(newVars,attrTTvars[[v]]) newVars[[length(newVars)]]<-NULL indxV<-vind+1 for(l in c(indxL:lind)) newLabels<-c(newLabels,attrTTlabels[[l]]) newLabels<-newLabels[-(length(newLabels))] indxL<-lind+1 ## deparse and fix the tag tagAsList <-.fixTag(.deparseTag(attrTTvars[[vind]])) for (tindx in 1:length(tagAsList)){ t<-tagAsList[[tindx]] if(((t$var %in% namesOfEquations)==FALSE) && t$var != "none" && t$var != "1"){ newVars<-c(newVars,parse(text=t$var)[[1]]) newLabels<-c(newLabels,t$var) } if(((t$id %in% namesOfEquations)==FALSE) && t$id !="none" && t$id !="1"){ ##print(t$id) newVars<-c(newVars,parse(text=t$id)[[1]]) newLabels<-c(newLabels,t$id) } ## constraints ? if(t$var !="none" && t$label !="none" && t$id =="none"){ nrConstr<-nrConstr+1 namesConstr<-c(namesConstr,t$label) constr[[nrConstr]]<-c(i,t$label,t$var) constrTmp<-c(constrTmp,t$var) ##???? what is constrTMP? } } } ## if there is any var/term remaining after tags ## add them to newVars and newLabels if(length(attrTTvars)>vind){ for(v in (vind+1):length(attrTTvars)) newVars<-c(newVars,attrTTvars[[v]]) } if(length(attrTTlabels)>lind){ for(l in (lind+1):length(attrTTlabels)) newLabels<-c(newLabels,attrTTlabels[[l]]) } XconstrEqn[[i]]<-constrTmp ## make newVars and newLabels unique newVars<-unique(newVars) newLabels <- unique(newLabels) } else{ ## there is no tag => newVars and newLabels remain unchanged newVars<-attrTTvars newLabels<-attrTTlabels } nrEquationsNew<-nrEquationsNew+1 objectNew[[namei]]<-eqni if (length(eqni)==3){ nreq=nreq+1 ## number of required equations lhs<-eqni[[2]] if (length(lhs)>1 && lhs[[1]]=="id"){ depVars[[namei]]<-lhs[[3]] depFactorVar<-c(depFactors,deparse(lhs[[2]])) depLevels<-c(depLevels,lhs[[3]]) }else depVars[[namei]]<-deparse(eqni[[2]]) } attr(TT,"variables")<-as.call(newVars) attr(TT,"term.labels")<-newLabels variables[[namei]]<-attr(TT,"variables") termlabels[[namei]]<-attr(TT,"term.labels") intercAttr[[namei]]<-attr(TT,"intercept") } ## end of for each equation namesOfEquations<-names(objectNew) myattr<-list() result<-objectNew constraints<-subs<-FALSE ## construct constraints namesConstr<-unique(namesConstr) if(length(constr)>0){ constraints<-matrix(NA,nrow=nrEquationsNew,ncol=length(namesConstr),dimnames=list(namesOfEquations,namesConstr)) for(i in 1:length(constr)){ constri<-constr[[i]] eqind<-constri[[1]] eq<-namesOfEquations[as.numeric(eqind)] lab<-constri[[2]] constraints[eq,lab]<-constri[[3]] } } indVars<-unique(unlist(termlabels)) if(length(depFactorVar) !=0) depFactors<-list("depFactorVar"=unique(unlist(depFactorVar)),"depLevels"=depLevels) else depFactors<-FALSE whiche<-which(lapply(termlabels,length)!=0) myattr$systEqns<-names(whiche) myattr$ancilEqns<-"%w/o%"(namesOfEquations,myattr$systEqns) myattr$variables<-variables myattr$term.labels<-termlabels myattr$indVars<-indVars myattr$depVars<-depVars myattr$depFactors<-depFactors myattr$constraints<-constraints myattr$subs<-subs myattr$response<-1 myattr$intercept<-intercAttr attributes(result)<-myattr names(result)<-namesOfEquations class(result)<-c("terms","multiple","list") return(result) } ### ## Fix the deparsed tag ## .fixTag <- function(l){ if(l$var == "1" && l$label!="none"){ ## tag(1,z1 | state) == tag (z1|state) l$var <- l$label l$label <- "none" } if(l$label =="none"){ ## tag(1+z1|state) vars<-.trim(unlist(strsplit(l$var,"+", fixed=TRUE))) }else{ ## tag(z1,w1+w2|state) vars<-.trim(unlist(strsplit(l$label,"+", fixed=TRUE))) } if(length(vars) == 1){ ## nothing to expand return (list(l)) }else{ alltgs<-list() for(i in 1:length(vars)){ if(l$label == "none") alltgs[[i]] <- list(label="none",var=vars[[i]],id=l$id) else alltgs[[i]] <- list(label="none",var=paste(l$var,":",vars[[i]],sep=""),id=l$id) } } return (alltgs) } #' Model Terms for 'vglm' Models #' @usage \method{terms}{vglm}(x, ...) #' @S3method terms vglm #' @param x a fitted model object from the VGAM library #' @param ... ignored parameters #' @return the models terms of this fitted model object #' @author Ferdinand Alimadhi, Kosuke Imai and Olivia Lau terms.vglm <- function(x, ...) x@terms$terms #' Model Terms for a Zelig Object #' #' This method simply extracts the model terms for the fitted model passed to #' the \code{zelig} function. #' @S3method terms zelig #' @usage \method{terms}{zelig}(x, ...) #' @param x a \code{zelig} object #' @param ... forwarded parameters #' @return terms of the original fitted model terms.zelig <- function (x, ...) { terms(x$result, ...) } Zelig/R/getResponseTerms.formula.R0000644000176000001440000001023012061700507016647 0ustar ripleyusers#' Get Response Terms from a Standard Formula #' #' This method gets the response terms from a standard formula #' @usage #' \method{getResponseTerms}{formula}(x, ..., single.only=FALSE, duplicates=TRUE) #' @param x a formula #' @param ... ignored parameters #' @param single.only a logical specifying whether 'cbind' or 'list' keywords #' are allowed #' @param duplicates a logical specifying whether the returned character-vector #' will only return duplicates. #' @return a character-vector specifying the response terms of the formula #' @S3method getResponseTerms formula #' @author Matt Owen getResponseTerms.formula <- function (x, ..., single.only=FALSE, duplicates=TRUE) { # Handle handle.formula.err <- function (e) { message("\n\n") message("The formula ", x, " seems to have no dependent variables") stop("The formula for the ") } rhs <- tryCatch(x[[3]], error = handle.formula.err) lhs <- tryCatch(x[[2]], error = handle.formula.err) # Reponse terms are always specified in the lefthand-side of the equation if (is.name(lhs)) { # If the lhs is a name, this implies it's a single variable with no function # applied to it. Thus, it's a term. return(tryCatch( callToString(lhs), error = function (e) as.character(lhs) )) } # Otherwise, it is either a function being applied or the keywords "cbind" or # "list" op <- callToString(lhs[[1]]) if (op %in% c("cbind", "list")) { if (single.only) { # If only single outcome response terms are allowed, then 'cbind' and # 'list' cannot be used. warning("'cbind' and 'list' may not be used ", "in this formula specification.") return(vector("character", 0)) } # If it is one of the keywords, we extract these terms individually lis <- as.list(lhs[-1]) lis <- unlist(Map(callToString, lis)) if (!duplicates) # If duplicates flag is FALSE, remove all duplicate entries lis <- unique(lis) # Remove all emptry strings and return Filter(nchar, lis) } else { # Otherwise, we can treat them as one single term. That is the formula: # x + y ~ 1 # will have a single response term: # x + y callToString(lhs) } } #' Get Response Terms from a ``Formula'' Object #' #' This method gets the response terms from a ``Formula'' Object #' @rdname getResponseTerms.Formula-not-formula #' @aliases getResponse.Formula #' @usage #' \method{getResponseTerms}{Formula}(x, ..., single.only=FALSE, duplicates=TRUE) #' @param x a formula #' @param ... ignored parameters #' @param single.only a logical specifying whether 'cbind' or 'list' keywords #' are allowed #' @param duplicates a logical specifying whether the returned character-vector #' will only return duplicates. #' @return a character-vector specifying the response terms of the formula #' @S3method getResponseTerms Formula #' @author Matt Owen getResponseTerms.Formula <- function (x, ..., single.only=FALSE, duplicates=TRUE) { # Create and empty list list.formula <- list() # This loop goes through all the list response and predictor terms and # creates a "Zelig-style" list based on it. This is so we can extract response # and predictor terms with "getResponstTerms" and "getPredictorTerms" in a # manageable way! for (resp in attr(x, "lhs")) { # Iterate through all response variables for (pred in attr(x, "rhs")) { # Iterate through all predictor variables # Append response variable and predictor terms # "ccc" is probably going to be convention for a call object in Zelig # models since "CALL", "call", "Call" all seem too similar to "call". # And we need to break ccc <- call("~", resp, pred) # Cast from a "call" object to a "formula" object ccc <- as.formula(ccc) # Append to list list.formula <- append(list.formula, ccc) } } # Important to send 'single.only'/'duplicates' into this function resp <- getResponseTerms(list.formula, ..., single.only, duplicates) # Apply unique only if 'duplicates' is FALSE # This ensures the list has the expected properties if (duplicates) resp else unique(resp) } Zelig/R/poisson.bayes.R0000644000176000001440000000450112110047000014455 0ustar ripleyusers#' Interface between the Zelig Model poisson.bayes and the Pre-existing Model-fitting Method #' @param formula a formula #' @param ... additonal parameters #' @param data a data.frame #' @return a list specifying '.function' #' @export zelig2poisson.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data ) { loadDependencies("MCMCpack", "coda") if (missing(verbose)) verbose <- round((mcmc + burnin)/10) list( .function = "MCMCpoisson", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param poisson.bayes param.poisson.bayes <- function(obj, num=1000, ...) { list( coef = coef(obj), fam = poisson() ) } #' @S3method qi normal.bayes qi.poisson.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { res1 <- poisson.ev(x, param) res2 <- poisson.ev(x1, param) list( "Expected Value: E(Y|X)" = res1$ev, "Predicted Value: Y|X" = res1$pv, "Expected Value (for X1): E(Y|X1)" = res2$ev, "Predicted Value (for X1): Y|X1" = res2$pv, "First Differences: E(Y|X1) - E(Y|X)" = res2$ev - res1$ev ) } poisson.ev <- function (x, param) { # If either of the parameters are invalid, # Then return NA for both qi's if (is.null(x) || is.na(x) || is.null(param)) return(list(ev=NA, pv=NA)) # Extract inverse-link and simulated parameters (respectively) inv <- linkinv(param) eta <- coef(param) %*% t(x) # Give matrix identical rows/columns to the simulated parameters ev <- pv <- matrix(NA, nrow(eta), ncol(eta)) dimnames(ev) <- dimnames(pv) <- dimnames(eta) # Compute Expected Values ev <- inv(eta) # Compute Predicted Values for (i in 1:ncol(ev)) pv[, i] <- rpois(length(ev[, i]), ev[, i]) list(ev=ev, pv=pv) } #' @S3method describe poisson.bayes describe.poisson.bayes <- function(...) { list( description = "Bayesian Poisson Regression", authors = c("Ben Goodrich", "Ying Lu"), year = 2013 ) } Zelig/R/multi.dataset.R0000644000176000001440000000772212110047000014447 0ustar ripleyusers# Make a ``multi.dataset'' Object # @param datasets a list containing data.frames # @param labels a character vector labeling indices of the dataset make.multi.dataset <- function (datasets, labels=NULL) { md <- datasets if (!missing(labels)) names(md) <- labels # Set super important attributes #attr(md, "something") <- "red" class(md) <- "multi.dataset" # Return object md } # Multiple Dataset Object multi.dataset <- function (obj, ...) { UseMethod("multi.dataset") } # Create a Multiple Dataset Object from a data.frame # @param obj a data.frame to conver # @return a ``multi.dataset'' object multi.dataset.data.frame <- function (obj, ...) { # Place inside a list and label according to the name from the function call label <- as.character(as.expression(substitute(obj))) make.multi.dataset(list(obj), label) } # Create a Multiple Dataset Object from a data.frame # @param obj a list of data.frame's # @return a ``multi.dataset'' object multi.dataset.list <- function (obj, ...) { # Iterate backwards through list, so that we can remove elements for (k in length(obj):1) { if (!is.data.frame(obj[[k]])) { warning('"obj" contains an element that is not a data.frame... removing.') obj[[k]] <- NULL } } LABELS <- names(obj) # If there are no labels, or they are uneven if (is.null(LABELS) || length(LABELS) != length(obj)) LABELS <- paste("data-set-", 1:length(obj), sep = "") # Otherwise, we have a nice matching of labels, but we might still have some # that are empty else { for (k in 1:length(LABELS)) { lab <- LABELS[k] if (is.na(lab) || is.null(lab) || (is.character(lab) && nchar(lab) == 0)) LABELS[k] <- paste("data-set-", k, sep = "") } } # Return object make.multi.dataset(obj, LABELS) } # Create a Multiple Dataset Object from a data.frame # @param obj a list of data.frame's # @return a ``multi.dataset'' object multi.dataset.amelia <- function (obj, ...) { data.frames <- obj$imputations class(data.frames) <- NULL make.multi.dataset(data.frames, names(data.frames)) } # Divide a Data Frame or Matrix Into Subsets # @param obj a data.frame or matrix to be split into subsets, divided by the # categorical variable # @param by a character-string, specifying the column to subset # @return a list, containing the subsetted data sets. The names of the list # correspond to the value of the subsetted list divide <- function (obj, by) { # Ensure that "obj" is valid (a data.frame or matrix) if (!is.data.frame(obj) && !is.matrix(obj)) { warning('"obj" is not a data.frame or matrix') return(list(obj)) } # Ensure that "by" is valid (a character-string) if (!is.character(by) && length(by) == 1) { warning('"by" is not a character-string') return(list(obj)) } # Ensure that "by" is a column in "obj" if (! by %in% colnames(obj)) { warning('"by" is not a valid column of "obj"') return(list(obj)) } # Get the set of possible values column.levels <-if (is.factor(obj[, by])) levels(obj[, by]) else unique(obj[, by]) # A list used to store each individual data.frame res <- list() # Iterate through all possible values and store each subset in a separate # entry in the list for (val in column.levels) { # Determine which rows match this value hits <- obj[, by] == val # Store data set temporarily in a local value data.set <- obj[hits, ] # Assign levels to the column. This adds levels to string data. levels(data.set[, by]) <- column.levels # Store data set in list res[[val]] <- data.set } # Return list res } # Print a ``multi.dataset'' Object # @param x a multi.dataset object, essentially a list of data.frames # @param ... parameters to pass to the print.data.frame object # @return x (invisibly) print.multi.dataset <- function (x, ...) { for (key in names(x)) { cat("label =", key, "\n") print(x[[key]], ...) cat("\n") } # Return printed object (invisibly) invisible(x) } Zelig/R/multipleUtil.R0000644000176000001440000000327012061700507014371 0ustar ripleyusers#' Build Formula ??? #' #' This function builds a formula #' @param Xnames a character-vector #' @param sepp a seperator (???) #' @return a character-string #' @author ??? toBuildFormula<-function(Xnames,sepp="+"){ lng<-length(Xnames) rhs<-NULL if (lng!=0){ if(lng==1){ rhs=Xnames }else{ for (j in 1:(lng-1)){ rhs<-paste(rhs,as.name(Xnames[[j]])) rhs<-paste(rhs,sepp) } rhs<-paste(rhs,Xnames[[lng]]) } } return (rhs) } #' Multilevel #' #' This function currently has no documentation, but is essential in Zelig 3.5's #' implementation of formulae. #' @param tt a terms object #' @param data a \code{data.frame} #' @param mode ??? #' @param eqn an integer specifying the number of equations in a model #' @param ... ignored parameters #' @return a list with the "terms" attribute specified #' @author Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi multilevel<-function(tt,data,mode,eqn,...){ if(!(mode %in% c(1,2))) stop("Wrong mode argument") if(is.null(eqn)) stop("Please provide an equations") res<-list() eqns<-attr(tt,"systEqns") subs<-attr(tt,"subs") depVars<-attr(tt,"depVars") nrEquations<-length(eqns) termlabels<-attr(tt,"term.labels") #for(i in 1:nrEquations){ rhs<-toBuildFormula(termlabels[[eqn]],"+") if(!is.null(rhs)) rhs<-paste("~",rhs) else rhs<-"~1" Ynamei<-depVars[[eqn]] if(!(Ynamei %in% colnames(subs))) lhs<-Ynamei else lhs<-NULL f<-as.formula(paste(lhs,rhs)) if(mode==1) res<-model.matrix.default(f,data) # res[[eqns[[i]]]]<-f else res<-model.frame.default(f,data) # res[[eqns[[i]]]]<-f #} attr(res,"terms")<-tt return(res) } Zelig/R/sim.R0000644000176000001440000001214412061700507012470 0ustar ripleyusers#' Generic Method for Computing and Organizing Simulated Quantities of Interest #' Simulate quantities of interest from the estimated model #' output from \code{zelig()} given specified values of explanatory #' variables established in \code{setx()}. For classical \emph{maximum #' likelihood} models, \code{sim()} uses asymptotic normal #' approximation to the log-likelihood. For \emph{Bayesian models}, #' Zelig simulates quantities of interest from the posterior density, #' whenever possible. For \emph{robust Bayesian models}, simulations #' are drawn from the identified class of Bayesian posteriors. #' Alternatively, you may generate quantities of interest using #' bootstrapped parameters. #' @param obj the output object from zelig #' @param x values of explanatory variables used for simulation, #' generated by setx #' @param x1 optional values of explanatory variables (generated by a #' second call of setx) #' particular computations of quantities of interest #' @param y a parameter reserved for the computation of particular #' quantities of interest (average treatment effects). Few #' models currently support this parameter #' @param num an integer specifying the number of simulations to compute #' @param bootstrap currently unsupported #' @param bootfn currently unsupported #' @param cond.data currently unsupported #' @param ... arguments reserved future versions of Zelig #' @return The output stored in \code{s.out} varies by model. Use the #' \code{names} command to view the output stored in \code{s.out}. #' Common elements include: #' \item{x}{the \code{\link{setx}} values for the explanatory variables, #' used to calculate the quantities of interest (expected values, #' predicted values, etc.). } #' \item{x1}{the optional \code{\link{setx}} object used to simulate #' first differences, and other model-specific quantities of #' interest, such as risk-ratios.} #' \item{call}{the options selected for \code{\link{sim}}, used to #' replicate quantities of interest. } #' \item{zelig.call}{the original command and options for #' \code{\link{zelig}}, used to replicate analyses. } #' \item{num}{the number of simulations requested. } #' \item{par}{the parameters (coefficients, and additional #' model-specific parameters). You may wish to use the same set of #' simulated parameters to calculate quantities of interest rather #' than simulating another set.} #' \item{qi\$ev}{simulations of the expected values given the #' model and \code{x}. } #' \item{qi\$pr}{simulations of the predicted values given by the #' fitted values. } #' \item{qi\$fd}{simulations of the first differences (or risk #' difference for binary models) for the given \code{x} and \code{x1}. #' The difference is calculated by subtracting the expected values #' given \code{x} from the expected values given \code{x1}. (If do not #' specify \code{x1}, you will not get first differences or risk #' ratios.) } #' \item{qi\$rr}{simulations of the risk ratios for binary and #' multinomial models. See specific models for details.} #' \item{qi\$ate.ev}{simulations of the average expected #' treatment effect for the treatment group, using conditional #' prediction. Let \eqn{t_i} be a binary explanatory variable defining #' the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the #' average expected treatment effect for the treatment group is #' \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - #' E[Y_i(t_i=0)] \mid t_i=1 \,],} #' where \eqn{Y_i(t_i=1)} is the value of the dependent variable for #' observation \eqn{i} in the treatment group. Variation in the #' simulations are due to uncertainty in simulating \eqn{E[Y_i(t_i=0)]}, #' the counterfactual expected value of \eqn{Y_i} for observations in the #' treatment group, under the assumption that everything stays the #' same except that the treatment indicator is switched to \eqn{t_i=0}. } #' \item{qi\$ate.pr}{simulations of the average predicted #' treatment effect for the treatment group, using conditional #' prediction. Let \eqn{t_i} be a binary explanatory variable defining #' the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the #' average predicted treatment effect for the treatment group is #' \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - #' \widehat{Y_i(t_i=0)} \mid t_i=1 \,],} #' where \eqn{Y_i(t_i=1)} is the value of the dependent variable for #' observation \eqn{i} in the treatment group. Variation in the #' simulations are due to uncertainty in simulating #' \eqn{\widehat{Y_i(t_i=0)}}, the counterfactual predicted value of #' \eqn{Y_i} for observations in the treatment group, under the #' assumption that everything stays the same except that the #' treatment indicator is switched to \eqn{t_i=0}.} #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Olivia Lau and Kosuke Imai sim <- function( obj, x=NULL, x1=NULL, y=NULL, num=1000, bootstrap=F, bootfn=NULL, cond.data=NULL, ... ) { UseMethod("sim") } Zelig/R/probit.R0000644000176000001440000000521312214411116013171 0ustar ripleyusers#' Interface between probit model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2probit <- function(formula, weights=NULL, ..., data) z( glm, # .hook = "robust.glm.hook", formula = formula, weights = weights, family = binomial(link="probit"), model = F, data = data ) #' Param Method for the 'probit' Zelig Model #' @note This method is used by the 'probit' Zelig model #' @usage \method{param}{probit}(obj, num=1000, ...) #' @S3method param negbinom #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.probit <- function(obj, num=1000, ...) { list( simulations = mvrnorm(n=num, mu=coef(.fitted), Sigma=vcov(.fitted)), alpha = NULL, fam = binomial(link="probit") ) } #' Compute quantities of interest for 'probit' Zelig models #' @usage \method{qi}{probit}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi probit #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.probit <- qi.logit #' Describe the `probit' model to Zelig #' @usage \method{describe}{probit}(...) #' @S3method describe poisson #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.probit <- function(...){ parameters <-list(mu = list( equations = c(1,1), tags.allowed = FALSE, dep.vars = TRUE, exp.vars = TRUE ) ) # return list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2007, category = "dichotomous", parameters = parameters, text = "Probit Regression for Dichotomous Dependent Variables" ) } Zelig/R/probit.survey.R0000644000176000001440000000627312071063367014550 0ustar ripleyusers#' @export zelig2probit.survey <- function( formula, weights=NULL, ids=NULL, probs=NULL, strata = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL, fpctype="fraction", return.replicates=FALSE, na.action="na.omit", start=NULL, etastart=NULL, mustart=NULL, offset=NULL, model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL, design=NULL, data ) { loadDependencies("survey") if (is.null(ids)) ids <- ~ 1 # the following lines designate the design # NOTE: nothing truly special goes on here; # the below just makes sure the design is created correctly # for whether or not the replication weights are set design <- if (is.null(repweights)) svydesign( data=data, ids=ids, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights ) else { .survey.prob.weights <- weights svrepdesign( data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc ) } z(.function = svyglm, formula = formula, design = design, family = quasibinomial(link="probit") ) } #' @S3method param probit.survey param.probit.survey <- function(obj, num=1000, ...) { list( simulations = mvrnorm(num, coef(obj), vcov(obj)), alpha = NULL, # note: assignment of link and link-inverse are # implicit when the family is assigned fam = binomial(link="probit") ) } #' @S3method qi probit.survey qi.probit.survey <- qi.logit.survey #' @S3method describe probit.survey describe.probit.survey <- function(...) { list( authors = "Nicholas Carnes", year = 2008, description = "Survey-Weighted Probit Regression for Continuous, Positive Dependent Variables" ) } Zelig/R/summary.R0000644000176000001440000002710012142745743013406 0ustar ripleyusers# Summary of MCMCZelig Object # # This method produces a summary object for \code{MCMCZelig} objects # @param object an "MCMCZelig" object # @param quantiles a numeric vector specifying the quantiles to use in the # summary object. # @param ... ignored parameters # @return a \code{summary.MCMCZelig} object #' @S3method summary MCMCZelig summary.MCMCZelig <- function(object, quantiles = c(0.025, 0.5, 0.975), ...) { out <- list() out$summary <- cbind( summary(coef(object))$statistics[,1:2], summary(coef(object), quantiles=quantiles)$quantiles ) colnames(out$summary) <- c("Mean", "SD", paste(quantiles*100, "%",sep="")) stuff <- attributes(coef(object)) out$call <- object$call out$start <- stuff$mcpar[1] out$end <- stuff$mcpar[2] out$thin <- stuff$mcpar[3] out$nchain <- 1 class(out) <- "summary.MCMCZelig" out } #' Method for summarizing simulations of multiply imputed quantities of interest #' #' @S3method summary MI.sim #' @usage \method{summary}{MI.sim}(object, ...) #' @param object a `MI.sim' object #' @param ... ignored parameters #' @return a `summarized.MI.sim' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.MI.sim <- function(object, ...) { summarized.list <- list() for (key in names(object)) { stats <- object[[key]]$stats for (qi.name in names(stats)) summarized.list[[qi.name]][[key]] <- stats[[qi.name]] } class(summarized.list) <- "summarySim.MI" summarized.list } #' Summary of Generalized Linear Model with Robust Error Estimates #' #' Returns summary of a glm model with robust error estimates. This only #' slightly differs from how the standard GLM's behave. #' @usage \method{summary}{glm.robust}(object, ...) #' @S3method summary glm.robust #' @param object a ``glm.robust'' fitted model #' @param ... parameters to pass to the standard ``summary.glm'' method #' @return a object of type ``summary.glm.robust'' and ``summary.glm'' summary.glm.robust <- function(object, ...) { class(object) <- c("glm", "lm") res <- summary.glm(object, ...) if (is.null(object$robust)) { res$cov.unscaled <- covmat.unscaled <- vcovHAC(object) res$robust <- "vcovHAC" } else { fn <- object$robust$method res$robust <- object$robust$method object$robust$method <- NULL arg <- object$robust arg$x <- object res$cov.unscaled <- covmat.unscaled <- eval(do.call(fn, args=arg)) } res$cov.scaled <- covmat <- covmat.unscaled*res$dispersion if (!is.null(res$correlation)) { dd <- sqrt(diag(res$cov.unscaled)) res$correlation <- res$cov.unscaled/outer(dd, dd) dimnames(res$correlation) <- dimnames(res$cov.unscaled) } res$coefficients[,2] <- s.err <- sqrt(diag(covmat)) res$coefficients[,3] <- tvalue <- coefficients(object)/s.err if (length(dimnames(res$coefficients)[[2]])>3) { if (dimnames(res$coefficients)[[2]][3]=="z value") res$coefficients[,4] <- 2 * pnorm(-abs(tvalue)) else res$coefficients[,4] <- 2 * pt(-abs(tvalue), object$df.residual) } class(res) <- c("summary.glm.robust","summary.glm") return(res) } #' Return a Summary of a Set of Pooled Simulated Interests #' #' Returns the summary information from a set of pooled simulated interests. #' The object returned contains the slots ``labels'', a character-vector #' specifying the labels (explanatory variable titles) of the qi's, ``titles'', #' a character vector specifying the names of the quantities of interest, and #" ``stats'', a list containing quantities of interests. #' @usage \method{summary}{pooled.sim}(object, ...) #' @S3method summary pooled.sim #' @param object a ``pooled.sim'' object, containing information about #' simulated quantities of interest #' @param ... Ignored parameters #' @return a ``summary.pooled.sim'' object storing the replicated quantities of #' interest #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.pooled.sim <- function (object, ...) { model <- list() stats <- list() titles <- list() original <- list() call <- list() x <- list() x1 <- list() # for (key in names(object)) { o <- object[[key]] stats[[key]] <- o$stats titles[[key]] <- o$titles } s <- list( labels = names(object), titles = names(object[[1]]$stats), stats = stats ) class(s) <- "summary.pooled.sim" s } #' Summary for ``Relogit'' Fitted Model #' #' Summarize important components of the ``relogit'' model #' @usage \method{summary}{Relogit}(object, ...) #' @S3method summary Relogit #' @param object a ``Relogit'' object #' @param ... other parameters #' @return a ``summary.relogit'' object summary.Relogit <- function(object, ...) { dta <- model.matrix(terms(object), data=model.frame(object)) class(object) <- class(object)[2] res <- summary(object, ...) if (object$bias.correct) { n <- nrow(dta) k <- ncol(dta) res$cov.unscaled <- res$cov.unscaled * (n/(n+k))^2 res$cov.scaled <- res$cov.unscaled * res$dispersion res$coefficients[,2] <- sqrt(diag(res$cov.scaled)) res$coefficients[,3] <- res$coefficients[,1] / res$coefficients[,2] res$coefficients[,4 ] <- 2*pt(-abs(res$coefficients[,3]), res$df.residual) } res$call <- object$call res$tau <- object$tau res$bias.correct <- object$bias.correct res$prior.correct <- object$prior.correct res$weighting <- object$weighting class(res) <- "summary.relogit" return(res) } #' Summary for ``Relogit2'' Fitted Model #' #' Summarize important components of the ``relogit'' model #' @usage \method{summary}{Relogit2}(object, ...) #' @S3method summary Relogit2 #' @param object a ``Relogit2'' object #' @param ... other parameters #' @return a ``summary.relogit2'' object summary.Relogit2 <- function(object, ...) { res <- list() res$lower.estimate <- summary.Relogit(object$lower.estimate) res$upper.estimate <- summary.Relogit(object$upper.estimate) res$call <- object$call class(res) <- "summary.relogit2" return(res) } #' Method for summarizing simulations of quantities of interest #' #' Return a ``summary.sim'' object (typically for display) #' @S3method summary sim #' @usage \method{summary}{sim}(object, ...) #' @param object a 'MI.sim' object #' @param ... ignored parameters #' @return a 'summarized.MI.sim' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.sim <- function(object, ...) { res <- list( model = object$model, stats = object$stats, titles = object$titles, original = object$result, call = object$call, zeligcall= object$zcall, x = object$x, x1 = object$x1, num = object$num ) class(res) <- c(object$name, "summary.sim") res } #' Zelig Object Summaries #' #' Compute summary data for zelig objects #' @S3method summary zelig #' @usage \method{summary}{zelig}(object, ...) #' @param object a zelig object #' @param ... parameters forwarded to the generic summary object #' @return the summary of the fitted model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.zelig <- function (object, ...) { # For now, simply get the summary of the result object obj <- eval(object$result) if (isS4(obj)) { sigs <- findMethodSignatures('summary') classes <- class(obj) # Remove classes that do not have 'summary' methods intersection <- classes[ ! sigs %in% classes ] intersection <- na.omit(intersection) intersection <- as.character(intersection) # Summary only has one parameter, so we only consider the first one # This may be slightly dangerous, but it should not fail sig <- intersection[1] # if an attempt to get the summary fails, replace with a call to the S3 SUMMARY <- tryCatch(getMethod('summary', sig), error = function(e) summary) # return SUMMARY(obj) } else # S3 objects have no problem figuring out which method to use summary(obj) } #' Sumary of ``setx'' Object #' #' Compute summary data for ``setx'' objects #' @S3method summary zelig #' @usage \method{summary}{zelig}(object, ...) #' @param object a zelig object #' @param ... parameters forwarded to the generic summary object #' @return the summary of the fitted model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.setx <- function (object, ...) { mm <- object$matrix attr(mm, "assign") <- NULL attr(mm, "contrasts") <- NULL structure( list( call = object$call, label = object$label, model.name = object$name, formula = object$formula, model.matrix = mm ), class = "summary.setx" ) } #' Summary of Multiply Imputed Statistical Models Using Rubin's Rule #' #' ... #' @S3method summary MI #' @usage \method{summary}{MI}(object, ...) #' @param object a set of fitted statistical models #' @param ... parameters to forward #' @return a list of summaries #' @author Matt Owen \email{mowen@@iq.harvard.edu} summary.MI <- function (object, subset = NULL, ...) { if (length(object) == 0) stop('Invalid input for "subset"') else if (length(object) == 1) return(summary(object[[1]])) # getcoef <- function(obj) { # S4 if (!isS4(obj)) coef(obj) else if ("coef3" %in% slotNames(obj)) obj@coef3 else obj@coef } # res <- list() # Get indices subset <- if (is.null(subset)) 1:length(object) else c(subset) # Compute the summary of all objects for (k in subset) { res[[k]] <- summary(object[[k]]) } # Answer ans <- list( zelig = object[[1]]$name, call = object[[1]]$result$call, all = res ) # coef1 <- se1 <- NULL # for (k in subset) { tmp <- getcoef(res[[k]]) coef1 <- cbind(coef1, tmp[, 1]) se1 <- cbind(se1, tmp[, 2]) } rows <- nrow(coef1) Q <- apply(coef1, 1, mean) U <- apply(se1^2, 1, mean) B <- apply((coef1-Q)^2, 1, sum)/(length(subset)-1) var <- U+(1+1/length(subset))*B nu <- (length(subset)-1)*(1+U/((1+1/length(subset))*B))^2 coef.table <- matrix(NA, nrow = rows, ncol = 4) dimnames(coef.table) <- list(rownames(coef1), c("Value", "Std. Error", "t-stat", "p-value")) coef.table[,1] <- Q coef.table[,2] <- sqrt(var) coef.table[,3] <- Q/sqrt(var) coef.table[,4] <- pt(abs(Q/sqrt(var)), df=nu, lower.tail=F)*2 ans$coefficients <- coef.table ans$cov.scaled <- ans$cov.unscaled <- NULL for (i in 1:length(ans)) { if (is.numeric(ans[[i]]) && !names(ans)[i] %in% c("coefficients")) { tmp <- NULL for (j in subset) { r <- res[[j]] tmp <- cbind(tmp, r[[pmatch(names(ans)[i], names(res[[j]]))]]) } ans[[i]] <- apply(tmp, 1, mean) } } class(ans) <- "summaryMI" ans } print.summaryMI <- function(x, subset = NULL, ...){ m <- length(x$all) if (m == 0) m <- 1 if (any(subset > max(m))) stop("the subset selected lies outside the range of available \n observations in the MI regression output.") cat("\n Model:", x$zelig) cat("\n Number of multiply imputed data sets:", m, "\n") if (is.null(subset)) { cat("\nCombined results:\n\n") cat("Call:\n") print(x$call) cat("\nCoefficients:\n") print(x$coefficients) cat("\nFor combined results from datasets i to j, use summary(x, subset = i:j).\nFor separate results, use print(summary(x), subset = i:j).\n\n") } else { if (is.function(subset)) M <- 1:m if (is.numeric(subset)) M <- subset for(i in M){ cat(paste("\nResult with dataset", i, "\n")) print(x$all[[i]], ...) } } } Zelig/R/ignore.R0000644000176000001440000000114512061700507013162 0ustar ripleyusers#' Constructor for the 'ignore' class #' This class is included for future use, and is currently #' not used in any Zelig model. It is designed for use with #' zelig2* functions #' @param default default value #' @param type ignored parameter #' @return an 'ignore' object #' @export #' @author Matt Owen \emph{mowen@@iq.harvard.edu} ignore <- function (default = NULL, type = "no pass") { self <- default class(self) <- "ignore" # store information, set class, and return self <- list( default = default, type = type ) class(self) <- "ignore" self } Zelig/R/attach.env.R0000644000176000001440000000347712061700507013744 0ustar ripleyusers#' Attach Variables to a Function #' #' Returns a function, specified by the user, with the variables of a specified #' environment attached. This, in essence, allows programmers to write functions #' that have forms of private memory. This makes the function behave similarly #' to an object. #' #' @note This function is used by Zelig to ensure that particular method calls - #' param, qi, bootstap - will contain the private variables: ``.fitted'', #' ``.model'', ``.call'' and ``.env'' which respectively contain the fitted #' model object, the name of the zelig model being invoked, the original call #' to the model-fitting function and the environment in which to call the #' function call. #' #' @param f a function which will be modified #' @param env an environment variable which will be attached to the function #' being returned #' @param ... arbitrary key-value paired parameters which will be assigned to #' the environment of the function being returned #' @return the original function ``f'' with a different environment attached to #' it. #' #' @author Matt Owen \email{mowen@@iq.harvard.edu} attach.env <- function (f, env = NULL, ...) { # Ensure that a valid environment is passed in if (is.null(env)) env <- new.env() # Expand dot parameters dots <- list(...) # Ensure that "env" is a valid environment if (is.null(env)) env <- new.env() else if (!inherits(env, "environment")) { warning('Environment "env" is not a valid environment variable. ', 'A default environment will be applied to "f" instead.') env <- new.env() } if (length(dots)) { # Add variables to the newly created environment for (key in names(dots)) assign(key, dots[[key]], env) } # Modify the default environment of the function environment(f) <- env # Return the modified function f } Zelig/R/qi.summarized.R0000644000176000001440000000553212061700507014473 0ustar ripleyusers#' Constructor for QI Summarized Class #' This class takes an arbitrary number of the _same_ type of #' quantities of interest labels them, then #' merges them into one simple printable block. In particular, #' this class determines which print function to use based on the #' the type and size od data to be passed to the print function. #' @param title a character-string specifying the title of the QI #' @param x a list of summarized quantities of interest #' @param ... additional quantities of interest (the parameter that #' titles these will be used as the name of the data.frame #' @return the list of QI's (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.summarize <- function (title, x, ...) { qis <- append(x, list(...)) attr(qis, 'title') <- title class(qis) <- 'qi.summarized' for (key in names(qis)) { val <- x[[key]] if (is.matrix(val)) next qis[[key]] <- matrix(val, nrow=1, ncol=length(val)) } nrows <- Map(nrow, qis) if (all(nrows == 1)) attr(qis, 'print') <- 'matrix' else attr(qis, 'print') <- 'list' invisible(qis) } #' Print Method for Summarized Quantities of Interest #' @usage \method{print}{qi.summarized}(x, \dots) #' @S3method print qi.summarized #' @param x a 'summarized.qi' object #' @param ... parameters to be passed to the specific print functions #' @return x (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @seealso \link{special_print_MATRIX} and #' \link{special_print_LIST} print.qi.summarized <- function (x, ...) { if (attr(x, 'print') == 'matrix') .print.qi.summarized.MATRIX(x, ...) else if (attr(x, 'print') == 'list') .print.qi.summarized.LIST(x, ...) else print(x, ...) } #' Method for Printing Summarized QI's in a Matrix Form #' @name special_print_MATRIX #' @aliases special_print_MATRIX .print.qi.summarized.MATRIX #' @note This function is used internall by Zelig #' @param x a 'summarized.qi' object #' @param ... additional parameters #' @return x (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} .print.qi.summarized.MATRIX <- function (x, ...) { m <- matrix(NA, 0, 0) for (key in names(x)) { m <- .bind(m, x[[key]]) } rownames(m) <- names(x) cat(attr(x, 'title'), "\n") print(m, ...) invisible(x) } #' Method for Printing Summarized QI's in a List Form #' @name special_print_LIST #' @aliases special_print_LIST .print.qi.summarized.LIST #' @note This function is used internall by Zelig #' @param x a 'summarized.qi' object #' @param ... additional parameters to be used by the 'print.matrix' method #' @return x (invisibly) #' @author Matt Owen \email{mowen@@iq.harvard.edu} .print.qi.summarized.LIST <- function (x, ...) { cat(attr(x, 'title'), "\n\n") for (key in names(x)) { cat('imputed data = ', key, "\n") print(x[[key]], ...) cat("\n") } invisible(x) } Zelig/R/gamma.R0000644000176000001440000000777512071063367013007 0ustar ripleyusers#' Interface between gamma model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2gamma <- function(formula, ..., data) z( glm, # .hook = "robust.glm.hook", formula = formula, family = Gamma(), model = F, data = data ) #' param method for the `gamma' Zelig model #' #' Return parameter estimates for the ``gamma'' GLM in Zelig. #' @usage \method{param}{gamma}(obj, num, ...) #' @S3method param gamma #' @param obj a `zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored parameters #' @return a list to be cast as a `parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.gamma <- function(obj, num = 1000, ...) { # Extract shape parameters, which will be used to simulate the ancillary # parameters shape <- gamma.shape(.object) # Simulate ancillary parameters alpha <- rnorm(n=num, mean=shape$alpha, sd=shape$SE) # list( simulations = mvrnorm(n=num, mu=coef(.object), Sigma=vcov(.object)), alpha = alpha, family = Gamma() ) } #' Compute quantities of interest for 'gamma' Zelig models #' @usage \method{qi}{gamma}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi gamma #' @param obj a \code{zelig} object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.gamma <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { # Get parameters shape <- gamma.shape(.fitted) alpha <- rnorm(num, mean = shape$alpha, sd = shape$SE) coef <- coef(param) # Compute eta eta <- coef %*% t(x) # Compute theta (apply inverse) theta <- matrix(1/eta, nrow = nrow(coef)) ev <- theta pr <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta)) # Default to not available ev1 <- pr1 <- fd <- NA # Compute predicted values for (i in 1:nrow(ev)) pr[i,] <- rgamma( ncol(ev), shape = alpha[i], scale = theta[i,]/alpha[i] ) # if x1 is not NULL, run more simultations # ... if (!is.null(x1)) { eta1 <- coef %*% t(x1) ev1 <- theta1 <- matrix(1/eta1, nrow = nrow(coef)) pr1 <- matrix(NA, nrow = nrow(theta1), ncol = ncol(theta1)) for (i in 1:nrow(ev1)) pr1[i, ] <- rgamma(ncol(ev1), shape = alpha[i], scale = theta1[i,]/alpha[i]) fd <- ev1 - ev } # Return list("Expected Values: E(Y|X)" = ev, "Expected Values: E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values: Y|X1" = pr1, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe the \code{gamma} model to Zelig #' @usage \method{describe}{gamma}(...) #' @S3method describe default #' @param ... ignored parameters #' @return a list of important information #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.gamma <- function(...) { # parameters object parameters <- list(lambda = list( equations = c(1, 1), tags.allowed = FALSE, dep.var = TRUE, exp.var = TRUE ) ) # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2007, category = "bounded", parameters = parameters, text = "Gamma Regression for Continuous, Positive Dependent Variables" ) } Zelig/R/logit.bayes.R0000644000176000001440000000442112110047000014102 0ustar ripleyusers#' @export zelig2logit.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose=0, ..., data ) { loadDependencies("MCMCpack", "coda") if (missing(verbose)) verbose <- round((mcmc + burnin)/10) list( .function = "MCMClogit", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param logit.bayes param.logit.bayes <- function(obj, num=1000, ...) { list( coef = coef(obj), fam = binomial(link="logit") ) } #' @S3method qi logit.bayes qi.logit.bayes <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # Use a Helper-Function that computes expected values and predicted values # simultaneously. res1 <- logit.ev(x, param) res2 <- logit.ev(x1, param) # Return quantities of interest list( "Expected Value: E(Y|X)" = res1$ev, "Predicted Value: Y|X" = res1$pv, "Expected Value (for X1): E(Y|X1)" = res2$ev, "Predicted Value (for X1): Y|X1" = res2$pv, "First Differences: E(Y|X1)-E(Y|X)" = res2$ev - res1$ev ) } logit.ev <- function (x, param) { # If either of the parameters are invalid, # Then return NA for both qi's if (is.null(x) || is.na(x) || is.null(param)) return(list(ev=NA, pv=NA)) # Extract inverse-link and simulated parameters (respectively) inv <- linkinv(param) eta <- coef(param) %*% t(x) # Give matrix identical rows/columns to the simulated parameters ev <- pv <- matrix(NA, nrow(eta), ncol(eta)) dimnames(ev) <- dimnames(pv) <- dimnames(eta) # Compute Expected Values ev <- inv(eta) # Compute Predicted Values for (i in 1:ncol(ev)) pv[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i])) # Return list(ev=ev, pv=pv) } #' @S3method describe logit.bayes describe.logit.bayes <- function(...) { list( authors = c("Ben Goodrich", "Ying Lu"), text = "Bayesian Logistic Regression for Dichotomous Dependent Variables", year = 2013 ) } Zelig/R/qi.R0000644000176000001440000000265512061700507012317 0ustar ripleyusers#' The \code{qi} function is used by developers to simulated quantities of #' interest. This method, as a result, is the most significant method of any #' Zelig statistical model. #' #' @title Generic Method for Computing Quantities of Interest #' @param obj a \code{zelig} object #' @param x a \code{setx} object or NULL #' @param x1 an optional \code{setx} object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a #' handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of #' quantities of interest with their simulations #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @note Run \code{example(qi)} to see a trivial version of #' @examples #' qi.some.model <- function(obj, x=NULL, x1=NULL, y=NULL, param=NULL) { #' list( #' "Expected Values: E(Y|X)" = NA, #' "Predicted Values: Y|X" = NA #' ) #' } qi <- function(obj, x=NULL, x1=NULL, y=NULL, num, param=NULL) { if (!inherits(obj, "zelig")) stop('"obj" must be of a "zelig" object') if (!(is.null(x) || inherits(x, "setx"))) stop('"x" must be a "setx" object"') if (!(is.null(x1) || inherits(x1, "setx"))) stop('"x1" must be a "setx" object') # then use the method UseMethod("qi") } Zelig/R/negbinom.R0000644000176000001440000000743212143273572013512 0ustar ripleyusers#' Interface between negbinom model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2negbinom <- function(formula, weights=NULL, ..., data) z( .function = "glm.nb", .hook = robust.glm.hook, weights = weights, formula = formula, data = data ) #' Param Method for the 'negbinom' Zelig Model #' @note This method is used by the 'negbinom' Zelig model #' @usage \method{param}{negbinom}(obj, num=1000, ...) #' @S3method param negbinom #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.negbinom <- function(obj, num=1000, ...) { list( simulations = mvrnorm(num, mu=coef(.fitted), Sigma=vcov(.fitted)), alpha = .fitted$theta, link = function (e) e, linkinv = function (e) e ) } #' Compute quantities of interest for 'negbinom' Zelig models #' @usage \method{qi}{negbinom}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi negbinom #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.negbinom <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # coef <- coef(param) alpha <- alpha(param) # get inverse function inverse <- obj[["family", "linkinv"]] # eta <- coef %*% t(x) theta <- matrix(inverse(eta), nrow=nrow(coef)) # ... ev <- theta pr <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta)) # default values ev1 <- pr1 <- fd <- NA # for (i in 1:ncol(ev)) pr[,i] <- as.character(rnegbin(nrow(ev), mu = ev[i,], theta = alpha[i])) if (!is.null(x1)) { # quantities of interest results <- qi(obj=obj, x=x1, num=num, param=param) # pass values over ev1 <- results[["Expected Values: E(Y|X)"]] pr1 <- results[["Predicted Values: Y|X"]] # compute first differences fd <- ev1 - ev } # Return quantities of interest, paired off with their titles list("Expected Values: E(Y|X)" = ev, "Expected Values: E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values: Y|X1" = pr1, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe the \code{negbinom} model to Zelig #' @note \code{negbinom} stands for "negative binomial" #' @usage \method{describe}{negbinom}(...) #' @S3method describe negbinom #' @param ... ignored parameters #' @return a list to be processed by \code{as.description} #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.negbinom <- function(...) { # parameters object parameters <- list(pi = list( equations = c(1, 1), tags.allowed = FALSE, dep.var = TRUE, exp.var = TRUE ) ) # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2008, category = "count", parameters = parameters, text = "Negative Binomial Regression for Event Count Dependent Variables" ) } Zelig/R/zelig.R0000644000176000001440000002422712110047000013002 0ustar ripleyusers#' Estimating a Statistical Model #' #' The zelig command estimates a variety of statistical #' models. Use \code{zelig} output with \code{setx} and \code{sim} to compute #' quantities of interest, such as predicted probabilities, expected values, and #' first differences, along with the associated measures of uncertainty #' (standard errors and confidence intervals). #' #' @param formula a symbolic representation of the model to be #' estimated, in the form \code{y \~\, x1 + x2}, where \code{y} is the #' dependent variable and \code{x1} and \code{x2} are the explanatory #' variables, and \code{y}, \code{x1}, and \code{x2} are contained in the #' same dataset. (You may include more than two explanatory variables, #' of course.) The \code{+} symbol means ``inclusion'' not #' ``addition.'' You may also include interaction terms and main #' effects in the form \code{x1*x2} without computing them in prior #' steps; \code{I(x1*x2)} to include only the interaction term and #' exclude the main effects; and quadratic terms in the form #' \code{I(x1^2)} #' @param model the name of a statistical model. #' Type \code{help.zelig("models")} to see a list of currently supported #' models #' @param data the name of a data frame containing the variables #' referenced in the formula, or a list of multiply imputed data frames #' each having the same variable names and row numbers (created by #' \code{mi}) #' @param ... additional arguments passed to \code{zelig}, #' depending on the model to be estimated #' @param by a factor variable contained in \code{data}. Zelig will subset #' the data frame based on the levels in the \code{by} variable, and #' estimate a model for each subset. This a particularly powerful option #' which will allow you to save a considerable amount of effort. For #' example, to run the same model on all fifty states, you could type: #' \code{z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")} #' You may also use \code{by} to run models using MatchIt subclass #' @param cite If is set to "TRUE" (default), the model citation will be #' @return Depending on the class of model selected, \code{zelig} will return #' an object with elements including \code{coefficients}, \code{residuals}, #' and \code{formula} which may be summarized using #' \code{summary(z.out)} or individually extracted using, for example, #' \code{z.out\$coefficients}. See the specific models listed above #' for additional output values, or simply type \code{names(z.out)}. #' #' @name zelig #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Kosuke Imai, Olivia Lau, and #' Gary King #' Maintainer: Matt Owen \email{mowen@@iq.harvard.edu} #' @keywords package zelig <- function (formula, model, data, ..., by=NULL, cite=T) { # Yea this forever model.warnings(model) # Split data.frame if (!missing(by)) { if (length(by) > 1) { warning("by cannot have length greater than 1") by <- NULL } if (!is.data.frame(data)) warning("") else if (any(by %in% all.vars(formula))) { warning("by cannot list contain a variable from the model's formula") by <- NULL } else data <- divide(data, by) } # Almost equivalient to: # data <- multi.dataset(data) # # but we want to keep the name of the original data object as our title (sometimes). divided.data <- eval(call("multi.dataset", substitute(data))) # Call <- match.call() # expand dot arguments dots <- list() # get non-dot arguments in a general fashion notdots <- as.list(match.call(expand.dots=F)[-1]) notdots[["..."]] <- NULL # only get the non-dot arguments # that do not exist in the dot arguments names.notdots <- Filter(function(x) !x%in%names(dots), names(notdots)) notdots <- notdots[names.notdots] # build parameter list (including optional parameters) params <- c(dots, notdots) # set up list res <- NULL old.style.oop <- TRUE # Call make.mi symbolically so that the function can implicitly label # data.frame's from context. For example, the expression: # mi(turnout[1:1000, ], ) # will contain a data.frame labeled: # turnout[1:1000, ] # m <- eval(call("multi.dataset", substitute(data), by=by)) # Ensure certain values remain consistent between any object on this list # by giving them all a pointer to the same environment object which contains # a few pieces of information state <- new.env() # Begin constructing zelig object object <- list() # Create zelig2* function zelig2 <- paste("zelig2", as.character(model), sep="") zelig2 <- get(zelig2, mode="function") # Get package name. This is useful for writing methods that apply to all # models within a particular software package package.name <- getPackageName(environment(zelig2), FALSE) # repeat for (key in names(divided.data)) { d.f <- divided.data[[key]] label <- key # catch end-of-list error if (is.null(d.f)) next zclist <- zelig2(formula, ..., data=d.f) new.call <- zclist$call env <- zclist$env if (!inherits(zclist, "z")) { if (!is.list(zclist)) warning("invalid object returned from `zelig2` method") else { wl <- zclist # reserved words taken from the zelig2 method .func <- as.name(wl$.function) .hook <- wl$.hook # remove the reserved words wl$.function <- NULL wl$.hook <- NULL wl$.post <- NULL wl$.model.matrix <- NULL new.call <- as.call(append(list(.func), wl)) mock.call <- match.call() env <- NULL } } else if (inherits(zclist, "z")) { new.call <- zclist$literal.call mock.call <- zclist$call env <- NULL } else { warning("zelig2 function is returning an invalid type of object") } # Default value for result object new.res <- NULL tryCatch( { new.res <- eval(new.call) }, error = function (e) { warning("There was an error fitting this statistical model.") print(e) } ) # Apply first hook if it exists if (!is.null(zclist$.hook)) { zclist$.hook <- get(zclist$.hook, mode='function') new.res <- zclist$.hook(new.res, new.call, match.call(), ..., data = d.f) } else if (!is.null(zclist$hook) && is.function(zclist$hook)) { new.res <- zclist$hook(new.res, new.call, match.call(), ..., data = d.f) } # Determine whether this is an S4 object old.style.oop <- ! isS4(new.res) if (exists("mock.call")) { if (isS4(new.res)) new.res@call <- mock.call else new.res$call <- mock.call } # This is the only "obj" assignment that matters obj <- makeZeligObject(new.res, model, new.call, match.call(), d.f, label, env, package.name = package.name ) # Specify the appropriate class # Attach shared environment as an attribtute attr(obj, 'state') <- state # Add to list of results object[[label]] <- obj } if (missing(by) && is.data.frame(data)) { object <- object[[1]] } else { attr(object, 'state') <- state class(object) <- c(model, paste(model, 'mi', sep='-'), "MI") } # Update the shared environment assign('old-formula', formula, state) assign('args', list(...), state) assign('parent', parent.frame(), state) assign('call', match.call(), state) assign('by', by, state) # assign('methods', methods.env, state) assign('methods', NULL, state) assign('model', model, state) # The below line should probably remain commented out # assign('mi', m, state) # Display citation information if (cite) { described <- describe(object) descr <- description( authors = described$authors, year = described$description, text = described$text, url = described$url, model = model ) cat("\n\n", cite(descr), "\n") } object } #' Make an Individual Zelig Object #' #' Returns a ``zelig'' object with the proper specifications #' @param object a fitted statistical model #' @param model a character-string specifying the name of the model #' @param call The call that produced the fitted model #' @param zelig_call The call made to the original zelig function #' @param data the data.frame used to fit the model #' @param label a character-string or symbol used as a human-readable label for #' the data-set #' @param env an environment variable that contains all variables to evaluate #' the call ``zelig_call'' #' @param package.name a character-string specifyign the name of the package #' that is the source of the model used to fit this object #' @return A ``zelig'' object makeZeligObject <- function (object, model, call, zelig_call, data, label, env, package.name = NULL ) { # This is a set of variables that will be visible to the following methods: # param, bootstrap, qi implied.variables <- new.env() # The fitted model assign(".fitted", object, implied.variables) # The name of the model assign(".model", model, implied.variables) # The call to the model-fitting function assign(".call", call, implied.variables) # The environment used to evaluate the model-fitting functino assign(".env", env, implied.variables) # Create list-object self <- list( result = object, formula = formula(object), zelig.call = zelig_call, name = model, label = label, env = env, call = call, data = data, S4 = isS4(object), method.env = implied.variables, package.name = package.name ) # Specify as a ``zelig'' object class(self) <- c("zelig", model) # Return self } Zelig/R/poisson.survey.R0000644000176000001440000001057412071063367014742 0ustar ripleyusers#' @export zelig2poisson.survey <- function( formula, weights=NULL, ids=NULL, probs=NULL, strata = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL, fpctype="fraction", return.replicates=FALSE, na.action="na.omit", start=NULL, etastart=NULL, mustart=NULL, offset=NULL, model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL, design=NULL, data ) { loadDependencies("survey") if (is.null(ids)) ids <- ~1 # the following lines designate the design # NOTE: nothing truly special goes on here; # the below just makes sure the design is created correctly # for whether or not the replication weights are set design <- if (is.null(repweights)) svydesign( data=data, ids=ids, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights ) else { .survey.prob.weights <- weights svrepdesign( data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc ) } z(.function = svyglm, formula = formula, design = design, family = poisson() ) } #' @S3method param poisson.survey param.poisson.survey <- function(obj, num=1000, ...) { list( simulations = mvrnorm(num, coef(obj), vcov(obj)), alpha = NULL, # note: assignment of link and link-inverse are # implicit when the family is assigned fam = poisson() ) } #' @S3method qi poisson.survey qi.poisson.survey <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { model <- GetObject(obj) coef <- coef(param) alpha <- alpha(param) eta <- coef %*% t(x) link.inverse <- linkinv(param) theta <- matrix(link.inverse(eta), nrow=nrow(coef)) pr <- ev <- matrix(NA, nrow=nrow(theta), ncol(theta)) dimnames(pr) <- dimnames(ev) <- dimnames(theta) ev <- theta for (k in 1:nrow(ev)) pr[k, ] <- rpois(length(ev[k, ]), lambda=ev[k, ]) ev1 <- pr1 <- fd <- NA if (!is.null(x1)) { ev1 <- theta1 <- matrix(link.inverse(coef %*% t(x1)), nrow = nrow(coef) ) fd <- ev1-ev } att.ev <- att.pr <- NA if (!is.null(y)) { yvar <- matrix(rep(y, nrow(coef)), nrow=nrow(coef), byrow=TRUE) tmp.ev <- yvar - ev tmp.pr <- yvar - pr att.ev <- matrix(apply(tmp.ev, 1, mean), nrow=nrow(coef)) att.pr <- matrix(apply(tmp.pr, 1, mean), nrow=nrow(coef)) } list( "Expected Values: E(Y|X)" = ev, "Expected Values for (X1): E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values (for X1): Y|X1" = pr1, "First Differences E(Y|X1)-E(Y|X)" = fd, "Average Treatment Effect: Y-EV" = att.ev, "Average Treatment Effect: Y-PR" = att.pr ) } #' @S3method describe poisson.survey describe.poisson.survey <- function(...) { list( authors = "Nicholas Carnes", year = 2008, description = "Survey-Weighted Poisson Regression for Continuous, Positive Dependent Variables" ) } Zelig/R/get.package.R0000644000176000001440000000452112061700507014051 0ustar ripleyusers#' Find the Zelig package that a particular model belong to #' #' This method is used to help transition Zelig v3.5 users to Zelig v4 #' @param model a character-string specifying a Zelig model #' @param quiet a logical indicating whether to display messages and warnings #' @param ... ignored parameters #' @return NA or a character-string specifying the name of the package which #' contains a specific model #' @author Matt Owen \email{mowen@@iq.harvard.edu} get.package <- function (model, quiet=TRUE, ...) { # Bad variable-types return NULL if (is.null(model)) return(NA) else if (!is.character(model)) return(NA) else if (length(model) != 1) return (NA) # Create list (auto-generated using another script. # This is a copy-and-paster of that below descr <- c( gamma = "Zelig", logit = "Zelig", ls = "Zelig", negbinom = "Zelig", normal = "Zelig", poisson = "Zelig", probit = "Zelig", gamma.gee = "Zelig", logit.gee = "Zelig", normal.gee = "Zelig", poisson.gee = "Zelig", probit.gee = "Zelig", factor.bayes = "Zelig", logit.bayes = "Zelig", mlogit.bayes = "Zelig", normal.bayes = "Zelig", oprobit.bayes = "Zelig", poisson.bayes = "Zelig", probit.bayes = "Zelig", aov = "Zelig", sur = "Zelig", twosls = "Zelig", threesls = "Zelig", blogit = "ZeligChoice", bprobit = "ZeligChoice", mlogit = "ZeligChoice", mprobit = "ZeligChoice", ologit = "ZeligChoice", oprobit = "ZeligChoice", logit.gam = "ZeligGAM", normal.gam = "ZeligGAM", poisson.gam = "ZeligGAM", probit.gam = "ZeligGAM", gamma.mixed = "ZeligMultilevel", logit.mixed = "ZeligMultilevel", ls.mixed = "ZeligMultilevel", normal.mixed = "ZeligMultilevel", poisson.mixed = "ZeligMultilevel", probit.mixed = "ZeligMultilevel", gamma.survey = "ZeligSurvey", logit.survey = "ZeligSurvey", normal.survey = "ZeligSurvey", poisson.survey = "ZeligSurvey", probit.survey = "ZeligSurvey", cloglog.net = "ZeligNetwork", gamma.net = "ZeligNetwork", logit.net = "ZeligNetwork", ls.net = "ZeligNetwork", negbinom.net = "ZeligNetwork", normal.net = "ZeligNetwork", poisson.net = "ZeligNetwork", probit.net = "ZeligNetwork" ) if (model %in% names(descr)) descr[[model]] else NA } Zelig/R/parameters.R0000644000176000001440000000770312061700507014050 0ustar ripleyusers#' Constructor for `parameters' class #' #' #' @param simulations a vector or matrix containing simulated values #' @param alpha ancillary parameters for the Zelig statistical model #' @param fam a family object which implicitly specifies the link #' and link-inverse functions for the #' @param link the link function of the specified statistical model. #' The `linkinv' parameter is implicitly defined by #' by the `link' parameter, when `linkinv' is omitted #' @param linkinv the inverse link function #' @return a `parameters' object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} parameters <- function(simulations, alpha, fam=NULL, link=NULL, linkinv=NULL ) { if (is.function(fam)) fam <- fam() # if (!missing(fam) && isS4(fam)) { link <- fam@link linkinv <- fam@inverse } else if (!missing(fam) && inherits(fam, "family")) { link <- fam$linkfun linkinv <- fam$linkinv } else if (missing(link)) { #warning("no link function") } else if (missing(linkinv)) { #warning("no inverse link function") linkinv <- .NumInverse(link) } # Construct object p <- list(coefficients = simulations, alpha = alpha, link = link, linkinv = linkinv ) # cast, and return class(p) <- "parameters" p } #' Extract ancillary parameters from #' `parameters' objects #' #' @param param a `parameters' object #' @return the ancillary parameters \emph{specified} for #' the statistical model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} alpha <- function(param) param$alpha #' Return Simulations of Parameter Coefficients #' #' Returns simulated parameters of coefficients for use in statistical #' simulation. The values are set by the model-fitting function and the #' developer of the qi. method. #' #' @note This function may not differ at all from coef.default #' @usage \method{coef}{parameters}(object, ...) #' @S3method coef parameters #' @param object a 'parameters' object #' @param \dots ignored #' @return simulations, specified by the Zelig model, of #' the ancillary parameters #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} coef.parameters <- function(object, ...) { object$coefficients } #' Return Simulations of Parameter Coefficients #' #' Returns simulated parameters of coefficients for use in statistical #' simulation. The values are set by the model-fitting function and the #' developer of the qi. method. #' #' @note This function does not differ at all from coef.default #' @usage \method{simulations}{parameters}(object, ...) #' @S3method coef parameters #' @param object a 'parameters' object #' @param \dots ignored #' @return simulations, specified by the Zelig model, of #' the ancillary parameters #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} simulations.parameters <- function(object, ...) object$coefficients #' Method for extracting the link function from 'parameters' objects #' @param param a 'parameters' object #' @return the link function specified by the `param' function for the given #' Zelig model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} link <- function(param) param$link #' Method for extracting the inverse link function from 'parameters' objects #' #' Returns the inverse link function of a ``parameters'' object. If the #' model's developer did not specify one (but did specify a link function) this #' function returns a numerical approximation of the link function. #' @param param a 'parameters' object #' @return the inverse link function specified by the 'param' function for the #' given Zelig model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} linkinv <- function(param) { if (is.null(param$linkinv)) .NumInverse(param$link) else param$linkinv } Zelig/R/probit.bayes.R0000644000176000001440000000217712110047000014271 0ustar ripleyusers#' @export zelig2probit.bayes <- function ( formula, burnin = 1000, mcmc = 10000, verbose=0, ..., data ) { loadDependencies("MCMCpack", "coda") if (missing(verbose)) verbose <- round((mcmc + burnin)/10) list( .function = "MCMCprobit", .hook = "MCMChook", formula = formula, data = data, burnin = burnin, mcmc = mcmc, verbose= verbose, # Most parameters can be simply passed forward ... ) } #' @S3method param probit.bayes param.probit.bayes <- function(obj, num=1000, ...) { list( coef = coef(obj), fam = binomial(link="probit") ) } #' @S3method qi probit.bayes qi.probit.bayes <- qi.logit.bayes #' @S3method describe probit.bayes describe.probit.bayes <- function(...) { list( description = "Bayesian Probit Regression for Dichotomous Dependent Variables", authors = c("Ben Goodrich", "Ying Lu"), year = 2013 ) } Zelig/R/robust.hook.R0000644000176000001440000000066412110047000014144 0ustar ripleyusers#' @export robust.gee.hook <- function(obj, Zall, Call, robust, ...) { # Assume robust, if nothing is specified if (missing(robust) || is.null(robust)) robust <- TRUE # Invalid robust parameters should stop program if (!is.logical(robust)) stop("robust must be a logical (TRUE or FALSE)") if (robust) class(obj) <- c("gee.robust", class(obj)) else class(obj) <- c("gee.naive", class(obj)) # obj } Zelig/R/poisson.R0000644000176000001440000000707112071063367013404 0ustar ripleyusers#' Interface between poisson model and Zelig #' This function is exclusively for use by the `zelig' function #' @param formula a formula #' @param weights a numeric vector #' @param ... ignored parameters #' @param data a data.frame #' @return a list to be coerced into a zelig.call object #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig2poisson <- function(formula, weights=NULL, ..., data) { z( glm, # .hook = "robust.glm.hook", formula = formula, weights = weights, family = poisson(), model = F, data = data ) } #' Param Method for the 'poisson' Zelig Model #' @note This method is used by the 'poisson' Zelig model #' @usage \method{param}{poisson}(obj, num=1000, ...) #' @S3method param negbinom #' @param obj a 'zelig' object #' @param num an integer specifying the number of simulations to sample #' @param ... ignored #' @return a list to be cast as a 'parameters' object #' @author Matt Owen \email{mowen@@iq.harvard.edu} param.poisson <- function (obj, num=1000, ...) { list( simulations = mvrnorm(num, mu=coef(.fitted), Sigma=vcov(.fitted)), fam = poisson() ) } #' Compute quantities of interest for 'poisson' Zelig models #' @usage \method{qi}{poisson}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) #' @S3method qi poisson #' @param obj a 'zelig' object #' @param x a 'setx' object or NULL #' @param x1 an optional 'setx' object #' @param y this parameter is reserved for simulating average treatment effects, #' though this feature is currentlysupported by only a handful of models #' @param num an integer specifying the number of simulations to compute #' @param param a parameters object #' @return a list of key-value pairs specifying pairing titles of quantities of #' interest with their simulations #' @author Matt Owen \email{mowen@@iq.harvard.edu} qi.poisson <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { # coef <- coef(param) # get inverse function inverse <- linkinv(param) # eta <- coef %*% t(x) theta <- matrix(inverse(eta), nrow=nrow(coef)) # ... ev <- theta pr <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta)) # default values ev1 <- pr1 <- fd <- NA for (i in 1:ncol(ev)) pr[,i] <- rpois(nrow(ev), lambda = ev[,i]) if (!is.null(x1)) { # quantities of interest results <- qi(obj, x1, num=num, param=param) # pass values over ev1 <- results[["Expected Values: E(Y|X)"]] pr1 <- results[["Predicted Values: Y|X"]] # compute first differences fd <- ev1 - ev } # Return quantities of interest list("Expected Values: E(Y|X)" = ev, "Expected Values: E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values: Y|X1" = pr1, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' Describe the `poisson' model to Zelig #' @usage \method{describe}{poisson}(...) #' @S3method describe poisson #' @param ... ignored parameters #' @return a list to be processed by `as.description' #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export describe.poisson <- function(...) { # parameters object parameters <- list(lambda = list( equations = c(1, 1), tags.allowed = FALSE, dep.vars = TRUE, exp.vars = TRUE ) ) # return list list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = 2007, category = "count", parameters = parameters, text = "Poisson Regression for Event Count Dependent Variables" ) } Zelig/R/zzz.R0000644000176000001440000012372212216377251012552 0ustar ripleyusers# THIS FILE CONTAINS PACKAGE HOOKS FOR ZELIG # ------------------------------------------ # @...: nothing # spill-over: output information about Zelig .onAttach <- function(...) { package.name <- "Zelig" mylib <- dirname(system.file(package = package.name)) ver <- packageDescription(package.name, lib.loc = mylib)$Version build.date <- packageDescription(package.name, lib.loc = mylib)$Date # build info packageStartupMessage("ZELIG (Versions ", ver, ", built: ", build.date, ")") # cat, for readability of the message text # Zelig info - do not exceed 80char/line packageStartupMessage(" +----------------------------------------------------------------+ | Please refer to http://gking.harvard.edu/zelig for full | | documentation or help.zelig() for help with commands and | | models support by Zelig. | | | | Zelig project citations: | | Kosuke Imai, Gary King, and Olivia Lau. (2009). | | ``Zelig: Everyone's Statistical Software,'' | | http://gking.harvard.edu/zelig | | and | | Kosuke Imai, Gary King, and Olivia Lau. (2008). | | ``Toward A Common Framework for Statistical Analysis | | and Development,'' Journal of Computational and | | Graphical Statistics, Vol. 17, No. 4 (December) | | pp. 892-913. | | | | To cite individual Zelig models, please use the citation | | format printed with each model run and in the documentation. | +----------------------------------------------------------------+ ") } # @param object a zelig object # @param envir an environment .GetGenericsS4 <- function(object, envir=parent.frame()) { if (inherits(object$result, "list")) { .ListS4Generics(classes=class(object$result[[1]]), env=envir) } else .ListS4Generics(classes=class(object$result), env=envir) } # @classes: classes # @where: compatibility with showMethods # @env: the environment to search for generics # return: a character-vector of function names # ******************************************** # this function searches .AllMTable within the namespace # of the functions environment .ListS4Generics <- function(classes=NULL, where=NULL, env=topenv(parent.frame())) { # get list of all generic functions functions <- if (missing(where)) getGenerics() else getGenerics(where) # matches <- c() functions <- as.character(functions) # for (f in functions) { fdef <- getGeneric(f) env <- environment(fdef) table <- tryCatch(get(".AllMTable", envir=env), error=function(e) NULL) if (is.null(table)) next if (any(classes %in% ls(table))) matches <- append(matches, f) } # return flist <- c("zelig", "param", "as.parameters", "sim", "setx", "register", 'summary') matches[ ! matches %in% flist ] } #' Describe a Zelig Model #' #' @param model.name #' @param ... ignored parameters #' @return a 'description' object containing citation information #' @author Matt Owen \email{mowen@@iq.harvard.edu} .ZeligDescribeModel <- function(model.name, ...) { # lie to zelig dummy.zelig <- "dummy" class(dummy.zelig) <- model.name # return as a description as.description(describe(dummy.zelig)) } #' Get a Character-Vector of All Models with a 'zelig2' Function #' #' @note In order for a Zelig model to either execute correctly or be listed as #' a legal Zelig model, the function name must be prefixed with 'zelig2'. #' @param zelig.only a boolean specifying whether we want to search only the #' Zelig namespace #' @return a character-vector of the Zelig models loaded on the user's machine #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} ZeligListModels <- function(zelig.only=FALSE) { results <- if (zelig.only) ls(pattern="^zelig2", envir=asNamespace("Zelig")) else apropos("^zelig2", mode="function") # substitute and return sub("^zelig2", "", results) } #' Get a Text-Block of Citation Information about a Zelig Model #' #' @note This function is strictly used internally by Zelig #' @param model.name the name of a Zelig model #' @return a block of text giving a human readable (and APA compliant) block #' citation text #' @author Matt Owen \email{mowen@@iq.harvard.edu} .GetModelCitationTex <- function(model.name) cite(ZeligDescribeModel(model.name)) #' Produce a 'description' Object from the Name of a Model #' @note The 'description' object is a list-style object containing citation #' information #' @param model.name a character-string specifying a Zelig model #' @return a 'description' object specified by the 'model.name' parameter. This #' object is created by executing the specified Zelig models' 'describe' #' function #' @export ZeligDescribeModel <- function(model.name) { dummy <- "I love baseball. You know, it doesn't have to mean anything. It's just very beautiful to watch." class(dummy) <- model.name # describe res <- describe(dummy) # add model name res$model <- model.name # return as.description(res) } #' Get a TeX-style Citation #' @param model a character-string specifying the name of the Zelig model of which #' to describe in TeX-style #' @return a string to be rendered as part of a LaTeX-style document #' @export TexCite <- function(model) { # description object descr <- ZeligDescribeModel(model) # url url <- "http://gking.harvard.edu/zelig" # define title title <- if (is.null(descr$text)) descr$model else paste(descr$model, ": ", descr$text, sep="") # quote title string title <- paste('"', title, '"', sep="") # construct string str <- paste( "{\bf To cite this model in Zelig:}", paste(descr$authors, descr$year, sep="."), paste(title, "in Kosuke Imai, Gary King and Olivia Lau,"), "\"Zelig: Everyone's Statistical Software,\"", url, sep = "\n" ) str } #' Get a List of Categories for Describing Zelig Models #' @note This feature is being deprecated, as original functionality with the #' Dataverse Project \url{thedata.org} is being reevaluated. #' @return a list of character-string specifying legal category types (as the #' keys of the list) and their human-counterparts (as the values) #' @export .ZeligModelCategories <- function() { list(continuous = "Models for Continuous Dependent Variables", dichotomous = "Models for Dichotomous Dependent Variables", ordinal = "Models for Ordinal Dependent Variables", bounded = "Models for Continous Bounded Dependent Variables", multinomial = "Multinomial Choice Models", count = "Event Count Models", mixed = "Models for Mixed Dependent Variables", ei = "Ecological Inference Models" ) } #' List the Titles of the Zelig Statistical Models #' @return a list of manual titles for the Zelig software #' @export ZeligListTitles <- function() { # models <- ZeligListModels() # lis <- list() # for (m in models) lis[[m]] <- ZeligDescribeModel(m)$text # turn into a vector with each entry having: # model_name: model_description # e.g. # probit: Probit Regression for Dichotomous Dependent Variables paste(names(lis), lis, sep=": ") } #' Whether an Arbitrary R-package has a Zelig2 Function within Its Namespace #' @note This function is used primarily internally to determine whether a #' a package is contributing a function to the Zelig software suite #' @param pkg a character-string representing a package name #' @return whether the package contains any zelig2-functions #' @export has.zelig2 <- function(pkg) { env <- asNamespace(pkg) hits <- grep("^zelig2*", ls(envir=env)) length(hits) > 0 } #' Whether a Statistical Package Depends on the Zelig Software Suite #' @note This function is used primarily internally to determine whether a #' a package is contributing a function to the Zelig software suite #' @param package a character-string representing a package name #' @return whether the package lists Zelig as a dependency in its DESCRIPTION #' @export depends.on.zelig <- function(package="") { zcomp <- packageDescription(package, fields="Depends") if (is.na(zcomp)) return(FALSE) zcomp <- unlist(strsplit(zcomp, " *, *")) # "Zelig" %in% zcomp # pattern to match things leading with Zelig, some spaces, and a parenthesis ending # ex: # Zelig # Zelig (>= 3) # Zelig (blah blah) pattern <- "^Zelig *(?:\\(.*?\\))$" length(grep(pattern, zcomp)) != 0 } #' Get a List of Packages Installed on the Current Machine that Depend on Zelig #' @note This function is used primarily internally to determine whether a #' a package is contributing a function to the Zelig software suite #' @return a character-vector of all zelig-dependent packages on the current #' machine list.zelig.dependent.packages <- function() Filter(depends.on.zelig, .packages(all.available=TRUE)) #' List Zelig Models Installed on the Current Machine #' @note This list is not necessarily complete #' @param with.namespace a boolean specifying whether #' @return list of all zelig models list.zelig.models <- function(with.namespace=TRUE) { # list the zelig-dependent packages pkgs <- list.zelig.dependent.packages() # include the core package pkgs <- c("Zelig", pkgs) # initialize functions variable functions <- NULL # create a list of every zelig2 function for (pkg in pkgs) { # get all zelig2 functions, then get their model name models <- ls(pattern="^zelig2", envir=asNamespace(pkg)) models <- sub("^zelig2", "", models) # add to results list functions[models] <- pkg } # return if (with.namespace) # with model-name as the key, and namespace as the value functions else # with just a list of models names(functions) } #' Append a Prefix to a Character String #' @note This function is exclusively used internally by Zelig #' @param name a character-string specifying the name of a variable #' @param envir an environment variable to search #' @param prefix a character-string to prefix the string with #' this is applied until the name is unique #' @param sep a character-string that separates prefix and name .prefix <- function(name, envir, prefix="zelig", sep=".") { # check to make sure this is an environment variable if (!is.environment(envir)) { warning() envir <- globalenv() } # ensure some name is returned if (!is.character(c(name, prefix, sep))) { warning() name } else if (length(name) > 1 || length(prefix) > 1 || length(sep) > 1) { warning() name } else if (!nchar(name)) { warning() sep <- "." } else { while(exists(name, envir=envir)) name <- paste(prefix, name, sep=sep) # return if nothing wonky happened name } } .GetGenerics <- function(...) UseMethod(".GetGenerics") # needs work .GetGenerics.MI <- function(...) new.env() # @zelig.object: a zelig object # @envir: namespace to search with 'ls' # return: a list of generic functions names to # to define for zelig .GetGenerics.default <- function(zelig.object, envir=parent.frame()) { if (is.null(zelig.object$S4)) stop(as.character(zelig.object$family[[1]])) else if (zelig.object$S4) suppressWarnings(.GetGenericsS4(zelig.object, envir)) else suppressWarnings(.GetGenericsS3(zelig.object, envir)) } .GetGenericsS3 <- function(zelig.object, envir=parent.frame()) { # hash <- list() cls <- class(zelig.object$result) method.list <- as.character(unlist(mapply(methods, class=cls))) regex <- paste("(", paste(cls, collapse="|"), ")", sep="|") method.list <- gsub(regex, "", method.list) meth.list <- c() for (cl in c(class(zelig.object$result), "default")) { method.list <- as.character(methods(class=cl)) method.list <- gsub(paste("\\.", cl, "$", sep=""), "", method.list) meth.list <- unique(c(meth.list, method.list)) } # final list flist <- c("zelig", "param", "as.parameters", "sim", "setx", "register", 'qi', 'summary') meth.list <- sort(unique(c(meth.list, names(get(".knownS3Generics"))))) meth.list[ ! meth.list %in% flist ] } # Numerical Derivative # # This method computes the numerical derivative at a point # @param f function (differentiable) # @param stencil number of points in stencil. This is currently ignored. # @param h size of mesh # @return anonymous function with the approximation # @note single variable numerical derivative .nderiv <- function(f, stencil=5, h=sqrt(.Machine$double.eps)) { # return approximated derivative function function (x) { # construct the 5-point mesh, middle point omitted # since it gets deleted anyway x.stencil <- rep(x, 4) + c(2, 1, -1, -2)*h # compute approximation sum(sapply(x.stencil, f) %*% c(-1, 8, -8, 1))/12/h } } # @F: function to invert # @f: derivative of function, or NULL to use numerical approximation # @x: initial guess # @tol: error-tolerance # @h: mesh size # @max.iter: number of iterations to perform before giving up # return: df(x_0)/dx # **note: newton-rhapson for single variables # **suggestion: replace with C code, otherwise won't be truly fast-enough .nr <- function(F, f=NULL, x = 1, a = 0, tol = sqrt(.Machine$double.eps), h = sqrt(.Machine$double.eps), max.iter = 50) { # save function to prevent recursions saved.function <- F # rewrite function to solve for a if (!missing(a)) F <- function(x) saved.function(x) - a # if NULL assign numerical derivative if (is.null(f)) f <- .nderiv(F) # count <- 1 # while (abs(F(x)) > tol && count <= max.iter) { # increment counter count <- count + 1 # if derivative is zero, or near it # (otherwise we have issues with solutions where x=0) if (abs(f(x)) < 10^-8) { x <- x + runif(1, min=-1, max=1) next } # iterate x <- x - F(x)/f(x) } if (count > max.iter) warning("approximation failed to converge given specified tolerance") # return result x } # @F: # @f: # @x: initial guess # @tol: # return: a functional form of the newton-rhapson approximation .NumInverse <- function(F, f=NULL, x = 1, tol = (.Machine$double.eps)^.5, h = sqrt(.Machine$double.eps), max.iter = 50) { function (a) { res <- c() # kludgey, but just a hold-over for now for (val in a) { val <- .nr(F=F, f=f, x=x, a=val, tol=tol, h=h, max.iter=max.iter) res <- c(res, val) } res } } # This file contains overloaded operators # However, developers - in general - should avoid the use of these features, # and instead use iterators when dealing with multiple fitted models or # quantities of interest. # The methods primarily come up when defining 'summarize' and 'plot' functions #' Extract a Value from a Fitted Model Object (Wrapped by Zelig) #' @S3method "[[" zelig #' @param z an object of type 'zelig' #' @param slot a character-string specifying the slot to extract from the fitted #' model object #' @param ... subsequent slots to extract from the fitted model object #' @return contents of the specified slots #' @author Matt Owen \emph{mowen@@iq.harvard.edu} "[[.zelig" <- GetSlot.zelig #' Extraction Operator for Quantities of Interest #' This function is exclusively used internally by Zelig, and behaves in a very #' fishy manner. \code{qi} objects maintain an internal list of indices which #' are used to find the appropriate slot which holds a particular quantity of #' interest. #' When a \code{qi} object is defined, all the quantities of interest are #' converted into acronyms, so that elements of the \code{qi} object can be #' stored without a lengthy name containing spaces (since most qi's are #' human-readable). As a result, these objects contain an \code{.index} #' attribute which pairs every quantity of interest with its acronym. This #' index is then used to extract (using the \code{$} operator) the appropriate #' element of the list. #' In short, it pairs the key "Expected Value" with the slot \code{ev}. This #' allows that the following will always be true (in the mentioned example): #' \code{qi$ev == qi[["Expected Value"]]} #' @note When possible, \code{qi} objects should be handled with iterators #' rather than list-style extraction operators. #' @S3method "[[" qi #' @param self the \code{qi} object #' @param key a character-string specifying the title of the quantity of #' interest to extract. #' @return if the quantity of interest exists, that entry. Otherwise, #' \code{NULL} #' @author Matt Owen \email{mowen@@iq.harvard.edu} "[[.qi" <- function(self, key) { # Produce the index of titles of qi's index <- attr(self, ".index") # Find the 'short-name' matching qi.short.name <- index[[key]] if (is.null(qi.short.name)) NULL else # if this title => key pair is found, invoke the "$" operator on the # shortname. In effect, this makes: # qi[['Expected Value']] # # equivalent to: # qi$ev do.call("$", list(self, qi.short.name)) } #' Receiver Operator Characteristic Plots #' #' The 'rocplot' command generates a receiver operator characteristic plot to #' compare the in-sample (default) or out-of-sample fit for two logit or probit #' regressions. #' #' @usage #' rocplot(y1, y2, fitted1, fitted2, #' cutoff = seq(from=0, to=1, length=100), lty1="solid", #' lty2="dashed", lwd1=par("lwd"), lwd2=par("lwd"), #' col1=par("col"), col2=par("col"), #' main="ROC Curve", #' xlab = "Proportion of 1's Correctly Predicted", #' ylab="Proportion of 0's Correctly Predicted", #' plot = TRUE, #' ... #' ) #' #' @param y1 response variable for the first model #' @param y2 response variable for the second model #' @param fitted1 fitted values for the first model. These values may represent #' either the in-sample or out-of-sample fitted values #' @param fitted2 fitted values for the second model #' @param cutoff A vector of cut-off values between 0 and 1, at which to #' evaluate the proportion of 0s and 1s correctly predicted by the first and #' second model. By default, this is 100 increments between 0 and 1 #' inclusive #' @param lty1 the line type of the first model (defaults to 'line') #' @param lty2 the line type of the second model (defaults to 'dashed') #' @param lwd1 the line width of the first model (defaults to 1) #' @param lwd2 the line width of the second model (defaults to 1) #' @param col1 the color of the first model (defaults to 'black') #' @param col2 the color of the second model (defaults to 'black') #' @param main a title for the plot (defaults to "ROC Curve") #' @param xlab a label for the X-axis #' @param ylab a lavel for the Y-axis #' @param plot whether to generate a plot to the selected device #' @param \dots additional parameters to be passed to the plot #' @return if plot is TRUE, rocplot simply generates a plot. Otherwise, a list #' with the following is produced: #' \item{roc1}{a matrix containing a vector of x-coordinates and #' y-coordinates corresponding to the number of ones and zeros correctly #' predicted for the first model.} #' \item{roc2}{a matrix containing a vector of x-coordinates and #' y-coordinates corresponding to the number of ones and zeros correctly #' predicted for the second model.} #' \item{area1}{the area under the first ROC curve, calculated using #' Reimann sums.} #' \item{area2}{the area under the second ROC curve, calculated using #' Reimann sums.} #' @export #" @author Kosuke Imai and Olivia Lau rocplot <- function(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100), lty1="solid", lty2="dashed", lwd1=par("lwd"), lwd2=par("lwd"), col1=par("col"), col2=par("col"), main="ROC Curve", xlab = "Proportion of 1's Correctly Predicted", ylab="Proportion of 0's Correctly Predicted", plot = TRUE, ...) { roc1 <- roc2 <- matrix(NA, nrow = length(cutoff), ncol = 2) colnames(roc1) <- colnames(roc2) <- c("ones", "zeros") for (i in 1:length(cutoff)) { roc1[i,1] <- mean(fitted1[y1==1] >= cutoff[i]) roc2[i,1] <- mean(fitted2[y2==1] >= cutoff[i]) roc1[i,2] <- mean(fitted1[y1==0] < cutoff[i]) roc2[i,2] <- mean(fitted2[y2==0] < cutoff[i]) } if (plot) { plot(0:1, 0:1, type = "n", xaxs = "i", yaxs = "i", main=main, xlab=xlab, ylab=ylab, ...) lines(roc1, lty = lty1, lwd = lwd1, col=col1) lines(roc2, lty = lty2, lwd = lwd2, col=col2) abline(1, -1, lty = "dotted") } else { area1 <- area2 <- array() for (i in 2:length(cutoff)) { area1[i-1] <- (roc1[i,2] - roc1[(i-1),2]) * roc1[i,1] area2[i-1] <- (roc2[i,2] - roc2[(i-1),2]) * roc2[i,1] } return(list(roc1 = roc1, roc2 = roc2, area1 = sum(na.omit(area1)), area2 = sum(na.omit(area2)))) } } #' Create Function Call #' #' #' @param Call a \code{call} object, typically specifying the original function #' call to \code{zelig} #' @param zelig2 the return-value of the \code{zelig2} method #' @param remove a list of character vectors specifying which parameters to #' ignore from the original call to \code{zelig} #' @return a function call used to fit the statistical model #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} zelig.call <- function(Call, zelig2, remove = NULL) { # envir <- new.env() # reserved words taken from the zelig2 method func <- as.name(zelig2$.function) hook <- zelig2$.hook # remove the reserved words zelig2$.function <- NULL zelig2$.hook <- NULL zelig2$.post <- NULL zelig2$.model.matrix <- NULL # make a list of the parameters to be passed to the external model args <- names(formals(as.character(func))) # remove certain parameters for (key in remove) { if (key %in% names(Call)) Call[[key]] <- NULL } # remove invalid params for (key in names(Call[-1])) { if (! key %in% args) Call[[key]] <- NULL } # A static list of objects that do not printout well or should be stored # within a separate environment messy.objects <- c("data.frame", "function", 'matrix', "family", "function") neat.objects <- c("formula", "family") skip <- c() # Store values within 'messy.objects' within another environment, and give a # pseudonym for (key in names(zelig2)) { obj <- zelig2[[key]] Class <- class(obj) first.class <- Class[1] if (is.object(obj)) { if (all(Class %in% neat.objects)) { Call[[key]] <- obj } else { Name <- store.object(obj, envir, ucfirst(first.class)) Call[[key]] <- as.name(Name) skip <- c(skip, key) } } else if (is.function(obj)) { Name <- store.object(obj, envir, "Function") Call[[key]] <- as.name(Name) skip <- c(skip, key) } else if (is.atomic(obj) && length(obj) > 5) { Name <- store.object(obj, envir, paste(toupper(Class[1]), length(obj), sep="")) Call[[key]] <- as.name(Name) skip <- c(skip, key) } else if (is.list(obj) && length(obj) > 5) { Name <- store.object(obj, envir, paste("List", length(obj), sep="")) Call[[key]] <- as.name(Name) skip <- c(skip, key) } else { # this is a hack to prevent removal of elements if the value is NULL null.list <- list(NULL) names(null.list) <- key # the two statement are *slightly* different if (is.null(obj)) { Call <- as.call(append(as.list(Call), null.list)) } else { Call[[key]] <- obj } } } # Guarantee all zelig2 names are included (including model, etc) for (key in names(zelig2)) { if (key %in% skip) next; if (!is.null(zelig2[[key]])) Call[[key]] <- zelig2[[key]] else { # Clear the entry. Don't worry. It's going to get re-added later in this # Else-block. Call[[key]] <- NULL # Create the NULL paramater dummylist <- list(NULL) names(dummylist) <- key # Cast as a list, so we can use append Call <- as.list(Call) # Append the entry Call <- as.call(append(Call, dummylist)) } } # Change function value Call[[1]] <- func list(call=Call, envir=envir) } #' Store Object in Environment with a Fake Name #' #' This function takes the value of an object and stores it within a specified #' environment. This is similar to simply using the \code{assign} function, but #' will not overwrite existing values in the specified environment. It #' accomplishes this by appending a prefix to the name of the variable until #' the name becomes unique. #' @note This method does not correct invalid names. That is, there is no test #' to determine whether the submitted name is valid. #' @param obj any object #' @param envir an environment object, which will contain the object with the #' assigned name #' @param name a character-string specifying the name that the object will be #' stored as in the specified environment #' @param prefix a character string specifying the prefixes to append to names #' that already have matches in the destination environment #' @return a character-string specifying the name of the object in the #' destination environment #' @author Matt Owen \email{mowen@@iq.harvard.edu} store.object <- function (obj, envir, name=NULL, prefix=".") { variables <- ls(envir=envir) # ensure name is unique while (name %in% variables) name <- paste(prefix, name, sep="") assign(name, obj, envir) name } #' Uppercase First Letter of a String #' #' This method sets the first character of a string to its uppercase, #' sets all other characters to lowercase. #' @param str a vector of charaqcter-strings #' @return a vector of character strings #' @author Matt Owen \email{mowen@@iq.harvard.edu} ucfirst <- function (str) { paste( toupper(substring(str, 1, 1)), tolower(substring(str, 2)), sep = "" ) } #' Search for, Copy, and Customize Template for a Newly Create Zelig Package #' This is used internally by \code{zelig.skeleton} #' @param model a character-string specifying the name of the model #' @param the file template to search for and copy #' @param pkg a character-string specifying the name of the package #' @param path a character-string specifying the base path to the package's u #' parent directory #' @return This function is used for its side-effects. .copy.templates <- function (model, pkg, path) { # path to R folder r.path <- file.path(path, pkg, 'R') # source files zelig2 <- system.file('templates', 'zelig2.R', package="Zelig") param <- system.file('templates', 'param.R', package="Zelig") qi <- system.file('templates', 'qi.R', package="Zelig") describe <- system.file('templates', 'describe.R', package="Zelig") # create R directory dir.create(r.path, showWarnings=FALSE) # destination files zelig2.dest <- file.path(r.path, paste('zelig2', model, '.R', sep="")) param.dest <- file.path(r.path, paste('param', model, 'R', sep=".")) qi.dest <- file.path(r.path, paste('qi', model, 'R', sep=".")) describe.dest <- file.path(r.path, paste('describe', model, 'R', sep=".")) # create blank files file.create(zelig2.dest, param.dest, qi.dest) # substitute zelig2.lines <- .substitute.expressions(zelig2, model=model) param.lines <- .substitute.expressions(param, model=model) qi.lines <- .substitute.expressions(qi, model=model) describe.lines <- .substitute.expressions(describe, model=model) # write to file writeLines(zelig2.lines, con = zelig2.dest) writeLines(param.lines, con = param.dest) writeLines(qi.lines, con = qi.dest) writeLines(describe.lines, con = describe.dest) TRUE } #' make a description file for a specific package #' param pkg a character-string specifying the name of the package #' param author a vector of strings specifying the names of contributors #' param email a character-string specifying the email of the maintainer #' param depends a vector of strings specifying package dependencies #' param url - ignored - #' param path a character-string specifying the location of the package #' return nothing .make.description <- function (pkg, author, email, depends, url, path='.') { model <- pkg description.file <- file.path(path, model, 'DESCRIPTION') # make author list human-readable author <- .get.list.as.text(author) maintainer <- paste(author[1L], ' <', email, '>', sep="") depends <- c("Zelig", depends) depends <- unique(depends) depends <- paste(depends, collapse=", ") fields <- c( Package = model, Version = .1, Date = as.character(Sys.Date()), Title = "A Zelig Model", Author = author, Maintainer = maintainer, Depends = depends, Description = "A Zelig Model", License = "GPL (>=2)", URL = "http://gking.harvard.edu/zelig", Packaged = gsub('\\s+', ' ', date()) ) # correctly write to file: # Package: 'model' # Version: .1 # etc. writeLines( paste(names(fields), ': ', fields, sep=""), con = description.file ) } #' @note This function fails if passed non-alphanumeric variable names. In #' particular, the parameters cannot contain periods, etc. #' @param .file the name of the file to replace #' @param ... #' @return a character-string .substitute.expressions <- function(.file, ...) { lines <- readLines(con = .file, warn = FALSE) replacements <- list(...) for (key in names(replacements)) { val <- replacements[[key]] expr <- paste('\\\\\\\\', key, '\\\\\\\\', sep="") lines <- gsub(expr, val, lines) } lines } #' Make \code{pkg}-package.R File for Roxygen Compliancy #' @param pkg the package name #' @param author a vector of characters specifying the authors of the Zelig #' models #' @param email the email address of the package's maintainer #' @param depends a vector specifying package dependencies #' @param URL specifying the package's website #' @param path location of the package #' @return NULL #' @author Matt Owen \email{mowen@@iq.harvard.edu} .make.package.R <- function (pkg, author, email, depends, url, path='.') { file <- system.file('templates', 'PACKAGE.R', package='Zelig') dest <- file.path(path, pkg, 'R', paste(pkg, 'package.R', sep='-')) author <- .get.list.as.text(author) depends <- paste(c('Zelig', depends), collapse=', ', sep=', ') lines <- .substitute.expressions(author=author, package=pkg, .file=file, depends=depends ) writeLines(lines, con = dest) } #' Convert Character-Strings into Human-Readable Lists #' This functions converts its parameters into a human-readable and #' grammatically correct series. #' @param ... character-vectors and list of characters #' @param final.comma whether to add the final comma to the series. Grammatical #' correctness is debateable #' @return a comma delineated string #' @author Matt Owen \email{mowen@@iq.harvard.edu} .get.list.as.text <- function (..., final.comma=FALSE) { authors <- c(...) length <- length(authors) if (!length) "" else if (length == 1) authors[[1L]] else if (length == 2) paste(authors, collapse = " and ") else { beginning <- head(authors, -1) beginning <- paste(beginning, collapse= ', ') end <- tail(authors, 1) final.sep <- ifelse(final.comma, ', and ', ' and ') paste(beginning, end, sep = final.sep) } } #' Compute the Statistical Mode of a Vector #' @param x a vector of numeric, factor, or ordered values #' @return the statistical mode of the vector. If two modes exist, one is #' randomly selected (by design) #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Mode <- function (x) { # build a table of values of x tab <- table(as.factor(x)) # find the mode, then if there's more than one, select one randomly v <- sample(names(which(tab == max(tab))), size=1) # if it came in as a factor, we need to re-cast it # as a factor, with the same exact levels if (is.factor(x)) return(factor(v, levels=levels(x))) # re-cast as any other data-type as(v, class(x)) } #' Compute the Statistical Median of a Vector #' @param x a vector of numeric or ordered values #' @param na.rm ignored #' @return the median of the vector #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Median <- function (x, na.rm=NULL) { v <- ifelse(is.numeric(x), median(v), levels(x)[ceiling(median(as.numeric(x)))] ) if (is.ordered(x)) v <- factor(v, levels(x)) v } #' Compute the Maximum Value of a Vector #' @param x a numeric or ordered vector #' @param na.rm ignored #' @return the maximum value of the vector #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Max <- function (x, na.rm=NULL) { if (is.numeric(x)) return(max(x)) else if (is.ordered(x)) return(factor(max(levels(x), na.rm=T ), levels=levels(x) ) ) else stop("Error: max cannot be computed for non-numeric and non-ordered values") } #' Compute the Minumum Value of a Vector #' @param x a vector of numeric or ordered values #' @param na.rm ignored #' @return the minimum value of the vector #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Min <- function (x, na.rm=NULL) { if (is.numeric(x)) return(min(x)) else if (is.ordered(x)) return(factor(min(levels(x), na.rm=T ), levels=levels(x) ) ) else stop("Error: min cannot be computed for non-numeric and non-ordered values") } #' @export loadDependencies <- function (..., character.only = FALSE) { # Get arguments that aren't "character.only" if (character.only) { packs <- match.call(expand.dots = TRUE)[-1] packs$character.only <- NULL packs <- as.character(packs) } else packs <- as.character(list(...)) # results <- list() # for (pkg in packs) results[pkg] <- require(pkg, character.only = TRUE) if (all(unlist(results))) invisible(TRUE) else { failed.packs <- Filter(function (x) { return(x == FALSE) }, results) list.of.packages <- paste('"', names(failed.packs), '"', sep = '', collapse = ', ') message('The following packages did not load: ') cat(' ') message(list.of.packages) message() install.string <- paste(' install.packages(', names(failed.packs), ')', sep = '', collapse = '\n') message('To run this model, install these packages with the following command:') message(install.string) message() stop('') } } #' Produce All Combinations of a Set of Lists #' @note This function is used internall by the 'mi' constructors in order to #' produce the complete set of combinations of data-frames and factors by to #' subset the data-frames. #' @param ... a set of lists to mix together #' @return all the combinations of the lists with repetition #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export mix <- function(...) { # expand dot arguments dots <- list(...) # error-catching if (length(dots) < 1) return(NULL) # prepare lists for first iteration res <- dots[[1]] dots <- dots[-1] # this entire algorithm could be optimized, # however, it will always be exponential time while(length(dots) > 0) { # get list to store new combinations in new.list <- list() # divide list first <- dots[[1]] # add new combinations for (f in first) { for (r in res) { row <- append(as.list(r), f) new.list[['']] <- row } } # Update list res <- new.list # Shift first entry off dots <- dots[-1] } # Appropriately name each entry for (k in 1:length(res)) names(res[[k]]) <- names(list(...)) res } #' Produce All Combinations of a Set of Lists #' @note This function is used internall by the 'mi' constructors in order to #' produce the complete set of combinations of data-frames and factors by #' to subset the data-frames. #' @param ... a set of lists to mix together #' @return all the combinations of the lists with repetition #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export combine <- function(...) { # expand dot arguments dots <- list(...) # error-catching if (length(dots) < 1) return(NULL) # prepare lists for first iteration res <- dots[[1]] dots <- dots[-1] # this entire algorithm could be optimized, # however, it will always be exponential time while(length(dots) > 0) { # get list to store new combinations in new.list <- list() # divide list first <- dots[[1]] # add new combinations for (f in first) for (r in res) new.list[['']] <- c(r, f) # update list res <- new.list # shift first entry off dots <- dots[-1] } # m, as in matrix m <- NULL # format results as a matrix for (r in res) m <- rbind(m, r) # name rows/cols rownames(m) <- 1:length(res) colnames(m) <- names(list(...)) # return m } #' Split a List into Two Lists #' This functions takes any list, and splits into two lists - one containing #' the values of arguments with specifically specified values and those without #' specified values. #' @note This function is a good candidate for deprecation #' @param args a list #' @return a list containing two entries: the key-value paired entires (titled #' wordful) and the unkeyed entried (titled wordless) #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export #' @examples #' #list(wordful = list(x=1, y=2), wordless=list(2, "red")) splitUp <- function(args) { wordless <- list() wordful <- list() k <- 1 if (is.null(names(args))) return(list(wordless=unlist(args), wordfull=NULL)) for (key in names(args)) { if (nchar(key) == 0) wordless <- c(wordless, args[[k]]) else wordful[[key]] <- args[[k]] k <- k+1 } list(wordless=wordless, wordful=wordful) } ## Honaker: I had to remove this utility function because ## CRAN has recently weighed against use of ":::" ## @topic: character-string representing help-topic ## @package: package containing help-topic ## return: character-string of processed Rd file #.get.help.file <- function(topic, package) { # # get package help-file if no topic is set # if (missing(topic)) # topic <- package # ## error-checking: ## ensure file and package are strings # if (!is.character(topic) && length(topic) > 1L) # stop() # # if (!is.character(package) && length(package) > 1L) # stop() # ## # directory <- system.file(package=package) # ## # path <- utils:::index.search( # topic=topic, # paths=directory # ) # ## search package-help-dataabase, get Rd file as string # utils:::.getHelpFile(file=path) #} # @package: character-string specifying the name of a package to # scan for help files # @as.table: boolean specifying whether the return value will be # a table or names of Rd files # return: either a named vector (table), or an unnamed vector .list.help.files <- function(package, as.table=TRUE) { # index for help files fi <- file.path( system.file(package=package), "help", "AnIndex" ) if (file.exists(fi)) { # get index of search-values and corresponding # Rd file index <- scan(fi, what = list(names="", values=""), sep = "\t", quote = "", na.strings = "", quiet = TRUE ) # the if-else below is a return value if (as.table) # return as an index structure(index$values, names=index$names) else # return only the names of the Rd files index$names } else { warning("nothing was found") NULL } } #' Compute the Intersection of Two Sets #' @note This function is used internally by Zelig #' @param a a vector #' @param b a vector #' @param unique a boolean determining whether a intersect b will contain only #' unique elements #' @return the intersection of a and b .intersection <- function(a, b, unique=TRUE) { intersection <- a[a %in% b] if (unique) intersection <- unique(intersection) if (is.null(intersection)) c() else intersection } #' Hook to Update the Zelig Call with the Appropriate Call Object #' @note This function is used internally by Zelig, and currently deprecated. #' @param zobj a 'zelig' object #' @param call1 the original call to Zelig #' @param call2 the manuafactured call to the model fitting function #' @return the 'zelig' object with a modified 'call' slot replace.call <- function(zobj, call1, call2) { # what if it doesn't exist? if (!is.null(zobj$result$call) && is.call(zobj$result$call2)) zobj$result$call <- call2 zobj } #' Wether an Installed R-Pack Depends on Zelig #' @note This package was used internally to determine whether an R-package is #' Zelig compliant, but is now likely deprecated. This test is useless if not #' paired with #' @param package a character-string naming a package #' @return whether this package depends on Zelig is.zelig.package <- function(package="") { "Zelig" %in% tools::pkgDepends(package)$Depends } #' Whether a R-Package Contains a 'Yes' in its DESCRIPTION File's 'Zelig' Field #' @note This package was used internally to determine whether an R-package is #' Zelig compliant, but is now likely deprecated. #' @param package a character-string specifying an installed R-package #' @return whether the package's DESCRIPTION file specifies Zelig-compliancy #' @seealso is.zelig.package #' @author Matt Owen \email{mowen@@iq.harvard.edu} is.zelig.compliant <- function(package="") { # zcomp <- packageDescription(package, fields="Zelig-Compliant") zcomp <- tolower(zcomp) # if (! zcomp %in% c('yes', 'no')) stop("") zcomp == "yes" } Zelig/R/t.setx.R0000644000176000001440000000073712061700507013132 0ustar ripleyusers#' Matrix Transpose of a ``setx'' Object #' #' Returns a ``setx'' object as column vector. If multiple values for each #' explanatory term has been set, then return a NxM matrix where `N' #' is the number of explanatory terms and `M' is the number of values set for #' each term. #' #' @S3method t setx #' @usage \method{t}{setx}(x) #' @param x a `setx' object #' @return a transposed matrix #' @author Matt Owen \email{mowen@@iq.harvard.edu} t.setx <- function(x) t(x$matrix) Zelig/R/getPredictorTerms.R0000644000176000001440000000252612061700507015351 0ustar ripleyusers#' Get Predictor Terms from Zelig-style Formulae #' #' This function extracts the predictor terms from a Zelig-style object. #' @note This function is used exclusively in the development of Zelig-core. #' @param x a Zelig-style formula ('formula' or 'list') #' @param ... ignored parameters #' @return a character-vector or NA #' @author Matt Owen \email{mowen@@iq.harvard.edu} #' @export getPredictorTerms <- function (x, ...) { # The following functions are unsafe for general input, so they are being # kept as nested functions. # Extract "predictor" terms from a formula # @param x a formula # @param ... ignored parameters # @return a character-vector specifying the # @author Matt Owen extractFromFormula <- function (form, ...) { TERMS <- terms(form) attr(TERMS, "term.labels") } # Extract "predictor" terms from a list of formulae # @param x a list # @param ... ignored parameters # @return a character-vector specifying the # @author Matt Owen extractFromList <- function (x, ...) { as.vector(unlist(Map(extractFromFormula, x))) } # Beginning of work for function if (is.list(x)) unique(extractFromList(x)) else if ("formula" %in% class(x)) unique(extractFromFormula(x)) else { warning("The model formula must either ", "be a list of formula to work properly") NA } } Zelig/R/gamma.gee.R0000644000176000001440000001134212110047000013503 0ustar ripleyusers#' Interface between the Zelig Model gamma.gee and #' the Pre-existing Model-fitting Method #' @param formula a formula #' @param id a character-string specifying the column of the data-set to use #' for clustering #' @param robust a logical specifying whether to robustly or naively compute #' the covariance matrix. This parameter is ignore in the \code{zelig2} #' method, and instead used in the \code{robust.hook} function, which #' executes after the call to the \code{gee} function #' @param ... ignored parameters #' @param R a square-matrix specifying the correlation #' @param corstr a character-string specifying the correlation structure #' @param data a data.frame #' @return a list specifying the call to the external model #' @export zelig2gamma.gee <- function (formula, id, robust = FALSE, ..., R = NULL, corstr = "independence", data) { loadDependencies("gee") if (corstr == "fixed" && is.null(R)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } z( .function = gee, .hook = robust.gee.hook, formula = formula, id = id, corstr = corstr, family = Gamma, data = data, ... ) } #' @S3method param gamma.gee param.gamma.gee <- function(obj, num=1000, ...) { # Extract means to compute maximum likelihood mu <- coef(.fitted) # Extract covariance matrix to compute maximum likelihood Sigma <- .fitted$naive.variance # list( coef = mvrnorm(num, mu, Sigma), fam = Gamma() ) } #' @S3method qi gamma.gee qi.gamma.gee <- function(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) { coef <- coef(param) inverse <- linkinv(param) eta1 <- coef %*% t(x) ev1 <- theta1 <- matrix(inverse(eta1), nrow=num) # default to NA ev2 <- fd <- NA if (!is.null(x1)) { eta2 <- coef %*% t(x1) ev2 <- theta1 <- matrix(inverse(eta2), nrow=num) fd <- ev2 - ev1 } list( "Expected Values (for x): E(Y|X)" = ev1, "Expected Values (for x1): E(Y|X1)" = ev2, "First Differences: E(Y|X1) - E(Y|X)" = fd ) } #' @S3method describe gamma.gee describe.gamma.gee <- function(...) { list( authors = "Patrick Lam", text = "General Estimating Equation for Gamma Regression", year = 2011 ) } # Remove Negative Simulations from Gamma GEE Parameter Simulations # @param object a \code{zelig} object # @param x a \code{setx} object # @param x1 a \code{setx} object # @param bootstrap a logical specifying whether the model is using a boot function # @param bootfn the boot function # @param data a data.frame used to simulated parameters # @param param the original \code{param} object # @param num an integer specifying the number of simulations to produce clean.up.gamma.gee <- function(object, x, x1=NULL, bootstrap = FALSE, bootfn = NULL, data = NULL, param, num = 1000) { coef <- coef(param) eta <- coef %*% t(x) if(!is.null(x1)) eta1 <- coef %*% t(x1) else eta1 <- NULL # define good.parameters (???) good.params <- function(par, x, x1=NULL) { eta <- par %*% t(x) if(!is.null(x1)) { eta1 <- par %*% t(x1) pos <- which(eta>0 & eta1>0) } else { pos <- which(apply(eta > 0,1,all)) } matrix(par[pos,], nrow=length(pos), ncol=ncol(par)) } if(length(which(apply(eta<=0,1,any)))>0 | (!is.null(eta1) & any(eta1<=0))){ warning(paste("Negative expected values in simulations. Rejection sampling method used.")) sum.neg <- length(which(apply(eta<=0,1,any))) coef <- good.params(par=coef, x=x, x1=x1) counter <- 1 while(sum.neg > 0){ if(!bootstrap) new.coef <- matrix(mvrnorm(sum.neg, mu = coef(object), Sigma = vcov(object)), nrow=sum.neg) #else # new.coef <- matrix(boot(data, bootfn, R = sum.neg, object = object)$t, nrow=sum.neg) new.coef <- good.params(par=new.coef, x=x, x1=x1) coef <- rbind(coef, new.coef) sum.neg <- num - nrow(coef) counter <- counter + 1 if(counter==200) warning(paste("Suitable parameters not found after 200 iterations of rejection sampling. Iterations will continue, but choosing another x is suggested for non-conditional prediction models.")) if(counter==2000) stop("Rejection sampling stopped after 2000 iterations. Please choose another x value.") } } # list( coefficients=coef, fam=Gamma(), linkinv = Gamma()$linkinv ) } Zelig/R/describe.R0000644000176000001440000000041312061700507013454 0ustar ripleyusers#' Method to describe a model to Zelig #' @param ... parameters which are typically ignored #' @return a list to be processed by `as.description' #' @export describe #' @author Matt Owen \email{mowen@@iq.harvard.edu} describe <- function(...) UseMethod("describe") Zelig/R/termsFromFormula.R0000644000176000001440000000072712061700507015210 0ustar ripleyusers#' Extract Terms from Zelig-style Formulae #' #' This method is a sugary function to extract terms from any type of #' Zelig-style formula. #' @param obj a Zelig-style formula #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} termsFromFormula <- function (obj) { # Do not put all of this function on one line, because it will make error # messages long and confusing obj <- parseFormula(obj) # Actually extract the terms, then return terms(obj) } Zelig/R/make.parameters.R0000644000176000001440000000271112061700507014756 0ustar ripleyusers#' ??? For use with cmvglm #' @param terms ??? #' @param shape ??? #' @param ancillary ??? #' @param eqns ??? #' @return ??? #' @export #' @author Kosuke Imai and Olivia Lau make.parameters <- function(terms, shape = "vector", ancillary = TRUE,eqns=NULL) { if (!shape %in% c("matrix", "vector")) stop("not a valid 'shape' for parameters. Choose from \"matrix\" or \"vector\".") #comment if(is.null(eqns)) eqns<-names(terms) ints <- attr(terms, "intercept")[eqns] labs <- attr(terms, "term.labels")[eqns] const <- attr(terms, "constraints") for (i in 1:length(eqns)) { if (ints[[i]] == 1) labs[[i]] <- c("(Intercept)", labs[[i]]) } fixed<-eqns[eqns %in% attr(terms,"ancilEqns")] syst<-eqns[eqns %in% attr(terms,"systEqns")] # syst<-eqns vars <- unique(unlist(labs)) pars <- matrix(NA, ncol = length(syst), nrow = length(vars)) colnames(pars) <- syst rownames(pars) <- vars for (i in syst) { idx <- which(!is.na(match(vars, labs[[i]]))) pars[idx,i] <- paste(labs[[i]], i, sep = ":") } if (!is.logical(const)) { const <- attr(terms, "constraints")[syst,,drop=FALSE] for (i in 1:ncol(const)) { cidx <- which(!is.na(const[,i])) ridx <- match(const[cidx, i], rownames(pars)) pars[cbind(ridx, cidx)] <- colnames(const)[i] } } if (shape == "matrix") out <- pars if (shape == "vector") { out <- unique(na.omit(c(t(pars)))) if (ancillary) out <- c(out, fixed) } out } Zelig/R/getResponseTerms.R0000644000176000001440000000055412061700507015213 0ustar ripleyusers#' Get Response Terms from a Zelig-style Formula #' #' This method acquires the response variables from Zelig-style input. #' @param x a formula or list of formulae #' @param ... ignored parameters #' @return a character-vector specifying a the of response terms in this formula #' @export getResponseTerms <- function (x, ...) { UseMethod("getResponseTerms") } Zelig/R/normal.survey.R0000644000176000001440000001071012071063367014530 0ustar ripleyusers#' @export zelig2normal.survey <- function( formula, weights=NULL, ids=NULL, probs=NULL, strata = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL, fpctype="fraction", return.replicates=FALSE, na.action="na.omit", start=NULL, etastart=NULL, mustart=NULL, offset=NULL, model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL, design=NULL, data ) { loadDependencies("survey") if (is.null(ids)) ids <- ~1 # the following lines designate the design # NOTE: nothing truly special goes on here; # the below just makes sure the design is created correctly # for whether or not the replication weights are set design <- if (is.null(repweights)) svydesign( data=data, ids=ids, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights ) else { .survey.prob.weights <- weights svrepdesign( data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc ) } z(.function = svyglm, formula = formula, design = design ) } #' @S3method param normal.survey param.normal.survey <- function(obj, num=1000, ...) { df <- obj$result$df.residual sig2 <- summary(obj)$dispersion list( simulations = mvrnorm(num, coef(obj), vcov(obj)), alpha = sqrt(df*sig2/rchisq(num, df=df)), # note: assignment of link and link-inverse are # implicit when the family is assigned fam = gaussian() ) } #' @S3method qi normal.survey qi.normal.survey <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) { model <- GetObject(obj) coef <- coef(param) alpha <- alpha(param) eta <- coef %*% t(x) link.inverse <- linkinv(param) theta <- matrix(link.inverse(eta), nrow=nrow(coef)) pr <- ev <- matrix(NA, nrow=nrow(theta), ncol(theta)) dimnames(pr) <- dimnames(ev) <- dimnames(theta) ev <- theta for (k in 1:nrow(ev)) pr[k, ] <- rnorm(length(ev[k, ]), ev[k,], alpha[k]) ev1 <- pr1 <- fd <- NA if (!is.null(x1)) { ev1 <- theta1 <- matrix(link.inverse(coef %*% t(x1)), nrow = nrow(coef) ) fd <- ev1-ev } att.ev <- att.pr <- NA if (!is.null(y)) { yvar <- matrix(rep(y, nrow(coef)), nrow=nrow(coef), byrow=TRUE) tmp.ev <- yvar - ev tmp.pr <- yvar - pr att.ev <- matrix(apply(tmp.ev, 1, mean), nrow=nrow(coef)) att.pr <- matrix(apply(tmp.pr, 1, mean), nrow=nrow(coef)) } list( "Expected Values: E(Y|X)" = ev, "Expected Values for (X1): E(Y|X1)" = ev1, "Predicted Values: Y|X" = pr, "Predicted Values (for X1): Y|X1" = pr1, "First Differences E(Y|X1)-E(Y|X)" = fd, "Average Treatment Effect: Y-EV" = att.ev, "Average Treatment Effect: Y-PR" = att.pr ) } #' @S3method describe normal.survey describe.normal.survey <- function(...) { list( authors = "Nicholas Carnes", year = 2008, description = "Survey-Weighted Normal Regression for Continuous, Positive Dependent Variables" ) } Zelig/vignettes/0000755000176000001440000000000012217153457013373 5ustar ripleyusersZelig/vignettes/Zelig.sty0000644000176000001440000000152712061700507015202 0ustar ripleyusers\usepackage{hyperref} \usepackage{Sweave} \DefineVerbatimEnvironment{Code}{Verbatim}{ samepage=TRUE, fontsize=\small } \newcommand{\CiteZelig}[0]{ To cite Zelig as a whole, please reference these two sources: \begin{verse} Kosuke Imai, Gary King, and Olivia Lau. 2007. ``Zelig: Everyone's Statistical Software,'' \url{http://GKing.harvard.edu/zelig}. \end{verse} \begin{verse} Imai, Kosuke, Gary King, and Olivia Lau. (2008). ``Toward A Common Framework for Statistical Analysis and Development.'' Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913. \end{verse} } \newcommand{\Sref}[1]{Section~\ref{#1}} \newcommand{\hlink}[2]{\href{#2}{#1}} \newcommand{\rvers}{2.5} \newcommand{\rwvers}{R-2.5.1} \newcommand{\fullrvers}{2.5.1} \newcommand{\code}[1]{{\tt #1}} \usepackage[all]{xy} Zelig/vignettes/gkpubs.bib0000644000176000001440000021174612061700507015346 0ustar ripleyusers% A bibtex-format file for papers by or coauthored with Gary King % % rules used for abbreviations: % % -if one author: use last name and last 2 digits of the year: King99. % -if multiple authors, use 1st 3 letters of each of UP TO the first three % authors and the last 2 digits of the year: KinTomWit00. % -if necessary add lower-case letters for multiple entries in a year: % King02, King02b (the first one should NOT have an 'a' afterwards) % -No string abbreviations are used % % entries are in separate sections (books, articles, software, data) % in reverse chronical order % % copies of all papers, articles, data, and software, and some books, % are available at http://gking.harvard.edu/ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Books %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @book{KinSchNie09, editor = {Gary King and Kay Schlozman and Norman Nie}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009} } @book{GirKin08, author = {Federico Girosi and Gary King}, title = {Demographic Forecasting}, publisher = {Princeton University Press}, year = {2008}, address = {Princeton}, note = {{http://gking.harvard.edu/files/smooth/}} } @book{KinRosTan04, editor = {Gary King and Ori Rosen and Martin A. Tanner}, title = {Ecological Inference: New Methodological Strategies}, publisher = {Cambridge University Press}, year = {2004}, address = {New York}, note = {{http://gking.harvard.edu/files/abs/ecinf04-abs.shtml}} } @book{King97, author = {Gary King}, title = {A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data}, publisher = {Princeton University Press}, year = {1997}, address = {Princeton}, note = {{http://gking.harvard.edu/eicamera/kinroot.html}} } @book{KinKeoVer94, author = {Gary King and Robert O. Keohane and Sidney Verba}, title = {Designing Social Inquiry: Scientific Inference in Qualitative Research}, publisher = {Princeton University Press}, year = {1994}, address = {Princeton}, note = {{http://www.pupress.princeton.edu/titles/5458.html}} } @book{King89, author = {Gary King}, title = {Unifying Political Methodology: The Likelihood Theory of Statistical Inference}, publisher = {Michigan University Press}, year = 1989, address = {Ann Arbor} } @book{KinRag88, author = {Gary King and Lyn Ragsdale}, title = {The Elusive Executive: Discovering Statistical Patterns in the Presidency}, publisher = {Congressional Quarterly Press}, year = {1988}, address = {Washington, D.C} } @book{BraHarKin89, author = {Paul Brace and Christine Harrington and Gary King}, title = {The Presidency in American Politics}, publisher = {New York University Press}, year = {1989}, address = {New York and London} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Articles %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{SonKin11, author = {Samir Soneji and Gary King}, title = {Statistical Security for Social Security}, journal = {Demography}, year = 2011, note = {{http://gking.harvard.edu/files/abs/ssc-abs.shtml}} } @Article{, author = {Gary King and Richard Nielsen and Carter Coberley and James Pope and Aaron Wells}, title = {Avoiding Randomization Failure in Program Evaluation}, journal = {Population Health Management}, year = 2011, volume = 14, number = 1, pages = {S11-S22}, note = {{http://gking.harvard.edu/gking/files/mhs.pdf}} } @Article{KinNieCob11, author = {Gary King and Richard Nielsen and Carter Coberley and James Pope and Aaron Wells}, title = {Comparative Effectiveness of Matching Methods for Causal Inference}, journal = { }, year = {2011}, OPTkey = {}, OPTvolume = {}, OPTnumber = {}, OPTpages = {}, OPTmonth = {}, OPTnote = {{http://gking.harvard.edu/files/abs/psparadox-abs.shtml}}, OPTannote = {} } @Article{SteKinShi10, author = {Gretchen Stevens, Gary King, and Kenji Shibuya}, title = {Deaths From Heart Failure: Using Coarsened Exact Matching to Correct Cause of Death Statistics}, journal = {Population Health Metrics}, year = 2010, volume = 8, number = 6, note = {{http://gking.harvard.edu/files/abs/heartfcem-abs.shtml}} } @Article{GriKin10, author = {Justin Grimmer and Gary King}, title = {Quantitative Discovery from Qualitative Information: A General-Purpose Document Clustering Methodology}, journal = { }, year = {2010}, note = {{http://gking.harvard.edu/files/abs/discov-abs.shtml}} } @Article{GriKin10b, author = {Justin Grimmer and Gary King}, title = {A General Purpose Computer-Assisted Document Clustering Methodology}, journal = { }, year = {2010}, note = {{http://gking.harvard.edu/files/abs/discovm-abs.shtml}} } @Article{IacKinPor11, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Multivariate Matching Methods That are Monotonic Imbalance Bounding}, journal = {Journal of the American Statistical Association}, year = {In press}, note = {{http://gking.harvard.edu/files/abs/cem-math-abs.shtml}} } @article{HopKin10, author = {Daniel Hopkins and Gary King}, title = {Improving Anchoring Vignettes: Designing Surveys to Correct Interpersonal Incomparability}, journal = {Public Opinion Quarterly}, year = {2010}, pages = {1-22}, note = {{http://gking.harvard.edu/files/abs/implement-abs.shtml}} } @Article{KinLuShi10, author = {Gary King and Ying Lu and Kenji Shibuya}, title = {Designing Verbal Autopsy Studies}, journal = {Population Health Metrics}, year = 2010, volume = 8, number = 19, note = {http://gking.harvard.edu/files/abs/desva-abs.shtml} } @article{LazPenAda09, author = {Lazer, David and Pentland, Alex and Adamic, Lada and Aral, Sinan and Barabasi, Albert-Laszlo and Brewer, Devon and Christakis, Nicholas and Contractor, Noshir and Fowler, James and Gutmann, Myron and Jebara, Tony and King, Gary and Macy, Michael and Roy, Deb and Van Alstyne, Marshall}, title = {{SOCIAL SCIENCE: Computational Social Science}}, journal = {Science}, volume = {323}, number = {5915}, pages = {721-723}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/LazPenAda09-abs.shtml}} } @Article{AbrBolGut09, author = {Mark Abrahamson and Kenneth A. Bollen and Myron Gutmann and Gary King and Amy M. Pienta}, title = {Preserving Data for Long Term Analyses}, journal = {Historical Social Research}, year = {2009}, OPTkey = {}, OPTvolume = {}, OPTnumber = {}, OPTpages = {}, OPTmonth = {Summer, forthcoming}, OPTnote = {}, OPTannote = {} } @article{KinGakIma09, Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and Jason Lakin and Ryan T. Moore and Clayton Nall and Nirmala Ravishankar and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme}, journal = {The Lancet}, volumne = {373}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/spi-abs.shtml}} } @Article{KinSon09, author = {Gary King and Samir Soneji}, title = {The Future of Death in America}, journal = {}, year = 2009, note = {{http://gking.harvard.edu/files/abs/mort-abs.shtml}} } @Article{IacKinPor11, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Causal Inference Without Balance Checking: Coarsened Exact Matching}, journal = {Political Analysis}, year = {2011, in press}, note = {{http://gking.harvard.edu/files/abs/cem-plus-abs.shtml}} } @article{ImaKinStu08, author = {Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Misunderstandings Among Experimentalists and Observationalists about Causal Inference}, journal = {Journal of the Royal Statistical Society, {S}eries {A}}, volume = {171, part 2}, year = {2008}, pages = {481--502}, note = {{http://gking.harvard.edu/files/abs/matchse-abs.shtml}} } @article{uImaKinStu08, author = {Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Misunderstandings Among Experimentalists and Observationalists about Causal Inference}, journal = {Journal of the Royal Statistical Society, {S}eries {A}}, volume = {171, part 2}, year = {2008}, pages = {481--502} } @article{KinLu08, author = {Gary King and Ying Lu}, title = {Verbal Autopsy Methods with Multiple Causes of Death}, journal = {Statistical Science}, volume = {23}, number = {1}, year = {2008}, pages = {78--91}, note = {{http://gking.harvard.edu/files/abs/vamc-abs.shtml}} } @article{AltKin07, author = {Micah Altman and Gary King}, title = {A Proposed Standard for the Scholarly Citation of Quantitative Data }, journal = {D-Lib Magazine}, volume = {13}, year = {2007}, month = {March / April}, number = {3/4}, note = {{http://gking.harvard.edu/files/abs/cite-abs.shtml}} } @article{GroKin07, author = {Bernard Grofman and Gary King}, title = {The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry}, journal = {Election Law Journal}, volume = {6}, year = {2007}, pages = {2-35}, month = {January}, number = {1}, note = {{http://gking.harvard.edu/files/abs/jp-abs.shtml}} } @article{uHoImaKin07, author = {Daniel Ho and Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, journal = {Political Analysis}, year = {2007}, volume = {15}, pages = {199--236} } @article{HoImaKin07, author = {Daniel Ho and Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, journal = {Political Analysis}, year = {2007}, volume = {15}, pages = {199--236}, note = {{http://gking.harvard.edu/files/abs/matchp-abs.shtml}} } @article{HopKin10b, author = {Daniel Hopkins and Gary King}, title = {A Method of Automated Nonparametric Content Analysis for Social Science}, journal = {American Journal of Political Science}, year = {2010}, volume = {54}, number = {1}, month = {January}, pages = {229--247}, note = {http://gking.harvard.edu/files/abs/words-abs.shtml} } @article{ImaKinLau07, author = {Kosuke Imai and Gary King and Olivia Lau}, title = {Toward A Common Framework for Statistical Analysis and Development}, journal = {Journal of Computational Graphics and Statistics}, volume = 17, number = 4, pages = {1--22}, year = 2008, note = {{http://gking.harvard.edu/files/abs/z-abs.shtml}} } @article{ImaKinNal09d, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Matched Pairs and the Future of Cluster-Randomized Experiments: A Rejoinder}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {64--72}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}} } @article{ImaKinNal09, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {29--53}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}} } @article{uImaKinNal09, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {29--53}, year = {2009} } @article{King07, author = {Gary King}, title = {An Introduction to the Dataverse Network as an Infrastructure for Data Sharing}, journal = {Sociological Methods and Research}, year = {2007}, volume = {36}, number = {2}, pages = {173--199}, note = {{http://gking.harvard.edu/files/abs/dvn-abs.shtml}} } @inbook{King09b, author = {Gary King}, chapter = {The Changing Evidence Base of Political Science Research}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009, forthcoming}, editor = {Gary King and Kay Schlozman and Norman Nie}, note = {{http://gking.harvard.edu/files/abs/evbase-abs.shtml}} } @inbook{King09c, author = {Gary King and Kay Schlozman and Norman Nie}, chapter = {An Introduction to the Future of Political Science}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009, forthcoming}, editor = {Gary King and Kay Schlozman and Norman Nie} } @article{KinGakRav07, Author = {Gary King and Emmanuela Gakidou and Nirmala Ravishankar and Ryan T. Moore and Jason Lakin and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {A `Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program}, journal = {Journal of Policy Analysis and Management}, volume = {26}, year = {2007}, pages = {479-506}, number = {3}, note = {{http://gking.harvard.edu/files/abs/spd-abs.shtml}} } @article{KinWan07, author = {Gary King and Jonathan Wand}, title = {Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes}, journal = {Political Analysis}, volume = {15}, year = {2007}, pages = {46-66}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/c-abs.shtml}} } @article{KinZen07, author = {Gary King and Langche Zeng}, title = {When Can History Be Our Guide? The Pitfalls of Counterfactual Inference}, journal = {International Studies Quarterly}, year = {2007}, pages = {183-210}, month = {March}, note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}} } @article{uKinZen07, author = {Gary King and Langche Zeng}, title = {When Can History Be Our Guide? The Pitfalls of Counterfactual Inference}, journal = {International Studies Quarterly}, year = {2007}, pages = {183-210}, month = {March} } @article{KinZen07b, author = {Gary King and Langche Zeng}, title = {Detecting Model Dependence in Statistical Inference: A Response}, journal = {International Studies Quarterly}, volume = {51}, year = {2007}, pages = {231-241}, month = {March}, note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}} } @article{WanKinLau07, author = {Jonathan Wand and Gary King and Olivia Lau}, title = {Anchors: Software for Anchoring Vignettes Data}, journal = {Journal of Statistical Software}, year = {2007, forthcoming} } @inbook{EpsHoKin06, author = {Lee Epstein and Daniel E. Ho and Gary King and Jeffrey A. Segal}, title = {Principles and Practice in American Politics: Classic and Contemporary Readings}, chapter = {The Effect of War on the Supreme Court}, year = {2006}, publisher = {Congressional Quarterly Press}, edition = {3rd}, address = {Washington, D.C.}, editor = {Samuel Kernell and Steven S. Smith}, note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}} } @article{GakKin06, author = {Emmanuela Gakidou and Gary King}, title = {Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data from Sibling Survival Data}, journal = {Demography}, volume = 43, year = 2006, pages = {569--585}, month = {August}, number = 3, note = {{http://gking.harvard.edu/files/abs/deathbys-abs.shtml}} } @article{HonKin10, author = {James Honaker and Gary King}, title = {What to do About Missing Values in Time Series Cross-Section Data}, journal = {American Journal of Political Science}, year = {2010}, volume = {54}, number = {2}, month = {April}, pages = {561--581}, note = {{http://gking.harvard.edu/files/abs/pr-abs.shtml}} } @article{King06, author = {Gary King}, title = {{Publication, Publication}}, journal = {PS: Political Science and Politics}, volume = {39}, year = {2006}, pages = {119--125}, month = {January}, number = {01}, note = {{http://gking.harvard.edu/files/abs/paperspub-abs.shtml}} } @inbook{KinRosTan06, author = {Gary King and Ori Rosen and Martin Tanner}, title = {The New Palgrave Dictionary of Economics}, chapter = {Ecological Inference}, year = {2006}, edition = {2nd}, editor = {Larry Blume and Steven N. Durlauf}, note = {{http://gking.harvard.edu/files/abs/newintro-abs.shtml}} } @article{KinZen06, author = {Gary King and Langche Zeng}, title = {The Dangers of Extreme Counterfactuals}, journal = {Political Analysis}, volume = {14}, year = 2006, pages = {131--159}, number = {2}, note = {{http://gking.harvard.edu/files/abs/counterft-abs.shtml}} } @article{GirKin07, author = {Federico Girosi and Gary King}, title = {Understanding the Lee-Carter Mortality Forecasting Method}, year = 2007, note = {{http://gking.harvard.edu/files/abs/lc-abs.shtml}} } @article{EpsHoKin05, author = {Lee Epstein and Daniel E. Ho and Gary King, and Jeffrey A. Segal}, title = {The Supreme Court During Crisis: How War Affects only Non-War Cases}, journal = {New York University Law Review}, volume = {80}, year = {2005}, pages = {1--116}, month = {April}, number = {1}, note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}} } @article{StoKinZen05, author = {Heather Stoll and Gary King and Langchee Zeng}, title = {WhatIf: Software for Evaluating Counterfactuals}, journal = {Journal of Statistical Software}, volume = {15}, year = {2005}, number = {4}, note = {{http://www.jstatsoft.org/index.php?vol=15}} } @article{BecKinZen04, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Theory and Evidence in International Conflict: Response to de Marchi, Gelpi, and Grynaviski}, journal = apsr, volume = {98}, year = {2004}, pages = {379-389}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/toe-resp-abs.shtml}} } @inbook{GelKatKin04, author = {Andrew Gelman and Jonathan Katz and Gary King}, title = {Rethinking the Vote: The Politics and Prospects of American Electoreal Reform}, chapter = {Chapter 5, Empirically Evaluating the Electoral College}, year = {2004}, publisher = {Oxford University Press}, pages = {75-88}, address = {New York}, editor = {Ann N. Crigler and Marion R. Just and Edward J. McCaffery}, note = {{http://gking.harvard.edu/files/abs/rethink-abs.shtml}} } @article{GilKin04, author = {Jeff Gill and Gary King}, title = {What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation}, journal = {Sociological Methods and Research}, volume = {32}, year = {2004}, pages = {54-87}, month = {August}, number = {1}, note = {{http://gking.harvard.edu/files/abs/help-abs.shtml}} } @article{ImaKin04, author = {Kosuke Imai and Gary King}, title = {Did Illegal Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?}, journal = {Perspectives on Politics}, volume = {2}, year = {2004}, pages = {537--549}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/ballots-abs.shtml}} } @article{King04, author = {Gary King}, title = {EI: A Program for Ecological Inference}, journal = {Journal of Statistical Software}, volume = {11}, year = {2004}, number = {7} } @article{King04b, author = {Gary King}, title = {Finding New Information for Ecological Inference Models: A Comment on Jon Wakefield, "Ecological Inference in 2 X 2 Tables"}, journal = {Journal of the Royal Statistical Society}, volume = {167}, year = {2004}, pages = {437}, number = {Series A} } @inbook{KinRosTan04b, author = {Gary King and Ori Rosen and Martin Tanner}, title = {Ecological Inference: New Methodological Strategies}, chapter = {Information in Ecological Inference: An Introduction}, year = {2004}, publisher = {Cambridge University Press}, address = {New York}, editor = {Gary King and Ori Rosen and Martin Tanner} } @inbook{KinZen04, author = {Gary King and Langche Zeng}, title = {Encyclopedia of Biopharmaceutical Statistics}, chapter = {Inference in Case-Control Studies}, year = {2004}, publisher = {Marcel Dekker}, edition = {2nd}, address = {New York}, editor = {Shein-Chung Chow}, note = {{http://gking.harvard.edu/files/abs/1s-enc-abs.shtml}} } @article{AdoKin03, author = {Christopher Adolph and Gary King}, title = {Analyzing Second Stage Ecological Regressions}, journal = {Political Analysis}, volume = {11}, year = {2003}, pages = {65-76}, month = {Winter}, number = {1} } @article{AdoKinHer03, author = {Christopher Adolph and Gary King, with Michael C. Herron and Kenneth W. Shotts}, title = {A Consensus on Second Stage Analyses in Ecological Inference Models}, journal = {Political Analysis}, volume = {11}, year = {2003}, pages = {86--94}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/akhs-abs.shtml}} } @article{EpsKin03, author = {Lee Epstein and Gary King}, title = {Building An Infrastructure for Empirical Research in the Law [with comments from four law school deans]}, journal = {Journal of Legal Education}, volume = {53}, year = {2003}, pages = {311--320}, number = {311}, note = {{http://gking.harvard.edu/files/abs/infra-abs.shtml}} } @inbook{GakKin03, author = {Emmanuela Gakidou and Gary King}, title = {Health Systems Performance Assessment: Debates, Methods and Empiricism}, chapter = {Chapter 36, Determinants of Inequality in Child Survival: Results from 39 Countries}, publisher = {World Health Organization}, pages = {497-502}, address = {Geneva}, editor = {Chrisopher Murray and David B. Evans} } @inbook{GilKin03, author = {Jeff Gill and Gary King}, title = {Numerical Issues in Statistical Computing for the Social Scientist}, chapter = {Chapter 6, Numerical Issues Involved in Inverting Hessian Matrices}, year = {2003}, publisher = {John Wiley and Sons, Inc.}, pages = {143-176}, address = {Hoboken, NJ}, editor = {Micah Altman and Jeff Gill and Michael P. McDonald} } @article{King03, author = {Gary King}, title = {The Future of Replication}, journal = {International Studies Perspectives}, volume = {4}, year = {2003}, pages = {443--499}, month = {February}, number = 1, note = {{http://gking.harvard.edu/files/abs/replvdc-abs.shtml}} } @article{KinLow03, author = {Gary King and Will Lowe}, title = {An Automated Information Extraction Tool For International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design}, journal = {International Organization}, volume = {57}, year = {2003}, pages = {617-642}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/infoex-abs.shtml}} } @article{KinMurSal04, author = {Gary King and Christopher J.L. Murray and Joshua A. Salomon and Ajay Tandon}, title = {Enhancing the Validity and Cross-cultural Comparability of Measurement in Survey Research}, journal = {American Political Science Review}, volume = {98}, year = {2004}, pages = {191--207}, month = {February}, number = {1}, note = {{http://gking.harvard.edu/files/abs/vign-abs.shtml}} } @article{KinRosTan07, author = {Gary King and Ori Rosen and Martin Tanner and Alexander F. Wagner.}, title = {Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler}, year = {2007}, note = {{http://gking.harvard.edu/files/abs/naziV-abs.shtml}} } @inbook{KinZen03, author = {Gary King and Langche Zeng}, title = {Inference in Case-Control Studies}, year = {2003}, publisher = {Marcel Dekker}, volume = {2nd edition}, address = {New York}, editor = {Shein-Chung Chow, ed.}, journal = {Encyclopedia of Biopharmaceutical Statistics} } @article{LowKin03, author = {Will Lowe and Gary King }, title = {Some Statistical Methods for Evaluating Information Extraction Systems}, journal = {Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics}, year = {2003}, pages = {19-26} } @article{TomKinZen03, author = {Michael Tomz and Gary King and Langche Zeng}, title = {ReLogit: Rare Events Logistic Regression}, journal = {Journal of Statistical Software}, volume = {8}, year = {2003}, number = {2}, note = {{http://gking.harvard.edu/stats.shtml#relogit}} } @article{TomWitKin03, author = {Michael Tomz and Jason Wittenberg and Gary King}, title = {CLARIFY: Software for Interpreting and Presenting Statistical Results}, journal = {Journal of Statistical Software}, volume = {8}, year = {2003}, number = {1}, note = {{http://gking.harvard.edu/stats.shtml}} } @article{EpsKin02, author = {Lee Epstein and Gary King}, title = {The Rules of Inference}, journal = {University of Chicago Law Review}, volume = {69}, year = {2002}, pages = {1--209}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}} } @article{EpsKin02b, author = {Lee Epstein and Gary King}, title = {Empirical Research and The Goals of Legal Scholarship: A Response}, journal = {University of Chicago Law Review}, volume = {69}, year = {2002}, pages = {1--209}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}} } @article{GakKin02, author = {Emmanuela Gakidou and Gary King}, title = {Measuring Total Health Inequality: Adding Individual Variation to Group-Level Differences}, journal = {BioMed Central: International Journal for Equity in Health}, volume = {1}, year = 2002, month = {August}, number = 3, note = {{http://gking.harvard.edu/files/abs/ebb-abs.shtml}} } @article{HonKinKat02, author = {James Honaker and Gary King and Jonathan N. Katz}, title = {A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data}, journal = {Political Analysis}, volume = {10}, year = {2002}, pages = {84--100}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/trip-abs.shtml}} } @article{King02b, author = {Gary King}, title = {Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological Inference}, journal = {Political Analysis}, volume = {10}, year = {2002}, pages = {298--300}, month = {Summer}, number = {3}, note = {{http://gking.harvard.edu/files/abs/ac-abs.shtml}} } @article{KinMur02, author = {Gary King and Christopher J.L. Murray}, title = {Rethinking Human Security}, journal = {Political Science Quarterly}, volume = {116}, year = {2002}, pages = {585--610}, month = {Winter}, number = {4}, note = {{http://gking.harvard.edu/files/abs/hs-abs.shtml}} } @article{KinZen02, author = {Gary King and Langche Zeng}, title = {Improving Forecasts of State Failure}, journal = {World Politics}, volume = 53, year = 2002, pages = {623--658}, month = {July}, number = 4 , note = {{http://gking.harvard.edu/files/abs/civil-abs.shtml}} } @article{KinZen02b, author = {Gary King and Langche Zeng}, title = {Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies}, journal = {Statistics in Medicine}, volume = 21, year = 2002, pages = {1409--1427}, note = {{http://gking.harvard.edu/files/abs/1s-abs.shtml}} } @article{MurKinLop02, author = {Christopher J.L. Murray and Gary King and Alan D. Lopez and Niels Tomijima and Etienne Krug}, title = {Armed Conflict as a Public Health Problem}, journal = {BMJ (British Medical Journal)}, volume = {324}, year = {2002}, pages = {346--349}, month = {February 9}, note = {{http://gking.harvard.edu/files/abs/armedph-abs.shtml}} } @article{AltAndDig01a, author = {Micah Altman and Leonid Andreev and Mark Diggory and Gary King and Daniel L. Kiskis and Elizabeth Kolster and M. Krot and Sidney Verba}, title = {A Digital Library for the Dissemination and Replication of Quantitative Social Science Research: The Virtual Data Center}, journal = {Social Science Computer Review}, volume = 19, year = 2001, pages = {458--470}, month = {Winter}, number = 4, note = {{http://gking.harvard.edu/files/abs/vdcwhitepaper-abs.shtml}} } @article{AltAndDig01b, author = {Micah Altman and Leonid Andreev and Mark Diggory and Gary King and Daniel L. Kiskis and Elizabeth Kolster and M. Krot and Sidney Verba}, title = {An Overview of the Virtual Data Center Project and Software}, journal = {JCDL '01: First Joint Conference on Digital Libraries}, year = 2001, pages = {203-204}, note = {{http://gking.harvard.edu/files/abs/jcdl01-abs.shtml}} } @article{AltKinSig01, author = {James E. Alt and Gary King and Curtis Signorino}, title = {Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data}, journal = {Political Analysis}, volume = {9}, year = {2001}, pages = {21--44}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/abcd-abs.shtml}} } @article{King01, author = {Gary King}, title = {Proper Nouns and Methodological Propriety: Pooling Dyads in International Relations Data}, journal = {International Organization}, volume = {55}, year = {2001}, pages = {497--507}, month = {Fall}, number = {2}, note = {{http://gking.harvard.edu/files/abs/pool-abs.shtml}} } @article{KinHonJos01, author = {Gary King and James Honaker and Anne Joseph and Kenneth Scheve}, title = {Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation}, journal = {American Political Science Review}, volume = 95, year = 2001, pages = {49--69}, month = {March}, number = 1 , note = {{http://gking.harvard.edu/files/abs/evil-abs.shtml}} } @article{KinZen01, author = {Gary King and Langche Zeng}, title = {Logistic Regression in Rare Events Data}, journal = {Political Analysis}, volume = 9, year = 2001, pages = {137--163}, month = {Spring}, number = 2 , note = {{http://gking.harvard.edu/files/abs/0s-abs.shtml}} } @article{KinZen01b, author = {Gary King and Langche Zeng}, title = {Explaining Rare Events in International Relations}, journal = {International Organization}, volume = 55, year = 2001, pages = {693--715}, month = {Summer}, number = 3 , note = {{http://gking.harvard.edu/files/abs/baby0s-abs.shtml}} } @article{RosJiaKin01, author = {Ori Rosen and Wenxin Jiang and Gary King and Martin A. Tanner}, title = {Bayesian and Frequentist Inference for Ecological Inference: The $R \times C$ Case}, journal = {Statistica Neerlandica}, volume = 55, year = 2001, pages = {134--156}, number = 2 , note = {{http://gking.harvard.edu/files/abs/rosen-abs.shtml}} } @article{BecKinZen00, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Improving Quantitative Studies of International Conflict}, journal = {American Political Science Review}, volume = 94, year = 2000, pages = {21--36}, month = {March}, number = 1 , note = {{http://gking.harvard.edu/files/abs/improv-abs.shtml}} } @article{King00, author = {Gary King}, title = {Geography, Statistics, and Ecological Inference}, journal = {Annals of the Association of American Geographers}, volume = {90}, year = {2000}, pages = {601--606}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/geog-abs.shtml}} } @article{KinTomWit00, author = {Gary King and Michael Tomz and Jason Wittenberg}, title = {Making the Most of Statistical Analyses: Improving Interpretation and Presentation}, journal = {American Journal of Political Science}, volume = 44, year = 2000, pages = {341--355}, month = {April}, number = 2, note = {{http://gking.harvard.edu/files/abs/making-abs.shtml}} } @article{GelKinLiu99, author = {Andrew Gelman and Gary King and Chuanhai Liu}, title = {Not Asked and Not Answered: Multiple Imputation for Multiple Surveys}, journal = {Journal of the American Statistical Association}, volume = 93, year = 1999, pages = {846--857}, month = {September}, number = 433 , note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}} } @article{GelKinLiu99b, author = {Andrew Gelman and Gary King and Chuanhai Liu}, title = {Rejoinder}, journal = {Journal of the American Statistical Association}, volume = 93, year = 1999, pages = {869--874}, month = {September}, number = 433 , note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}} } @article{KatKin99, author = {Jonathan Katz and Gary King}, title = {A Statistical Model for Multiparty Electoral Data}, journal = {American Political Science Review}, volume = 93, year = 1999, pages = {15--32}, month = {March}, number = {1}, note = {{http://gking.harvard.edu/files/abs/multiparty-abs.shtml}} } @article{King99, author = {Gary King}, title = {The Future of Ecological Inference Research: A Reply to Freedman et al.}, journal = {Journal of the American Statistical Association}, volume = {94}, year = {1999}, pages = {352-355}, month = {March}, number = {445}, note = {{http://gking.harvard.edu/files/abs/reply-abs.shtml}} } @article{KinLav99, author = {Gary King and Michael Laver}, title = {Many Publications, but Still No Evidence}, journal = {Electoral Studies}, volume = {18}, year = {1999}, pages = {597--598}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/manypub-abs.shtml}} } @article{KinRosTan99, author = {Gary King and Ori Rosen and Martin A. Tanner}, title = {Binomial-Beta Hierarchical Models for Ecological Inference}, journal = {Sociological Methods and Research}, volume = 28, year = 1999, pages = {61--90}, month = {August}, number = 1 , note = {{http://gking.harvard.edu/files/abs/binom-abs.shtml}} } @article{LewKin99, author = {Jeffrey Lewis and Gary King}, title = {No Evidence on Directional vs. Proximity Voting}, journal = {Political Analysis}, volume = {8}, year = {1999}, pages = {21--33}, month = {August}, number = {1}, note = {{http://gking.harvard.edu/files/abs/spatial-abs.shtml}} } @article{GelKinBos98, author = {Andrew Gelman and Gary King and John Boscardin}, title = {Estimating the Probability of Events that Have Never Occurred: When Is Your Vote Decisive?}, journal = {Journal of the American Statistical Association}, volume = {93}, year = {1998}, pages = {1--9}, month = {March}, number = {441}, note = {{http://gking.harvard.edu/files/abs/estimatprob-abs.shtml}} } @article{KinPal98, author = {Gary King and Bradley Palmquist}, title = {The Record of American Democracy, 1984-1990}, journal = {Sociological Methods and Research}, volume = {26}, year = {1998}, pages = {424--427}, month = {February}, number = {3}, note = {{http://www.hmdc.harvard.edu/ROAD/}} } @article{BenKin96, author = {Kenneth Benoit and Gary King}, title = {A Preview of EI and EzI: Programs for Ecological Inference}, journal = {Social Science Computer Review}, volume = {14}, year = {1996}, pages = {433--438}, month = {Winter}, number = {4}, note = {{http://gking.harvard.edu/files/abs/preview-abs.shtml}} } @inbook{GelKin96, author = {Andrew Gelman and Gary King}, title = {Advantages of Conflictual Redistricting}, year = {1996}, publisher = {Dartmouth Publishing Company}, pages = {207--218 }, address = {Aldershot, England}, editor = {Iain McLean and David Butler, eds}, note = {{http://gking.harvard.edu/files/abs/advant-abs.shtml}}, journal = {Fixing the Boundary: Defining and Redefining Single-Member Electoral Districts} } @inbook{KinBruGel96, author = {Gary King and John Bruce and Andrew Gelman}, title = {Racial Fairness in Legislative Redistricting}, year = {1996}, publisher = {Princeton University Press}, editor = {Paul E. Peterson, ed.}, note = {{http://gking.harvard.edu/files/abs/racial-abs.shtml}}, journal = {Classifying by Race} } @article{King96, author = {Gary King}, title = {Why Context Should Not Count}, journal = {Political Geography }, volume = {15}, year = {1996}, pages = {159--164}, month = {February}, number = {2}, note = {{http://gking.harvard.edu/files/abs/contxt-abs.shtml}} } @article{KinSig96, author = {Gary King and Curtis S. Signorino}, title = {The Generalization in the Generalized Event Count Model}, journal = {Political Analysis}, volume = 6, year = 1996, pages = {225--252}, note = {{http://gking.harvard.edu/files/abs/generaliz-abs.shtml}} } @article{King95, author = {Gary King}, title = {Replication, Replication}, journal = {PS: Political Science and Politics}, volume = {28}, year = 1995, pages = {443--499}, month = {September}, number = 3, note = {{http://gking.harvard.edu/files/abs/replication-abs.shtml}} } @article{King95b, author = {Gary King}, title = {A Revised Proposal, Proposal}, journal = {PS: Political Science and Politics}, volume = {XXVIII}, year = 1995, pages = {494--499}, month = {September}, number = 3, note = {{http://gking.harvard.edu/files/abs/replication-abs.shtml}} } @article{KinKeoVer95, author = {Gary King and Robert O. Keohane and Sidney Verba}, title = {The Importance of Research Design in Political Science}, journal = {American Political Science Review}, volume = {89}, year = {1995}, pages = {454--481 }, month = {June}, number = {2}, note = {{http://gking.harvard.edu/files/abs/kkvresp-abs.shtml}} } @article{VosGelKin95, author = {D. Steven Voss and Andrew Gelman and Gary King}, title = {Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992}, journal = {Public Opinion Quarterly}, volume = {59}, year = {1995}, pages = {98--132}, month = {Spring}, number = {1}, note = {{http://gking.harvard.edu/files/abs/preelection-abs.shtml}} } @article{WinSigKin95, author = {Rainer Winkelmann and Curtis Signorino and Gary King}, title = {A Correction for an Underdispersed Event Count Probability Distribution}, journal = {Political Analysis}, year = {1995}, pages = {215--228}, note = {{http://gking.harvard.edu/files/abs/correction-abs.shtml}} } @article{AltKin94, author = {James E. Alt and Gary King}, title = {Transfers of Governmental Power: The Meaning of Time Dependence}, journal = {Comparative Political Studies}, volume = {27}, year = {1994}, pages = {190--210}, month = {July}, number = {2}, note = {{http://gking.harvard.edu/files/abs/transfers-abs.shtml}} } @article{GelKin94, author = {Andrew Gelman and Gary King}, title = {A Unified Method of Evaluating Electoral Systems and Redistricting Plans}, journal = {American Journal of Political Science}, volume = 38, year = 1994, pages = {514--554}, month = {May}, number = 2, note = {{http://gking.harvard.edu/files/abs/writeit-abs.shtml}} } @article{GelKin94b, author = {Andrew Gelman and Gary King}, title = {Enhancing Democracy Through Legislative Redistricting}, journal = {American Political Science Review}, volume = {88}, year = {1994}, pages = {541--559}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/red-abs.shtml}} } @incollection{GelKin94c, author = {Andrew Gelman and Gary King}, title = {Party Competition and Media Messages in U.S. Presidential Election Campaigns}, booktitle = {The Parties Respond: Changes in the American Party System}, publisher = {Westview Press}, year = 1994, address = {Boulder, Colorado}, editor = {L. Sandy Maisel}, pages = {255-295}, note = {{http://gking.harvard.edu/files/abs/partycomp-abs.shtml}} } @article{GelKin93, author = {Andrew Gelman and Gary King}, title = {Why are American Presidential Election Campaign Polls so Variable when Votes are so Predictable?}, journal = {British Journal of Political Science}, volume = 23, year = 1993, pages = {409--451}, month = {October}, number = 1, note = {{http://gking.harvard.edu/files/abs/variable-abs.shtml}} } @inbook{King93, author = {Gary King}, title = {The Methodology of Presidential Research}, year = {1993}, publisher = {University of Pittsburgh}, pages = {387--412}, address = {Pittsburgh}, editor = {George Edwards, III, John H. Kessel, and Bert A. Rockman, eds.}, note = {{http://gking.harvard.edu/files/abs/methpres-abs.shtml}}, journal = {Researching the Presidency: Vital Questions, New Approaches} } @article{KingBruGil93, author = {Gary King and John M. Bruce and Michael Gilligan}, title = {The Science of Political Science Graduate Admissions}, journal = {PS: Political Science and Politics}, volume = {XXVI}, year = {1993}, pages = {772--778}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/admis-abs.shtml}} } @article{KinLav93, author = {Gary King and Michael Laver}, title = {On Party Platforms, Mandates, and Government Spending}, journal = {American Political Science Review}, volume = {87}, year = {1993}, pages = {744--750}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/hoff-abs.shtml}} } @article{KinWal93, author = {Gary King and Daniel J. Walsh}, title = {Good Research and Bad Research: Extending Zimile's Criticism}, journal = {Early Childhood Research Quarterly}, volume = {8}, year = {1993}, pages = {397--401}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/good-abs.shtml}} } @article{King91, author = {Gary King}, title = {'Truth' is Stranger than Prediction, More Questionable Than Causal Inference}, journal = {American Journal of Political Science}, volume = {35}, year = {1991}, pages = {1047--1053}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/truth-abs.shtml}} } @article{King91b, author = {Gary King}, title = {Constituency Service and Incumbency Advantage}, journal = {British Journal of Political Science}, volume = {21}, year = {1991}, pages = {119--128}, month = {January}, number = {1}, note = {{http://gking.harvard.edu/files/abs/constit-abs.shtml}} } @article{King91c, author = {Gary King}, title = {On Political Methodology}, journal = {Political Analysis}, volume = {2}, year = {1991}, pages = {1--30}, note = {{http://gking.harvard.edu/files/abs/polmeth-abs.shtml}} } @article{King91d, author = {Gary King}, title = {Stochastic Variation: A Comment on Lewis-Beck and Skalaban's `The R-Square'}, journal = {Political Analysis}, volume = {2}, year = {1991}, pages = {185--200}, note = {{http://gking.harvard.edu/files/abs/stoch-abs.shtml}} } @article{King91e, author = {Gary King}, title = {Calculating Standard Errors of Predicted Values based on Nonlinear Functional Forms}, journal = {The Political Methodologist}, volume = {4}, year = {1991}, month = {Fall}, number = {2} } @article{KinGel91, author = {Gary King and Andrew Gelman}, title = {Systemic Consequences of Incumbency Advantage in the U.S. House}, journal = {American Journal of Political Science}, volume = 35, year = 1991, pages = {110--138}, month = {February}, number = 1 , note = {{http://gking.harvard.edu/files/abs/sysconseq-abs.shtml}} } @article{AnsKin90, author = {Stephen Ansolabehere and Gary King}, title = {Measuring the Consequences of Delegate Selection Rules in Presidential Nominations}, journal = {Journal of Politics}, volume = {52}, year = {1990}, pages = {609--621}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/pri-abs.shtml}} } @article{GelKin90, author = {Andrew Gelman and Gary King}, title = {Estimating the Electoral Consequences of Legislative Redistricting}, journal = {Journal of the American Statistical Association}, volume = {85}, year = {1990}, pages = {274--282}, month = {June}, number = {410}, note = {{http://gking.harvard.edu/files/abs/svstat-abs.shtml}} } @article{GelKin90b, author = {Andrew Gelman and Gary King}, title = {Estimating Incumbency Advantage Without Bias}, journal = {American Journal of Political Science}, volume = {34}, year = {1990}, pages = {1142--1164}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/inc-abs.shtml}} } @article{KinAltBur90, author = {Gary King and James Alt and Nancy Burns and Michael Laver}, title = {A Unified Model of Cabinet Dissolution in Parliamentary Democracies}, journal = {American Journal of Political Science}, volume = {34}, year = {1990}, pages = {846--871}, month = {August}, number = {3}, note = {{http://gking.harvard.edu/files/abs/coal-abs.shtml}} } @article{King90, author = {Gary King}, title = {Electoral Responsiveness and Partisan Bias in Multiparty Democracies}, journal = {Legislative Studies Quarterly}, volume = {XV}, year = {1990}, pages = {159--181}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/electresp-abs.shtml}} } @article{GelKin89, author = {Andrew Gelman and Gary King}, title = {Electoral Responsiveness in U.S. Congressional Elections, 1946-1986}, journal = {Proceedings of the Social Statistics Section, American Statistical Association}, year = {1989}, pages = {208} } @article{King89b, author = {Gary King}, title = {Representation Through Legislative Redistricting: A Stochastic Model}, journal = {American Journal of Political Science}, volume = {33}, year = {1989}, pages = {787--824}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/repstoch-abs.shtml}} } @article{King89c, author = {Gary King}, title = {Event Count Models for International Relations: Generalizations and Applications}, journal = {International Studies Quarterly}, volume = {33}, year = {1989}, pages = {123--147}, month = {June}, number = {2}, note = {{http://gking.harvard.edu/files/abs/ISQ33-abs.shtml}} } @article{King89d, author = {Gary King}, title = {Variance Specification in Event Count Models: From Restrictive Assumptions to a Generalized Estimator}, journal = {American Journal of Political Science}, volume = 33, year = 1989, pages = {762--784}, month = {August}, number = 3 , note = {{http://gking.harvard.edu/files/abs/varspecec-abs.shtml}} } @article{King89e, author = {Gary King}, title = {A Seemingly Unrelated Poisson Regression Model}, journal = {Sociological Methods and Research}, volume = {17}, year = {1989}, pages = {235--255}, month = {February}, number = {3}, note = {{http://gking.harvard.edu/files/abs/SMR17-abs.shtml}} } @article{King88, author = {Gary King}, title = {Statistical Models for Political Science Event Counts: Bias in Conventional Procedures and Evidence for The Exponential Poisson Regression Model}, journal = {American Journal of Political Science}, volume = 32, year = 1988, pages = {838-863}, month = {August}, number = 3 , note = {{http://gking.harvard.edu/files/abs/epr-abs.shtml}} } @article{BroKin87, author = {Robert X Browning and Gary King}, title = {Seats, Votes, and Gerrymandering: Measuring Bias and Representation in Legislative Redistricting}, journal = {Law and Policy}, volume = {9}, year = {1987}, pages = {305--322}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/LP9-abs.shtml}} } @article{KinBro87, author = {Gary King and Robert X Browning}, title = {Democratic Representation and Partisan Bias in Congressional Elections}, journal = {American Political Science Review}, volume = {81}, year = {1987}, pages = {1252--1273}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/sv-abs.shtml}} } @article{King87, author = {Gary King}, title = {Presidential Appointments to the Supreme Court: Adding Systematic Explanation to Probabilistic Description}, journal = {American Politics Quarterly}, volume = {15}, year = {1987}, pages = {373--386}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/sct-abs.shtml}} } @article{King86, author = {Gary King}, title = {How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science}, journal = {American Journal of Political Science}, volume = {30}, year = {1986}, pages = {666--687}, month = {August}, number = {3}, note = {{http://gking.harvard.edu/files/abs/mist-abs.shtml}} } @article{King86b, author = {Gary King}, title = {The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation}, journal = {Social Science Research}, volume = {15}, year = {1986}, pages = {135--152}, month = {June}, note = {{http://gking.harvard.edu/files/abs/SSR15-abs.shtml}} } @article{King86c, author = {Gary King}, title = {Political Parties and Foreign Policy: A Structuralist Approach}, journal = {Political Psychology}, volume = {7}, year = {1986}, pages = {83--101}, month = {March}, number = {1}, note = {{http://gking.harvard.edu/files/abs/PP7-abs.shtml}} } @article{KinMer86, author = {Gary King and Richard Merelman}, title = {The Development of Political Activists: A Model of Early Learning}, journal = {Social Science Quarterly}, volume = {67}, year = {1986}, pages = {473--490}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/poliactiv-abs.shtml}} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{IacKinPor11b, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Replication data for: Causal Inference Without Balance Checking: Coarsened Exact Matching}, journal = { }, year = 2011, note = {http://hdl.handle.net/1902.1/15601 Murray Research Archive [Distributor] V1 [Version]} } @article{HopKin09b, author = {Daniel Hopkins and Gary King}, title = {Replication Data for: A Method of Automated Nonparametric Content Analysis for Social Science}, journal = { }, year = 2009, note = {\underline{UNF:3:xlE5stLgKvpeMvxzlLxzEQ==} hdl:1902.1/12898 Murray Research Archive [Distributor]} } @article{KinGakIma09b, Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and Jason Lakin and Clayton Nall and Ryan T. Moore and Nirmala Ravishankar and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {Replication Data for: Public Policy for the Poor? A Randomized Ten-Month Evaluation of the Mexican Universal Health Insurance Program}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/11044} UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research Archive [Distributor]} } @article{ImaKinNal09c, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Replication Data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/12730} UNF:3:CKs4T0iVYxP36LQSMgAkuw== Murray Research Archive [Distributor]} } @article{ImaKinNal09b, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Replication Data for: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/11047} UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research Archive [Distributor]} } @Article{KinZen08, author = {Gary King and Langche Zeng}, title = {Replication data for: Empirical vs. Theoretical Claims about Extreme Counterfactuals: A Response}, journal = { }, year = 2008, note = {\underline{hdl:1902.1/11903}, Murray Research Archive [Distributor]} } @article{GakKin06b, author = {Emmanuela Gakidou and Gary King}, title = {Replication data for: Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data}, year = 2006, note = {{\underline{hdl:1902.1/ZMESWNECZW} Murray Research Archive [Distributor]}} } @article{GirKin06, author = {Federico Girosi and Gary King}, title = {Cause of Death Data}, year = {2006}, note = {{\underline{hdl:1902.1/UOVMCPSWOL} UNF:3:9JU+SmVyHgwRhAKclQ85Cg== Murray Research Archive [Distributor]}} } @article{HoImaKin06, author = {Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart}, title = {Replication Data Set for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, year = 2006, note = {{\underline{hdl:1902.1/YVDZEQIYDS} Murray Research Archive [distributor]}} } @article{KinAlt06, author = {Gary King and James E. Alt and Nancy Burns and Michael Laver}, title = {Replication data for: A Unified Model of Cabinet Dissolution in Parliamentary Democracies}, year = {2006}, note = {{\underline{hdl:1902.1/RMPXNUSBBS} UNF:3:lfKIeFJKgejkOzXEY1i6lw== Murray Research Archive [Distributor]}} } @article{KinZen06b, author = {Gary King and Langche Zeng}, title = {Replication Data Set for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference}, year = 2006, note = {{\underline{hdl:1902.1/DXRXCFAWPK} Murray Research Archive [distributor]}} } @article{KinZen06c, author = {Gary King and Langche Zeng}, title = {Replication data for: Detecting Model Depedence in Statistical Inference: A Response}, year = {2006}, note = {{\underline{hdl:1902.1/FGSRBXXIYT} UNF:3:K4/CgnMYDMV6izc5RVOZTA== Murray Research Archive [Distributor]}} } @article{KinZen06d, author = {Gary King and Langche Zeng}, title = {Replication data for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference}, year = {2006}, note = {{\underline{hdl:1902.1/DXRXCFAWPK} UNF:3:DaYlT6QSX9r0D50ye+tXpA== Murray Research Archive [Distributor]}} } @article{KinZen06e, author = {Gary King and Langche Zeng}, title = {Replication data for: The Dangers of Extreme Counterfactuals }, year = {2006}, note = {{\underline{hdl:1902.1/UTVMBVNGMX} UNF:3:ytKKNjK+yR8Pq3H0RcV6eg== Murray Research Archive [Distributor]}} } @article{EpsHoKin05b, author = {Lee Epstein and Daniel E. Ho and Gary King and Jeffrey A. Segal}, title = {Replication data for: The Supreme Court During Crisis: How War Affects Only Nonwar Cases}, year = {2005}, note = {{\underline{hdl:1902.1/RESUDVYWPE} UNF:3:ZmbzFbfqogNM0Gb6CcV52A== Murray Research Archive [Distributor]}} } @article{BecKinZen04b, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Replication data for: Gelpi and Grynaviski}, year = {2004}, note = {{\underline{hdl:1902.1/LAAYCJJGDS} UNF:3:N0bEAswAlPPVXCxPOZYyqw== Murray Research Archive [Distributor]}} } @article{King03b, author = {Gary King}, title = {10 Million International Dyadic Events}, year = {2003}, note = {{\underline{hdl:1902.1/FYXLAWZRIA} UNF:3:um06qkr/1tAwpS4roUqAiw== Murray Research Archive [Distributor]}} } @article{KinZen01c, author = {Gary King and Langche Zeng}, title = {Replication data for: Explaining Rare Events in International Relations}, year = {2001}, note = {\underline{hdl:1902.1/OUCBSJKXIC} UNF:3:vyct3c8fMCdWOdp03NUhaA== Murray Research Archive [Distributor]} } @article{KinZen01d, author = {Gary King and Langche Zeng}, title = {Replication data for: Improving Forecats of State Failure}, year = {2001}, note = {{\underline{hdl:1902.1/RPQIODIANR} UNF:3:CEsbEgPxbxExfYuh2NWwWQ== Murray Research Archive [Distributor]}} } @article{BecKinZen00b, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Replication data for: Improving Quantitative Studies of International Conflict: A Conjecture}, volume = {2000}, note = {{\underline{hdl:1902.1/SZKONDGOMF} UNF:3:rYRDzT8dCJ/BR7V9u8fObA== Murray Research Archive [Distributor]}} } @article{KinTomWit00b, author = {Gary King and Michael Tomz and Jason Wittenberg}, title = {Replication data for: Making the Most of Statistical Analyses: Improving Interpretation and Presentation}, year = {2000}, note = {{\underline{hdl:1902.1/QTCABXZZRQ} UNF:3:1VaLflZ/LfB+AISX+hBm1w== Murray Research Archive [Distributor]}} } @article{KatKin99b, author = {Jonathan Katz and Gary King}, title = {Replication data for: A Statistical Model of Multiparty Electoral Data}, year = {1999}, note = {{\underline{hdl:1902.1/QIGTWZYTLZ} UNF:3:gwGcKylle0BKJTGv3Zv4OA== Murray Research Archive [Distributor]}} } @article{GelKinBos98b, author = {Andrew Gelman and Gary King and John Boscardin}, title = {Replication data for: Estimating the Probability of Events that have Never Occurred: When is your Vote Decisive}, year = {1998}, note = {{\underline{hdl:1902.1/NOLXXTUHNZ} UNF:3:ORDulVH6qEb4lsCyDn5W3A== Murray Research Archive [Distributor]}} } @article{King97b, author = {Gary King}, title = {Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individuals Behavior from Aggregate Data}, year = {1997}, note = {{\underline{hdl:1902.1/LWMMKUTYXS} UNF:3:DRWozWd89+vNLO7lY2AHbg== Murray Research Archive [Distributor]}} } @article{GelKin94d, author = {Andrew Gelman and Gary King}, title = {Replication data for: Enhancing Democracy Through Legislative Redistricting}, year = {1994}, note = {{\underline{hdl:1902.1/BNCOWNVERH} UNF:3:ZXahi7PBFxLRb46sVKOAuQ== Murray Research Archive [Distributor]}} } @article{GelKin94e, author = {Andrew Gelman and Gary King}, title = {Replication data for: Unified Methods of Evaluating Electoral Systems and Redistricting Plans: United States House of Representatives adn Ohio State Legislature}, year = {1994}, note = {{\underline{hdl:1902.1/JWFTSFKOBK} UNF:3:Fi01DWj4Sx+0ZEOEo4TOXA== Murray Research Archive [Distributor]}} } @article{King94, author = {Gary King}, title = {Elections to the United States House of Representatives, 1898-1992}, year = {1994}, note = {{\underline{hdl:1902.1/TQDSSPRDDZ} UNF:3:tD8SznMFjKIxWxOqTQaamQ== Murray Research Archive [Distributor]}} } @article{GelKin93b, author = {Andrew Gelman and Gary King}, title = {Replication data for: Why Are American Presidential Election Campaign Polls so Variable When Votes are so Predictable?}, year = {1993}, note = {{\underline{hdl:1902.1/SBBXEUSSCW} Murray Research Archive [Distributor]}} } @article{KinLav93b, author = {Gary King and Michael Laver}, title = {Replication data for: On Party Platforms, Mandates, and Government Spending}, year = {1993}, note = {{\underline{hdl:1902.1/XEHYCJAWQD} UNF:3:cwNXuRQ/6Lp72obLkttmGg== Murray Research Archive [Distributor]}} } @article{King91e, author = {Gary King}, title = {Replication data for: Constituency Service and Incumbency Advantage}, year = {1991}, note = {{\underline{hdl:1902.1/JTMXGSZXIZ} UNF:3:IE4ZSAs8ZzUK+fRXNbVvGw== Murray Research Archive [Distributor]}} } @article{King91f, author = {Gary King}, title = {Replication data for: On Political Methodology}, year = {1991}, note = {{\underline{hdl:1902.1/KHTLSQXAEJ} Murray Research Archive [Distributor]}} } @article{AnsKin90b, author = {Stephen Ansolabehere and Gary King}, title = {Replication data for: Measuring the Consequences of Delegate Selection Rules in Presidential Nominations}, year = {1990}, note = {{\underline{hdl:1902.1/BUJXCEPXQK} UNF:3:OdFPcQcvfO5hc3WJ5ty8vQ== Murray Research Archive [Distributor]}} } @article{KinBen86, author = {Gary King and Gerald Benjamin}, title = {Replication data for: The Stability of Partisan Identification in the U.S. House of Representatives, 1789-1984}, year = {1986}, note = {{\underline{hdl:1902.1/HINHTJQYFO} Murray Research Archive [Distributor]}} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Software %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{IacKinPor09b, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {CEM: Coarsened Exact Matching Software}, journal = {Journal of Statistical Software}, volume = 30, issue = 9, year = 2009, note = {{http://gking.harvard.edu/cem}} } @article{WanKinLau07, author = {Jonathan Wand and Gary King and Olivia Lau}, title = {Anchors: Software for Anchoring Vignettes Data}, journal = {Journal of Statistical Software}, year = {2007, forthcoming} } @Article{HonKinBLa10, author = {James Honaker and Gary King and Matthew Blackwell}, title = {Amelia II: A Program for Missing Data}, year = 2010, note = {{http://gking.harvard.edu/amelia}} } @article{ImaKinLau06, author = {Kosuke Imai and Gary King and Olivia Lau}, title = {Zelig: Everyone's Statistical Software}, year = 2006, note = {{http://gking.harvard.edu/zelig}} } @article{TomWitKin05, author = {Michael Tomz and Jason Wittenberg and Gary King}, title = {CLARIFY: Software for Interpreting and Presenting Statistical Results}, year = {1998-2005}, note = {{http://gking.harvard.edu/stats.shtml#clarify}} } @article{HonJosKin98, author = {James Honaker and Anne Joseph and Gary King and Kenneth Scheve and Naunihal Singh.}, title = {AMELIA: A Program for Missing Data}, year = {1998-2002}, note = {{http://gking.harvard.edu/amelia}} } @article{King98, author = {Gary King}, title = {MAXLIK, a set of Gauss programs, annotated for pedagogical purposes, to implement the maximum likelihood models in Unifying Political Methodology: The Likelihood Theory of Statistical Inference}, year = {1998}, note = {{http://gking.harvard.edu/stats.shtml#maxlik}} } @article{King96b, author = {Gary King}, title = {EI: Program for Ecological Inference}, year = {1996-2003}, note = {{http://gking.harvard.edu/stats.shtml#ei}} } @article{GelKin92, author = {Andrew Gelman and Gary King}, title = {JudgeIt: A Program for Evaluating Electoral Systems and Redistricting Plans}, year = {1992-2002}, note = {{http://gking.harvard.edu/stats.shtml#judgeit}} } @article{HoImaKin07a, author = {Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart}, title = {MatchIt: Nonparametric Preprocessing for Parametric Causal Inference}, year = {Forthcoming}, journal = {Journal of Statistical Software}, note = {{http://gking.harvard.edu/matchit}} } @InCollection{Gelman04, author = {Andrew Gelman}, title = {Treatment Effects in Before-After Data}, booktitle = {Applied Bayesian Modeling and Causal Inference from an Incomplete Data Perspective}, publisher = {Wiley}, year = 2004, editor = {Andrew Gelman and Xiao-Li Meng}, chapter = 18, address = {London} } Zelig/vignettes/gk.bib0000644000176000001440000204601212061700507014446 0ustar ripleyusers% A bibtex file for papers by or coauthored with Gary King % % To add references, first please CHECK that your doesn't already % exist in this file and % then add entries only at the end. % % Use these rules for the reference label: % % -if one author: use last name and last 2 digits of the year: Tobler79. % -if multiple authors, use 1st 3 letters of each of UP TO the first three % authors and the last 2 digits of the year: KinTomWit00. % -if necessary add lower-case letters for multiple entries in a year: King02, King02b % (the first one should NOT have an 'a' afterwards) % % -feel free to use the abbreviations at the start, or add to them. % -Use authors full names when known. % % please be sure to commit changes to CVS regularly as a number of % people are all using this at the same time. @STRING{ prq = "Political Research Quarterly"} @STRING{ apsr = "American Political Science Review"} @STRING{ ajps = "American Journal of Political Science"} @STRING{ jop = "Journal of Politics"} @STRING{ bjps = "British Journal of Political Science"} @STRING{ jleo = "Journal of Law, Economics, and Organization"} @STRING{ isa = "Paper presented at the annual meetings of the International Studies Association"} @STRING{ apsa = "Paper presented at the annual meetings of the American Political Science Association"} @STRING{ cp = "Comparative Politics"} @STRING{ io = "International Organization"} @STRING{ midwest = "Paper presented at the Annual Meeting of the Midwest Political Science Association"} @STRING{ mpsa = "midwest"} @STRING{ southern = "Paper presented at the Annual Meeting of the Southern Political Science Association"} @STRING{ icpsr = "Inter-University Consortium for Political and Social Research"} @STRING{ jasa = "Journal of the American Statistical Association"} @STRING{ lsq = "Legislative Studies Quarterly"} @STRING{ isq = "International Studies Quarterly"} @STRING{ tas = "The American Statistician"} @STRING{ jbes = "Journal of Business \& Economic Statistics"} @STRING{ joe = "Journal of Econometrics"} @STRING{ wp = "World Politics"} @STRING{ cup = "Cambridge University Press"} @STRING{ hup = "Harvard University Press"} @STRING{ ny = "New York"} @STRING{ sv = "Springer Verlag"} @STRING{ pup = "Princeton University Press"} @STRING{ ucp = "University of California Press"} @STRING{ ap = "Academic Press"} @STRING{ wb = "The World Bank"} @STRING{ eas = "Europe-Asia Studies"} @STRING{ jet = "Journal of Economic Theory"} @STRING{ jrssA = "Journal of the Royal Statistical Society, A"} @STRING{ jrssb = "Journal of the Royal Statistical Society, B"} @STRING{ poq = "Public Opinion Quarterly"} @STRING{ pnas = "Proceedings of the National Academy of Sciences"} @STRING{ ai = "Artificial Intelligence"} @STRING{ pa = "Political Analysis"} @STRING{ ps = "PS: Political Science and Politics"} @STRING{ smr = "Sociological Methods and Research"} @STRING{ sim = "Statistics in Medicine"} @STRING{ asr = "American Sociological Review"} @STRING{ bmj = "British Medical Journal"} @STRING{ lan = "Lancet"} @STRING{ dem = "Demography"} @STRING{ bull = "Bulletin of WHO"} @STRING{ ssm = "Social Science and Medicine"} @STRING{ mitai = "Artificial Intelligence Laboratory, Massachusetts Institute of Technology"} @STRING{ nc = "Neural Computation"} @article{AbaDruLeb02, author = {Alberto Abadie and David Druckker and Jane Leber Herr and Guido W. Imbens}, title = {Implementing Matching Estimators for Average Treatment Effects in Stata}, journal = {The Stata Journal}, volume = 1, year = 2002, pages = {1--18}, number = 1 } @article{Bin83, author = {David A. Binder}, title = {On the Variance of Asymptoticaly Normal Estimators from Complex Surveys}, year = {1983}, journal = {International Statistical Review}, volume = {51}, number = {3}, pages = {279--292} } @article{HorTho52, author = {D. G. Horvitz and D.J. Thompson}, title = {A Generalization of Sampling without Replacement from a Finite Universe}, year = {1952}, journal = {Journal of the American Statistical Association}, volume = {47}, pages = {663--685} } @article{AbaGelLev08, author = {Kobi Abayomi and Andrew Gelman and Marc Levy}, title = {Diagnostics for Multivariate Imputations}, journal = {Applied Statistics}, volume = {57}, number = {3}, pages = {273--291}, year = {2008} } @misc{AbaImb05, author = {Alberto Abadie and Guido Imbens}, title = {Estimation of the Conditional Variance in Paired Experiments}, year = 2006, howpublished = {KSG Working Paper}, note = {{http://ksghome.harvard.edu/\~{}.aabadie.academic.ksg/cve.pdf}} } @misc{AbaImb09, author = {Alberto Abadie and Guido Imbens}, title = {A Martingale Representation for Matching Estimators}, year = 2009, howpublished = {IZA Discussion Papers number 4073}, note = {{http://ftp.iza.org/dp4073.pdf}} } @article{Imb00, author = {Guido Imbens}, title = {The role of the propensity score in estimating the dose-response functions}, year = {2000}, journal = {Biometrika}, pages = {706--710}, volume = {87}, issue = {3} } @article{AbaImb07b, author = {Alberto Abadie and Guido Imbens}, title = {On the Failure of the Bootstrap for Matching Estimators}, year = {2007}, journal = {Econometrica}, pages = {1537--1557}, volume = {76}, issue = {6} } @article{AbaImb06, author = {Abadie, Alberto and Imbens, Guido W.}, title = {Large Sample Properties of Matching Estimators for Average Treatment Effects}, journal = {Econometrica}, volume = {74}, year = {2006}, pages = {235--267}, number = {1} } @inproceedings{Abu-Mostafa92, author={Y. Abu-Mostafa}, title={A Method for Learning from Hints}, booktitle={Advances in Neural information processings systems 5}, year={1992}, publisher={Morgan Kaufmann Publishers}, address={San Mateo, CA}, editor={S. J. Hanson and Jack D. Cowan and C. Lee Giles} } @article{Abu-Mostafa92, author={Y. Abu-Mostafa}, title={A Method for Learning from Hints}, journal={Advances in Neural information processings systems 5}, volume={1992}, pages={Morgan Kaufmann Publishers}, month={San Mateo CA}, number={S. J. Hanson and Jack D. Cowan and C. Lee Giles} } @book{Achen86, author={Christopher Achen}, title={Statistical Analysis of Quasi-experiments}, publisher={University of California Press}, year={1986}, address={Berkeley} } @techreport{AdaCoaRue00, author={Michelle Adato and David Coady and Marie Ruel}, title={An Operations Evaluation of Progresa from the Perspective of Beneficiaries, Promotoras, School Directors and Health Staff}, institution={International Food Policy Research Institute}, year={2000}, month={August}, type={Final Report}, address={2033 K Street, NW Washington, DC 20006} } @article{AdaGla05, author={Adamic, L.A. and Glance, N.}, title={{The political blogosphere and the 2004 US election: divided they blog}}, journal={Proceedings of the 3rd international workshop on Link discovery}, year={2005}, pages={36--43}, publisher={ACM Press New York, NY, USA} } @article{AgoDyn04, author={Roberto Agodini and Mark Dynarski}, title={Are experiments the only option? {A} look at dropout prevention programs}, journal={Review of Economics and Statistics}, volume= 86, year= 2004, pages={180-194}, month={February}, number= 1 } @unpublished{AgrRajSri03, author={Rakesh Agrawal and Sridhar Rajagopolan and Ramakrishnan Srikant and Yirong Xu}, title={Mining Newsgroups Using Networks Arising from Social Behavior}, note={IBM ALmaden Research Center 650 Harry Rd., San Jose, CA 95120}, year={2003}, month={May} } @book{Aitchison86, author={J. Aitchison}, title={The Statistical Analysis of Compositional Data}, publisher={Chapman and Hall}, year= 1986, address={London} } @article{Albert88, author={James H. Albert}, title={Computational Methods Using a Bayesian Hierarchical Generalized Linear Model}, journal={Journal of the American Statistical Association}, volume={83}, year={1988}, pages={1037-1004}, month={December}, number={404} } @article{AldMcK77, author={John H. Aldrich and Richard D. McKelvey}, title={A Method of Scaling With Applications to the 1968 and 1972 Presidential Elections}, journal= apsr, volume= 71, year= 1977, pages={111-130}, month={March} } @article{AleTab90, author={Alberto Alesina and Guido Tabellini}, title={A Positive Theory of Fiscal Deficits and Government Debt}, journal={The Review of Economic Studies}, volume={57}, year={1990}, pages={403-414}, month={July}, number={3} } @article{Alho00, author={J. M. Alho}, title={Discussion}, journal={North American Actuarial Journal}, volume= 4, year= 2000, pages={91--93}, number= 1 } @article{Alho92, author={J. M. Alho}, title={{Comment on ``Modeling and Forecasting U.S. Mortality'' by R. Lee and L. Carter}}, journal= jasa, volume= 87, year= 1992, pages={673--674}, month={September}, number= 419 } @article{AlSaCr76, author={James Alt and Bo Sarlvik and Ivor Crewe}, title={Individual Differences Scaling and Group Attitude Structures: British Party Imagery in 1974}, journal={Quality and Quantity}, volume= 10, year= 1976, pages={297--320}, month={October} } @book{AltGilMcD03, author={Micah Altman and Jeff Gill and Michael P. McDonald}, title={Numerical Issues in Statistical Computing for the Social Scientist}, publisher={John Wiley and Sons}, year= 2003, address={New York} } @article{Altman85, author={Douglas G. Altman}, title={Comparability of Randomised Groups}, journal={The Statistician}, volume={34}, year={1985}, pages={125-136}, number={1} } @article{Altman98, author={Douglas G. Altman and Jonathan J. Deeks and David L. Sackett}, title={Odds Ratios Should be Avoided When Events are Common}, journal={British Medical Journal}, volume= 317, year= 1998, pages= 1318, month={Nov. 7} } @article{AltMcD03, author={Micah Altman and Michael P. McDonald}, title={Replication with Attention to Numerical Accuracy}, journal={Political Analysis}, volume={11}, year={2003}, pages={302-307}, number={3} } @article{AltRub70, author={Robert P. Althauser and Donald B. Rubin}, title={The computerized construction of a matched sample}, journal={American Journal of Sociology}, volume= 76, year= 1970, pages={325-346}, month={September} } @article{AlvBre95, author={Michael R. Alvarez and John Brehm}, title={American Ambivalence Toward Abortion Policy: A Heteroskedastic probit Method for Assessing Conflicting Values}, journal={American Journal of Political Science}, volume={39}, year={1995}, pages={1055-82}, month={November} } @article{AlvBre97, author={Michael R. Alvarez and John Brehm}, title={Are Americans Ambivalent Towards Racial Policies}, journal={American Journal of Political Science}, volume={41}, year={1997}, pages={345-374}, month={April}, number={2} } @article{AlvGarLan91, author={Michael R.\ Alvarez and Geoffrey Garrett and Peter Lange}, title={Government Partisanship, Labor Organization, and Macroeconomic Performance}, journal= apsr, volume= 85, year= 1991, pages={539--556} } @article{AmoMccZim97, author={A.F. Amos and D.J. McCarty and P. Zimmet}, title={The Rising Global Burden of Diabetes and its Complications: Estimates and Projections to the Year 2010}, journal={Diabetic Medicine}, volume= 14, year= 1997, tpages={S7--S85} } @book{AndBasHum83, author={Andy B. Anderson and Alexander Basilevsky and Derek P.J. Hum}, title={Missing Data: A Review of the Literature}, publisher={Academic Press, Inc}, year={1983}, editor={Peter H. Rossi and James D. Writght and Andy B. Anderson} } @article{AndGib06, author={Krister Andersson and Clark C. Gibson}, title={Decentralized Governance and Environmental Change: Local Institutional Moderation of Defroestation in Bolivia}, journal={Journal of Policy Analysis and Management}, volume={26}, year={2006}, pages={99-123}, number={1} } @article{AndGreMcc05, author={Richard G. Anderson and William H. Greene and B.D. McCullough and H.D. Vinod}, title={The Role of Data \& Program Code Archives in the Future of Economic Research}, year= 2005, month={July}, note={Federal Reserve Bank of St. Louis Research Division} } @article{Andrews91, author={Donald W.K. Andrews}, title={Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation}, journal={Econometrica}, volume={59}, year={1991}, pages={817--858}, month={May}, number={3} } @article{AndZom01, author={A.S. Andreou and G.A. Zombanakis}, title={A Neural Network Measurement of Relative Military Security--The Case of Greece and Cyprus}, journal={Defence and Peace Economics}, volume= 12, year= 2001, pages={303--324}, number= 4, annote={have not read primary source. looks promising, given secondary source comments: all input variables are financial and the output variable--relative security--is population/demographics based. Arms race scenarios are simulated by increasing and decreasing financial covariates.} } @article{AngAngFro94, author={G. De Angelis and R. De Angelis and L. Frova and A. Verdecchia}, title={MIAMOD: A Computer Package to Estimate Chronic Disease Morbidity Using Mortality and Survival Data}, journal={Computer Methods and Programs in Biomedicine}, volume= 44, year= 1994, pages={99--107} } @article{AngImb95, author={Joshua D. Angrist and Guido W. Imbens}, title={Two-Stage Least Squares Estimation of Average Causal Effects in Models withVariable Treatment Intensity}, journal={Journal of the American Statistical Association}, volume={90}, year={1995}, pages={431-442}, month={June}, number={430} } @article{AngImbRub96, author={Angrist, Joshua D. and Imbens, Guido W. and Rubin, Donald B.}, title={Identification of Causal Effects Using Instrumental Variables (with discussion)}, journal={Journal of the American Statistical Association}, volume={91}, year={1996}, pages={444--455}, optnumber={434} } @article{Angress59, author={Werner T. Angress}, title={The Political Role of the Peasantry in the Weimar Republic}, journal={The Review of Politics}, volume= 21, year= 1959, pages={530--549}, number= 3 } @unpublished{AnkBlaCol99, author = {Martha Anker and Robert E. Black and Christopher Coldham and Henry D. Kalter and Maria A. Quigley and David Ross and Robert W. Snow}, title = {A Standard Verbal Autopsy Method for Investigating Causes of Death in Infants and Children}, note = {World Health Organization, Department of communicable Disease Surveillance and Response}, year = {1999}, journal = {World Health Organization} } @book{Anker03, author = {Martha Anker}, title = {Investigating Cause of Death During an Outbreak of Ebola Virus Haemorrhagic Fever: Draft Verbal Autopsy Instrument}, publisher = {World Health Organization}, year = 2003, address = {Geneva} } @article{Anker97, author = {Martha Anker}, title = {The Effect of Misclassification Error on Reported Cause-Specific Mortality Fractions from Verbal Autopsy}, journal = {International Journal of Epidemiology}, volume = {26}, year = {1997}, pages = {1090-1096} } @article{AppBosGra96, author = {A. Appels, et al}, title = {Self-Rated Health and Mortality in a Lithuanian and Dutch Population}, journal = {Social Science and Medicine}, volume = 42, year = 1996, pages = {{681-89}}, number = 5 } @techreport{Arendt03, author = {Jacob N. Arendt}, title = {Social gradients in self-rated health in Denmark - gender differences and health risk factors in dynamic context}, institution = {AKF, Institute of Local Government Studies}, year = 2003, month = {May}, address = {Nyropsgade 37, 1602 Copenhagen V, Denmark} } @book{Arendt73, author = {Arendt, Hannah}, title = {The Origins of Totalitarianism}, publisher = {Harcourt Brace Jovanovich}, year = 1973, address = {New York} } @incollection{Armstrong01, author = {J. Scott Armstrong}, title = {Extrapolation of Time Series and Cross-Sectional Data}, booktitle = {Principles of Forecasting: A Handbook for Researchers and Practitioners}, publisher = {Kluwer}, year = 2001, editor = {J. Scott Armstrong}, pages = {217--243} } @unpublished{Ashworth01, author = {Scott Ashworth}, title = {Reputational Dynamics and Congressional Careers}, note = {Harvard University}, year = 2001, annote = {introduce the single crossing property in political science} } @article{AssPocEno00, author = {Susan F. Assmann and Stuart J. Pocock, Laura E. Enos and Linda E. Kasten}, title = {Subgroup analysis and other (mis)uses of baseline data in clinical trials}, journal = {The Lancet}, volume = {355}, year = {2000}, pages = {1064-1069}, month = {March} } @article{AusMam06, author={Peter C. Austin and Muhammad M. Mamdani}, title={A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use}, journal={Statistics in Medicine}, volume={25}, year={2006}, pages={2084-2106} } @article{AusMamStu05, author={Peter C. Austin and Muhammad M. Mamdani and Therese A. Stukel and Geoffrey M. Anderson and Jack V. Tu}, title={The use of the propensity score for estimating treatment effects: {A}dministrative versus clinical data}, journal={Statistics in Medicine}, volume={24}, year={2005}, pages={1563-1578} } @article{AvlSchDav98, author={Kirsten Avlund, Kirsten Schultz-Larsen, and Michael Davidson}, title={Tiredness in Daily Activities at Age 70 as a Predictor of Mortality During the Next 10 Years}, journal={Journal of Clinical Epidemiology}, volume= 51, year= 1998, pages={{323-33}} } @article{BacKin04, author={Bachrach, Christine A. and King, Roslind B.}, title={{Data Sharing and Duplication: Is There a Problem?}}, journal={Archives of Pediatric and Adolescent Medicine}, volume= 158, year={2004}, month={September}, number= 9 } @article{BagHopMas02, author={A. Bagust and P.K. Hopkinson and L. Maslove and C.J. Currie}, title={The Projected Health Care Burden of Type 2 Diabetes in the UK from 2000 to 2060}, journal={Diabetic Medicine}, volume= 19, year= 2002, pages={1--5}, number= 4 } @book{Balderston02, author={Theo Balderston}, title={Economics and Politics in the Weimar Republic}, publisher={Cambridge University Press}, year= 2002, address={Cambridge} } @article{BanBan92, author={A.T. Bang and R.A. Bang and the SEARCH team}, title={Diagnosis of causes of childhood deaths in developing countries by verbal autosy: suggested criteria}, journal={Bulletin of the World Health Organization}, volume={70}, year={1992}, pages={499-507}, number={4} } @article{BaqBlaAri98, author={A.H. Baqui and R.E. Black and S.E. Arifeen and K. Hill and S.N. Mitra and A.Al Sabir}, title={Causes of childhood deaths in Bangladesh: results of a nationwide verbal autopsy study}, journal={Bulletin of the World Health Organization}, volume={76}, year={1998}, pages={161}, number={2} } @article{BarFraHil03, author={John Barnard and Constantine E. Frangakis and Jennifer L. Hill and Donald B. Rubin}, title={{Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City.}}, journal={Journal of the American Statistical Association}, volume={98}, year={2003}, pages={299-324}, number={462} } @book{Barkai90, author={Barkai, Avram}, title={Nazi Economics: Ideology, Theory, and Policy}, address={Oxford}, publisher={Berg Press}, year={1990} } @article{BarPonCor00, author={Ivana C. H. C. Barr{\^e}to and L{\'i}gia Kerr Pontes and e Luciano Corr{\^e}a}, title={Vigil{\^a}ncia de {\'o}bitos infantis em sistemas locais de sa{\'u}de: avalia{\c{c}}{\~a}o da aut{\'o}psia verbal e das informa{\c{c}}{\~o}es de agentes de sa{\'u}de}, journal={Rev Panam Salud Publica / Pan Am Journal of Public Health}, volume={7}, year={2000}, pages={303-312}, number={5} } @article{Bartels96, author={Bartels, Larry M.}, title={Uninformed Votes: Information Effects in Presidential Elections}, journal={American Journal of Political Science}, volume= 40, year= 1996, pages={194--230} } @unpublished{Bartels98, author={Larry Bartels}, title={Panel Attrition and Panel Conditioning in American National Election Sudies}, note={Paper prepared for the 1998 meetings of the Society for Political Methodology, San Deigo}, year={1998} } @article{BasEst01, author={S.A. Bashir and J. Esteve}, title={Projecting Cancer Incidence and Mortality Using Bayesian Age-Period-Cohort Models}, journal={Journal of Epidemiology and Biostatistics}, volume= 6, year= 2001, pages={287--296}, number= 3 } @unpublished{BatFerHab06, author={Robert Bates and Karen Feree and James Habyarimana and Macartan Humphreys and Smita Singh}, title={The Africa Research Program}, note={{http://africa.gov.harvard.edu}}, year= 2006 } @article{Bath03, author={Peter A. Bath, PhD}, title={Differences Between Older Men and Women in the Self-Rated Health-Mortality Relationship}, journal={The Gerontologist}, volume= 43, year= 2003, pages={{387-95}} } @article{Baum88, author={Lawrence Baum}, title={Measuring Policy Change in the U.S. Supreme Court}, journal= apsr, volume= 82, year= 1988, pages={905--912}, month={September}, number= 3 } @article{BeaMei89, author={Michael L. Beach and Paul Meier}, title={Choosing Covariates in the Analysis of Clinical Trials}, journal={Controlled Clinical Trials}, volume={10}, year={1989}, pages={161S-175S} } @incollection{Bearce00, author={David Bearce}, title={Economic Sanctions and Neural Networks: Forecasting Effectiveness and Reconsidering Cooperation}, booktitle={Political Complexity: Non Linear Models of Politics}, publisher={University of Michigan Press}, year= 2000, address={Ann Arbor, MI}, editor={Diana Richards}, pages={269--295}, annote={asks whether real-world forecasting needs make NN preferable to traditional (and linear) analysis. Looks at effectiveness of sanctions, using about 100 quantitative cases first examined in 1980s. NNs are shown to forecast twice as well as traditional methods.} } @book{BecChaWil88, author={Richard A. Becker and John M. Chambers and Allan R. Wilks}, title={The New S. language}, publisher={Wadsworth}, year={1988}, address={New York} } @article{BecIch02, author={Sascha O. Becker and Andrea Ichino}, title={Estimation of average treatment effects based on propensity scores}, journal={The Stata Journal}, volume= 2, year= 2002, pages={358-377}, number= 4 } @article{BecIch02, author={Sascha O. Becker and Andrea Ichino}, title={Stata programs for ATT estimation based on propensity score matching}, journal={The Stata Journal}, volume= 2, year= 2002, pages={358--377}, number= 4 } @article{BecJac98, author={Nathaniel Beck and Simon Jackman}, title={Beyond Linearity by Default: Generalized Additive Model}, journal= ajps, volume= 42, year= 1998, pages={596--627}, month={April}, number= 2 } @article{BecKat95, author={Nathaniel Beck and Jonathan Katz}, title={``What to Do (and Not to Do) with Time-Series-Cross-Section Data''}, journal= apsr, volume= 89, year= 1995, pages={634--647} } @article{BecKat96, author={Nathaniel Beck and Jonathan Katz}, title={Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Model}, journal= pa, volume={VI}, year= 1996, pages={1--36} } @article{BecKatTuc98, author={Nathaniel Beck and Jonathan Katz and Richard Tucker}, title={Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable}, journal= apsr, volume= 42, year= 1998, pages={1260-1288} } @article{BedChrJoh96, author={Edward J. Bedrick and Ronald Christensen and Wesley Johnson}, title={A New Perspective on Priors for Generalized Linear models}, journal={Journal of the American Statistical Association}, volume={91}, year={1996}, pages={1450-1460}, number={436} } @techreport{BehTod99, author={Jere R. Behrman and Petra E. Todd}, title={Randomness in the Experimental Samples of Progresa (Education, Health, and Nutrition Program)}, institution={International Food Policy Research Institute}, year={1999}, month={March}, type={Research Report}, address={2033 K Street, NW Washington, DC 20006} } @article{Bell97, author={W.R. Bell}, title={{Comparing and Assessing Time Series Methods for Forecasting Age-Specific Fertility and Mortality Rates}}, journal={Journal of Official Statistics}, volume= 13, year= 1997, pages={279--303}, number= 3 } @article{Bello93, author={Abdul Lateef Bello}, title={Choosing Among Imputation Techniques for Incomplete Multivariate Data: A Simulation Study}, journal={Communications in Statistics A: Theory and Methods}, volume={22}, year={1993}, pages={853-877}, number={3} } @article{BelMon91, author={W.R. Bell and B.C. Monsell}, title={Using Principal Components in time Series modeling and Forecasting of Age-Specific Mortality Rates}, journal={Proceedings of the American Statistical Association, Social Statistics Section}, year= 1991, pages={154--159} } @article{Beltrami1873, author={E. Beltrami}, title={Sulle funzioni bilineari}, journal={Giornale di Matematiche ad Uso degli Studenti Delle Universit{\'a}}, volume= 11, year= 1873, pages={98--106}, note={{An English translation by D. Boley is available as University of Minnesota, Department of Computer Science, Technical Report 90-37, 1990}} } @article{BenBluLus03, author={Yael Benyamini, et al.}, title={Gender differences in the self-rated health-mortality association: Is it poor self-rated health that predicts mortality or excellent self-rated health that predicts survival?}, journal={The Gerontologist}, volume={43}, year={2003}, pages={{396-405}}, number={3} } @incollection{Bendix53, author={Bendix, Reinhard}, title={Social Stratification and Political Power}, booktitle={Class Status and Power}, publisher={The Free Press}, year= 1953, address={Glencoe, IL}, editor={Bendix, Reinhard and Lipset, Seymour Martin} } @article{BenHumEbe04, author={Maureen Reindl Benjamins}, title={Self-Reported Health and Adult Mortality Risk: An Analysis of Cause Specific Mortality}, journal={Social Science and Medicine (Forthcoming 2004)} } @article{benichou95, author={J. Benichou and M. Gail}, title={Methods of Inference for Estimates of Absolute Risk Derived From Population-Based Case-Control Studies}, journal={Biometrics}, volume= 51, year= 1995, pages={182-194} } @article{BenIdl99, author={Yael Benyamini, and Ellen Idler}, title={Community Studies Reporting Association Between Self-Rated Health and Mortality}, journal={Research on Aging}, volume= 21, year= 1999, pages={{392-401}}, number= 3 } @article{BenIdlLev00, author={Yael Benyamini, Ellen Idler, Howard Leventhal, and Elaine A. Leventhal}, title={Positive-Affect and Function as Influences on Self-Assessments of Health: Expanding ou View Beyond Illness and Disability}, journal={Journal of Gerontology: Psychological Sciences}, volume={{55B}}, year= 2000, pages={{P107-116}} } @article{BenLav03, author={Kenneth Benoit and Michael Laver}, title={Estimating Irish party policy positions using computer wordscoring: the 2002 election - a research note}, journal={Irish Political Studies}, volume={18}, year={2003}, pages={97--107}, number={1} } @article{BenLevLev00, author={Yael Benyamini, et al}, title={Gender Differences in Processing Information for Making Self-Assessments of Health }, journal={American Psychosomatic Society}, volume= 62, year= 2000, pages={{354-64}}, number= 2 } @article{BenLevLev99, author={Yael Benyamini, Elaine A. Leventhal, and Howard Leventhal}, title={Self-Assessments of Health. What Do People Know that Predicts their Mortality?}, journal={Reasearch on Aging}, volume= 21, year= 1999, pages={{477-500}}, month={{May}}, number= 3 } @article{BenLip59, author={Bendix, Reinhard and Lipset, Seymour Martin}, title={On the Social Structure of Western Societies: Some Reflections on Comparative Analysis}, journal={Berkeley Journal of Sociology}, volume= 5, year= 1959, pages={1-15} } @article{BenSin99, author={S.K. Benara and Padam Singh}, title={Validity of Causes of Infant Death by Verbal Autopsy}, journal={Indian Journal of Pediatrics}, volume={66}, year={1999}, pages={647-650} } @article{BenSin99, author={S.K. Benara and Padam Singh}, title={Validity of Causes of Infant Death by Verbal Autopsy}, journal={Indian Journal of Pediatrics}, volume={66}, year={1999}, pages={647-650} } @article{BerdeG47, author={Berelson, B. and de Grazia, S.}, title={{Detecting Collaboration in Propaganda}}, journal={Public Opinion Quarterly}, volume={11}, year={1947}, pages={244--253}, number={2} } @proceedings{BerDegLin88, editor={J. M. Bernardo and M.H. Degroot and D.V. Lindley and A.F.M. Smith}, title={Bayesian Statistics 3}, publisher={Clarendon Press, Oxford}, year={1987}, month={June 1-5}, organization={Proceedings of the Third Valencia International Meeting} } @article{Berenson04, author={Robert Berenson}, title={The Medicare Chronic Care Improvement Program}, journal={The Urban Institute}, year={2004}, month={May}, note={{http://www.urban.org/url.cfm?ID=900714}} } @article{Berger04, author={Vance W. Berger}, title={Selection Bias and Baseline Imbalances in Randomized Trials}, journal={Drug Information Journal}, volume={38}, year={2004}, pages={1-2} } @article{Berger05a, author={Vance W. Berger}, title={Quantifying the Magnitude of Baseline Covariate Imbalances Resulting fronmSelection Bias in Randomized Clinical Trials}, journal={Biometrical Journal}, volume={47}, year={2005}, pages={119-127}, number={2} } @book{Berger05b, author={Vance W. Berger}, title={Selection Bias and covariate Imbalances in Randomized Clinical Trials}, publisher={John Wiley \& Sons, Ltd.}, year={2005}, editor={Stephen Senn and Vic Barnett}, series={Statistics in Practice} } @article{Berger94, author={James Berger}, title={An Overview of Robust Bayesian Analysis (With Discussion)}, journal={Test}, volume= 3, year= 1994, pages={5-124} } @article{BerHenSav77, author={E. Berndt and D. Hendry and N.E. Savin}, title={Conflict Among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model}, journal={Econometrica}, volume={45}, year={1977}, pages={1263-1277} } @article{BerKinKon97, author={Shulamit L. Bernard, Jean E. Kincade, Thomas R. Conrad, et al}, title={Predicting Mortality from Community Surveys of Older Adults: The Importance of Self-Rated Functional Ability}, journal={Journal of Gerontology: Social Sciences}, volume={{52B}}, year= 1997, pages={{S155-63}} } @misc{BerKos03, author={Erik Bergstralh and Jon Kosanke}, title={dist, gmatch, and vmatch: SAS Macros}, year= 2003, howpublished={Mayo Clinic, Division of Biostatistics}, note={{http://mayoresearch.mayo.edu/mayo/research/biostat/sasmacros.cfm}} } @book{BerKreOve98, author={Mark de Berg and Marc van Krevald and Mark Overmars and Otfried Schwarzkopf}, title={Computational Geometry: Algorithms and Applications}, publisher={Springer}, year= 1998, address={New York}, edition={2nd, revised edition} } @book{ImbRub10, author={Guido Imbens and Donald Rubin}, title={Causal Inference}, year= {2010}, note={Unpublished manuscript} } @article{Bernstein32, author={F. Bernstein}, title={{\"U}ber eine Methode, die soziologische und bev{\"o}lkerungsstatistische Gliederung von Abstimmungen bei geheimen Wahlverfahren zu ermittlen}, journal={Allgemeines Statistisches Archiv}, volume= 22, year= 1932, pages={253--256} } @article{Besag74, author={Julian Besag}, title={Spatial Interaction and the Statistical Analysis of Lattice Systems (With Discussion)}, journal= jrssb, volume= 36, year= 1974, pages={192-236} } @article{Besag75, author={Julian Besag}, title={Statistical Analysis of Non-Lattice Data}, journal={The Statistician}, volume= 24, year= 1975, pages={179--195}, number= 3 } @article{Besag83, author={Julian E. Besag}, title={Discussion of paper by {P}. {S}witzer}, journal={Bull. Intern. Statist. Inst.}, volume= 50, year= 1983, pages={422-425}, number={Bk. 3} } @article{Besag86, author={Julian Besag}, title={On the Statistical Analysis of Dirty Pictures}, journal={Journal of the Royal Statistical Society B}, volume={48}, year={1986}, pages={259--302}, number={3} } @article{Besancon05, author={Marie L. Besancon}, title={Relative Resources: Inequality in Ethnic Wars, Revolutions, and Genocides}, abstract={Political scientists and economists have exhaustively examined the nexus between economic inequality and political conflict (EI-PC nexus) in aggregated civil wars. This article revisits the nexus and its related theories, empirically and parsimoniously testing the effects of inequality on disaggregated intrastate conflicts. The results buttress the notion that traditionally deprived identity groups are more likely to engage in conflict under more economically equal conditions, while class or revolutionary wars fall under the conditions of greater economic inequality and war. Of the three types of conflicts tested - ethnic conflicts, revolutions, and genocides - economic inequality seems to have the most ambiguous bearing on genocides. Support follows for recent findings that political and social equalities are of greater importance in mitigating ethnic violence and that greed factors might exacerbate violence in all civil conflicts, including genocides. The theoretical argument proposes that the context within which intrastate violence takes place affects the requisite level of relative resources needed for the escalation of violence between groups. The results have policy implications for ethnically divided states that are in the process of equalizing their income differential, but neglect the substantial inclusion of all groups within the political process and the distribution of public goods. The social contracts between the governors and the governed then require careful crafting for a peaceful coexistence of diverse identity groups.}, journal={The Journal of Peace Research}, volume={42}, year={2005}, pages={393-415}, month={July}, number={4} } @article{BesGreHigMen95, author={Julian Besag and Peter Green and David Higdon and Kerrie Mengersen}, title={Bayesian Computation and Stochastic Systems (With Discussion)}, journal={Statistical Science}, volume= 10, year= 1995, pages={3-66}, number= 1 } @article{BesHig99, author={Julian Besag and David M. Higdon}, title={Bayesian Analysis of Agricultural Field Experiments (With Discussion)}, journal= jrssb, volume= 61, year= 1999, pages={691-746}, number={4} } @article{BesKoo95, author={Julian Besag and Charles Kooperberg}, title={On Conditional and Intrinsic Autoregressions}, journal={Biometrika}, volume={82}, year= 1995, pages={733-746}, number={4} } @article{BesYorMol91, author={Julian Besag and Jeremy York and Annie Molli{\'e}}, title={Bayesian Image Restoration with Two Applications in Spatial Statistics (With Discussion)}, journal={Annals of the Institute of Statistical Mathematics}, volume= 43, year= 1991, pages={1-59}, number= 1 } @article{Bicego97, author={G. Bicego}, title={Estimating adult mortality rates in the context of the AIDS epidemic in sub-Saharan Africa: analysis of DHS sibling histories}, journal={Health Transition Review}, volume= 7, year= 1997, pages={7--22}, number={S2} } @book{Biggs93, author={N.L. Biggs}, title={Algebraic Graph Theory}, publisher={Cambridge University Press}, year= 1993, address={Cambridge, UK}, edition={2nd} } @article{Billordo05a, author={Libia Billordo}, title={Publishing in French Political Science Journals}, journal={French Politics}, volume={3}, year={2005}, pages={178-186}, number={2} } @article{Billordo05b, author={Libia Billordo}, title={Methods Training in French Political Science}, journal={French Politics}, volume={3}, year={2005}, pages={352-0357}, number={3} } @article{BinBreEar05, author={J.B. Bingenheimer and R.T. Brennan and F.J. Earls}, title={Firearm violence exposure and serious violent behavior}, journal={Science}, volume={308}, year={2005}, pages={1323-1326}, month={May} } @book{BisFieHol75, author={Y.M. M. Bishop and S.E. Fienberg and P.W. Holland}, title={Multivariate Analysis: Theory and Practice}, publisher={MIT Press}, year= 1975, address={Cambridge, MA} } @book{Bishop95, author={Christopher M. Bishop}, title={Neural Networks for Pattern Recognition}, publisher={Oxford University Press}, year= 1995, address={Oxford} } @article{BisSteWil06, author={Benjamin Bishin and Daniel Stevens and Christian Wilson}, title={{Character Counts: Honesty and Fairness in Election 2000}}, journal={Public Opinion Quarterly}, volume={70}, year={2006}, pages={235-248}, number={2} } @article{BjoKri99, author={Jakob Bue Bjorner and Tage Sondergaard Kristensen}, title={Multi-item Scales for Measuring Global Self Rated Health}, journal={Research on Aging}, volume= 21, year= 1999, pages={{417-39}}, number= 3 } @article{BlaGeo91, author={R.C. Blattberg and E.I. George}, title={Shrinkage Estimation of Price and Promotional Elasticities: Seemingly Unrelated Equations}, journal= jasa, volume= 86, year= 1991, pages={304--315}, month={Jun}, number= 414 } @article{BlaRas04, author={Grant Blank and Karsten B. Rasmussen}, title={The Data Documentation Initiative: The Value and Significance of a Worldwide Standard.}, journal={Social Science Computer Review}, volume={22}, year={2004}, pages={306-318}, number={3} } @article{BlaSmi04, author={Dan A. Black and Jeffrey A. Smith}, title={How robust is the evidence on the effects of college quality? Evidence from matching}, journal={Journal of Econometrics}, volume={121}, year={2004}, pages={99-124}, number={1} } @article{BloHilRic03, author={Howard S. Bloom and Carolyn J. Hill and James A. Riccio}, title={Linking Program Implementation and Effectiveness: Lessons from a Pooled Sample of Welfare-to-Work Experiments}, journal={Journal of Policy Analysis and Management}, volume={22}, year={2003}, pages={551-575}, number={4} } @book{Bloom05, title={Learning More from Social Experiments}, publisher={Russell Sage Foundation}, year={2005}, editor={Howard S. Bloom}, address={New York} } @article{BloRicBla07, author={Howard S. Bloom and Lashawn Richburg-Hayes and Alison Black}, title={Using Covariates to Improve Precision for Studies that Randomize Schools to Evaluate Educational Interventions}, journal={Educational Evaluation and Policy Analysis}, year={2007} } @book{BLS03, author={{Board on Life Sciences}}, title={Sharing Publication-Related Data and Materials: Responsibilities of Authorship in the Life Sciences}, publisher={National Academies Press}, year= 2003, address={Washington, D.C.} } @article{Blumer48, author = {Herbert Blumer}, title = {Public Opinion and Public Opinion Polling}, journal = {American Sociological Review}, volume = {13}, year = {1948}, pages = {542-549}, month = {October}, number = {5} } @incollection{Bohm84, author={Peter Bohm}, title={Are thee Practicable Demand-Revealing Mechanisms?}, booktitle={Public Finance and the Quest for Efficiency}, publisher={Wayne State University Press}, year={1984}, address={Detroit}, editor={H. 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Boruch}, title={Randomized Experiments for Planning and Evaluation}, publisher={Sage Publications}, year={1997}, address={Thousand Oaks} } @article{BouChaWel01, author={Andrew Boulle and Daniel Chandramohan and Peter Weller}, title={A Case Study of Using Artificial Neural Networks for Classifying Cause of Death from Verbal Autopsy}, journal={International Journal of Epidemiology}, volume={30}, year={2001}, pages={515-520} } @unpublished{BowHan05, author={Jake Bowers and Ben Hansen}, title={Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: An Application of Randomization Inference Using Full Matching}, note={Departments of Political Science and Statistics: University of Michigan}, year={2005}, month={July} } @book{BoxHunHun78, author={George E.P. Box and William G. Hunger and J. Stuart Hunter}, title={Statistics for Experimenters}, publisher={Wiley-Interscience}, year={1978}, address={New York} } @article{BoyHonNar01, author={James P. Boyle and Amanda A. Honeycutt and K.M. Venkat Narayan and Thomas J. Hoerger and Linda S. Geiss and Hong Chen and Theodore J. Thompson}, title={Projection of Diabetes Burden Through 2050}, journal={Diabetes Care}, volume= 24, year= 2001, pages={1936--1940}, number= 11 } @article{BozBel87, author={J.E. Bozik and W.R. Bell}, title={Forecasting Age Specific Fertility Using Principal Components}, journal={Proceedings of the Americal Statistical Association, Social Statistics Section}, year= 1987, pages={396--401} } @article{bracken98, author={Michael B. and John C. Bracken}, title={Avoidable Systematic Error in Estimating Treatment Effects Must not be Tolerated}, journal={British Medical Journal}, volume= 317, year= 1998, pages={11-56}, month={October 24} } @article{Brady85, author={Henry E. Brady}, title={The Perils of Survey Research: Inter-Personally Incomparable Responses}, journal={Political Methodology}, volume= 11, year= 1985, pages={269--290}, month={June}, number={3--4} } @article{Brady89, author={Henry E. Brady}, title={Factor and Ideal Point Analysis for Interpersonally Incomparable Data}, journal={Psychometrika}, volume= 54, year= 1989, pages={181--202}, month={June}, number= 2 } @inproceedings{BraHil73, author={William Brass and Kenneth Hill}, title={Estimating Adult Mortality in Africa from Orphanhood}, booktitle={Proceedings of the International Population Conference Liege}, year= 1973, organization={International Union for the Scientific Study of Population} } @article{BraTuc01, author={Ted Brader and Joshua Tucker}, title={The Emergence of Mass Partisanship in Russia, 1993-96}, journal={American Journal of Political Science}, volume={45}, year={2001}, pages={69-83} } @book{BreDay80, author={Norman E. Breslow and N.E. Day }, title={Methods in Cancer Research}, publisher={Lyon}, year= 1980 } @book{BreFriOls84, author={Leo Breiman and Jerome H. Friedman and Richard A. Olshen and Charles J. Stone}, title={Classification and Regression Trees}, publisher={Chapman \& Hall}, year={1984}, address={New York, New York} } @book{Brehm93, author={John Brehm}, title={The Phantom Respondents: Opinion Surveys and Political Respresentation}, publisher={University of Michigan Press}, year={1993}, address={Ann Arbor} } @article{Breiman01, author={Leo Breiman}, title={Statistical Modeling: The Two Cultures}, journal={Statistical Science}, volume={16}, year={2001}, pages={199-215}, month={August}, number={3} } @article{Breslow96, author={Norman E. Breslow}, title={Statistics in Epidemiology: The Case-Control Study}, journal= jasa, volume= 91, year= 1996, pages={14--28} } @unpublished{Breyer04, author={L.A. Breyer}, title={The Dbacl Text Classifier}, note={laird@lbreyer.com}, year={04}, month={June} } @unpublished{Breyer04, author={L.A. Breyer}, title={The Dbacl Text Classifier}, note={laird@lbreyer.com}, year={04}, month={June} } @article{BreZie92, author={Hermann Brenner and Hartwig Ziegler}, title={Monitoring and Projecting Cancer Incidence in Saarland, Germany, Based on Age-Cohort Analyses}, journal={Journal of Epidemiology and Community Health}, volume= 46, year= 1992, pages={15--20} } @book{BroDav91, author={Peter J. Brockwell and Richard A. Davis}, title={Time Series: Theory and Methods}, publisher={Springer-Verlag}, year={1991}, edition={2nd} } @article{BroDenVer02, author={N. Brouhns and M. Denuit and J. 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Glynn and Jerry Avorn and Til Sturmer}, title={Variable Selection for Propensity Score Models}, journal={American Journal of Epidemiology}, volume={163}, year={2006}, pages={1149-1156}, month={April} } @book{Brown58, author={Ralph Brown}, title={Loyalty and Security}, publisher={Yale University Press}, year= 1958, address={New Haven, CT} } @article{Brown82, author={Brown, Courtney}, title={The Nazi Vote: A National Ecological Study}, journal={American Political Science Review}, volume= 76, year= 1982, pages={285-302}, number= 2 } @article{BruFal94, author={Brustein, William and Falter, J{\"u}rgen W.}, title={The Sociology of Nazism: An Interest-Based Account}, journal={Rationality and Society}, volume= 6, year= 1994, pages={369-399}, number= 3 } @book{Brustein96, author={William Brustein}, title={The Logic of Evil: Social Origins of the Nazi Party, 1925-1933}, publisher={Yale University Press}, year= 1996 } @article{BruUrd05, author={Helge Brunborg and Henrik Urdal}, title={The Demography of Conflict and Violence: An Introduction}, abstract={The demography of armed conflict is an emerging field among demographers and peace researchers alike. The articles in this special issue treat demography as both a cause and a consequence of armed conflict, and they carry important policy implications. A study of German-allied countries during World War II addresses the role of refugees and territorial loss in paving the way for genocide. Other articles focusing on the demographic causes of conflict discuss highly contentious issues of whether economic and social inequality, high population pressure on natural resources, and youth bulges and limited migration opportunities can lead to different forms of armed conflict and state failure. The articles on demographic responses to armed conflict analyze the destructiveness of pre-industrial warfare, differences in short- and long-term mortality trends after armed conflict, and migratory responses in war. Another set of articles on demographic responses to war is published simultaneously in the European Journal of Population.}, journal={The Journal of Peace Research}, volume={42}, year={2005}, pages={371-374}, month={July}, number={4} } @book{BSER02, author={{Board on Earth Sciences and Resources}}, title={Geoscience Data and Collections: National Resources in Peril}, publisher={National Academies Press}, year= 2002, address={Washington, D.C.} } @article{Buchheim03, author={Christoph Buchheim}, title={Die Erholung von der Weltwirtschaftskrise 1932/33 in Deutschland}, journal={Jahrbuch fuer Wirtschaftsgeschicht}, year={2003}, pages={13-26}, number={1} } @article{BurFred01, author={B Burstrom and P Fredlund}, title={Self-rated health: Is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes?}, journal={Journal of Epidemiology and Community Health}, volume= 55, year= 2001, pages={{836-40}} } @article{Burgoon06, author={Brian Burgoon}, title={On Welfare and Terror}, journal={Journal of Conflict Resolution}, volume={50}, year={2006}, pages={176-203}, month={April}, number={2} } @article{Burnham72, author={Walter Dean Burnham}, title={Political Immunisation and Political Confessionalism: The United States and Weimar Germany}, journal={Journal of Interdisciplinary History}, volume= 3, year= 1972, pages={1--30} } @article{Burtless95, author={Gary Burtless}, title={The Case for Randomized Field Trials in Economic and Policy Research}, journal={The Journal of Economic Perspectives}, volume={9}, year={1995}, pages={63-84}, number={2} } @article{ButBurMit87, author={J.S. Butler, et al.}, title={Measurement Error in Self-Reported Health Variables}, journal={The Review of Economics and Statistics}, volume= 69, year= 1987, pages={{644-50}} } @techreport{ButCar01, author={C.T.\ Butts and K.M.\ Carley}, title={Multivariate Methods for Interstructural Analysis}, institution={CASOS working paper, Carnegie Mellon University}, year={2001} } @incollection{Butler51, author={David E. Butler}, title={Appendix}, booktitle={The British General Election of 1950}, publisher={Macmillan}, year= 1951, address={London}, editor={H.G. Nicholas} } @article{BuuEyrTenHop03, author={S. van Buuren and S. Eyres and A. Tennant and M. 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Caroll and J. J. Chang}, title={Analysis of Individual Differences in Multidimensional Scaling}, journal={Psychometrika}, volume= 35, year= 1970, pages={283--319}, month={September} } @article{CarGre97, author={J. Douglas Carroll and Paul E. Green}, title={Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling}, journal={Journal of Marketing Research}, volume={XXXIV}, year= 1997, pages={193--204}, month={May} } @article{CarKucLom96, author={Raymond J. Carroll and helmut Kuchenhoff and F. Lombard and Leonard A. Stefanski}, title={Asymptotics for the SIMEX estimator in structural measurement error models. }, journal={Journal of the American Statistical Association}, volume={91}, year={1996}, pages={242-250} } @book{CarLou00, author={Bardley P. Carlin and Thomas A. Louis}, title={Bayes and Empirical Bayes Methods for Data Analysis}, publisher={CRC Press}, year= 2000, edition={2nd} } @article{CarMacRup99, author={Raymond J. Carroll and Jeffrey D. Maca and David Ruppert}, title={Nonparametric regression in the presence of measurement error}, journal={Biometrika}, volume={86}, year={1999}, pages={3}, month={541-554} } @article{Carpenter02, author={Daniel Paul Carpenter}, title={Groups, the Media, Agency Waiting Costs, and FDA Drug Approval}, journal= ajps, volume= 46, year= 2002, pages={490--505}, month={July}, number= 2 } @techreport{CarPrs00, author={Lawrence R. Carter and Alexia Prskawetz}, title={Examining Structural Shifts in Mortality using the Lee-Carter Method}, institution={Bundesinstitut fur Bevolkerungswissenschaften}, year= 2000, address={Germany}, note={Demographische Vorausschatzungen --- Abhandlungen des Arbeitkreises Bevolkerrungswissenschaftlicher Methoden der Statistischen Woche} } @article{Carr07, author={David Carr}, title={24-Hour Newspaper People}, journal={New York Times}, year= 2007, month={15 January} } @article{CavTre94, author={W.B. Cavnar and J.M. Trenkle}, title={{N-Gram-Based Text Categorization}}, journal={Proceedings of the Third Annual Symposium on Document Analysis and Information Retrival}, year={1994}, pages={161-175} } @article{Chafee19, author={Zechariah Chafee}, title={Freedom of Speech in War Time}, journal= hlr, volume= 32, year={1919}, pages={932--??} } @book{Chafee41, author={Zechariah Chafee}, title={Free Speech in the United States}, publisher= hup, year= 1941, address={Cambridge, MA} } @article{ChaMauRod94, author={Daniel Chandramohan and Gillian H. Maude and Laura C. Rodrigues and Richard J. Hayes}, title={Verbal Autopsies for Adult Deaths: Issues in their Development and Validation}, journal={International Journal of Epidemiology}, volume={23}, year={1994}, pages={213-222}, number={2} } @article{Chamberlain80, author={Gary Chamberlain}, title={Analysis of Covariance with Qualitative Data}, journal={Review of Economic Studies}, volume={XLVII}, year= 1980, pages={225-238} } @article{ChaRodMau98, author={Daniel Chandramohan and Laura C. Rodriques and Gillian H. Maude and Richard Hayes}, title={The Validy of Verbal Autopsies for Assessing the Causes of Institutional Maternal Death}, journal={Studies in Family Planning}, volume={29}, year={1998}, pages={414-422}, month={December}, number={4} } @article{Chase68, author={G.R. Chase}, title={On the Efficiency of Matched Pairs in Bernoulli Trials}, journal={Biometrika}, volume={55}, year={1968}, pages={365-369}, month={July}, number={2} } @article{ChaSetQui01, author={Daniel Chandramohan and Philip Setel and Maria Quigley}, title={Effect of misclassification of causes of death in verbal autopsy: can it be adjusted}, journal={International Journal of Epidemiology}, volume={30}, year={2001}, pages={509-514} } @article{ChaSolShi05, author={Daniel Chandramohan and Nadia Soleman and Kenji Shibuya and John Porter}, title={Editorial: Ethical issues in the application of verbal autopsies in mortality surveillance systems}, journal={Tropical Medicine and International Health}, volume={10}, year={2005}, pages={1087-1089}, month={November}, number={11} } @article{CheCumDum03, author={Lee Cheng, et al}, title={Health related quality of life in pregeriatric patients with chronic diseases at urban, public supported clinics}, journal={Health and Quality of Life Outcomes}, volume= 1, year= 2003, pages={{1-8}}, month={October}, number= 63 } @book{CheRon83, author={G. Shabbir Cheema and Dennis A. Rondinelli}, title={Decentralization and Development: Policy Implementation in Developing Countries}, publisher={Sage Publications}, year={1983}, address={Beverly Hills, CA} } @article{Childers76, author={Thomas Childers}, title={The Social Bases of the Nationalist Socialist Vote}, journal={Journal of Contemporary History}, volume= 11, year= 1976, pages={17-42} } @book{Childers83, author={Childers, Thomas}, title={The Nazi Voter}, publisher={University of North Carolina Press}, year= 1983 } @article{ChiLwa91, author={J. Chin and S.K. Lwanga}, title={Estimation and Projection of Adult AIDS Cases: a Simple Epidemiological Model}, journal={Bulletin of the World Health Organization}, volume= 69, year= 1991, pages={399--406}, number= 4 } @article{ChiZamGra92, author={J.D. Chiphangwi and T.P. Zamaere and W Graham and B. Duncan and T. Kenyon and R. Chinyama}, title={Maternal mortality in the Thyolo district of southern Malawi}, journal={East African Medical Journal}, volume= 69, year= 1992, pages={675--679} } @article{Chochran53, author={William G. Cochran}, title={Matching in Analytical Studies}, journal={American Journal of Public Health}, volume={43}, year={1953}, pages={684-691}, month={June} } @book{Christensen96, author={Ronadl Christensen}, title={Plane Answers to Complex Questions: The Theory of Linear Models}, publisher={Springer-Verlag New York}, year={1996}, edition={Second} } @article{Church75, author={Thomas Church}, title={Conspiracy Doctrine and Speech Offenses: A Reexamination of Yates v. U.S. from the Perspective of U.S. v. Spock}, journal={Cornell Law Review}, volume= 60, year={1975}, pages={569--??} } @incollection{ClaBer92, author={David G. Clayton and Luisa Bernardinelli}, title={Bayesian Methods For Mapping Disease Risk}, booktitle={Geographical and Environmental Epidemiology: Methods for Small-Area Studies}, publisher={Oxford University Press}, year= 1992, address={Oxford}, editor={P. Elliott and J.Cuzick and D. English and R. Stern}, pages={205-220} } @article{ClaJanHob01, author={W. Crawford Clark and Malvin N. Janal and Elaine K. Hoben and J. Douglas Carroll}, title={How Separate are the Sensory, Emotional, and Motivational Dimensions of Pain? A Multidimensional Scaling Analysis}, journal={Somatosensory and Motor Research}, volume= 18, year= 2001, pages={31-39}, number= 1 } @article{ClaMarLie04, author={Tim Clark and Sean Martin and Ted Liefeld}, title={Globally Distributed Object Identification for Biological Knowledgebases}, journal={Briefings in Bioinformatics}, volume={5}, year={2004}, pages={59-71}, month={March}, number={1} } @article{Clarkson00, author={Douglas B. Clarkson}, title={A Random Effects Individual Difference Multidimensional Scaling Model}, journal={Computational Statistics and Data Analysis}, volume= 32, year= 2000, pages={337--347}, month={January} } @incollection{Clayton96, author={David G. Clayton}, title={Generalized Linear Mixed Models}, booktitle={Markov Chain {M}onte {C}arlo in Practice}, publisher={Chapman \& Hall}, year= 1996, address={London}, editor={W.R. Gilks and S. Richardson and D.J. Spiegelhalter}, pages={275-301} } @article{CleDev88, author={W.S. cleveland and S.J. Devlin}, title={Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting}, journal={Journal of the American Statistical Association}, volume={83}, year={1988}, pages={596-610} } @book{CleHen98, author={M.P. Clements and D.F. Hendry}, title={{Forecasting Economic Time Series}}, publisher= cup, year= 1998, address={Cambridge, U.K.} } @misc{CliJacRiv00, author={Joshua Clinton and Simon Jackman and Douglas Rivers}, title={The Statistical Analysis of Legislative Behavior: A Unified Approach}, year={2000}, howpublished={Paper presented at the Annual Meeting of the Political Methodology Society} } @unpublished{CliJacRiv02, author={Joshua Clinton and Simon Jackman and Douglas Rivers}, title={The Statistical Analysis of Roll Call Data}, note={Stanford University}, year= 2002 } @article{CloRubSch91, author={Clifford C. Clogg and Donald B. Rubin and Nathaniel Schenker and Bradley Schultz and Lynn Weidman}, title={Multiple Imputation of Industry and Occupation Codes in Census Public-Use Samples Using Bayesian Logistic Regression}, journal={Journal of the American Statistical Association}, volume={86}, year={1991}, pages={68-78}, month={March}, number={413} } @book{CoaDem66, author={Ansley J. Coale and Paul Demeny}, title={Regional Model Life Tables and Stable Populations}, publisher={Princeton University Press}, year= 1966, address={Princeton, N.J.} } @book{CocCox57, author={WG Cochran and GM Cox}, title={Experimental Designs}, publisher={Wiley}, year={1957}, address={New York} } @article{Cochran65, author={William G. Cochran}, title={The Planning of Observational Studies of Human Populations}, journal={Journal of the Royal Statistical Society. Series A (General)}, volume= 128, year= 1965, pages={234-266}, number= 2 } @article{Cochran68, author={Cochran, William G.}, title={The effectiveness of adjustment by subclassification in removing bias in observational studies}, journal={Biometrics}, volume={24}, year={1968}, pages={295-313} } @article{CocRub73, author={Cochran, William G. and Rubin, Donald B.}, title={Controlling bias in observational studies: A review}, journal={Sankhya: The Indian Journal of Statistics, Series A}, volume={35, Part 4}, year={1973}, pages={417-466} } @article{ColMah93, author = {David Collier and Mahon, Jr., James E.}, title = {Conceptual `Stretching' Revisited}, journal = apsr, volume = 87, year = 1993, pages = {845-855}, month = {December}, number = 4 } @article{ColSul02, author={James E. Coleman and Barry Sullivan}, title={Enduring and Empowering: The Bill of Rights in the Third Millennium}, journal={Law and Contemporary Problems}, volume= 65, year={2002}, pages={1--??} } @book{Colton06, author={Timothy Colton}, title={Transitional Citizens: Voters and What Influences Them in the New Russia}, publisher={Harvard University Press}, year={2006 in press}, address={Cambridge, MA} } @article{ComMolGri01, author={Campbell, M.K. and Mollison, J. and Grimshaw, J.M.}, title={{Cluster trials in implementation research: estimation of intracluster correlation coefficients and sample size}}, journal={Statistics in Medicine}, volume={20}, year={2001}, pages={391--399}, number={3} } @unpublished{Congdon06, author={Peter Congdon}, title={A Model Framework for Mortality and Health Data Classified by Age, Area, and Time}, note={to be published in Biometrics Peter congdon, Dept. of Geography, Queen Mary (University of London), Mile end Road, London E1 4NS, England p.congdon@qmul.ac.uk}, year={2006} } @book{CooCam79, author={Thomas D. 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Cornelius}, title = {Mobilized Voting in the 2000 Elections: The Changing Efficacy of Vote Buying and Coercion in Mexican Electoral Politics}, booktitle = {Mexico's Pivotal Democratic Election: Candidates, Voters, and the Presidential Campaign of 2000}, OPTcrossref = {}, OPTkey = {}, OPTpages = {}, publisher = {Stanford University Press}, year = {2004}, editor = {Jorge I. Dom\'{i}nguez and Chappell Lawson}, OPTvolume = {}, OPTnumber = {}, OPTseries = {}, OPTtype = {}, OPTchapter = {}, address = {Stanford and La Jolla, CA}, OPTedition = {}, OPTmonth = {}, OPTnote = {}, OPTannote = {} } @article{Cornfield51, author={Jerome Cornfield}, title={A Method of Estimating Comparative Rates from Clinical Data: Application to Cancer of the Lung, Breast and Cervix}, journal={Journal of the National Cancer Institute}, volume= 11, year= 1951, number={1269-1275} } @article{CowBra96, author={Mary Kathryn Cowles and Bradley P. 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Barendregt}, title={Current and Future Costs of Cancer}, journal={European Journal of Cancer}, volume={30A}, year= 1994, pages={60--65}, number= 1 } @unpublished{KopSch05, author={Moshe Koppel and Jonathan Schler}, title={The Importance of Neutral Examples for Learning Sentiment}, note={Dept. of Computer Science Bar-Ilan University, Ramat-Gan Israel koppel,schlerj@cs.biu.ac.il}, year={05} } @unpublished{KopSch05, author={Moshe Koppel and Jonathan Schler}, title={The Importance of Neutral Examples for Learning Sentiment}, note={Dept. of Computer Science Bar-Ilan University, Ramat-Gan Israel koppel,schlerj@cs.biu.ac.il}, year={05} } @article{KorJorLet99, author={A E Kirtne, A F Jorm, Z Jiao, et al}, title={Health, Cognitive and psychosocial factors as predictors of mortality in an elderly community sample}, journal={Journal of Epidemiology and Communtiy Health }, volume= 53, year= 1999, pages={{83-8}} } @book{Kornhauser59, author={Kornhauser, W.}, title={The Politics of Mass Society}, publisher={The Free Press}, year= 1959, address={New York} } @article{KorWilGou03, author={Eline L. Korenromp and Brian G. Williams and Eleanor Gouws and christopher Dye and Robert W. Snow}, title={Measurement of trends in childhood malaria mortality in Africa: an assessment of progress toward targets based on verbal autopsy}, journal={The Lancet Infectious Diseases}, volume={3}, year={2003}, pages={349-58} } @book{Koshar86, author={Koshar, R.}, title={Social Life, Local Politics, and Nazism}, publisher={University of North Carolina Press}, year= 1986, address={Chapel Hill} } @article{KosHeiZak05, author={Michael Kosfeld and Markus Heinrichs and Paul J. Zak and Urs Fischbacher and Ernst Fehr}, title={Oxytocin Increases Trust in Humans}, journal={Nature}, volume={435}, year={2005}, pages={673-676}, month={June} } @article{KraJay94, author={Neal M. Krause, PhD, and Gina M. Jay, PhD}, title={What do Global Self-Rated Health Items Measure?}, journal={Medical Care}, volume= 32, year= 1994, pages={{930-42}} } @article{KraSha84, author={M.S. Kramer and S.H. Shapiro}, title={{Scientific challenges in the application of randomized trials}}, journal={Journal of the American Medical Association}, volume= 252, year= 1984 , pages={2739-45}, number= 19 } @article{KriBacRob07, author={Samuel Krislov and Charles Backstrom and Leonard Robins}, title={When Texans Gerrymander: Much Power, Continuous Politics, Little Law} } @book{Krippendorff04, author={Krippendorff, D.K.}, title={{Content Analysis: An Introduction to Its Methodology}}, publisher={Thousand Oaks, CA: Sage}, year={2004} } @article{Kruedener85, author={J{\"u}rgen von Kruedener}, title={Die {\"U}berforderung der Weimarer Republic als Sozialstaat}, journal={Geschichte und Gesellschaft}, volume= 1, year= 1985, pages={358--376}, number= 3 } @article{Krueger90, author={Anne O. Krueger}, title={Government Failures in Development}, journal={The Journal of Economic Perspectives}, volume={4}, year={1990}, pages={9-23}, number={3} } @article{Krueger99, author={Alan Krueger}, title={Experimental Estimates of Education Production Functions}, journal={Quarterly Journal of Economics}, volume={114}, year={1999}, pages={497-532}, month={May}, number={2} } @article{KrzWys86, author={Mical Krzyzanowski and Miroslaw Wysocki}, title={The Relation of Thirteen-Year Mortality to Ventilatory Impairment and Other Respiratory Symptoms: The Cracow Study}, journal={International Journal of Epidemiology}, volume= 15, year= 1986, pages={{56-64}}, number= 1 } @article{KucMwaLes06, author={Helmut K{\"u}chenohoff and Samuel M. Mwalili and Emmanuel Lassaffre}, title={A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX}, journal={Biometrics}, volume={62}, year={2006}, pages={85-96}, month={March} } @article{KulLei51, author={S. Kullback and R.A. Leibler}, title={On Information and Sufficiency}, journal={Annals of Mathematical Statistics}, volume= 22, year= 1951, pages={79--86}, month={March}, number= 1 } @unpublished{KumAll04, author={Giridhar Kumaran and James Allan}, title={Text Classification and Named Entities for New Event Detection}, note={Center for Intelligent Information Retreival, Department of Computer Science, Univ of MA, Amherst}, year={2004}, month={July} } @unpublished{KumAll04, author={Giridhar Kumaran and James Allan}, title={Text Classification and Named Entities for New Event Detection}, note={Center for Intelligent Information Retreival, Department of Computer Science, Univ of MA, Amherst}, year={2004}, month={July} } @article{KunGeuvan95, author={Anton Kunst and J.J. Geurts and J. van den Berg}, title={International variation in socioeconomic inequalities in self reported health}, journal={Journal of Epidemiology and Community Health}, year={1995}, optnumber={2}, optvolume={49}, optpages={117--123} } @article{KunGroMac98, author={A.E. Kunst and F. Gorenhof and J.P. Mackenbach and E.W. Health}, title={Occupational class and cause specific mortality in middle aged men in 11 European countries: comparison of population based studies. EU Working Group on Socioeconomic Inequalities in Health }, journal= bmj, year={1998}, optvolume={316}, optpages={1636--1642} } @article{KunGroMac98b, author={Anton Kunst and F. Groenhof and Johann Mackenbach}, title={Mortality by occupational class among men 30--64 years in 11 European countries}, journal= ssm, volume= 46, year= 1998, pages={1459-1476}, number= 11 } @article{Kunsch87, author={Hans R. K{\"u}nsch}, title={Intrinsic Autoregressions and Related Models on the Two-Dimensional Lattice}, journal={Biometrika}, volume= 74, year= 1987, pages={517-524}, number= 3 } @article{Kuo01, author={Yen-Hong Kuo}, title={Extrapolation of Association Between Two Variables in Four General Medical Journals}, year= 2001 , month={September}, note={Forth International Congress on Peer Review in Biomedical Publication}, key={Barcelona, Spain} } @book{kvart86, author={Igal Kvart}, title={A Theory of Counterfactuals}, publisher={Indianapolis: Hackett Publishing Company}, year= 1986 } @article{KwoShuHov06, author={Namhee Kwon and Stuart W. Shulman and Eduard Hovy}, title={{Collective Text Analysis for eRulemaking}}, journal={7th Annual International Conference on Digital Government Research}, year={2006} } @incollection{LagRus02, author={Monica Lagazio and Bruce Russett}, title={A Neural Network Analysis of Militarized International Disputes, 1885-1992: Temporal Stability and Causal Complexity}, booktitle={The Scourge of War: New Extensions on an Old Problem}, publisher={University of Michigan Press}, year={2002}, address={Ann Arbor}, editor={Paul Diehl}, optpages={269--295}, optannote={This paper fits a model for Cold War militarized disputes and assess how well the Cold War model fits pre-Cold War data (so-called 'post-diction'). So it gets fitted values. Also, runs test and training, and discusses merits of neural net approach.} } @article{Lahlrl03, author={P. Lahlrl}, title={On the Impact of Boostrapping in Survey Sampling and Small Area Estimation}, journal={Statistical Science}, volume= 18, year= 2003, pages={199-210}, number= 2 } @article{LaiLou87, author={Nan M. Laird and Thomas A. Louis}, title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples}, journal={Journal of the American Statistical Association}, volume={82}, year={1987}, pages={739-750}, month={September}, number={399} } @article{LaiLou87b, author={Nan M. Laird and Thomas A. Louis}, title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples: Rejoinder}, journal={Journal of the American Statistical Association}, volume={399}, year={1987}, pages={756-757}, month={September} } @unpublished{Lakin05, author={Jason Lakin}, title={Letting the Outsiders in: Democratization and Health Reform in Mexico}, note={American Political Science Association}, year= 2005 , address={Washington D.C.} } @article{Lalonde86, author={Robert Lalonde}, title={Evaluating the Econometric Evaluations of Training Programs}, journal={American Economic Review}, volume={76}, year={1986}, pages={604-620} } @article{Landers05, author={John Landers}, title={The Destructiveness of Pre-Industrial Warfare: Political and Technological Determinants}, journal={Journal for Peace Research}, volume={42}, year={2005}, pages={455-470}, month={July}, number={4} } @article{langholz91, author={B. Langholz and D.C. Thomas}, title={Efficiency of Cohort Sampling Designs: Some Surprising Results}, journal={Biometrics}, volume= 47, year= 1991, pages={1563-1571} } @article{langholz96, author={Bryan Langholz and Larry Goldstein}, title={Risk Set Sampling in Epidemiologic Cohort Studies}, journal={Statistical Science}, volume= 11, year= 1996, pages={35-53}, number= 1 } @article{langholz97, author={Bryan Langholz and Boran {\O }rnulf}, title={Estimation of Absolute Risk from Nested Case-Control Data}, journal={Biometrics}, volume= 53, year= 1997, pages={767-774}, month={June} } @article{LaPalombara68, author={Joseph LaPalombara}, title={Macrotheories and Microapplications in Comparative Politics: A Widening Chasm}, journal= cp, year= 1968, pages={52--78}, month={October} } @book{Laplace1820, author={P.S. Laplace}, title={Philosophical Essays on Probaiblities}, publisher={Dover}, year={1951, original: 1820}, address={New York} } @article{LaRBanJar79, author={Asenath LaRue, PhD, Lew Bank, MA, Lissy Jarvick, MD, PhD, and Monte Hetland, BA}, title={Health in Old Age: How Do Physicians' Ratings and Self-Ratings Compare?}, journal={Journal of Gerontology}, volume= 34, year= 1979, pages={{687-91}} } @article{Lassen05, author={David Dreyer Lassen}, title={The Effect of Information on Voter Turnout: Evidence from a Natural Experiement}, journal={American Journal of Political Science}, volume={2005}, year={49}, pages={103-118}, month={January}, number={1} } @article{Lau97, author={Tai-Shing Lau}, title={The Latent Class Model for Multiple Binary Screening Tests}, journal={Statistics in Medicine}, volume={16}, year={1997}, pages={2283-2295} } @article{LauIbr95, author={Purushottam W. Laud and Joseph G. Ibrahim}, title={Predictive Model Selection}, journal= jrssb, volume= 57, year= 1995, pages={247--262}, number= 1 } @article{LauIbr96, author={Purushottam W. Laud and Joseph G. Ibrahim}, title={Predictive Specification of Prior Model Probabilities in Variable Selection}, journal={Biometrika}, volume= 83, year= 1996, pages={267--274}, number= 2 } @article{LauSmiSta00, author={Jennifer L. Lauby and Philip J. Smith and Michael Stark and Bobbie Person and Janet Adams}, title={A Community-Level HIV Prevention Intervention for Inner-City Women: Results of the Women and Infants Demonstration Projects}, journal={American Journal of Public Health}, volume={90}, year={2000}, pages={216-222}, month={February}, number={2} } @article{LavBenGar03, author={Michael Laver and Kenneth Benoit and John Garry}, title={{Extracting Policy Positions from Political Texts Using Words as Data}}, journal={American Political Science Review}, volume={97}, year={2003}, pages={311-331}, number={2} } @book{Leamer78, author={Edward Leamer}, title={Specification Searches}, publisher={Wiley}, year= 1978, address={New York} } @article{Lebow00, author={Richard Ned Lebow}, title={What's so Different About a Counterfactual?}, journal= wp, volume= 52, year= 2000, pages={550--85}, month={July} } @misc{Lechner00, author={Michael Lechner}, title={A note on the common support problem in applied evaluation studies}, year= 2000, howpublished={{http://www.siaw.unisg.ch/lechner}}, note={University of St. Galen} } @incollection{Lechner99, author={Michael Lechner}, title={Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional Independence Assumption}, booktitle={Econometric Evaluation of Labour Market Policies}, publisher={Physica}, address={Heidelberg}, editor={Lechner, M. and Pfeiffer, F.}, year= 2001, pages={43--58} } @article{LedBre59, author={S. Ledermann and J. Breas}, title={{Les Dimensions de la Mortalit{\'e}}}, journal={Population}, volume= 14, year= 1959, pages={637--682}, note={[in French]} } @article{Lee00, author={Ronald D. Lee}, title={The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications}, journal={North American Actuarial Journal}, volume= 4, year= 2000, pages={80--93}, number= 1 } @article{Lee00a, author={Ronald D. Lee}, title={{Long-Term Projections and the US Social Security System}}, journal={Population and Development Review}, volume= 26, year= 2000, pages={137--143}, month={March}, number= 1 } @article{Lee93, author={Ronald D. Lee}, title={{Modeling and Forecasting the Time Series of US Fertility: Age Patterns, Range, and Ultimate Level}}, journal={International Journal of Forecasting}, volume= 9, year= 1993, pages={187--202} } @article{LeeCar92, author = {Ronald D. Lee and Lawrence R. Carter}, title = {{Modeling and Forecasting U.S. Mortality}}, journal = jasa, volume = 87, year = 1992, month = {September}, pages = {659--675}, number = 419 } @article{LeeCar92b, author={Ronald D. Lee and Lawrence R. Carter}, title={Rejoinder}, journal= jasa, volume= 87, year= 1992, pages={674--675}, month={September}, number= 419 } @article{LeeCarTul95, author={Ronald D. Lee and Lawrence Carter and S. Tuljapurkar}, title={Disaggregation in Population Forecasting: Do We Need It? And How to Do it Simply}, journal={Mathematical Population Studies}, volume={5}, year={1995}, pages={217--234}, month={July}, number={3}, annote={Authors describe a model for reducing the dimensionality of the forecasting problem by modeling the evolution over time of the age schedules of vital rates, reducing the problem to forecasting a single parameter for fertility and another for mortality. Authors also show how one can fit the model more simply and prepare integrated forecasts for a collection of regions, and discuss alternate approaches to forecasting the estimated indices of fertility and mortality, including state-space methods.} } @misc{LeeHigBiFerWes00, author={Herbert Lee and David Higdon and Zhuoxin Bi and Marco Ferreira and Mike West}, title={Markov Random Field Models for High-Dimensional Parameters in Simulations of Fluid Flow in Porous Media}, year= 2000, howpublished={Discussion Paper \#00-35, Institute of Statistics and Decision Sciences, Duke University} } @book{LeeJohZel96, author={Jack C. Lee and Wesley O. Johnson and Arnold Zellner}, title={Modelling and Prediction: Honoring Seymour Geisser}, publisher={Springer}, year={1996}, editor={Jack C. Lee and Wesley O. Johnson and Arnold Zellner} } @article{LeeMil01, author={Ronald D. Lee and Timothy Miller}, title={Evaluating the Performance of the Lee-Carter Approach to Modeling and Forecasting Mortality}, journal={Demography}, volume= 38, year= 2001, pages={537--549}, month={November}, number= 4 } @article{LeeRof94, author={Ronald D. Lee and R. Rofman}, title={Modeling and Projecting Mortality in Chile}, journal={Notas Poblacion}, volume={22}, year={1994}, pages={183--213}, month={Jun}, number={59}, annote={Authors extend the Lee-Carter method to deal with various problems of incomplete data common in Third World populations, and then apply the method to forecast mortality in Chile.} } @article{LeeSki99, author={Ronald D. Lee and Jonathan Skinner}, title={Will Aging Baby Boomers Bust the Federal Budget}, journal={Journal of Economic Perspectives}, volume= 13, year= 1999, pages={117--140}, month={Winter}, number= 1 } @article{LeeTul94, author={Ronald D. Lee and S. Tuljapurkar}, title={{Stochastic Population Projections for the U.S.: Beyond High, Medium and Low}}, journal= jasa, volume= 89, year= 1994, pages={1175--1189}, month={December}, number= 428 } @article{LeeTul98, author={Ronald D. Lee and S. Tuljapurkar}, title={{Uncertain Demographic Futures and Social Security Finances}}, journal={American Economic Review: Papers and Proceedings}, year= 1998, pages={237--241}, month={May} } @incollection{LeeTul98a, author={Ronald D. Lee and S. Tuljapurkar}, title={{Stochastic Forecasts for Social Security}}, booktitle={Frontiers in the Economics of Aging}, publisher={University of Chicago Press}, year= 1998, address={Chicago}, editor={David Wise}, pages={393--420} } @techreport{LenFox06, author={Amanda Lenhart and Susannah Fox}, title={{Bloggers: A Portrait of the Internet's New Storytellers}}, institution={Pew Internet and American Life Project}, year= 2006, note={{http://207.21.232.103/pdfs/PIP\%20Bloggers\%20Report\%20July\%2019\%202006.pdf}} } @book{LenHsu99, author={T. Leonard and J.S.J. Hsu}, title={Bayesian Methods}, publisher={Cambridge University Press}, year= 1999, address={Cambridge} } @unpublished{LeuSia03, author={E. Leuven and B. Sianesi}, title={psmatch2}, note={{Stata module to perform full {M}ahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Available at: http://www1.fee.uva.nl/scholar/mdw/leuven/stata}}, year= 2003 } @misc{LeuSia04, author={Edwin Leuven and Barbara Sianesi}, title={PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing}, year= 2004, howpublished={EconPapers}, note={{http://econpapers.repec.org/software/bocbocode/S432001.htm}} } @article{LeuTanLue97, author={Kai-Kuen Leung, Li-Yu Tang, and Bee-Horng Lue}, title={Self-Rated Health and Mortality in Chinese Institutional Elderly Persons}, journal={Journal of Clinical Epidemiology}, volume= 50, year= 1997, pages={{1107-16}}, number= 10 } @book{Levinson01, author={Sanford Levinson}, title={What is the Constitution's Role in Wartime: Why Free Speech and Other Rights Are Not as Safe as You Might Think}, publisher={?}, year= 2001 } @article{LevKas70, author={P.S. Levy and E. H. Kass}, title={A three population model for sequential screening for Bacteriuria}, journal={American Journal of Epidemiology}, volume={91}, year={1970}, pages={148-154} } @article{Lewis01, author={Jeffrey B. Lewis}, title={Estimating Voter Preference Distributions from Individual-Level Voting Data}, journal={Political Analysis}, volume= 9, year= 2001, pages={275-297}, month={June}, number= 3 } @article{Lewis03, author={Anthony Lewis}, title={Marbury v. Madison v. Ashcroft}, journal={New York Times}, year={2003}, month={February 24, A17} } @incollection{Lewis05, author={Maureen Lewis}, title={Improving Efficiency and Impact in Health Care Services: Lessons from Central America}, booktitle={Health Systems Innovation in Central America}, publisher={The World Bank}, year= 2005, address={Washington, D.C.}, editor={Gerard M. La Forgia} } @inbook{Lewis05, author={Maureen Lewis}, title={Health Sysems Innovatin in Central American}, chapter={Improving Efficiency and Impact in Health Care Services - Lessons from Central American }, year={2005}, publisher={The World Bank}, address={Washington DC}, editor={Gerard M. La Forgia} } @book{lewis73, author={David K. Lewis}, title={Counterfactuals}, publisher={Cambridge: Harvard University Press}, year= 1973 } @article{Lewis99b, author={John A. Lewis}, title={Statistical Principles for Clinical Trials (ICH E9) An Introductory Note on an International Guideline}, journal={Statistics in Medicine}, volume={18}, year={1999}, pages={1903-1904} } @article{LewLev89, author={R.A. Lew and P.S. Levy}, title={Estimation of prevalence on the basis of screening tests}, journal={Statistics in Medicine}, volume={8}, year={1989} } @article{LewLev89, author={Robert A. Lew and Paul S. Levy}, title={Estimation of Prevalence on the Basis of Screening Tests}, journal={Statistics in Medicine}, volume={8}, year={1989}, pages={1225-1230} } @book{Li95, author={S.Z. Li}, title={Markov Random Field Modeling in Computer Vision}, publisher={Springer-Verlag}, year= 1995 } @article{LicLipDan92, author={Allen S. Lichter and Marc E. Lippman and David N. Danforth Jr and Teresa d'Angelo and Seth M. Steinberg and Ernest deMoss and Harold D. MacDonald and Cheryl M. Reichert and Maria Merino and Sandra M. Swain and Kenneth Cowan and Lynn H. Gerber and Judith L. Bader and Peggie A. Findlay and Wendy Schain and Catherine R. Gorrell and Karen Straus and Steven A. Rosenberg and Eli Glatstein}, title={Mastectomy Versus Breast-Conserving Therapy in the Treatment of Stage I and II Carcinoma of the Breast: A Randomized Trial at the National Cancer Institute}, journal={Journal of Clinical Oncology}, volume= 10, year= 1992, pages={976-983}, month= June , number= 6 } @article{LilPer94, author={David E. Lilienfeld and Daniel P. Perl}, title={Projected Neurodegenerative Disease Mortality among Minorities in the United States}, journal={Neuroepidemiology}, volume= 13, year= 1994, pages={179--186} } @article{LinCutZwi02, author={Shin Lin and David L. Cutler and Michael E. Zwick and Aravinda Chakravarti}, title={Haplotype Inference in Random Population Samples}, journal={American Journal of Human Genetics}, volume= 71, year= 2002, pages={1129--1137} } @book{LinHam97, title={Handbook of Modern Item Response Theory}, publisher={Springer}, year= 1997, editor={Wim Van Der Linden and Ronald K. Hambleton}, address={New York} } @article{LinLin80, author={Bernard S. Linn and Margaret W. Linn}, title={Objective and self-assessed health in the old and very old}, journal={Social Science and Medicine }, volume={{14A}}, year= 1980, pages={{311-15}} } @article{LinPekWan05, author={Peter K. Lindenauer and Penelope Pekow and Kaijun Wang and Dheeresh K. Mamidi and Benjamin Guierrez and Evan M. Benjamin}, title={Perioperative beta-blocker therapy and mortality after major noncardiac surgery}, journal={New England Journal of Medicine}, volume={353}, year={2005}, pages={349-361}, month={July}, number={4} } @article{LinSmi72, author={D. V. Lindley and A. F. M. Smith}, title={{B}ayes Estimates for the Linear Model}, journal={Journal of the Royal Statistical Society B}, volume= 34, year= 1972, pages={1-41}, number= 1 } @book{Lipset63, author={Lipset, Seymour Martin}, title={Political Man: The Social Basis of Politics}, publisher={Anchor Books}, year= 1963, address={Garden City, NY} } @article{LisMilFre03, author={John A. List and Daniel L. Millimet and Per G. Fredriksson and W. Warren McHone}, title={Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator}, journal={The Review of Economics and Statistics}, volume={85}, year={2003}, pages={944-952}, month={November}, number={4} } @article{LitAn04, author={Roderick Little and Hyonggin An}, title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values}, journal={Statistica Sinica}, volume={14}, year={2004}, pages={949-968} } @article{LitAn04, author={Roderick Little and Hyonggin An}, title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values}, journal={Statistica Sinica}, volume={14}, year={2004}, pages={949-968} } @book{LitRub02, author={Roderick J.A. Little and Donald B. Rubin}, title={Statistical Analysis with Missing Data, 2nd Edition}, publisher={John Wiley and Sons}, year={2002}, address={New York, New York} } @article{LitRub89, author={Rodrick J. Little and Donald Rubin}, title={The Analysis of Social Science Data with Missing Values}, journal={Sociological Methods and Research}, volume={18}, year={1989}, pages={292-326} } @inbook{LitSch95, author={Rodrick J. Little and N. Schenker}, title={Handbook of Statistical Modeling for the Social and Behavioral Sciences}, chapter={Missing Data}, year={1995}, pages={39-75} } @unpublished{Little05, author={Roderick Little}, title={Calibrated Bayes: A Bayes/Frequentist Roadmap}, note={University of Michigan}, year={2005}, month={September} } @article{Little92, author={Roderick J. Little}, title={Regression with Missing X's: A Review}, journal={Journal of the American Statistical Association}, volume={87}, year={1992}, pages={1227-1237} } @unpublished{LiuHuChe05, author={Bing Liu and Minqing Hu and Junsheng Cheng}, title={Opinion Observer: Analyzing and Comparing Opinions on the Web}, note={Bing Liu Dept of Computer Science; Univ of Illinois at Chicago, 851 south Morgan St. Chicago, IL 60607-7053, liub@cs.uic.edu}, year={2005}, month={May} } @article{LiuWonKon94, author={J. Liu and W.H. Wong and A. Kong}, title={Covariance Structure of the Gibbs Sampler with Applications to the Comparisons of Estimators and Augmentation Schemes}, journal={Biometrika}, volume={81}, year={1994}, pages={27-40} } @article{LiWen05, author={Quan Li and Ming Wen}, title={The Immediate and Lingering Effects of Armed Conflict on Adult Mortality: A Time-Series Cross-National Analysis}, abstract={This research investigates the effect of armed conflict on adult mortality across countries and over time. Theoretical mechanisms are specified for how military violence influences adult mortality, both immediately and over time after conflict. The effects of aggregate conflict, interstate and intrastate conflicts, and conflict severity are explored. The Heckman selection model is applied to account for the conflict-induced missing data problem. A pooled analysis across 84 countries for the period from 1961 to 1998 provides broad empirical support for the proposed theoretical expectations across both genders. This study confirms the importance of both the immediate and the lingering effect of military conflict on the mortality of the working-age population. The immediate effect of civil conflict is much stronger than that of the interstate conflict, while the reverse applies to the lingering effect. Both the immediate and the lingering effects of severe conflict are much stronger than those of minor conflict. While men tend to suffer higher mortality immediately from intrastate conflict and severe conflict, women in the long run experience as much mortality owing to the lingering effects of these conflicts. The mortality data show a strong data selection bias caused by military conflict. The research findings highlight the imperative for negotiating peace. Preventing a contest from escalating into a severe conflict can produce noticeable gains in saved human lives.}, journal={Journal of Peace Research}, volume={42}, year={2005}, pages={471-492}, month={July}, number={4} } @article{LoeFulKag03, author={Susanna Loeb and Bruce Fuller and Sharon Lynn Kagan and Bidemi Carrol}, title={How Welfare Reform Affects Young Children: Experimental Findings from Connecticut - A Research Note}, journal={Journal of Policy Analysis and Management}, volume={22}, year={2003}, pages={537-550}, number={4} } @article{Londregan00, author={John Londregan}, title={Estimating Legislator's Preferred Points}, journal={Political Analysis}, volume= 8, year= 2000, pages={21--34}, month={Winter}, number= 1 } @book{Londregan00b, author={John Londregan}, title={Legislative Institutions and Ideology in Chile}, publisher={Cambridge University Press}, year= 2000, address={New York} } @book{LonFre06, author={J. Scott Long and Jeremy Freese}, title={Regression Models for Categorical Dependent Variables Using Stata}, publisher={Stata Press}, year={2006}, address={College Station, TX} } @article{LooBee46, author={Loomis, C. P. and Beegle, J. A.}, title={The Spread of German Nazism in Rural Areas}, journal= asr, volume= 11, year= 1946, pages={724-734} } @book{LopAhmGui00, author = {Alan Lopez and O. Ahmed and M. Guillot and B.D. Ferguson and J.A. Salomon and C.J.L. Murray and K.H. Hill}, title = {World Mortality in 2000: Life Tables for 191 Countries}, publisher = {World Health Organization}, year = 2000, address = {Geneva} } @article{LowAltFer98, author={Robert Lowry and James E. Alt and Karen Ferree}, title={Fiscal Policy Outcomes and Electoral Accountability in the American States}, journal={American Political Science Review}, volume= 92, year= 1998, pages={759-777}, month={December} } @article{Lowenstein2006, author={Daniel H. Lowenstein}, title={Vieth's Gap: Has the Supreme Court Gone from Bad to Worse on Partisan Gerrymandering?}, journal={Cornell Journal of Law and Public Policy}, volume={N:X}, year={2006}, pages={101-130}, month={January} } @article{LozSolGak06, author = {Rafael Lozano and Patricia Soliz and Emmanuela Gakidou and Jesse Abbott-Klafter and Dennis M. Feehan and Cecilia Vidal and Juan Pablo Ortiz and Christopher J.L. Murray}, title = {Benchmarking of performance of Mexican States with Effective Coverage}, journal = {The Lancet}, volume = {368}, year = {2006}, pages = {1729-1741}, month = {November} } @article{lubin94, author = {J.H. Lubin and M.H. Gail}, title = {Sampling Strategies in Nested Case-Control Studies}, journal = {Environmental Health Perspectives}, volume = 102, year = 1994, pages = {47-51}, number = {suppl 8} } @article{Lubkemann05, author = {Stephen C. Lubkemann}, title = {Migratory Coping in Wartime Mozambique: An Anthropology of Violence and Displacement in Fragmented Wars}, journal = {Journal of Peace Research}, volume = {42}, year = {2005}, pages = {493-508}, month = {July}, number = {4} } @article{LumHea99, author = {Thomas Lumley and Patrick Heagerty}, title = {Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression}, journal = {jrssb}, volume = {61}, year = {1999}, pages = {459--477}, number = {2} } @article{LunSmi05, author = {Jennifer Hickes Lundquist and Herbert L. Smith}, title = {Family Formation Among Women in the U.S. Military: Evidence from the NLSY}, journal = {Journal of Marriage and Family}, volume = {67}, year = {2005}, pages = {1-13}, month = {February} } @article{LuRos04, author={Bo Lu and Paul R. Rosenbaum}, title={Optimal Pair Matching With Two Control Groups}, journal={Journal of Computational and Graphical Statistics}, volume={13}, year={2004}, pages={422-434}, number={2} } @inbook{Lustig94, author={Nora Lustig}, title={Solidarity as a Strategy of Poverty Alleviation}, chapter={5}, year={1994}, publisher={Center for U.S.-Mexican Studies}, pages={79-96}, series={U.S.-Mexico Contemporary Perspectives Series, 6}, address={University of California, San Diego} } @article{LuZanHor01, author = {Bo Lu and Elaine Zanuto and Robert Hornik and Paul R. Rosenbaum}, title = {Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse}, journal = {Journal of the American Statistical Association}, volume = {96}, year = {2001}, pages = {1245-1253}, month = {December}, number = {456} } @techreport{LymVar03, author = {Peter Lyman and Hal R. Varian}, title = {{How much information 2003}}, institution = {University of California}, year = 2003, note = {{http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/}} } @article{Lynch03, author = {Lynch, C.A.}, title = {{Institutional Repositories: Essential Infrastructure For Scholarship In The Digital Age}}, journal = {portal: Libraries and the Academy}, volume = {3}, year = {2003}, pages = {327--336}, number = {2} } @article{LynMcc92, author = {Henry S. Lynn and Charles E. McCulloch}, title = {When Does it Pay to Break the Matches for Analysis of a Matched-Pairs Design?}, journal = {Biometrics}, volume = {48}, year = {1992}, pages = {397-409}, month = {June} } @article{MacKunGro99, author = {J.P. Mackenbach and A.E. Kunst and F. Groenhof and J.K. Borgan and G. Costa and F. Faggiano }, title = {Socioeconomic inequalities in mortality among women and among men: an international study}, journal = {American Journal of Public Health}, volume = 89, year = 1999, pages = {1800-1806}, number = 12 } @book{Macridis55, author={Roy C. Macridis}, title={The Study of Comparative Government}, publisher={Doubleday and Co.}, year= 1955, address={New York} } @article{MacRivJur06, author = {Ellen J. MacKenzie and Frederick P. Rivara and Gregory J. Jurkovich and Avery B. Nathens and Katherine P. Frey and Brian L. Egleston and David S. Salkever and Daniel O Scharfstein}, title = {A National Evaluation of the Effect of Trauma-Center Care on Mortality}, journal = {New England Journal of Medicine}, volume = {354}, year = {2006}, pages = {366-378}, month = {January} } @article{MadDou64, author = {George L. Maddox and Elizabeth B. Douglas}, title = {Self-Assessments of Health: A Longitudinal Study of Elderly Subjects}, journal = {Journal of Health and Social Behavior }, volume = 14, year = 1973, pages = {{87-93}} } @article{MadNel92, author = {W.R. Madych and S.A. Nelson}, title = {Bounds on Multivariate Polynomials and Exponential Error Estimates for Multiquadric Interpolation}, journal = {Journal of Approximation Theory}, volume = 70, year = 1992, pages = {94--114} } @article{Malaker86, author={CR Malaker}, title={Estimation of Adult Mortality in India: 1971--1981}, journal={Demography India}, volume= 15, year= 1986, pages={126--136} } @article{ManKarMar03, author = {Kristiina Manderbacka, et al}, title = {The Effect of Point of Reference on the Association Between Self-Rated Health and Mortality}, journal = {Social Science and Medicine}, volume = 56, year = 2003, pages = {{1447-52}} } @book{ManSch99, author={Christopher D. Manning and Hinrich Sch{\"u}tze}, title={Foundations of Statistical Natural Language Processing}, publisher={Massachusetts Institute of Technology}, year={1999}, address={Cambridge, MA} } @book{Manski05, author={Charles F. Manski}, title={Social choice with Partial Knowledge of Treatment Response}, publisher={Princeton University Press}, year={2005}, series={Econometric Institute Lectures} } @article{Manski77, author={Charles F. Manski}, title={The Estimation of Choice Probabilities from Choice Based Samples}, journal={Econometrics}, volume= 45, year= 1977, pages={1977--88}, month={November}, number= 8 } @article{Manski90, author = {Charles F. Manski}, title = {The Use of Intentions Data to Predict Behavior: A Best-Case Analysis}, journal = {Journal of the American Statistical Association}, volume = {85}, year = {1990}, pages = {934-940}, month = {December}, number = {412} } @book{Manski95, author={Charles F. Manski}, title={Identification Problems in the Social Sciences}, publisher={Harvard University Press}, year= 1995 } @inproceedings{manski99, author = {Charles F. Manski}, title = {Nonlinear Statistical Inference: Essays in Honor of Takeshi Amemiya}, booktitle = {Nonparametric Identification Under Response-Based Sampling}, year = 1999 , publisher = cup, editor = {C. Hsiao and K. Morimune and J. Powell} } @article{ManSzoKoh01, author = {Mandl, K.D. and Szolovits, P. and Kohane, I.S.}, title = {{Public standards and patients' control: how to keep electronic medical records accessible but private}}, journal = {BMJ}, volume = {322}, year = {2001}, pages = {283--7}, number = {7281} } @article{ManSzoKoh01, author={Kenneth D. Mandl and Peter Szolovits and Isaac S. Kohane}, title={Public standards and patients' control: how to keep electronic medical records accessible but private}, journal={British Medical Journal}, volume={322}, year={2001}, pages={283-287}, month={February}, publisher={British Medical Journal} } @article{mantel73, author={N. Mantel}, title={Synthetic Retrospective Studies and Related Topics}, journal={Biometrics}, volume= 29, year= 1973, number={479-486} } @article{ManTudDie06, author={Dennis T. Mangano and Julia C. Tudor and Cynthia Dietzel}, title={The Risk Associated wtih Aprotinin in Cardiac Surgery}, journal={The New England Journal of Medicine}, volume={354}, year={2006}, pages={353-365}, month={January}, number={4} } @article{ManRao04, title = {{Community-Based and-Driven Development: A Critical Review}}, author = {Mansuri, G. and Rao, V.}, journal = {The World Bank Research Observer}, volume = {19}, number = {1}, pages = {1-39}, year = {2004} } @article{MarCamFay91, author = {Elizabeth A. Martin and Pamela C. Campanelli and Robert E. Fay}, title = {An Application of Rasch Analysis to Questionnaire Design: Using Vignettes to Study the Meaning of `Work' in the Current Population Survey}, journal = {The Statistician}, volume = 40, year = 1991, pages = {265--276}, month = {September}, number = 3 } @article{March57, author={James G. March}, title={party Legislative Representation as a Function of Election Results}, journal={1957}, volume={21}, year={1957-1958}, pages={521-542}, month={Winter}, number={4} } @article{Marcus00, author={George E. Marcus}, title={{Emotions in Politics}}, journal={Annual Review of Political Science}, volume={3}, year={2006}, pages={221-50} } @article{Marcus88, author={George E. Marcus}, title={{The Structure of Emotional Response: The 1984 Presidential Candidates}}, journal={American Political Science Review}, volume={82}, year={1988}, pages={737-761}, number={3} } @article{MarDiePer93, author={Donald C. Martin and Paula Diehr and Edward B. Perrin and Thomas D. Koepsell}, title={The Effect of Matching on the Power of Randomized Community Intervention Studies}, journal={Statistics in Medicine}, volume={12}, year={1993}, pages={329-338} } @article{MarHusLob95, author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and Stephen Luby}, title={Verbal autopsy in Karachi slums: comparing single and multiple cause of child deaths}, journal={Health Policy and Planning}, volume={10}, year={1995}, pages={395-403}, number={4} } @article{MarHusLob95, author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and Stephen Luby}, title={Verbal autopsy in Karachi slums: comparing single and multiple cause of child deaths}, journal={Health Policy and Planning}, volume={10}, year={1995}, pages={395-403}, number={4} } @book{MarKenBib79, author={K. V. Mardia and J. T. Kent and J. M. Bibby}, title={Multivariate Analysis}, publisher= ap, year={1979}, address={London} } @article{MarMajRas93, author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif Amin Khan}, title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By Verbal Autopsy}, journal={Journal of the Pakistan Medical Association}, volume={43}, year={1993}, pages={226-229}, month={November}, number={11} } @article{MarMajRas93, author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif Amin Khan}, title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By Verbal Autopsy}, journal={Journal of the Pakistan Medical Association}, volume={43}, year={1993}, pages={226-229}, month={November}, number={11} } @misc{MarQui01, author={Andrew D. Martin and Kevin M. Quinn}, title={The Dimensions of {S}upreme {C}ourt Decision Making: Again Revisiting {T}he {J}udicial {M}ind}, year= 2001, howpublished={Paper presented at the Annual Meeting of the Midwest Political Science Association} } @manual{MarQui05, author={Andrew D. Martin and Kevin M. Quinn}, title={MCMCpack: Markov chain Monte Carlo (MCMC) Package}, year={2005}, url={{http://mcmcpack.wustl.edu}} } @article{MarSadFik03, author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan and Gary L. Darmstadt}, title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths in Karachi, Pakistan}, journal={2003}, volume={17}, year={2003}, pages={132-142} } @article{MarSadFik03, author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan and Gary L. Darmstadt}, title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths in Karachi, Pakistan}, journal={2003}, volume={17}, year={2003}, pages={132-142} } @article{Marshall91, author={R.J. Marshall}, title={Mapping Disease and Mortality Rates using Empirical Bayes Estimators}, journal={Applied Statistics}, volume= 40, year= 1991, pages={283--294}, number= 2 } @article{MarSmiSta91, author={M.G. Marmot and G.D. Smith and S. Stansfeld and C. Patel and F. North and J. Head and I. White and E. Brunner and A. Feeney}, title={Health inequalities among British civil servants: the Whitehall II study [see comments].}, journal= lan, year={1991}, optvolume={337}, optpages={1387--1393} } @book{Martin92, author={Lisa Martin}, title={Coercive Cooperation: Explaining Multilateral Economic Sanctions}, publisher={Princeton University Press}, year={1992}, note={Please inquire with Lisa Martin before publishing results from these data, as this dataset includes errors that have since been corrected.} } @article{MatFatIno05, author = {Colin D. Mathers and Doris Ma Fat and Mie Inoue and Chalapati Rao and Alan Lopez}, title = {Counting the dead and what they died from: an assessment of the global status of cause of death data}, journal = {Bulletin of the World Health Organization}, volume = 83, year = 2005, pages = {171--177}, month = {March}, number = 3 } @unpublished{MatLopSte03, author={Colin D. Mathers and Alan Lopez and Claudia Stein and Doris Ma Fat and Chalapati Rao and Mie Unoue and Kenji Shiubuya and Niels Tomijima and Christina Bernard and Hongyi Xu}, title={Deaths and Disease Burden by Cause: Global Burden of Disease Estimates for 2001 by World Bank Country Groups}, note={Evidence and Information for Policy, World Health Organization, Geneva and School of Population health, University of Queensland, Brisbane, Austrailia}, year={2003} } @unpublished{MatSteFat02, author={Colin D. Mathers and Claudia Stein and Doris Ma Fat and Chalapati Rao and Mie Unoue and Niels Tomijima and Christina Bernard and Alan D. Lopez and Christopher J.L. Murray}, title={Global Burden of Disease 2000: Version 2 Methods and Results}, note={Global Programme on Evidence for Health Policy Discussion Paper No. 50 World Health Organization}, year={2002}, month={October} } @article{MauRos97, author={Gillian H. Maude and David A. Ross}, title={The Effect of Different Sensitivity, Specificity and Cause-Specific Mortality Fractions on the Estimation of Differences in Cause-Specific Mortality Rates in Children from Studies Using Verbal Autopsies}, journal={International Journal of Epidemiology}, volume={26}, year={1997}, pages={1097-1106}, number={5} } @techreport{Mayaud01, author={Philippe Mayaud}, title={Aids-Related Research Projects}, institution={The London School of Hygiene \& Tropical Medicine}, year={2001}, address={London School of Hygiene \& Tropical Medicine www.ishtm.ac.uk} } @book{McCNel89, author={Peter McCullagh and James A. Nelder}, title={Generalized Linear Models}, publisher={Chapman \& Hall}, year={1989}, series={Monograph on Statistics and Applied Probability}, edition={2nd}, number={37} } @inbook{McConahay86, author={John B. McConahay}, title={Prejudice, Discrimination, and Racism: Theory and Research}, chapter={Modern Racism Ambivalence, and the Modern Racism Scale}, year={1986}, publisher={New York: Academic Press}, editor={J. Dovidio and S.L. Gaertner} } @article{MccRidMor04, author={Daniel F. McCaffrey and Greg Ridgeway and Andrew R. Morral}, title={Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies}, journal={Psychological Methods}, volume={9}, year={2004}, pages={403-425}, number={4} } @article{McCShaWan94, author={John McCallum, DPhil, MPhil, Bruce Shadbolt, PhD, and Dong Wang, BSc}, title={Self-Rated Health and Survival: A 7-year Follow-Up study of Australian Elderly.}, journal={American Journal of Public Health}, volume= 84, year= 1994, pages={{1100-05}} } @unpublished{McDonald06a, author={Michael P. McDonald}, title={Seats to Votes Ratios in the United States}, note={George Mason University, Dept of Public and International Affairs 4400 University Dr., 3-F4 Fairfax, VA 22030-4444 (703)-993-4191 mmcdon@gmu.edu}, year={2006} } @unpublished{McDonald06b, author={Michael D. McDonald}, title={A Standard for Detecting and Remedying Gerrymanders}, note={Department of Political Science, Binghamton University- SUNY, Binghamton NY 13902-6000, (617) 777-2946, mdmcd@binghamton.edu}, year={2006} } @article{McKibbin69, author={Ross McKibbin}, title={The Myth of the Unemployed: Who did Vote for the Nazis?}, journal={Australian Journal of Politics and History}, volume= 15, year= 1969, pages={25--40}, number= 2 } @book{McLThr97, author={Geoffrey J. McLachlan and Thriyambakam Krishan}, title={The EM Algorithm and Extensions}, publisher={New York: Wiley} } @article{McNown92, author={Robert McNown}, title={Comment}, journal= jasa, volume= 87, year= 1992, pages={671--672}, number= 419 } @article{McNRog89, author={Rober McNown and Andrei Rogers}, title={Forecasting Mortality: A Parameterized Time Series Approach}, journal={Demography}, volume= 26, year= 1989, pages={645--660}, number= 4 } @article{McNRog92, author={Robert McNown and Andrei Rogers}, title={Forecasting Cause-Specific Mortality Using Time Series Methods}, journal={International Journal of Forecasting}, volume= 8, year= 1992, pages={413--432} } @unpublished{McqLasLai06, author={Matthew B. McQueen and Jessica Lasky-Su and Nan M. Laird and Christoph Lange}, title={Screening and Testing using the Same Data Set: A Testing Strategy for Genome-Wide Association Studies for Case-Control and Case-Cohort Designs}, year={2006} } @article{Mead92, author={A. Mead}, title={Review of the Development of Multidimensional Scaling Methods}, journal={The Statistician}, volume= 41, year= 1992, pages={27--39}, month={April}, number= 1 } @article{MebSek04, author={Walter Mebane and Jasjeet Sekhon}, title={Robust Estimation and Outlier Detection in Overdispersed Multinomial Models of Count Data}, journal= ajps, volume= 48, year= 2004, pages={391--410}, month={April} } @article{MeiKarPar04, author={Bettina Meinow et al.}, title={The effect of the duration of follow-up in mortality analysis: The temporal pattern of different predictors}, journal={Journal of Gerontology: Social Sciences}, volume={{59B}}, year={2004}, pages={{S181-89}}, number={3} } @article{Meng94, author={Xiao-Li Meng}, title={Multiple-Imputation Inferences with Uncongenial Sources of input}, journal={Statistical Science}, volume={9}, year={1994}, pages={538-573}, number={4} } @article{Meng94b, author={X.L. Meng}, title={Posterior Predictive p-Values}, journal={Annals of Statistics}, volume={22}, year={1994}, pages={1142-1160}, number={3} } @article{MenRom03, author={Xiao-Li Meng and Marin Romero}, title={Discussion: Efficiency and Self-Efficiency with Multiple Imputation Inference}, journal={International Statistical Review}, volume={71}, year={2003}, pages={607-618}, number={3} } @article{MenRub92, author={X.L. Meng and Donald Rubin}, title={Performing Likelihood Ratio Tests with Multiply-imputed Data Sets}, journal={Biometrika}, volume={79}, year={192}, pages={103-111} } @article{Metetal53, author={N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth and A. H. Teller and E. Teller}, title={Equation of State Calculations by Fast Computing Machines}, journal={Journal of Chemical Physics}, volume={21}, year= 1953, pages={1087-1092} } @article{MicBloHil04, author={Charles Michalopoulos and Howard S. Bloom and Carolyn J. Hill}, title={Can propensity-score methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs?}, journal={Review of Economics and Statistics}, volume= 56, year= 2004, pages={156-179}, number= 1 } @article{Midlarsky05, author={Halbert White}, title={A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity}, abstract={This study seeks to distinguish between instances where genocide occurred and others where it might have been expected to occur but did not. Territorial loss, a corollary refugee influx, and a resulting contraction of socio-economic space are suggested to provide that distinction. Four analytic perspectives based on emotional reactions, class envy, prospect theory, and territoriality indicate the critical importance of loss. The theory is examined in the context of the mass murder of European Jewry including, of course, Germany and Austria, and all European German allies that allowed an indigenous genocidal impulse, willingness to comply with German genocidal policies, or an ability to resist German pressures for Jewish deportation. Three instances of perpetrating states - Italy, Vichy France, and Romania - emerge from the analysis. The latter two governments willingly collaborated with the Germans in victimizing their own Jewish citizenry, while Italy was on a genocidal path just prior to the German occupation. All five states mentioned above were found to experience considerable territorial loss and a contraction of socio-economic space. Bulgaria and Finland, on the other hand, actually expanded their borders at the start of the war and saved virtually all of their Jewish citizens. The importance of loss is demonstrated not only cross-sectionally, in the comparison between the five victimizers, on the one hand, and Bulgaria and Finland, on the other, but also diachronically, in the changing behavior over time of the genocidal and perpetrating states.}, journal={Econometrica}, volume={42}, year={1980}, pages={375-391}, month={July}, number={4}, optnumber={4}, optvolume={48}, optpages={817--838}, optmonth={May} } @article{Mierendorff30, author={Carl Mierendorff}, title={Gesicht und Charakter der nationalsozialistischen Bewegung}, journal={Gesellschaft}, volume= 7, year= 1930, pages={489--540}, number= 1 } @article{MiiVuoOja97, author={Seppo Miilunpalo, Ilkka Vuori, Pekka Oja, Matti Pasanen, and Helka Urponen}, title={Self Rated Health as a Health Measure: The Predictive Value of Self-Reported Health Status on the Use of Physician Services and on Mortality in the Working Age Population}, journal={Journal of Clinical Epidemiology}, volume= 50, year= 1997, pages={{517-28}} } @misc{MikSuc05, author={Gerome Miklau and Dan Suciu}, title={Managing Integrity for Data Exchanged on the Web}, year= 2005, month={16--17 June}, howpublished={Eighth International Workshop on the Web and Databases, Baltimore}, note={{http://webdb2005.uhasselt.be/papers/1-3.pdf}} } @article{Miller01, author={Tim Miller}, title={Increasing Longevity and Medicare Expenditures}, journal={Demography}, volume= 38, year= 2001, pages={215--226}, month={May}, number= 2 } @inbook{MilReiHes98, author={Arthur H. Miller and William M. Reisinger and Vicki L. Hesli}, title={Elections and Voters in Post-Communist Russia}, chapter={Leader Popularity and Party Development in Post-Soviet Russia }, year={100-135}, publisher={London: Edward Elgar}, editor={Matthew Wyman and Stephen White and Sarah Oates} } @article{MilRob89, author={Miller, Abraham H. and Robbins, James S.}, title={Who Did Vote for Hitler? A Reanalysis of the Lipset/Bendix Controversy}, journal={Polity}, volume= 21, year= 1989, pages={655-677}, number= 4 } @article{MilSha94, author={Paul Milgrom and Chris Shannon}, title={Monotone Comparative Statics}, journal={Econometrica}, volume= 62, year= 1994, pages={157--180}, month={January}, number= 1, annote={introduction of the single crossing property as a way to ensure monotonicity} } @article{MinRos00, author={Ming, K. and Rosenbaum, Paul R.}, title={Substantial gains in bias reduction from matching with a variable number of controls}, journal={Biometrics}, volume={56}, year={2000}, pages={118-124} } @article{MoeWal01, author={Karl Ove Moene and Michael Wallerstein}, title={Inequality, Social Insurance, and Redistribution}, journal={American Political Science Review}, volume={95}, year={2001}, pages={859-874}, month={December}, number={4} } @article{MoeWal03, author={Karl Ove Moene and Michael Wallerstein}, title={Earnings Inequality and Welfare Spending: A Disaggregated Analysis}, journal={World Politics}, volume={55}, year={2003}, pages={485-516}, month={July} } @book{Mommsen89, author={Hans Mommsen}, title={Die verspielte Freiheit --- der Weg der Republik von Weimar in den Untergang, 1918 bis 1933}, publisher={Propyl{\"a}en}, year= 1989 } @techreport{MonLopGel99, author={Manuel Montes-y-Gomez and Aurelio Lopez-Loez and Alexander F. Gelbukh and Grigori Sidorov and Adolfo Guzman-Arenas}, title={Text Mining: New Techniques and Applications}, institution={Center for computing Research of the National Polytechnic Institute, Mexico City}, year={1999}, month={August}, address={CIC, IPN, Laboratorio de Lenguaje Natural, Av. Juan de Dios Batiz, Mexico DF.}, volume={34} } @techreport{MonLopGel99b, author={Manuel Montes y Gomez and Aurelio Lopez Lopez and Alexander F. Gelbukh}, title={Text Mining as a Social Thermometer}, institution={Centro de Investigaci{\'o}n en Computaci{\'o}n}, year={1999}, address={CIC, IPN Laboratorio de Lenguaje Natural. Ave. Juan de Dios Batiz, Mexico DF} } @book{MonSam04, title={Decentralization and Democracy in Latin America}, publisher={University of Notre Dame Press}, year={2004}, editor={Alfred P. Montero and David J. Samuels}, address={Notre Dame, Indiana} } @book{Montgomery2001, author={Douglas C. Montgomery}, title={Design and Analysis of Experiments}, publisher={Wiley}, year={2001}, address={New York}, edition={5th} } @article{MorBlaTom03, author={Saul S. Morris and Robert E. Black and Lana Tomaskovic}, title={Predicting the distribution of under-five deaths by cause in countries without adequate vital registration systems}, journal={International Journal of Epidemiology}, volume={32}, year={2003}, pages={1041-1051} } @book{Morozov84, author={V.A. Morozov}, title={Methods for solving incorrectly posed problems}, publisher={Springer-Verlag}, year= 1984 , address={Berlin} } @article{MorSpi99, author={Mary J. Morrissey and Donna Spiegelman}, title={Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons}, journal={Biometrics}, volume={55}, year={1999}, pages={338-344}, month={June} } @unpublished{MorYamTat02, author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima}, title={Mining Product Reputations on the Web}, note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga@cw.jp.nec.com}, year={2002} } @unpublished{MorYamTat02, author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima}, title={Mining Product Reputations on the Web}, note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga@cw.jp.nec.com}, year={2002} } @article{MosSha82, author={Jana Mossey MPH, PhD, and Evelyn SHapira, MA}, title={Self-Rated Health: A Predictor of Mortality Among the Elderly}, journal={American Journal of Public Health}, volume= 72, year= 1982, pages={{800-08}} } @article{moynihan00, author={Ray Moynihan and Lisa Bero and Dennis Ross-Degnan and David Henry and Kriby Lee and Judy Watkins and Connie Mah and Stephen B. 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Thompson}, title={Letters to the Editor: The Merits of Matching in Community Intervention Trials: A Cautionary Tale}, journal={Statistics in Medicine}, volume={17}, year={1998}, pages={2147-2151} } @article{ThoPanLee06, author={Matt Thomas and Bo Pang and Lillian Lee}, title={Get out the vote: Determining support or opposition from Congressional floor-debate transcripts}, journal={Proceedings of EMNLP}, year= 2006, pages={327--335}, note={{http://www.cs.cornell.edu/home/llee/papers/tpl-convote.home.html}} } @article{ThoSyl82, author={Stuart J. Thorson and Donald A. Sylvan}, title={Counterfactuals and the Cuban Missle Crisis}, journal={International Studies Quarterly}, volume= 26, year= 1982, pages={539--571}, number= 4 } @book{Thurstone59, author={L.L. Thurstone}, title={The Measurement of Values}, publisher={University of Chicago Press}, year= 1959, address={Chicago} } @book{TikArs77, author={A. N. Tikhonov and V. Y. Arsenin}, title={Solutions of Ill-posed Problems}, publisher={W. H. Winston}, year= 1977 , address={Washington, D.C.} } @article{Tikhonov63, author={A. N. Tikhonov}, title={Solution of incorrectly formulated problems and the regularization method}, journal={Soviet Math. Dokl.}, volume={4}, year= 1963 , pages={1035--1038} } @article{Timaeus86, author={Ian Timaeus}, title={An Assessment of Methods for Estimating Adult Mortality from Two Sets of Data on Maternal Orphanhood}, journal={Demography}, volume= 23, year= 1986, pages={435--450} } @article{Timaeus91, author={Iain Timaeus}, title={Measurement of Adult Mortality in Developing Countries: A Comparative Review}, journal={Population Index}, volume= 57, year= 1991, pages={552-568}, number= 4 } @article{Timaeus91b, author={Ian Timaeus}, title={Estimation of Adult Mortality from Orphanhood Before and Since Marriage}, journal={Population Studies}, volume= 45, year={1991b}, pages={455--472} } @article{Timpone98, author={Richard J. Timpone}, title={Structure, Behavior, and Voter Turnout in the United States}, journal={American Poltical Science Review}, volume={92}, year={1998}, pages={145-158}, month={March}, number={1} } @incollection{TimZabAli01, author={Ian M. Timaeus Basia Zaba and Mohammed Ali}, title={Estimation of Adult Mortality from Data on Adult Siblings}, booktitle={Brass Tacks: Essays in Medical Demography}, publisher={Athlone}, year= 2001, editor={B. Zaba and J. Blacker}, pages={43--66} } @incollection{Tobler79, author={Waldo Tobler}, title={Cellular Geography}, booktitle={Philosophy in Geography}, publisher={Dordrecht: Reidel}, year= 1979, editor={S.\ Gale and G.\ Olssen} } @article{TodDefOde94, author = {J.E. Todd and A. De Francisco and T.J.D. O'Dempsey and B.M. Greenwood}, title = {The limitations of verbal autopsy in a malaria-endemic region}, journal = {Annals of Tropical Paediatrics}, volume = {14}, year = {1994}, pages = {31-36} } @book{Torgerson58, author = {Warren S. Torgerson}, title = {Theory and Methods of Scaling}, publisher = {Wiley and Sons}, year = 1958, address = {New York} } @incollection{TorRauHer93, author = {Hege Torp and O. Rauum and E. Hernaes and H. Goldstein}, title = {The First Norwegian Experiment}, booktitle = {Measuring Labour Market Measures: Evaluating the Effects of Active Labour Market Policies}, publisher = {Ministry of Labour}, year = {1993}, address = {Copenhagen, Denmark}, editor = {K. Jensen and Per Kongshoj Madsen} } @article{TruRod90, author = {J. Trussell and G. Rodriguez}, title = {A Note on the Sisterhood Estimator of Maternal Mortality}, journal = {Studies in Family Planning}, volume = 21, year = 1990, pages = {344--346}, month = {Nov-Dec}, number = 6 } @article{TsuLin86, author = {Robert K. Tsutakawa and Hsin Ying Lin}, title = {Bayesian Estimation of Item Response Curves}, journal = {Psychometrika}, volume = {51}, year = {1986}, pages = {251-267}, month = {June}, number = {2} } @article{TsuMinKey94, author = {Ichiro Tsuji, MD, et al}, title = {The Predictive Power of Self-Rated Health, Activities of Daily Living, and Ambulatory Activity for Cause Specific Mortality among the Elderly: A Three-year Follow-up in Urban Japan. }, journal = {Journal of the American Geriatric Society}, volume = 42, year = 1994, pages = {{153-56}} } @techreport{Tsutakawa75, author = {Robert K. Tsutakawa}, title = {Bayesian Inference for Bioassay}, institution = {University of Missouri - Columbia}, year = {1975}, month = {August}, number = {52} } @article{Tsutakawa84, author = {Robert K. 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Tsutakawa}, title = {Prior Distribution for Item Response Curves}, journal = {British Journal of Mathematical and Statistical Psychology}, volume = {45}, year = {1992}, pages = {51-74} } @article{TulBoe98, author = {Shripad Tuljapurkar and Carl Boe}, title = {Mortality Change and Forecasting: How Much and How Little Do We Know?}, journal = {North American Actuarial Journal}, volume = {2}, year = {1998}, number = {4}, annote = {This paper makes a critical assessment of knowledge about mortality change and the potential of existing work to contribute to the development of useful forecasts in Canada, Mexico, and the United States. Methods of forecasting are reviewed, including the scenario method used by the US Social Security Administration and the time series method of Lee and Carter.} } @article{TulLiBoe00, author = {S. Tuljapurkar and N. Li and C. Boe}, title = {A Universal Pattern of Mortality Decline in the {G7} Countries}, journal = {Nature}, volume = 405, year = 2000, pages = {789--792}, month = {June} } @article{tumbarello98, author = {M. Tumbarello and E. Tacconelli and K. de Gaetano and F. Ardit and T. Pirronti and R. Claudia and L. Ortona}, title = {Bacterial Pneumonia in HIV-Infected Patients: Analysis of Risk Factors and Prognostic Indicators}, journal = {Journal of Acquired Immune Deficiency Syndromes and Human Retroviology}, volume = 18, year = 1998, number = {39-45} } @unpublished{TurLit02, author = {P.D. Turney and M.L. Littman}, title = {Unsupervised Learning of Semantic Orientation}, note = {National Research Council Canada}, year = {2002}, month = {May} } @unpublished{TurLit02, author = {P.D. Turney and M.L. Littman}, title = {Unsupervised Learning of Semantic Orientation}, note = {National Research Council Canada}, year = {2002}, month = {May} } @article{TurMat01, author = {G. Turrell and Colin Mathers}, title = {Socioeconomic inequalities in all-cause and specific-cause mortality in Australia: 1985--1987 and 1995--1997}, journal = {International Journal of Epidemiology}, volume = 30, year = 2001, pages = {231--239}, number = 2 } @book{Turner85, author = {Turner, Henry-Ashbury}, title = {German big business and the rise of Hitler}, publisher = {Oxford University Press}, year = {1985} } @proceedings{Turney02, editor = {Peter D. Turney}, title = {Thumbs Up or Thumbs Down? Semantic Orientation Applied to}, publisher = {40th Annual Meeting of the Associatin for Computational Linguistics}, year = {2002}, month = {July}, organization = {Institute for Information Technology}, address = {National Research Council of Canada, Ottawa, Ontario, Canada K1A0R6} } @proceedings{Turney02, editor = {Peter D. Turney}, title = {Thumbs Up or Thumbs Down? Semantic Orientation Applied to}, publisher = {40th Annual Meeting of the Associatin for Computational Linguistics}, year = {2002}, month = {July}, organization = {Institute for Information Technology}, address = {National Research Council of Canada, Ottawa, Ontario, Canada K1A0R6} } @article{Urdal05, author = {Henrik Urdal}, title = {People vs. Malthus: Population Pressure, Environmental Degradation, and Armed Conflict Revisited}, journal = {Journal of Peace Research}, volume = {42}, year = {2005}, pages = {417-434}, month = {July}, number = {4}, publisher = {Journal for Peace Research} } @book{UttLock02, title = {American Political Scientists: a Dictionary}, publisher = {Greenwood Press}, year = {2002}, editor = {Glenn H. 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Vapnick}, title={The Nature of Statistical Learning Theory}, publisher={Springer}, year= 1995, address={New York} } @book{Vapnik98, author={Vladimir N. Vapnik}, title={Statistical Learning Theory}, publisher={Wiley}, year= 1998 , address={New York} } @book{VenRip02, author={William N. Venables and Brian D. Ripley}, title={Modern Applied Statistics with S}, publisher={Springer-Verlag}, year={2002}, edition={4th} } @article{VerAngCap02, author={Arduino Verdecchia and Giovanni De Angelis and Riccardo Capocaccia}, title={Estimation and Projections of Cancer Prevalence from Cancer Registry Data}, journal={Statistics in Medicine}, volume= 21, year= 2002, pages={3511--3526} } @article{VerCapEgi89, author={A. Verdecchia and R. Capocaccia and V. Egidi and A. Golini}, title={A Method for the Estimation of Chronic Disease Morbidity and Trends from Mortality Data}, journal={Statistics in Medicine}, volume= 8, year= 1989, pages={201--216} } @article{Verrall93, author={R.J. Verrall}, title={A state space formulation of Whittaker graduation, with extensions}, journal={Insurance: Mathematics and Economics}, volume= 13, year= 1993, pages={7--14} } @article{VerSch77, author={Sidney Verba and Kay Lehman Schlozman}, title={Unemployment, Class Consciousness, and Radical Politics: What Didn't Happen in the Thirties}, journal={Journal of Politics}, volume= 39, year= 1977, pages={291--323}, number= 2 } @book{VerSchBra95, author={Sidney Verba and Kay Lehman Schlozman and Henry E. Brady}, title={Voice and Equality: Civic Volunteerism in American Politics}, publisher={Harvard University Press}, year= 1995, address={Cambridge, MA} } @article{Villalonga04, author={Belen Villalonga}, title={Does Diversification Cause the "Diversification Discount"?}, journal={Financial Management}, volume={33}, year={2004}, pages={5-27}, number={2} } @article{Voth03, author = {Voth, Hans-Joachim}, title = {With a Bang, not a Whimper: Pricking Germany's Stock Market Bubble in 1927 and the Slide into Depression}, journal = {Journal of Economic History}, volume = 63, year = 2003, pages = {65--99}, number = 1 } @article{Voth95, author = {Voth, Hans-Joachim}, title = {Did High Wages or High Interest Rates Bring Down the Weimar Republic? A Cointegration Model of Investment in Germany, 1925-1930}, journal = {Journal of Economic History}, volume = 55, year = 1995, pages = {801--821}, month = {December}, number = 4 } @article{WacWei82, author={Wacholder, S. and Weinberg, C.R.}, title={{Paired versus Two-Sample Design for a Clinical Trial of Treatments with Dichotomous Outcome: Power Considerations}}, journal={Biometrics}, volume={38}, year={1982}, pages={801--812}, number={3} } @article{Wagstaff00, author={Adam Wagstaff}, title={Socioeconomic inequalities in Child Mortality: Comparisons Across Nine Developing Countries}, journal= bull, year={2000}, optnumber={1}, optvolume={78}, optpages={19--29} } @article{Wahba75, author={G. Wahba}, title={Smoothing noisy data by spline functions}, journal={Numer. Math}, volume= 24, year= 1975, pages={383--393} } @article{Wahba77, author={G. Wahba}, title={Practical approximate solutions to linear operator equations when the data are noisy}, journal={SIAM J. Numer. 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Wahba}, title={Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data}, booktitle={Approximation theory III}, publisher={Academic Press}, year= 1980, address={New York}, editor={W. Cheney}, pages={905--912} } @article{Wahba85, author={G. Wahba}, title={A comparison of {GCV} and {GML} for choosing the smoothing parameter in the generalized splines smoothing problem}, journal={The Annals of Statistics}, volume= 13, year= 1985, pages={1378--1402} } @book{Wahba90, author={G. Wahba}, title={Splines Models for Observational Data}, publisher={{Series in Applied Mathematics, Vol. 59, SIAM}}, year= 1990 , address={Philadelphia} } @techreport{WahLinZha99, author={G. Wahba and Y. Lin and H. Zhang}, title={Generalized Approximate Cross Validation for SVM, or, anather way to look at margin-like quantities}, institution={Department of Statistics, University of Wisconsin}, year={1999}, type={Tech. Report}, number={1006} } @misc{Wakefield01, author={Jon Wakefield}, title={Ecological Inference for $2 \times 2$ Tables}, year= 2001, howpublished={Working Paper \# 12, Center for Statistics and the Social Sciences, University of Washington} } @article{WalCarXiaGel97, author={Lance A. Waller and Bradley P. Carlin and Hong Xia and Alan E. Gelfand}, title={Hierarchical Spatio-Temporal Mapping of Disease Rates}, journal= jasa, volume= 92, year= 1996, pages={607-617} } @article{WalDon94, author={G.E.L. Walraven and P.W.J. van Dongen}, title={Assessment of maternal mortality in Tanzania}, journal={British Journal of Obstetrics and Gynaecology}, volume= 101, year= 1994, pages={414--417} } @book{Waldron99, author={Jeremy Waldron}, title={Law and disagreement}, publisher={Oxford University Press}, year={1999}, address={New York} } @article{Waletal97, author={L.A. Waller and B.P. Carlin and H. Xia and A.E. Gelfand}, title={{Hierarchical Spatio-Temporal Mapping of Disease Rates}}, journal= jasa, volume= 92, year= 1997, pages={607--617}, number= 438 } @techreport{WalHogHam06, author={Robert Walker and Lesley Hoggart and Gayle Hamilton with Susan Blank}, title={Making random assignment happen: Evidence from the UK Employment Retention and Advancement (ERA) demonstration}, institution={Department for Work and Pensions, Corporate Document Services}, year={2006}, month={March}, type={research report}, note={ISBN 1 84 123981 X, Research Report 330} } @article{WanRob98, author={Naisyin Wang and James Robins}, title={Large-sample theory for parametric multiple imputation procedures}, journal={Biometrika}, volume={85}, year={1998}, pages={935-948} } @article{WanSchAvo05, author={Philip S. Wang and Sebastian Schneeweiss and Jerry Avorn and Michael A. Fischer and Helen Mogun and Daniel H. Solomon and M. Alan Brookhart}, title={Risk of Death inelderly Users of Conventional vs. Atypical Antipsychotic Medications}, journal={New England Journal of Medicine}, volume={353}, year={2005}, pages={2335-2341}, month={December} } @article{WanSha91, author={Goya Wannamethee and A. G. Shaper }, title={Self-assessment of Health Status and Mortality in Middle Aged British Men}, journal={International Journal of Epidemiology}, volume= 20, year= 1991, pages={{239-45}}, number= 1 } @unpublished{WanYanMa06, author={L Wang and G. Yang and J Ma and C Rao and X Wan and AD Lopez}, title={Evaluation of the quality of cause of death statistics in rural China using verbal autopsies}, year={2006}, journal={Journal of Epidemiology and Community Health} } @book{WapBerBra40, author={Waples, D. and Berelson, B. and Bradshaw, F.R.}, title={{What Reading Does to People: A Summary of Evidence on the Social Effects of Reading and a Statement of Problems for Research}}, publisher={The University of Chicago Press}, year={1940} } @article{Ware05, author={Helen Ware}, title={Demography, Migration and Conflict in the Pacific}, abstract={This article explores the relationships between demography and internal conflict in the Pacific Island countries, focusing on the three subregions Polynesia, Micronesia and Melanesia. These countries confront distinctive challenges and opportunities because of their unique cultures and non-militarized status, combined with very small size and remote locations. The use of the MIRAB model of island economies based on migration, remittances, aid and bureaucracy is extended to examine its impact on social cohesion and the avoidance of internal conflict. For Polynesia, MIRAB is found to be a sustainable development strategy. Continuous emigration from Polynesia serves to reduce population pressure and communal tensions. Further, remittance income supports the Polynesian economies, and this also reduces the potential for conflict. For Micronesia, except Kiribati and Nauru, migration access to the USA is assured. In contrast, for the Melanesian countries, there is minimal emigration, rapid population growth and considerable intercommunal tension, which has resulted in several coups and one 'failed state'. Demographic pressure created by rapid population growth results in a lack of employment opportunities for youths (who provide the majority of participators in civil unrest and conflicts) rather than in direct pressure on land and other natural resources.}, journal={Journal for Peace Research}, volume={42}, year={2005}, pages={435-454}, month={July}, number={4} } @unpublished{WarSivCao05, author={Michael D. Ward and Randolph M. Siverson and Xun Cao}, title={Everybody Out of the Pool!}, note={Michael Ward, Dept of Politcal Science, Univ of WA, Seattle mdw@u.washington.edu}, year={2005}, month={August} } @techreport{WasRoe06, author={Larry Wasserman and Kathryn Roeder}, title={Weighted Hypothesis Testing}, institution={Carnegie Mellon University}, year={2006}, month={April} } @article{Weibe04, author={Janyce M. Wiebe}, title={Tracking Point of View in Narrative}, journal={Computational Linguistics}, volume={20}, year={1994}, pages={233-287}, number={2} } @article{Weibe04, author={Janyce M. Wiebe}, title={Tracking Point of View in Narrative}, journal={Computational Linguistics}, volume={20}, year={1994}, pages={233-287}, number={2} } @article{WeiCoxWil87, author={Milton C. Weinstein and Pamela G. Coxson and Lawrence W. Williams and Theodore M. Pass and William B Stason and Lee Goldman}, title={Forecasting Coronary Heart Disease Incidence, Mortality, and Cost: The Coronary Heart Disease Policy Model}, journal={American Journal of Public Health}, volume= 77, year= 1987, pages={1417--1426}, number= 11 } @book{Weiss86, author={N.S. Weiss}, title={Clinical Epidemiology: the Study of Outcome of Disease}, publisher={Oxford University Press, NY}, year={1986} } @book{Weiss86, author={Noel S. Weiss}, title={Clinical Epidemiology: The Study of the Outcome of Illness}, publisher={Oxford University Press}, year={1986}, volume={Volume 11}, address={New York}, series={Monographs in Epidemiology and Biostatistics } } @article{WeiTan90, author={Greg C. Wei and Martin A. Tanner}, title={A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms}, journal={Journal of the American Statistical Association}, volume={85}, year={1990}, pages={699-704}, month={September} } @article{WeiWanIbr97, author={Robert E. Weiss and Yan Wang and Joseph G. Ibrahim}, title={Predictive Model Selection for Repeated Measures Random Effects Models Using Bayes Factors}, journal={Biometrics}, volume= 53, year= 1997, pages={592--602}, month={June} } @article{Wellhofer03, title = {{Democracy and Fascism: Class, Civil Society, and Rational Choice in Italy}}, author = {E. Spencer Wellhofer}, journal = {American Political Science Review}, volume = {97}, number = {01}, pages = {91--106}, year = {2003} } @article{Werner00, author={Suzanne Werner}, title={The Effects of Political Similarity on the Onset of Militarized Disputes, 1816-1985}, journal= prq, volume= 53, year= 2000, pages={343--374}, month={June} } @article{Wernette1977, author={Wernette, Dee Richard}, title={Quantitative Methods in Studying Political Mobilization in Late Weimar Germany}, journal={Historical Methods Newsletter}, volume= 10, year= 1977, pages={97-101} } @book{WesHar97, author={Mike West and Jeff Harrison}, title={Bayesian Forecasting and Dynamic Linear Models}, publisher={Springer}, year= 1997, address={New York} } @article{WesHarMig85, author={Mike West and P. Jeff Harrison and Helio S. Migon}, title={Dynamic Generalized Linear Models and Bayesian Forecasting}, journal={Journal of the American Statistical Association}, volume={80}, year={1985}, pages={73-83}, month={March}, number={389} } @article{Western95, author={Bruce Western}, title={{Concepts and Suggestions for Robust Regression Analysis}}, journal={American Journal of Political Science}, volume={39}, year={1995}, pages={786--817}, number={3} } @article{Western98, author={Bruce Western}, title={{Causal Heterogeneity in Comparative Research: a Bayesian Hierarchical Modelling Approach}}, journal={American Journal of Political Science}, volume= 42, year= 1998, pages={1233--1259}, month={October}, number= 4 } @article{WhiEva96, author={Stephen Whitefield and Geoffrey Evans}, title={Support for Democracy and Poltical Opposition in Russia 1993-95}, journal={Post Soviet Affairs}, volume={12}, year={1996}, pages={218-52}, number={3} } @book{WhiRosMcA97, author={Stephen White and Richard Rose and Ian McAllister}, title={How Russia Votes}, publisher={Chatham House Publishers, Inc.}, year={1997}, address={Chatham, NJ} } @unpublished{WhiSetCha06, author={David R. Whiting and Philip W. Setel and Daniel Chandramohan and Lara J. Wolfson and Yusuf Hemed and Alan D. Lopez}, title={Estimating Cause-Specific Mortality from Community- and Facility-Based Data Sources in Tanzania: Options and Implications for Mortality Burden Estimates}, note={Whiting, MEASURE Evaluation, Carolina Population Center, Univ. of NC at Chapel Hill, Dept of Medicine, School of Clinical Medical Sciences, Univ of Newcastle upon Tyne England; david.whiting@ncl.ac.uk}, year={2006} } @article{Whitbeck05, author={Caroline Whitbeck}, title={The Responsible Collection, Retention, Sharing, and Interpretation of Data}, journal={Online Ethics Center for Engineering and Science}, year= 2005, note={{http://onlineethics.org/reseth/mod/data.html}} } @article{White02, author={Kevin M. White}, title={Longevity Advances in High-IncomeCountries, 1955-96}, journal={Population and Development Review}, volume= 28, year= 2002, pages={59--76}, month={March}, number= 1 } @book{White82, author={Halbert L. White}, title={Asymptotic Theory For Econometricians}, publisher={Academic Press}, year= 1984, address={New York} } @book{White92, author={Halbert H. White}, title={Artificial Neural Networks, Approximation and Learning Theory}, publisher={Blackwell}, year= 1992, address={Cambridge, MA} } @article{Whittaker23, author={{Whittaker E.T.}}, title={On a New Method of Graduation}, journal={Proceedings of the Edinburgh Mathematical Society}, volume= 41, year= 1923, pages={63--75} } @article{WidKub96, author={Widmer, G. and Kubat, M.}, title={{Learning in the presence of concept drift and hidden contexts}}, journal={Machine Learning}, volume={23}, year={1996}, pages={69--101}, number={1}, publisher={Springer} } @unpublished{WieWilBel01, author={Janyce Wiebe and Theresa Wilson and Matthew Bell}, title={Identifying Collocations for Recognizing Opinions}, note={University of Pittsburgh wiebe, twilson, mbell@cs.pitt.edu}, year={2001}, month={April} } @article{WilBerNob01, author={B.P. Will and J.M. Berthelot and K.M. Nobrega and W. Flanagan and W.K. Evans}, title={Canada's Population Health Model (POHEM): A Tool for Performing Economic Evaluations of Cancer Control Interventions}, journal={European Journal of Cancer}, volume= 37, year= 2001, pages={1797--1804} } @article{WilGouBos02, author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer Bryce and Christopher Dye}, title={Estimates of world-wide distribution of child deaths from acute respiratory infections}, journal={The Lancet Infectious Diseases}, volume={2}, year={2002}, pages={25-32}, month={January} } @article{WilGouBos02, author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer Bryce and Christopher Dye}, title={Estimates of world-wide distribution of child deaths from acute respiratory infections}, journal={The Lancet Infectious Diseases}, volume={2}, year={2002}, pages={25-32}, month={January} } @article{WilHol07, author={Elizabeth Ty Wilde and Robinson Hollister}, title={How Close is Close Enough? Evaluating Propensity Score Matching Using Data from a Class-Size Reduction Experiment}, journal={Journal of Policy Analysis and Management}, volume={26}, year={2007}, number={3} } @techreport{Wilmoth93, author={John Wilmoth}, title={{Computational Methods for Fitting and Extrapolating the Lee-Carter Model of Mortality Change}}, institution={Department of Demography, University of California, Berkeley}, year= 1993 } @incollection{Wilmoth96, author={John R. Wilmoth}, title={Mortality Projections for Japan: A Comparison of Four Methods}, booktitle={Health and Mortality Among Elderly Populations}, publisher={Oxford University Press}, year= 1996, address={Oxford}, editor={G. 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a35da60f3f139a9a7cd6749353eb430f *vignettes/gkpubs.bib Zelig/README0000644000176000001440000003076112214411116012234 0ustar ripleyusers4.0-6 (August 25th, 2011): Experimental branch to improve the formula parser. 4.0-5 (August 25th, 2011): Stable release for R.13.1. Removed dependency on the 'iterators' package. This is part of a general move towards shrinking the size of Zelig's dependency list. To facilitate this change, the 'mi' object has been made more robust, and the 'zframe' helper object has been removed. For specifics, please refer to the CHANGES file 4.0-2 (May 16, 2011): Stable release for R 2.12,1. Major version update, and and addition of numerous API features. Core package now contains a mere 7 models. Dependencies having correspondingly been reduced to: MASS iterators survival methods For the missing models, please see the software packages: bivariate.zelig: bivaraite generalized linear regressions mixed.zelig: multilevel (mixed) generalized linear regressions multinomial.zelig: multinomial logit and probit regressions ordinal.zelig: ordinal logit and probit regressions survey.zelig: survey-weighted generalized linear models These models can be found on the Harvard IQSS website at: http://gking.harvard.edu/zelig/ Or via installations with: source('http://people.iq.harvard.edu/~mowen/install.R') 2.8-3 (May 29, 2007): Stable release for R 2.4.0-2.5.0. Fixed bugs in help.zelig(), and summary for multinomial logit, bivariate probit, and bivariate logit with multiple imputation. First version dependencies are as follows: MASS 7.2-34 boot 1.2-27 VGAM 0.7-1 MCMCpack 0.8-2 mvtnorm 0.7-5 survival 2.31 sandwich 2.0-0 zoo 1.2-1 coda 0.10-7 nnet 7.2-34 sna 1.4 2.8-2 (March 3, 2007): Stable release for R 2.4.0-2.4.1. Fixed bug in ARIMA simulation process. 2.8-1 (February 21, 2007): Stable release for R 2.4.0-2.4.1. Made setx() compatible with ordred factor variables (thanks to Mike Ward and Kirill Kalinin). First order dependencies as in version 2.8-1. 2.8-0 (February 12, 2007): Stable release for R 2.4.0-2.4.1. Released ARIMA models and network analysis models (least squares and logit) for sociomatrices. First version dependencies are as follows: MASS 7.2-31 boot 1.2-27 VGAM 0.7-1 MCMCpack 0.7-4 mvtnorm 0.7-5 survival 2.31 sandwich 2.0-0 zoo 1.2-1 coda 0.10-7 nnet 7.2-31 sna 1.4 2.7-5 (December 25, 2006): Stable release for R 2.4.0-2.4.1. Fixed bug related to {\tt names.default()}, summary for multiple imputation methods, and prediction for ordinal response models (thanks to Brian Ripley, Chris Lawrence, and Ian Yohai). 2.7-3 (November 9, 2006): Stable release for R 2.4.0. Fixed bugs related to R check. 2.7-2 (November 5, 2006): Stable release for R 2.4.0. Temporarily removed ARIMA models. 2.7-1 (November 3, 2006): Stable release for R 2.4.0. Made changes regarding the S4 classes in VGAM. The ARIMA model for time series data added by Justin Grimmer. First level dependencies are as follows: MASS 7.2-29 boot 1.2-26 VGAM 0.7-1 MCMCpack 0.7-4 mvtnorm 0.7-5 survival 2.29 sandwich 2.0-0 zoo 1.2-1 coda 0.10-7 2.6-5 (September 14, 2006): Stable release for R 2.3.0-2.3.1. Fixed bugs in bivariate logit, bivariate probit, multinomial logit, and model.matrix.multiple (related to version 2.6-4, but not previous versions, thanks to Chris Lawrence). First level dependencies are as follows: MASS 7.2-27.1 boot 1.2-26 VGAM 0.6-9 MCMCpack 0.7-1 mvtnorm 0.7-2 survival 2.28 sandwich 1.1-1 zoo 1.0-6 coda 0.10-5 2.6-4 (September 8, 2006): Stable release for R 2.3.0-2.3.1. Fixed bugs in {\tt setx()}, and bugs related to {\tt multiple}. Added instructions for installing Fortran tools for Intel macs. Added the RxC ecological inference model. 2.6-3 (June 19, 2006): Stable release for R 2.0.0-2.3.1. Fixed bug in VDC interface functions, and parse.formula(). 2.6-2 (June 7, 2006): Stable release for R 2.0.0-2.3.1. Removed R x C EI. Changed data = list() to data = mi() for multiply-imputed data frames. First level version compatabilities are as for version 2.6-1. 2.6-1 (April 29, 2006): Stable release for R 2.0.0-2.2.1. Fixed major bug in ordinal logit and ordinal probit expected value simulation procedure (does not affect Bayesian ordinal probit). (reported by Ian Yohai) Added the following ecological inference (EI) models: Bayesian hierarchical EI, Bayesian dynamic EI, and RxC EI. First level version compatabilties (at time of release) are as follows: MASS 7.2-24 boot 1.2-24 VGAM 0.6-8 MCMCpack 0.7-1 mvtnorm 0.7-2 survival 2.24 sandwich 1.1-1 zoo 1.0-6 coda 0.10-5 2.5-4 (March 16, 2006): Stable release for R 2.0.0-2.2.1. Fixed bug related to windows build. First-level dependencies are the same as in version 2.5-1. 2.5-3 (March 9, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs related to VDC GUI. First level dependencies are the same as in version 2.5-1. 2.5-2 (February 3, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs related to VDC GUI. First level dependencies are the same as in version 2.5-1. 2.5-1 (January 31, 2006): Stable release for R 2.0.0-2.2.1. Added methods for multiple equation models. Fixed bugs related to robust estimation and upgrade of sandwich and zoo packages. Revised setx() to use environments. Added current.packages() to retrieve version of packages upon which Zelig depends. First level version dependencies are as follows: MASS 7.2-24 boot 1.2-24 VGAM 0.6-7 mvtnorm 0.7-2 survival 2.20 sandwich 1.1-0 zoo 1.0-4 MCMCpack 0.6-6 coda 0.10-3 2.4-6 (October 27, 2005): Stable release for R 2.0.0-2.2.0. Fixed bug related to simulation for Bayesian Normal regression. 2.4-5 (October 18, 2005): Stable release for R 2.0.0-2.2.0. Updated installation instructions. 2.4-4 (September 29, 2005): Stable release for R 2.0.0-2.2.0. Fixed links for help.zelig(). 2.4-3 (September 29, 2005): Stable release for R 2.0.0-2.2.0. 2.4-2 (August 30, 2005): Stable release for R 2.0.0-2.1.1. Fixed bug in setx() related to as.factor() and I(). Streamlined qi.survreg(). 2.4-1 (August 15, 2005): Stable release for R 2.0.0-2.1.1. Added the following Bayesian models: factor analysis, mixed factor analysis, ordinal factor analysis, unidimensional item response theory, k-dimensional item response theory, logit, multinomial logit, normal, ordinal probit, Poisson, and tobit. Also fixed minor bug in formula (long variable names coerced to list). 2.3-2 (August 5, 2005): Stable release for R 2.0.0-2.1.1. Fixed bug in simulation procedure for lognormal model. 2.3-1 (August 4, 2005): Stable release for R 2.0.0-2.1.1. Fixed documentation errors related to model parameterization and code bugs related to first differences and conditional prediction for exponential, lognormal, and Weibull models. (reported by Alison Post) 2.2-4 (July 30, 2005): Stable release for R 2.0.0-2.1.1. Revised relogit, additing options for weightig in addition to prior correction. (reported by Martin Plöderl) 2.2-3 (July 24, 2005): Stable release for R 2.0.0-2.1.1. Fixed bug associated with robust standard errors for negative binomial. 2.2-2 (July 13, 2005): Stable release for R 2.0.0-2.1.1. Fixed bug in setx(). (reported by Ying Lu) 2.2-1 (July 11, 2005): Stable release for R 2.0.0-2.1.0. Revised ordinal probit to use MASS library. Added robust standard errors for the following regression models: exponential, gamma, logit, lognormal, least squares, negative binomial, normal (Gaussian), poisson, probit, and weibull. 2.1-4 (May 22, 2005): Stable release for R 1.9.1-2.1.0. Revised help.zelig() to deal with CRAN build of Windows version. Added recode of slots to lists in NAMESPACE. Revised install.R script to deal with changes to install.packages(). (reported by Dan Powers and Ying Lu) 2.1-3 (May 9, 2005): Stable release for R 1.9.1-2.1.0. Revised param.lm() function to work with bootstrap simulation. (reported by Jens Hainmueller) 2.1-2 (April 14, 2005): Stable release for R 1.9.1-2.1. Revised summary.zelig(). 2.1-1 (April 7, 2005): Stable release for R 1.9.1-2.1. Fixed bugs in NAMESPACE and summary.vglm(). 2.0-13 (March 11, 2005): Stable release for R 1.9.1-2.0.1. Fixed bugs in NAMESPACE and rocplot.Rd. 2.0-12 (February 20, 2005): Stable release for R 1.9.1-2.0.1. Added plot = TRUE option to rocplot(). 2.0-11 (January 14, 2005): Stable release for R 1.9.1-2.0.1. Changed class name for subsettted models from "multiple" to "strata", and modified affected functions. 2.0-10 (January 4, 2005): Stable release for R 1.9.1 and R 2.0.0. Fixed bug in simulation procedure for ordinal logit. (Reported by Ian Yohai.) 2.0-9 (October 21, 2004): Stable release for R 1.9.1 and R 2.0.0 (Linux only). Fixed bugs in NAMESPACE. 2.0-8 (October 18, 2004): Stable release for R 1.9.1 and R 2.0.0 (Linux only). Revised Zelig for submission to CRAN. 2.0-7 (October 14, 2004): Stable release for R 1.9.1 \emph{and} R 2.0.0 (Linux only). Fixed bugs in summary.zelig(), NAMESPACE, and assorted bugs related to new R release. Revised syntax for multiple equation models. 2.0-6 (October 4, 2004): Stable release for R 1.9.1. Fixed problem with NAMESPACE. 2.0-5 (September 25, 2004): Stable release for R 1.9.1. Changed installation procedure to source install.R from Zelig website. 2.0-4 (September 22, 2004): Stable release for R 1.9.1. Fixed typo in installation directions, implemented NAMESPACE, rationalized summary.zelig(), and tweaked documentation for least squares. 2.0-3 (September 1, 2004): Stable release for R 1.9.1. Fixed bug in conditional prediction for survival models. 2.0-2 (August 25, 2004): Stable release for R 1.9.1. Removed predicted values from ls. 2.0-1b (July 16, 2004): Stable release for R 1.9.1. MD5 checksum problem fixed. Revised plot.zelig() command to be a generic function with methods assigned by the model. Revised entire architecture to accept multiply imputed data sets with strata. Added functions to simplify adding models. Completely restructured reference manual. Fixed bugs related to conditional prediction in setx and summarizing strata in summary.zelig. 1.1-2 (June 24, 2004): Stable release for R 1.9.1 (MD5 checksum problem not fixed, but does not seem to cause problems). Fixed bug in help.zelig(). (reported by Michael L. Levitan) 1.1-1 (June 14, 2004): Stable release for R 1.9.0. Revised zelig() procedure to use zelig2model() wrappers, revised help.zelig() to use a data file with extension .url.tab, and revised setx() procedure to take a list of fn to apply to variables, and such that fn = NULL returns the entire model.matrix(). 1.0-8 (May 27, 2004): Stable release for R 1.9.0. Fixed bug in simulation procedure for survival models. (reported by Elizabeth Stuart) 1.0-7 (May 26, 2004): Stable release for R 1.9.0. Fixed bug in relogit simulation procedure. (reported by Tom Vanwellingham) 1.0-6 (May 11, 2004): Stable release for R 1.9.0. Fixed bug in setx.default, which had previously failed to ignore extraneous variables in data frame. (reported by Steve Purpura) 1.0-5 (May 7, 2004): Replaced relogit procedure with memory-efficient version. (reported by Tom Vanwellingham) 1.0-4 (April 19, 2004): Stable release for R 1.9.0. Added vcov.lm method; changed print for summary.relogit. 1.0-2 (April 16, 2004): Testing distribution for R 1.9.0. 1.0-1 (March, 23, 2004): Stable release for R 1.8.1. Zelig/build/0000755000176000001440000000000012217153457012462 5ustar ripleyusersZelig/build/vignette.rds0000644000176000001440000000044212217153457015021 0ustar ripleyusersN0 ӵl i]З`ж-nU)KF)ڍGMF BU\k'MJ8cO1 q]J77:[  iGCbԼ+_?Jz+K(@bJ2zLz90W'7 License: GPL (>= 2) Title: Everyone's Statistical Software Author: Matt Owen , Kosuke Imai , Gary King , Olivia Lau Description: Zelig is an easy-to-use program that can estimate, and help interpret the results of, an enormous range of statistical models. It literally is ``everyone's statistical software'' because Zelig's simple unified framework incorporates everyone else's (R) code. We also hope it will become ``everyone's statistical software'' for applications and teaching, and so have designed Zelig so that anyone can easily use it or add their programs to it. Zelig also comes with infrastructure that facilitates the use of any existing method, such as by allowing multiply imputed data for any model, and mimicking the program Clarify (for Stata) that takes the raw output of existing statistical procedures and translates them into quantities of direct interest. Version: 4.2-1 URL: http://gking.harvard.edu/zelig Date: 2013-09-12 Depends: R (>= 2.14), boot, MASS, methods, sandwich Suggests: Amelia, mvtnorm, Formula, gee, survey, survival, systemfit, MatchIt, MCMCpack, coda Packaged: 2013-09-20 23:15:59 UTC; jhonaker NeedsCompilation: no Repository: CRAN Date/Publication: 2013-09-21 08:14:26 Zelig/man/0000755000176000001440000000000012217153457012136 5ustar ripleyusersZelig/man/sna.ex.Rd0000644000176000001440000000063112061700507013610 0ustar ripleyusers\name{sna.ex} \alias{sna.ex} \title{Simulated Example of Social Network Data} \description{ This data set contains five sociomatrices of simulated data social network data.} \usage{data(sna.ex)} \format{ Each variable in the dataset is a 25 by 25 matrix of simulated social network data. The matrices are labeled "Var1", "Var2", "Var3", "Var4", and "Var5". } \source{fictitious} \keyword{datasets} Zelig/man/repl.default.Rd0000644000176000001440000000063212061700507015002 0ustar ripleyusers\name{repl.default} \alias{repl.default} \title{Default Method for Replicating Statistics} \usage{ \method{repl}{default}(object, data=NULL, ...) } \arguments{ \item{object}{an object to replicate} \item{data}{a data.frame} \item{...}{ignored parameters} } \value{ a replicated object } \description{ Replicate a simulation } \author{ Kosuke Imai and Olivia Lau \email{mowen@iq.harvard.edu} } Zelig/man/TexCite.Rd0000644000176000001440000000051112061700507013756 0ustar ripleyusers\name{TexCite} \alias{TexCite} \title{Get a TeX-style Citation} \usage{ TexCite(model) } \arguments{ \item{model}{a character-string specifying the name of the Zelig model of which to describe in TeX-style} } \value{ a string to be rendered as part of a LaTeX-style document } \description{ Get a TeX-style Citation } Zelig/man/repl.Rd0000644000176000001440000000051112061700507013353 0ustar ripleyusers\name{repl} \alias{repl} \title{Generic Method for Replicating Data} \usage{ repl(object, ...) } \arguments{ \item{object}{a 'zelig' object} \item{...}{parameters} } \value{ a replicated object } \description{ Generic Method for Replicating Data } \author{ Kosuke Imai and Olivia Lau \email{mowen@iq.harvard.edu} } Zelig/man/homerun.Rd0000644000176000001440000000233612061700507014075 0ustar ripleyusers\name{homerun} \alias{homerun} \docType{data} \title{Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.} \description{ Game-by-game information for the 1998 season for Mark McGwire and Sammy Sosa. Data are a subset of the dataset provided in Simonoff (1998). } \usage{data(homerun)} \format{ A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatus", "player") and 326 observations. \describe{ \item{\code{gameno}}{an integer variable denoting the game number} \item{\code{month}}{a factor variable taking with levels "March" through "September" denoting the month of the game} \item{\code{homeruns}}{an integer vector denoting the number of homeruns hit in that game for that player} \item{\code{playerstatus}}{an integer vector equal to "0" if the player played in the game, and "1" if they did not.} \item{\code{player}}{an integer vector equal to "0" (McGwire) or "1" (Sosa)} } } \source{\url{http://www.amstat.org}} \references{Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' \emph{Journal of Statistics Education} 6(3). Data used are a subset of the data in the article.} \keyword{datasets} Zelig/man/describe.Rd0000644000176000001440000000051412061700507014174 0ustar ripleyusers\name{describe} \alias{describe} \title{Method to describe a model to Zelig} \usage{ describe(...) } \arguments{ \item{...}{parameters which are typically ignored} } \value{ a list to be processed by `as.description' } \description{ Method to describe a model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/parseFormula.Rd0000644000176000001440000000077012061700507015060 0ustar ripleyusers\name{parseFormula} \alias{parseFormula} \title{Parse Zelig-style Formulae} \usage{ parseFormula(obj, data = NULL) } \arguments{ \item{obj}{a list or formula} \item{data}{the data set associated with the formula object} } \value{ an object of type "parseFormula". This object has slots specifying: } \description{ Zelig uses three distinct types of formulae. This method is a re-design of the Zelig function \code{parse.formula}. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/free2.Rd0000644000176000001440000000776512061700507013436 0ustar ripleyusers\name{free2} \alias{free2} \title{Freedom of Speech Data} \usage{data(free2)} \description{ Selection of individual-level survey data for freedom of speech. } \details{ A table with 150 observations and 12 variables. \itemize{ \item{sex}{1 for men and 0 for women} \item{age}{Age of respondent in years} \item{educ}{Levels of education, coded as a numeric variable with \itemize{ \item{1}{No formal education} \item{2}{Less than primary school education} \item{3}{Completed primary school} \item{4}{Completed secondary school} \item{5}{Completed high school} \item{6}{Completed college} \item{7}{Completed post-graduate degree} } } \item{country}{Character strings consisting of "Oceana", "Eurasia", and "Eastasia", after Orwell's \emph{1984}.} \item{y}{Self assessment (see below).} \item{v1-v6}{Response to vignettes (see below).} } Survey respondents were asked in almost the same language for a self-assessment and for an assessment of several hypothetical persons described by written vignettes. The self assessment (\code{self}, in the data set), "How free do you think [name/you] [is/are] to express [him-her/your]self without fear of government reprisal?" was first asked of the survey respondent with respect to him or herself, and then after each of vignette. The possible response categories are: \itemize{ \item{1}{Completely free} \item{2}{Very free} \item{3}{Moderately free} \item{4}{Slightly free} \item{5}{Not free at all} } The vignettes, ordered from most free to least free, are: \itemize{ \item{vign1}{[Kay] does not like many of the government's policies. She frequently publishes her opinion in newspapers, criticizing decisions by officials and calling for change. She sees little reason these actions could lead to government reprisal.} \item{vign2}{[Michael] disagrees with many of the government's policies. Though he knows criticism is frowned upon, he doesn't believe the government would punish someone for expressing critical views. He makes his opinion known on most issues without regard to who is listening.} \item{vign3}{[Bob] has political views at odds with the government. He has heard of people occasionally being arrested for speaking out against the government, and government leaders sometimes make political speeches condemning those who criticize. He sometimes writes letters to newspapers about politics, but he is careful not to use his real name.} \item{vign4}{[Connie] does not like the government's stance on many issues. She has a friend who was arrested for being too openly critical of governmental leaders, and so she avoids voicing her opinions in public places.} \item{vign5}{[Vito] disagrees with many of the government's policies, and is very careful about whom he says this to, reserving his real opinions for family and close friends only. He knows several men who have been taken away by government officials for saying negative things in public.} \item{vign6}{[Sonny] lives in fear of being harassed for his political views. Everyone he knows who has spoken out against the government has been arrested or taken away. He never says a word about anything the government does, not even when he is at home alone with his family. } } } \references{ \emph{WHO's World Health Survey} by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana, Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria Villanueva Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming. copy at http://wand.stanford.edu/research/anchors-jss.pdf Gary King and Jonathan Wand. "Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes," Political Analysis, 15, 1 (Winter, 2007): Pp. 46-66, copy at http://gking.harvard.edu/files/abs/c-abs.shtml. } \keyword{datasets} Zelig/man/Median.Rd0000644000176000001440000000054412061700507013614 0ustar ripleyusers\name{Median} \alias{Median} \title{Compute the Statistical Median of a Vector} \usage{ Median(x, na.rm = NULL) } \arguments{ \item{x}{a vector of numeric or ordered values} \item{na.rm}{ignored} } \value{ the median of the vector } \description{ Compute the Statistical Median of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/simulations.plot.Rd0000644000176000001440000000235012061700507015740 0ustar ripleyusers\name{simulations.plot} \alias{simulations.plot} \title{Plot Quantities of Interest in a Zelig-fashion} \usage{ simulations.plot(y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, axisnames=TRUE) } \arguments{ \item{y}{A matrix or vector of simulated results generated by Zelig, to be graphed.} \item{y1}{For comparison of two sets of simulated results at different choices of covariates, this should be an object of the same type and dimension as y. If no comparison is to be made, this should be NULL.} \item{xlab}{Label for the x-axis.} \item{ylab}{Label for the y-axis.} \item{main}{Main plot title.} \item{col}{A vector of colors. Colors will be used in turn as the graph is built for main plot objects. For nominal/categorical data, this colors renders as the bar color, while for numeric data it renders as the background color.} \item{line.col}{A vector of colors. Colors will be used in turn as the graph is built for line color shading of plot objects.} \item{axisnames}{a character-vector, specifying the names of the axes} } \value{ nothing } \description{ Various graph generation for different common types of simulated results from Zelig } \author{ James Honaker } Zelig/man/link.Rd0000644000176000001440000000062612061700507013355 0ustar ripleyusers\name{link} \alias{link} \title{Method for extracting the link function from 'parameters' objects} \usage{ link(param) } \arguments{ \item{param}{a 'parameters' object} } \value{ the link function specified by the `param' function for the given Zelig model } \description{ Method for extracting the link function from 'parameters' objects } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/summarize.Rd0000644000176000001440000000063312061700507014432 0ustar ripleyusers\name{summarize} \alias{summarize} \title{Generic methonf for summarizing simualted quantities of interest} \usage{ summarize(obj) } \arguments{ \item{obj}{a \code{qi} object, storing simulations of quantities of interest} } \value{ a \code{summarized.qi} object } \description{ Generic methonf for summarizing simualted quantities of interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/find.match.Rd0000644000176000001440000000176212061700507014435 0ustar ripleyusers\name{find.match} \alias{find.match} \title{Find a Partial or Exact Match from a Vector of Strings Searches a vector of character-string, and returns the best match.} \usage{ find.match(needle, haystack, fail = NA) } \arguments{ \item{needle}{a character-string to search for in the} \item{haystack}{a vector of character-strings} \item{fail}{the value to return in case no match is found. Defaults to NA} } \value{ the best-matched string or NA } \description{ Find a Partial or Exact Match from a Vector of Strings Searches a vector of character-string, and returns the best match. } \details{ ``find.match'' attempts to use several common matching functions in an order that sequentially prefers less strict matching, until a suitable match is found. If none is found, then return the value of the ``fail'' parameter (defaults to NA). The functions used for matching are: ``match'', ``charmatch'', and finally ``grep''. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.relogit2.Rd0000644000176000001440000000113012061700507015235 0ustar ripleyusers\name{param.relogit2} \alias{param.relogit2} \title{Estimate Parameters for the ``relogit'' Zelig Mdoel} \usage{ \method{param}{relogit2}(obj, num, x, ...) } \arguments{ \item{obj}{a zelig object containing the fitted model} \item{num}{an integer specifying the number of simulations to compute} \item{x}{ideally we should be able to remove this parameter} \item{...}{unspecified parameters} } \value{ a list specifying important parameters for the ``relogit'' model } \description{ Returns estimates on parameters, as well as, specifying link and inverse-link functions. } Zelig/man/getResponseTerms.Rd0000644000176000001440000000062312061700507015726 0ustar ripleyusers\name{getResponseTerms} \alias{getResponseTerms} \title{Get Response Terms from a Zelig-style Formula} \usage{ getResponseTerms(x, ...) } \arguments{ \item{x}{a formula or list of formulae} \item{...}{ignored parameters} } \value{ a character-vector specifying a the of response terms in this formula } \description{ This method acquires the response variables from Zelig-style input. } Zelig/man/parseFormula.formula.Rd0000644000176000001440000000104012061700507016513 0ustar ripleyusers\name{parseFormula.Formula} \alias{parseFormula.Formula} \title{Parse ``Formula''-style Zelig Formulae} \usage{ \method{parseFormula}{Formula}(obj, data=NULL) } \arguments{ \item{obj}{a list of formulae} \item{data}{a data frame} } \value{ an object of type ``parseFormula'' } \description{ This method parses a ``Formula''-style Zelig formula. This is to support the ``Formula'' object. It seems like it has the right idea when it comes to expressing multiple responses. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/description.Rd0000644000176000001440000000170612061700507014743 0ustar ripleyusers\name{description} \alias{description} \title{Constructor for the 'description' class} \usage{ description(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"), year = NULL, model = "", text = "", url = "", category = NULL) } \arguments{ \item{authors}{a character-vector of author names} \item{year}{a numeric specifying the year} \item{model}{a character-string specifying model name} \item{text}{a character-string specifying the title of the model. This typically includes more exact information than 'model'. E.g., for the 'logit' the title 'Logistic Regression for Dichotomous Variables' would be a suitable text parameter.} \item{url}{a character-string specifying the model's software page} \item{category}{deprecated until data-verse bindings are reevaluated} } \value{ an object of type 'description' } \description{ Constructor for the 'description' class } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/z.Rd0000644000176000001440000000121512110047000012647 0ustar ripleyusers\name{z} \alias{z} \title{Return value for a zelig2-function} \usage{ z(.function, ..., .hook = NULL) } \arguments{ \item{.function}{a function} \item{...}{a set of parameters to be evaluated symbolically} \item{.hook}{a function to be applied after the external, model-fitting function is called} } \value{ a ``z'' object which specifies how to evaluate the fitted model } \description{ This is an API-function that bridges a model-fitting function with a zelig interface. } \note{ This is used internally by Zelig-dependent packages to instruct Zelig how to evaluate the function call to a particular statistical model. } Zelig/man/depends.on.zelig.Rd0000644000176000001440000000106112061700507015560 0ustar ripleyusers\name{depends.on.zelig} \alias{depends.on.zelig} \title{Whether a Statistical Package Depends on the Zelig Software Suite} \usage{ depends.on.zelig(package = "") } \arguments{ \item{package}{a character-string representing a package name} } \value{ whether the package lists Zelig as a dependency in its DESCRIPTION } \description{ Whether a Statistical Package Depends on the Zelig Software Suite } \note{ This function is used primarily internally to determine whether a a package is contributing a function to the Zelig software suite } Zelig/man/param.negbinom.Rd0000644000176000001440000000102012061700507015302 0ustar ripleyusers\name{param.negbinom} \alias{param.negbinom} \title{Param Method for the 'negbinom' Zelig Model} \usage{ \method{param}{negbinom}(obj, num=1000, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the 'negbinom' Zelig Model } \note{ This method is used by the 'negbinom' Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/terms.zelig.Rd0000644000176000001440000000056612061700507014666 0ustar ripleyusers\name{terms.zelig} \alias{terms.zelig} \title{Model Terms for a Zelig Object} \usage{ \method{terms}{zelig}(x, ...) } \arguments{ \item{x}{a \code{zelig} object} \item{...}{forwarded parameters} } \value{ terms of the original fitted model } \description{ This method simply extracts the model terms for the fitted model passed to the \code{zelig} function. } Zelig/man/param.probit.Rd0000644000176000001440000000100412061700507015005 0ustar ripleyusers\name{param.probit} \alias{param.probit} \title{Param Method for the 'probit' Zelig Model} \usage{ \method{param}{probit}(obj, num=1000, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the 'probit' Zelig Model } \note{ This method is used by the 'probit' Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/mix.Rd0000644000176000001440000000100012061700507013200 0ustar ripleyusers\name{mix} \alias{mix} \title{Produce All Combinations of a Set of Lists} \usage{ mix(...) } \arguments{ \item{...}{a set of lists to mix together} } \value{ all the combinations of the lists with repetition } \description{ Produce All Combinations of a Set of Lists } \note{ This function is used internall by the 'mi' constructors in order to produce the complete set of combinations of data-frames and factors by to subset the data-frames. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/setx.default.Rd0000644000176000001440000000117412061700507015025 0ustar ripleyusers\name{setx.default} \alias{setx.default} \title{Set explanatory variables} \usage{ \method{setx}{default}(obj, fn=NULL, data=NULL, cond=FALSE, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{fn}{a list of key-value pairs specifying which function apply to columns of the keys data-types} \item{data}{a data.frame} \item{cond}{ignored} \item{...}{parameters specifying what to explicitly set each column as. This is used to produce counterfactuals} } \value{ a 'setx' object } \description{ Set explanatory variables } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Kosuke Imai, and Olivia Lau } Zelig/man/robust.gee.hook.Rd0000644000176000001440000000144112110047000015413 0ustar ripleyusers\name{robust.gee.hook} \alias{robust.gee.hook} \title{Classify Fitted Object as Naive or Robust} \usage{ robust.gee.hook(obj, Zall, Call, robust, ...) } \arguments{ \item{obj}{a \code{zelig} object} \item{Zall}{the call made to the \code{zelig} function} \item{Call}{the call made to the external model} \item{robust}{a logical specifying whether to use the naive or robust covariance matrix} \item{...}{ignored parameters} } \value{ a \code{zelig} object with the additional class \code{gee.robust} or \code{gee.naive} } \description{ This hook is ran after the call to the external mode. It sets the class of the object (in addition to its other designations) as 'gee.naive' or 'gee.robust' depending on the value of the \code{robust} parameter. } \author{ Skyler } Zelig/man/mexico.Rd0000644000176000001440000000116512061700507013703 0ustar ripleyusers\name{mexico} \alias{mexico} \title{Voting Data from the 1988 Mexican Presidental Election} \description{ This dataset contains voting data for the 1988 Mexican presidential election. } \usage{data(mexico)} \format{A table containing 33 variables and 1,359 observations.} \source{ICPSR} \references{ King, Gary, Michael Tomz and Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp. 341-355. King, Tomz and Wittenberg. ICPSR Publication Related Archive, 1255. } \keyword{datasets} Zelig/man/summary.glm.robust.Rd0000644000176000001440000000102712061700507016204 0ustar ripleyusers\name{summary.glm.robust} \alias{summary.glm.robust} \title{Summary of Generalized Linear Model with Robust Error Estimates} \usage{ \method{summary}{glm.robust}(object, ...) } \arguments{ \item{object}{a ``glm.robust'' fitted model} \item{...}{parameters to pass to the standard ``summary.glm'' method} } \value{ a object of type ``summary.glm.robust'' and ``summary.glm'' } \description{ Returns summary of a glm model with robust error estimates. This only slightly differs from how the standard GLM's behave. } Zelig/man/describe.gamma.Rd0000644000176000001440000000052312061700507015255 0ustar ripleyusers\name{describe.gamma} \alias{describe.gamma} \title{Describe the \code{gamma} model to Zelig} \usage{ \method{describe}{gamma}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list of important information } \description{ Describe the \code{gamma} model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/bootstrap.Rd0000644000176000001440000000155612061700507014440 0ustar ripleyusers\name{bootstrap} \alias{bootstrap} \title{Generic Method for ``bootstrap''} \usage{ bootstrap(obj, ...) } \arguments{ \item{obj}{a fitted model object that will be used to produce boot-strapped parameters. This object usually inherits the class ``glm'' or ``lm'' object} \item{...}{unspecified parameters} } \value{ a list with the ``alpha'' and ``beta'' slots set. Note that ``alpha'' corresponds to ancillary parameters and ``beta'' corresponds to systematic components of the model } \description{ This method is intended to be overried by statistical models that would like to support statistical bootstrapping. } \note{ This method has private memory storage and can reference the objects: ``.fitted'', ``.data'', ``.call'', ``.env'', despite having no declaration in the argument list. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Zelig-package.Rd0000644000176000001440000000251712061700507015064 0ustar ripleyusers\docType{package} \name{Zelig-package} \alias{Zelig} \alias{Zelig-package} \title{Zelig Everyone's Statistical Software} \description{ Zelig is an easy-to-use program that can estimate, and help interpret the results of, an enormous range of statistical models. It literally is ``everyone's statistical software'' because Zelig's simple unified framework incorporates everyone else's (R) code. We also hope it will become ``everyone's statistical software'' for applications and teaching, and so have designed Zelig so that anyone can easily use it or add their programs to it. Zelig also comes with infrastructure that facilitates the use of any existing method, such as by allowing multiply imputed data for any model, and mimicking the program Clarify (for Stata) that takes the raw output of existing statistical procedures and translates them into quantities of direct interest. } \details{ \tabular{ll}{ Package: \tab Zelig\cr Version: \tab 4.0-11\cr Date: \tab 2012-10-28\cr Depends: \tab R (>= 2.14), boot, MASS, methods, sandwich, survival\cr Suggests: \tab mvtnorm, Formula \cr License: \tab GPL version 2 or newer\cr URL: \tab http://gking.harvard.edu/zelig\cr } } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Kosuke Imai, Olivia Lau, and Gary King } \seealso{ zelig setx sim } \keyword{package} Zelig/man/ZeligDescribeModel.Rd0000644000176000001440000000107512061700507016113 0ustar ripleyusers\name{ZeligDescribeModel} \alias{ZeligDescribeModel} \title{Produce a 'description' Object from the Name of a Model} \usage{ ZeligDescribeModel(model.name) } \arguments{ \item{model.name}{a character-string specifying a Zelig model} } \value{ a 'description' object specified by the 'model.name' parameter. This object is created by executing the specified Zelig models' 'describe' function } \description{ Produce a 'description' Object from the Name of a Model } \note{ The 'description' object is a list-style object containing citation information } Zelig/man/kmenta.Rd0000644000176000001440000000117212061700507013674 0ustar ripleyusers\name{kmenta} \alias{kmenta} \title{Simulation Data for model Three-Stage Least Square (threesls) that corresponds to method 3SLS of systemfit} \description{ Dataframe contains 20 annual observations of a supply/demand model with 5 variables. Columns are q=Food consumption per capita, p=Ratio of food price to general consumer prices, d=Disposable income in contstant dollars, f=Ratio of preceding year's prices received by farmers to general consumer prices, a=Time index. } \usage{data(kmenta)} \format{ A table containing 5 variables ("q", "p", "d", "f","a") and 20 observations. } \keyword{datasets} Zelig/man/klein.Rd0000644000176000001440000000142212061700507013515 0ustar ripleyusers\name{klein} \alias{klein} \title{Simulation Data for model Two-Stage Least Square (twosls) that corresponds to method 2SLS of systemfit} \description{ Dataframe contains annual observations of US economy from 1920 to 1940. The columns are, Year, C=Consumption, P=Corporate profits, P1=Previous year corporate profit,Wtot=Total wage, Wp=Private wage bill, Wg=Government wage bill,I=Investment, K1=Previous year capital stock,X=GNP,G=Government spending, T=Taxes, X1=Previous year GNP, Tm=Year-1931. } \usage{data(klein)} \format{ A table containing 14 variables ("year", "C", "P", "P1","Wtot", "Wp", "Wg", "I", "K1","X", "G", "T", "X1", "Tm") and 21 observations. } \source{http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm} \keyword{datasets} Zelig/man/SupremeCourt.Rd0000644000176000001440000000200712061700507015050 0ustar ripleyusers\name{SupremeCourt} \alias{SupremeCourt} \title{U.S. Supreme Court Vote Matrix} \description{ This dataframe contains a matrix votes cast by U.S. Supreme Court justices in all cases in the 2000 term. } \usage{data(SupremeCourt)} \format{ The dataframe has contains data for justices Rehnquist, Stevens, O'Connor, Scalia, Kennedy, Souter, Thomas, Ginsburg, and Breyer for the 2000 term of the U.S. Supreme Court. It contains data from 43 non-unanimous cases. The votes are coded liberal (1) and conservative (0) using the protocol of Spaeth (2003). The unit of analysis is the case citation (ANALU=0). We are concerned with formally decided cases issued with written opinions, after full oral argument and cases decided by an equally divided vote (DECTYPE=1,5,6,7).} \source{ Harold J. Spaeth (2005). ``Original United States Supreme Court Database: 1953-2004 Terms.'' . } \keyword{datasets} Zelig/man/qi.summarize.Rd0000644000176000001440000000214112061700507015036 0ustar ripleyusers\name{qi.summarize} \alias{qi.summarize} \title{Constructor for QI Summarized Class This class takes an arbitrary number of the _same_ type of quantities of interest labels them, then merges them into one simple printable block. In particular, this class determines which print function to use based on the the type and size od data to be passed to the print function.} \usage{ qi.summarize(title, x, ...) } \arguments{ \item{title}{a character-string specifying the title of the QI} \item{x}{a list of summarized quantities of interest} \item{...}{additional quantities of interest (the parameter that titles these will be used as the name of the data.frame} } \value{ the list of QI's (invisibly) } \description{ Constructor for QI Summarized Class This class takes an arbitrary number of the _same_ type of quantities of interest labels them, then merges them into one simple printable block. In particular, this class determines which print function to use based on the the type and size od data to be passed to the print function. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/eidat.Rd0000644000176000001440000000051212061700507013500 0ustar ripleyusers\name{eidat} \alias{eidat} \title{Simulation Data for Ecological Inference} \description{ This dataframe contains a simulated data set to illustrate the models for ecological inference. } \usage{data(eidat)} \format{ A table containing 4 variables ("t0", "t1", "x0", "x1") and 10 observations. } \keyword{datasets} Zelig/man/linkinv.Rd0000644000176000001440000000110412061700507014062 0ustar ripleyusers\name{linkinv} \alias{linkinv} \title{Method for extracting the inverse link function from 'parameters' objects} \usage{ linkinv(param) } \arguments{ \item{param}{a 'parameters' object} } \value{ the inverse link function specified by the 'param' function for the given Zelig model } \description{ Returns the inverse link function of a ``parameters'' object. If the model's developer did not specify one (but did specify a link function) this function returns a numerical approximation of the link function. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Max.Rd0000644000176000001440000000051612061700507013143 0ustar ripleyusers\name{Max} \alias{Max} \title{Compute the Maximum Value of a Vector} \usage{ Max(x, na.rm = NULL) } \arguments{ \item{x}{a numeric or ordered vector} \item{na.rm}{ignored} } \value{ the maximum value of the vector } \description{ Compute the Maximum Value of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/rocplot.Rd0000644000176000001440000000464712061700507014111 0ustar ripleyusers\name{rocplot} \alias{rocplot} \title{Receiver Operator Characteristic Plots} \usage{ rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100), lty1="solid", lty2="dashed", lwd1=par("lwd"), lwd2=par("lwd"), col1=par("col"), col2=par("col"), main="ROC Curve", xlab = "Proportion of 1's Correctly Predicted", ylab="Proportion of 0's Correctly Predicted", plot = TRUE, ... ) } \arguments{ \item{y1}{response variable for the first model} \item{y2}{response variable for the second model} \item{fitted1}{fitted values for the first model. These values may represent either the in-sample or out-of-sample fitted values} \item{fitted2}{fitted values for the second model} \item{cutoff}{A vector of cut-off values between 0 and 1, at which to evaluate the proportion of 0s and 1s correctly predicted by the first and second model. By default, this is 100 increments between 0 and 1 inclusive} \item{lty1}{the line type of the first model (defaults to 'line')} \item{lty2}{the line type of the second model (defaults to 'dashed')} \item{lwd1}{the line width of the first model (defaults to 1)} \item{lwd2}{the line width of the second model (defaults to 1)} \item{col1}{the color of the first model (defaults to 'black')} \item{col2}{the color of the second model (defaults to 'black')} \item{main}{a title for the plot (defaults to "ROC Curve")} \item{xlab}{a label for the X-axis} \item{ylab}{a lavel for the Y-axis} \item{plot}{whether to generate a plot to the selected device} \item{\dots}{additional parameters to be passed to the plot} } \value{ if plot is TRUE, rocplot simply generates a plot. Otherwise, a list with the following is produced: \item{roc1}{a matrix containing a vector of x-coordinates and y-coordinates corresponding to the number of ones and zeros correctly predicted for the first model.} \item{roc2}{a matrix containing a vector of x-coordinates and y-coordinates corresponding to the number of ones and zeros correctly predicted for the second model.} \item{area1}{the area under the first ROC curve, calculated using Reimann sums.} \item{area2}{the area under the second ROC curve, calculated using Reimann sums.} } \description{ The 'rocplot' command generates a receiver operator characteristic plot to compare the in-sample (default) or out-of-sample fit for two logit or probit regressions. } Zelig/man/model.matrix.multiple.Rd0000644000176000001440000000130512061700507016650 0ustar ripleyusers\name{model.matrix.multiple} \alias{model.matrix.multiple} \title{Create Design Matrix of a \code{multiple} Object} \usage{ \method{model.matrix}{multiple}(object,data,shape="compact",eqn=NULL,...) } \arguments{ \item{object}{an object of type \code{multiple}. This represents a Zelig 3.5 formula} \item{data}{a \code{data.frame}} \item{shape}{a character-string specifying the shape of the matrix} \item{eqn}{an integer specifying the number of equations} \item{...}{ignored parameters} } \description{ This method is used to generate a \code{model.matrix} adhering to the specifications in the help document "model.matrix". } \note{ This method is scheduled to be deprecated. } Zelig/man/combine.Rd0000644000176000001440000000101412061700507014024 0ustar ripleyusers\name{combine} \alias{combine} \title{Produce All Combinations of a Set of Lists} \usage{ combine(...) } \arguments{ \item{...}{a set of lists to mix together} } \value{ all the combinations of the lists with repetition } \description{ Produce All Combinations of a Set of Lists } \note{ This function is used internall by the 'mi' constructors in order to produce the complete set of combinations of data-frames and factors by to subset the data-frames. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/sanction.Rd0000644000176000001440000000134012061700507014230 0ustar ripleyusers\name{sanction} \alias{sanction} \title{Multilateral Economic Sanctions} \description{ Data on bilateral sanctions behavior for selected years during the general period 1939-1983. This data contains errors that have since been corrected. Please contact Lisa Martin before using this data for publication. } \usage{data(sanction)} \format{A table containing 8 variables ("mil", "coop", "target", "import", "export", "cost", "num", and "ncost") and 78 observations. For full variable description, see Martin, 1992. } \source{Martin, 1992} \references{ Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral Economic Sanctions}, Princeton: Princeton University Press. } \keyword{datasets} Zelig/man/multilevel.Rd0000644000176000001440000000106512061700507014600 0ustar ripleyusers\name{multilevel} \alias{multilevel} \title{Multilevel} \usage{ multilevel(tt, data, mode, eqn, ...) } \arguments{ \item{tt}{a terms object} \item{data}{a \code{data.frame}} \item{mode}{???} \item{eqn}{an integer specifying the number of equations in a model} \item{...}{ignored parameters} } \value{ a list with the "terms" attribute specified } \description{ This function currently has no documentation, but is essential in Zelig 3.5's implementation of formulae. } \author{ Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi } Zelig/man/param.Rd0000644000176000001440000000410712061700507013516 0ustar ripleyusers\name{param} \alias{param} \title{Generic Method for Simulating Ancillary/Auxillary Parameters of Zelig Models} \usage{ param(obj, num, ...) } \arguments{ \item{obj}{a \code{zelig} object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{optional parameters which will likely be ignored} } \value{ The main purpose of the \code{param} function is to return a list of key-value pairs, specifuing information that should be shared between the \code{qi} function and the fitted statistical model (produced by the \code{zelig2} function. This list can contain the following entries: \item{\code{simulations}}{specifies a set of simulated parameters used to describe the statistical model's underlying distribution} \item{\code{alpha}}{specifies the fixed (non-simulated) ancillary parameters used by the statistical model's underlying distribution} \item{\code{family}}{specifies a family object used to implicitly define the \code{link} and \code{linkinv} functions. That is, this specifies the "link" and "inverse link" functions of generalized linear models} \item{\code{link}}{specifies the \code{link} function to be used. This parameter is largely unimportant compared to the "inverse link" function} \item{\code{linkinv}}{specifies the \code{linkinv} function to be used.} } \description{ The \code{param} method is used by developers to specify simulated and fixed ancillary parameters of the Zelig statistical model. That is, this method is used between the \code{zelig2} function and the \link{qi} as a helper function that specifies all the necessary details needed to simulate quantities of interest, given the fitted statistical model produced by the \code{zelig2} function. } \note{ The 'param' function is a method meant to be overloaded by Zelig Developers } \examples{ param.some.model <- function (obj, num, ...) { list( simulations = NULL, alpha = NULL, link = NULL, linkinv = NULL, fam = NULL ) } } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/splitUp.Rd0000644000176000001440000000150612061700507014056 0ustar ripleyusers\name{splitUp} \alias{splitUp} \title{Split a List into Two Lists This functions takes any list, and splits into two lists - one containing the values of arguments with specifically specified values and those without specified values.} \usage{ splitUp(args) } \arguments{ \item{args}{a list} } \value{ a list containing two entries: the key-value paired entires (titled wordful) and the unkeyed entried (titled wordless) } \description{ Split a List into Two Lists This functions takes any list, and splits into two lists - one containing the values of arguments with specifically specified values and those without specified values. } \note{ This function is a good candidate for deprecation } \examples{ #list(wordful = list(x=1, y=2), wordless=list(2, "red")) } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/describe.logit.Rd0000644000176000001440000000052012061700507015306 0ustar ripleyusers\name{describe.logit} \alias{describe.logit} \title{Describe a `logit' model to Zelig} \usage{ \method{describe}{logit}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe a `logit' model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.sim.Rd0000644000176000001440000000057212061700507014343 0ustar ripleyusers\name{print.sim} \alias{print.sim} \title{Print values of `sim' objects} \usage{ \method{print}{sim}(x, ...) } \arguments{ \item{x}{a `sim' object (ignored)} \item{...}{ignored parameters} } \value{ NULL (invisibly) } \description{ This function is currently unimplemented, and included for future development } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.zelig.Rd0000644000176000001440000000053312061700507014662 0ustar ripleyusers\name{print.zelig} \alias{print.zelig} \title{Print values of ``zelig'' objects} \usage{ \method{print}{zelig}(x, ...) } \arguments{ \item{x}{a `zelig' object} \item{...}{ignored parameters} } \value{ the `zelig' object (invisibly) } \description{ Print the zelig object as a list } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/structuralToReduced.Rd0000644000176000001440000000061112061700507016421 0ustar ripleyusers\name{structuralToReduced} \alias{structuralToReduced} \title{Transform the Multilevel's Structural Formulas Into Reduced Form} \usage{ structuralToReduced(f) } \arguments{ \item{f}{a list of formulas} } \value{ a formula in reduced form } \description{ Transform the Multilevel's Structural Formulas Into Reduced Form } \author{ Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau } Zelig/man/is.formula.Rd0000644000176000001440000000060012061700507014467 0ustar ripleyusers\name{is.formula} \alias{is.formula} \title{Whether an Object is a Formula} \usage{ is.formula(x) } \arguments{ \item{x}{an object} } \value{ a logical specifying whether an object is a formula } \description{ This is a boolean-check to see whether an object is a formula. } \note{ This will not be shared in the Zelig/ZeligFormulae namespace. } \author{ Matt Owen } Zelig/man/setx.MI.Rd0000644000176000001440000000143612061700507013707 0ustar ripleyusers\name{setx.MI} \alias{setx.MI} \title{Set Explanatory Variables for Multiply Imputed Data-sets This function simply calls setx.default once for every fitted model within the 'zelig.MI' object} \usage{ \method{setx}{MI}(obj, ..., data=NULL) } \arguments{ \item{obj}{a 'zelig' object} \item{...}{user-defined values of specific variables for overwriting the default values set by the function \code{fn}} \item{data}{a new data-frame} } \value{ a 'setx.mi' object used for computing Quantities of Interest by the 'sim' method } \description{ Set Explanatory Variables for Multiply Imputed Data-sets This function simply calls setx.default once for every fitted model within the 'zelig.MI' object } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ \link{setx} } Zelig/man/parseFormula.list.Rd0000644000176000001440000000060212061700507016024 0ustar ripleyusers\name{parseFormula.list} \alias{parseFormula.list} \title{Parse List-Style Zelig Formulae} \usage{ \method{parseFormula}{list}(obj, data=NULL) } \arguments{ \item{obj}{a list of formulae} \item{data}{a data frame} } \value{ an object of type "parseFormula" } \description{ This method parses a list-style Zelig formula. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/plot.ci.Rd0000644000176000001440000000410012216135406013761 0ustar ripleyusers\name{plot.ci} \alias{plot.ci} \title{Method for plotting pooled simulations by confidence intervals} \usage{ \method{plot}{ci}(x, qi="ev", var=NULL, ..., main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, legcol="gray20", col=NULL, leg=1, legpos=NULL, ci=c(80,95,99.9)) } \arguments{ \item{x}{A `sim' object} \item{qi}{a character-string specifying the quantity of interest to plot: "ev" expected values, "pv" predicted values, "fd" first differences} \item{var}{The variable to be used on the x-axis} \item{...}{Parameters to be passed to the `truehist' function which is implicitly called for numeric simulations} \item{main}{A character-string, specifying the main title of the plot} \item{sub}{A character-string, specifying the sub-title of the plot} \item{xlab}{A character-string, specifying the label for the x-axis} \item{ylab}{A character-string, specifying the label for the y-axis} \item{xlim}{A vector of length 2, specifying the left-most and right-most values for the plot} \item{ylim}{A vector of length 2, specifying the bottom-most and top-most values for the plot} \item{legcol}{``legend color'', an valid color used for plotting the line colors in the legend} \item{col}{a valid vector of colors of at least length 3 to use to color the confidence intervals} \item{leg}{``legend position'', an integer from 1 to 4, specifying the position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and ``NE'' respectively} \item{legpos}{``legend type'', exact coordinates and sizes for legend. Overrides argment ``leg.type''} \item{ci}{A vector of length up to 3, specifying the three confidence interval levels to plot on the graph (where confidence is expressed on the scale 0-100).} } \value{ the current graphical parameters. This is subject to change in future implementations of Zelig } \description{ Plot confidence intervals of simulated quantities of interest, across a range of a variable. } \author{ James Honaker, adapted by Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/has.zelig2.Rd0000644000176000001440000000101712061700507014361 0ustar ripleyusers\name{has.zelig2} \alias{has.zelig2} \title{Whether an Arbitrary R-package has a Zelig2 Function within Its Namespace} \usage{ has.zelig2(pkg) } \arguments{ \item{pkg}{a character-string representing a package name} } \value{ whether the package contains any zelig2-functions } \description{ Whether an Arbitrary R-package has a Zelig2 Function within Its Namespace } \note{ This function is used primarily internally to determine whether a a package is contributing a function to the Zelig software suite } Zelig/man/relogit.Rd0000644000176000001440000000071212061700507014061 0ustar ripleyusers\name{relogit} \alias{relogit} \title{Fit a rare-event logistic model in Zelig} \usage{ relogit(formula, data = sys.parent(), tau = NULL, bias.correct = TRUE, case.control = "prior", ...) } \arguments{ \item{formula}{a formula object} \item{data}{...} \item{tau}{...} \item{bias.correct}{...} \item{case.control}{...} \item{...}{???} } \value{ a ``relogit'' ``glm'' object } \description{ Fits a rare-event (``relogit'') model. } Zelig/man/toBuildFormula.Rd0000644000176000001440000000045412061700507015347 0ustar ripleyusers\name{toBuildFormula} \alias{toBuildFormula} \title{Build Formula ???} \usage{ toBuildFormula(Xnames, sepp = "+") } \arguments{ \item{Xnames}{a character-vector} \item{sepp}{a seperator (???)} } \value{ a character-string } \description{ This function builds a formula } \author{ ??? } Zelig/man/as.parameters.Rd0000644000176000001440000000234112061700507015161 0ustar ripleyusers\name{as.parameters} \alias{as.parameters} \title{Generic Method for Converting Objects into 'parameters'} \usage{ as.parameters(params, ...) } \arguments{ \item{params}{the object to be casted} \item{...}{parameters reserved for future revisions} } \value{ an object of type `parameters' } \description{ Converts list-style objects into Parameter lists primarily used by the 'qi' methods. These list-style objects may contain keys specifying: 'link' (the link function of a statistical model), 'linkinv' (the inverse-link function), 'family' (a object of 'family' class used to specify the model's classification), 'alpha' (a vector of ancillary parameters, and 'simulations' (a vector of simulated draws from the model's underlying distribution. } \note{ Only three scenarios may exist - converting 'parameters' to 'parameters', 'list' to 'parameters', and vectors to 'parameters'. The third in particular is needed only for backwards compatibility, and support will likely be deprecated. Furthermore, this function should be exlusively used implicitly and by Zelig. } \author{ Matt Owen \email{mowen@ig.harvard.edu} } \seealso{ as.parameters.list as.parameters.parameters, as.parameters.default } Zelig/man/reduceMI.Rd0000644000176000001440000000104412061700507014110 0ustar ripleyusers\name{reduceMI} \alias{reduceMI} \title{Reduce MI Formulas Take a formula in any of the reduced form or in a structural form and return the most reduced form of that formula} \usage{ reduceMI(f) } \arguments{ \item{f}{a formula} } \description{ Reduce MI Formulas Take a formula in any of the reduced form or in a structural form and return the most reduced form of that formula } \note{ This formula is used primarily by 'zelig2' functions of multivariate Zelig models } \author{ Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau } Zelig/man/help.zelig.Rd0000644000176000001440000000047112061700507014457 0ustar ripleyusers\name{help.zelig} \alias{help.zelig} \title{Help system for Zelig models} \usage{ help.zelig(...) } \arguments{ \item{...}{the help files to look-up} } \value{ results of calling the specific help function } \description{ Help system for Zelig models } \author{ Matt Owen \emph{mowen@iq.harvard.edu} } Zelig/man/as.description.Rd0000644000176000001440000000117112061700507015341 0ustar ripleyusers\name{as.description} \alias{as.description} \title{Generic Method for Casting 'description' Objects} \usage{ as.description(descr, ...) } \arguments{ \item{descr}{an object to cast an object of type 'description'} \item{...}{parameters which are reserved for future Zelig revisions} } \value{ an object of type 'description' } \description{ Convert the result of a call to the 'describe' method into an object parseble by Zelig. Currently conversions only exist for lists and description objects. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ as.description.description as.description.list } Zelig/man/describe.probit.Rd0000644000176000001440000000053112061700507015471 0ustar ripleyusers\name{describe.probit} \alias{describe.probit} \title{Describe the `probit' model to Zelig} \usage{ \method{describe}{probit}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe the `probit' model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/bootstrap.gamma.Rd0000644000176000001440000000114012061700507015506 0ustar ripleyusers\name{bootstrap.gamma} \alias{bootstrap.gamma} \title{Bootstrap Parameters for Zelig ``gamma'' GLM} \usage{ \method{bootstrap}{gamma}(obj, ...) } \arguments{ \item{obj}{a ``zelig'' object that will be used to produce boot-strapped parameters} \item{...}{extra parameters to be passed to the ``boot'' method. These are typically ignored, but is included for further expansion.} } \value{ a list containing information concerning link, link-inverses, etc. } \description{ Returns bootstrapped parameter estimates for a ``gamma'' GLM. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.qi.list.Rd0000644000176000001440000000143512061700507014404 0ustar ripleyusers\name{as.qi.list} \alias{as.qi.list} \title{list -> qi This function has a lot of room to go wrong. It tries o detect whether the zelig model is old-style or new-style (as of 4/4/2011). Eventually this feature should be phased out.} \usage{ as.qi.list(s) } \arguments{ \item{s}{a list} } \value{ an object of type `qi' } \description{ list -> qi This function has a lot of room to go wrong. It tries o detect whether the zelig model is old-style or new-style (as of 4/4/2011). Eventually this feature should be phased out. } \note{ This method has peculiar behavior when the list contains only two elements. The crucial fix is to simply remove the portion of code which intentionally implements this perculiar behavior. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/tobin.Rd0000644000176000001440000000103312061700507013524 0ustar ripleyusers\name{tobin} \alias{tobin} \title{Tobin's Tobit Data} \description{ Economists fit a parametric censored data model called the `tobit'. These data are from Tobin's original paper. } \usage{data(tobin)} \format{ A data frame with 20 observations on the following 3 variables. durable: Durable goods purchase age: Age in years quant: Liquidity ratio (x 1000) } \source{ J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, v26, 24-36, 1958. } \keyword{datasets} Zelig/man/free1.Rd0000644000176000001440000000776512061700507013435 0ustar ripleyusers\name{free1} \alias{free1} \title{Freedom of Speech Data} \usage{data(free1)} \description{ Selection of individual-level survey data for freedom of speech. } \details{ A table with 150 observations and 12 variables. \itemize{ \item{sex}{1 for men and 0 for women} \item{age}{Age of respondent in years} \item{educ}{Levels of education, coded as a numeric variable with \itemize{ \item{1}{No formal education} \item{2}{Less than primary school education} \item{3}{Completed primary school} \item{4}{Completed secondary school} \item{5}{Completed high school} \item{6}{Completed college} \item{7}{Completed post-graduate degree} } } \item{country}{Character strings consisting of "Oceana", "Eurasia", and "Eastasia", after Orwell's \emph{1984}.} \item{y}{Self assessment (see below).} \item{v1-v6}{Response to vignettes (see below).} } Survey respondents were asked in almost the same language for a self-assessment and for an assessment of several hypothetical persons described by written vignettes. The self assessment (\code{self}, in the data set), "How free do you think [name/you] [is/are] to express [him-her/your]self without fear of government reprisal?" was first asked of the survey respondent with respect to him or herself, and then after each of vignette. The possible response categories are: \itemize{ \item{1}{Completely free} \item{2}{Very free} \item{3}{Moderately free} \item{4}{Slightly free} \item{5}{Not free at all} } The vignettes, ordered from most free to least free, are: \itemize{ \item{vign1}{[Kay] does not like many of the government's policies. She frequently publishes her opinion in newspapers, criticizing decisions by officials and calling for change. She sees little reason these actions could lead to government reprisal.} \item{vign2}{[Michael] disagrees with many of the government's policies. Though he knows criticism is frowned upon, he doesn't believe the government would punish someone for expressing critical views. He makes his opinion known on most issues without regard to who is listening.} \item{vign3}{[Bob] has political views at odds with the government. He has heard of people occasionally being arrested for speaking out against the government, and government leaders sometimes make political speeches condemning those who criticize. He sometimes writes letters to newspapers about politics, but he is careful not to use his real name.} \item{vign4}{[Connie] does not like the government's stance on many issues. She has a friend who was arrested for being too openly critical of governmental leaders, and so she avoids voicing her opinions in public places.} \item{vign5}{[Vito] disagrees with many of the government's policies, and is very careful about whom he says this to, reserving his real opinions for family and close friends only. He knows several men who have been taken away by government officials for saying negative things in public.} \item{vign6}{[Sonny] lives in fear of being harassed for his political views. Everyone he knows who has spoken out against the government has been arrested or taken away. He never says a word about anything the government does, not even when he is at home alone with his family. } } } \references{ \emph{WHO's World Health Survey} by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana, Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria Villanueva Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming. copy at http://wand.stanford.edu/research/anchors-jss.pdf Gary King and Jonathan Wand. "Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes," Political Analysis, 15, 1 (Winter, 2007): Pp. 46-66, copy at http://gking.harvard.edu/files/abs/c-abs.shtml. } \keyword{datasets} Zelig/man/as.parameters.default.Rd0000644000176000001440000000072712061700507016612 0ustar ripleyusers\name{as.parameters.default} \alias{as.parameters.default} \title{??? -> parameters} \usage{ as.parameters.default(params, num = NULL, ...) } \arguments{ \item{params}{any non-supported data-type} \item{num}{an integer specifying the number of simulations to compute} \item{...}{ignored} } \value{ the object passed in } \description{ ??? -> parameters } \note{ This function should be deprecated. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/model.frame.multiple.Rd0000644000176000001440000000124512061700507016441 0ustar ripleyusers\name{model.frame.multiple} \alias{model.frame.multiple} \title{Create Model Frame from \code{multiple} Object} \usage{ \method{model.frame}{multiple}(formula,data,eqn=NULL,...) } \arguments{ \item{formula}{an object of both type \code{formula} and \code{multiple}} \item{data}{a \code{data.frame}} \item{eqn}{the number of equations in the formula} \item{...}{ignored parameters} } \value{ a \code{model.frame} object } \description{ This method creates a \code{model.frame} from a \code{multiple} object. This method will be deprecated as the development of Zelig 4 progresses. } \author{ Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi } Zelig/man/macro.Rd0000644000176000001440000000146512061700507013523 0ustar ripleyusers\name{macro} \alias{macro} \title{Macroeconomic Data} \description{ Selected macroeconomic indicators for Austria, Belgium, Canada, Denmark, Finland, France, Italy, Japan, the Netherlands, Norway, Sweden, the United Kingdom, the United States, and West Germany for the period 1966-1990. } \usage{data(macro)} \format{ A table containing 6 variables ("country", "year", "gdp", "unem", "capmob", and "trade") and 350 observations. } \source{ICPSR} \references{ King, Gary, Michael Tomz and Jason Wittenberg. ICPSR Publication Related Archive, 1225. King, Gary, Michael Tomz and Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp. 341-355. } \keyword{datasets} Zelig/man/Zelig.url.Rd0000644000176000001440000000026412061700507014271 0ustar ripleyusers\name{Zelig.url} \alias{Zelig.url} \title{Table of links for Zelig} \description{ Table of linds for \code{help.zelig} for the core Zelig package. } \keyword{datasets} Zelig/man/simulation.matrix.Rd0000644000176000001440000000125512061700507016106 0ustar ripleyusers\name{simulation.matrix} \alias{simulation.matrix} \title{Get Simulations as a Matrix} \usage{ simulation.matrix(obj, which = NULL, ...) } \arguments{ \item{obj}{an object, typically a ``sim'' or ``pooled.sim'' object.} \item{which}{a character-vector specifying the \emph{titles} of quantities of interest to extract} \item{...}{additional parameters} } \value{ a simulation matrix } \description{ Returns a MxN matrix where N is the number of simulations and M is the number of predicted values. Additionally, a ``labels'' attribute is attached that produces a human-readable identifier for each column. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/getPredictorTerms.Rd0000644000176000001440000000077412061700507016072 0ustar ripleyusers\name{getPredictorTerms} \alias{getPredictorTerms} \title{Get Predictor Terms from Zelig-style Formulae} \usage{ getPredictorTerms(x, ...) } \arguments{ \item{x}{a Zelig-style formula ('formula' or 'list')} \item{...}{ignored parameters} } \value{ a character-vector or NA } \description{ This function extracts the predictor terms from a Zelig-style object. } \note{ This function is used exclusively in the development of Zelig-core. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.exp.Rd0000644000176000001440000000101112061700507014300 0ustar ripleyusers\name{param.exp} \alias{param.exp} \title{Param Method for the \code{exp} Zelig Model} \usage{ \method{param}{exp}(obj, num, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored parameters} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the \code{exp} Zelig Model } \note{ This method is used by the \code{param} Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/robust.glm.hook.Rd0000644000176000001440000000105212061700507015445 0ustar ripleyusers\name{robust.glm.hook} \alias{robust.glm.hook} \title{Hook for ``glm'' Models in Zelig} \usage{ robust.glm.hook(obj, zcall, call, robust = FALSE, ...) } \arguments{ \item{obj}{a zelig object} \item{zcall}{the original call to the zelig model} \item{call}{the call that will be evaluated for the} \item{robust}{a logical specifying whether or not to use robust error estimates} \item{...}{ignored parameters} } \value{ the fitted model object } \description{ Adds support for robust error-estimates in the Zelig ``glm'' models. } Zelig/man/list.depth.Rd0000644000176000001440000000065012061700507014473 0ustar ripleyusers\name{list.depth} \alias{list.depth} \title{Count the Depth of a List Object} \usage{ list.depth(obj) } \arguments{ \item{obj}{a vector or list object} } \description{ This function recursively computes the depth of a list object. That is, it determines how many layers or levels exist within the object. } \note{ This function is used internally by Zelig. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/setx.Rd0000644000176000001440000000442612061700507013405 0ustar ripleyusers\name{setx} \alias{setx} \title{Setting Explanatory Variable Values} \usage{ setx(obj, fn = NULL, data = NULL, cond = FALSE, ...) } \arguments{ \item{obj}{the saved output from zelig} \item{fn}{a list of functions to apply to the data frame} \item{data}{a new data frame used to set the values of explanatory variables. If data = NULL (the default), the data frame called in zelig is used} \item{cond}{a logical value indicating whether unconditional (default) or conditional (choose \code{cond = TRUE}) prediction should be performed. If you choose \code{cond = TRUE}, \code{setx} will coerce \code{fn = NULL} and ignore the additional arguments in \code{\dots}. If \code{cond = TRUE} and \code{data = NULL}, \code{setx} will prompt you for a data frame.} \item{...}{user-defined values of specific variables for overwriting the default values set by the function \code{fn}. For example, adding \code{var1 = mean(data\$var1)} or \code{x1 = 12} explicitly sets the value of \code{x1} to 12. In addition, you may specify one explanatory variable as a range of values, creating one observation for every unique value in the range of values} } \value{ For unconditional prediction, \code{x.out} is a model matrix based on the specified values for the explanatory variables. For multiple analyses (i.e., when choosing the \code{by} option in \code{\link{zelig}}, \code{setx} returns the selected values calculated over the entire data frame. If you wish to calculate values over just one subset of the data frame, the 5th subset for example, you may use: \code{x.out <- setx(z.out[[5]])} } \description{ The \code{setx} command uses the variables identified in the \code{formula} generated by \code{zelig} and sets the values of the explanatory variables to the selected values. Use \code{setx} after \code{zelig} and before \code{sim} to simulate quantities of interest. } \examples{ # Unconditional prediction: data(turnout) z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout) x.out <- setx(z.out) s.out <- sim(z.out, x = x.out) } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Olivia Lau and Kosuke Imai } \seealso{ The full Zelig manual may be accessed online at \url{http://gking.harvard.edu/zelig} } \keyword{file} Zelig/man/GetObject.Rd0000644000176000001440000000052312061700507014262 0ustar ripleyusers\name{GetObject} \alias{GetObject} \title{Extract the fitted model object from the Zelig object} \usage{ GetObject(obj) } \arguments{ \item{obj}{an object of type `zelig'} } \value{ the fitted model object } \description{ Extract the fitted model object from the Zelig object } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/is.zelig.compliant.Rd0000644000176000001440000000122712061700507016127 0ustar ripleyusers\name{is.zelig.compliant} \alias{is.zelig.compliant} \title{Whether a R-Package Contains a 'Yes' in its DESCRIPTION File's 'Zelig' Field} \usage{ is.zelig.compliant(package = "") } \arguments{ \item{package}{a character-string specifying an installed R-package} } \value{ whether the package's DESCRIPTION file specifies Zelig-compliancy } \description{ Whether a R-Package Contains a 'Yes' in its DESCRIPTION File's 'Zelig' Field } \note{ This package was used internally to determine whether an R-package is Zelig compliant, but is now likely deprecated. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ is.zelig.package } Zelig/man/coalition.Rd0000644000176000001440000000212412061700507014374 0ustar ripleyusers\name{coalition} \alias{coalition} \title{Coalition Dissolution in Parliamentary Democracies} \description{ This data set contains survival data on government coalitions in parliamentary democracies (Belgium, Canada, Denmark, Finland, France, Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom) for the period 1945-1987. For parsimony, country indicator variables are omitted in the sample data. } \usage{data(coalition)} \format{ A table containing 7 variables ("duration", "ciep12", "invest", "fract", "polar", "numst2", "crisis") and 314 observations. For variable descriptions, please refer to King, Alt, Burns and Laver (1990). } \source{ICPSR} \references{ King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990). ``A Unified Model of Cabinet Dissolution in Parliamentary Democracies,'' \emph{American Journal of Political Science}, vol. 34, no. 3, pp. 846-870. Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR Publication Related Archive, 1115. } \keyword{datasets} Zelig/man/cluster.formula.Rd0000644000176000001440000000056412061700507015546 0ustar ripleyusers\name{cluster.formula} \alias{cluster.formula} \title{Generate Formulae that Consider Clustering} \usage{ cluster.formula(formula, cluster) } \arguments{ \item{formula}{a formula object} \item{cluster}{a vector} } \value{ a formula object describing clustering } \description{ This method is used internally by the "Zelig" Package to interpret clustering. } Zelig/man/param.relogit.Rd0000644000176000001440000000110512061700507015155 0ustar ripleyusers\name{param.relogit} \alias{param.relogit} \title{Estimate Parameters for the ``relogit'' Zelig Mdoel} \usage{ \method{param}{relogit}(obj, num, ...) } \arguments{ \item{obj}{a zelig object containing the fitted model} \item{num}{an integer specifying the number of simulations to compute} \item{...}{unspecified parameters} } \value{ a list specifying important parameters for the ``relogit'' model } \description{ Returns estimates on parameters, as well as, specifying link and inverse-link functions. } \note{ This method merely calls ``param.logit''. } Zelig/man/bootstrap.default.Rd0000644000176000001440000000130412061700507016052 0ustar ripleyusers\name{bootstrap.default} \alias{bootstrap.default} \title{Produce Boot-strapped Parameters for a Statistical Model} \usage{ \method{bootstrap}{default}(obj, ...) } \arguments{ \item{obj}{a fitted model object. This is typically of type ``glm'' or ``lm''} \item{...}{unspecified parameters} } \value{ a list with the ``alpha'' and ``beta'' slots set } \description{ This method is a fallback for bootstrapping models that do not have a defined ``bootstrap'' method. For most models, this default is sufficient, so long as the model follows the usual convention that ``coef(obj)'' returns the systematic parameters of a fitted model. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.bootlist.Rd0000644000176000001440000000077512061700507014666 0ustar ripleyusers\name{as.bootlist} \alias{as.bootlist} \title{Convert of Vector of Bootstrapped Parameters to a List-style Boot Object} \usage{ as.bootlist(bootstraps, lengths, names) } \arguments{ \item{bootstraps}{...} \item{lengths}{...} \item{names}{a character-vector specifying the names of the boot terms} } \value{ ... } \description{ This inverts the ``as.bootvector'' function, and returns a list containing the slots ``alpha'' and ``beta''. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/tolmerFormat.Rd0000644000176000001440000000130612061700507015067 0ustar ripleyusers\name{tolmerFormat} \alias{tolmerFormat} \title{Convert a Formula into 'lmer' Representation from Reduced Form Take a formula in its reducd from and return it as a 'lmer' representation (from the lme4 package). This is basically removing the starting 'tag' from each term.} \usage{ tolmerFormat(f) } \arguments{ \item{f}{a formula in reduced form} } \value{ the 'lmer' representation of 'f' } \description{ Convert a Formula into 'lmer' Representation from Reduced Form Take a formula in its reducd from and return it as a 'lmer' representation (from the lme4 package). This is basically removing the starting 'tag' from each term. } \author{ Ferdinand Alimadhi, Kosuke Imai, and Olivia Lau } Zelig/man/summary.pooled.sim.Rd0000644000176000001440000000143212061700507016161 0ustar ripleyusers\name{summary.pooled.sim} \alias{summary.pooled.sim} \title{Return a Summary of a Set of Pooled Simulated Interests} \usage{ \method{summary}{pooled.sim}(object, ...) } \arguments{ \item{object}{a ``pooled.sim'' object, containing information about simulated quantities of interest} \item{...}{Ignored parameters} } \value{ a ``summary.pooled.sim'' object storing the replicated quantities of interest } \description{ Returns the summary information from a set of pooled simulated interests. The object returned contains the slots ``labels'', a character-vector specifying the labels (explanatory variable titles) of the qi's, ``titles'', a character vector specifying the names of the quantities of interest, and } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/model.matrix.parseFormula.Rd0000644000176000001440000000137412061700507017463 0ustar ripleyusers\name{model.matrix.parseFormula} \alias{model.matrix.parseFormula} \title{Construct Design Matrix from a Parsed, Zelig-style Formula} \usage{ \method{model.matrix}{parseFormula}(object, data = NULL, ...) } \arguments{ \item{object}{a "parseFormula" object} \item{data}{a "data.frame"} \item{...}{ignored parameters} } \value{ a "model.matrix" specifying information relevant to a statistical model } \description{ This method constructs a design matrix from a Zelig-style formula. This matrix is commonly used in statistical simulation, and will likely be relevent as the relevant form of a \code{setx} object. } \note{ This method is primarily used by the \code{setx} function. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.tobit.Rd0000644000176000001440000000102312061700507014630 0ustar ripleyusers\name{param.tobit} \alias{param.tobit} \title{Param Method for the \code{tobit} Zelig Model} \usage{ \method{param}{tobit}(obj, num, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored parameters} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the \code{tobit} Zelig Model } \note{ This method is used by the \code{tobit} Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.summary.relogit2.Rd0000644000176000001440000000064212061700507016774 0ustar ripleyusers\name{print.summary.relogit2} \alias{print.summary.relogit2} \title{Print Summary of a Rare-event Logistic Model} \usage{ \method{print}{summary.relogit2}(x, digits = max(3, getOption("digits") - 3), ...) } \arguments{ \item{x}{the object to print} \item{digits}{an integer specifying the number of digits of precision} \item{...}{ignored parameters} } \value{ x (invisibly) } \description{ ... } Zelig/man/ucfirst.Rd0000644000176000001440000000057412061700507014101 0ustar ripleyusers\name{ucfirst} \alias{ucfirst} \title{Uppercase First Letter of a String} \usage{ ucfirst(str) } \arguments{ \item{str}{a vector of charaqcter-strings} } \value{ a vector of character strings } \description{ This method sets the first character of a string to its uppercase, sets all other characters to lowercase. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/qi.exp.Rd0000644000176000001440000000144712061700507013626 0ustar ripleyusers\name{qi.exp} \alias{qi.exp} \title{Compute quantities of interest for 'exp' Zelig models} \usage{ \method{qi}{exp}(obj, x=NULL, x1=NULL, y=NULL, num=1000, param=NULL) } \arguments{ \item{obj}{a 'zelig' object} \item{x}{a 'setx' object or NULL} \item{x1}{an optional 'setx' object} \item{y}{this parameter is reserved for simulating average treatment effects, though this feature is currentlysupported by only a handful of models} \item{num}{an integer specifying the number of simulations to compute} \item{param}{a parameters object} } \value{ a list of key-value pairs specifying pairing titles of quantities of interest with their simulations } \description{ Compute quantities of interest for 'exp' Zelig models } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/sim.MI.Rd0000644000176000001440000000147112061700507013513 0ustar ripleyusers\name{sim.MI} \alias{sim.MI} \title{Simulate Multiply Imputed Data} \usage{ \method{sim}{MI}(obj, x=NULL, x1=NULL, y=NULL, num=1000, ...) } \arguments{ \item{obj}{a 'zelig.MI' object containing several fits for two or more subsetted data-frames} \item{x}{a 'setx.mi' object containing explanatory variables for each fitted model} \item{x1}{a 'setx.mi' object containing explanatory variables for each fitted model} \item{y}{this feature is currently unimplemented} \item{num}{an integer specifying the number of simulations to compute} \item{...}{ignored parameters} } \value{ a 'sim.MI' with simulated quantities of interest for each fitted contained by 'obj' } \description{ Simulate Multiply Imputed Data } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ \link{sim} } Zelig/man/print.summarySim.MI.Rd0000644000176000001440000000071612061700507016225 0ustar ripleyusers\name{print.summarySim.MI} \alias{print.summarySim.MI} \title{Print Multiply Imputed Simulations Summary} \usage{ \method{print}{summarySim.MI}(x, digits=3, ...) } \arguments{ \item{x}{a 'summarySim.MI' object} \item{digits}{an integer specifying the number of digits of precision to print} \item{...}{ignored parameters} } \description{ Prints summary information about Multiply Imputed Fits } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/summarize.default.Rd0000644000176000001440000000061212061700507016052 0ustar ripleyusers\name{summarize.default} \alias{summarize.default} \title{Summarize Simualted Quantities of Interest} \usage{ \method{summarize}{default}(obj) } \arguments{ \item{obj}{a \code{qi} object, storing simulations of quantities of interest} } \value{ a 'summarized.qi' object } \description{ Summarize Simualted Quantities of Interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/cite.Rd0000644000176000001440000000052212061700507013337 0ustar ripleyusers\name{cite} \alias{cite} \title{Citation information for a 'description' object} \usage{ cite(descr) } \arguments{ \item{descr}{an object of type 'description'} } \value{ a character-string giving citation info } \description{ Citation information for a 'description' object } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/McmcHookFactor.Rd0000644000176000001440000000134712061700507015260 0ustar ripleyusers\name{McmcHookFactor} \alias{McmcHookFactor} \title{Hook to Clean-up MCMC Factor Object} \usage{ McmcHookFactor(obj, model.call, zelig.call, seed = NULL, ...) } \arguments{ \item{obj}{the fitted model object (in this case a \code{mcmc} object.} \item{model.call}{the call made to the external model} \item{zelig.call}{the actual call to zelig itself} \item{seed}{a seed for the MCMC algorithm} \item{...}{ignored parameters} } \value{ an object useable by Zelig } \description{ This method gives valid methods to the resulting MCMC object so that it can be used with Zelig. } \note{ This function is used internally by the ZeligBayesian package. } \author{ Olivia Lau, Kosuke Imai, Gary King and Matt Owen } Zelig/man/param.ls.Rd0000644000176000001440000000077612061700507014143 0ustar ripleyusers\name{param.ls} \alias{param.ls} \title{Param Method for the 'ls' Zelig Model} \usage{ \method{param}{ls}(obj, num, \dots) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored parameters} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the 'ls' Zelig Model } \note{ This method currently returns via a deprectated style } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.matrix.setx.Rd0000644000176000001440000000162612061700507015311 0ustar ripleyusers\name{as.matrix.setx} \alias{as.matrix.setx} \title{Convert a 'setx' Object to a Matrix} \usage{ \method{as.matrix}{setx}(x, ...) } \arguments{ \item{x}{a setx object} \item{...}{ignored parameters} } \value{ a matrix containing columns and rows corrseponding to the explanatory variables specified in the call to the 'setx' function } \description{ The setx object is, in its most basic form, a list of column names and values specified for each of these column names. This function simply converts the key-value pairs of column-name and specified value into a matrix. } \note{ This method allows basic matrix arithmetic operations on data objects, which mirror values stored within setx objects. In many scenarios, simulations require matrix-multiplication, etc. to be performed on a data-set. This function faciliates that need. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zelig.call.Rd0000644000176000001440000000110512061700507014435 0ustar ripleyusers\name{zelig.call} \alias{zelig.call} \title{Create Function Call} \usage{ zelig.call(Call, zelig2, remove = NULL) } \arguments{ \item{Call}{a \code{call} object, typically specifying the original function call to \code{zelig}} \item{zelig2}{the return-value of the \code{zelig2} method} \item{remove}{a list of character vectors specifying which parameters to ignore from the original call to \code{zelig}} } \value{ a function call used to fit the statistical model } \description{ Create Function Call } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/describe.default.Rd0000644000176000001440000000131612061700507015620 0ustar ripleyusers\name{describe.default} \alias{describe.default} \title{Default describe function for an arbitrary model This method exists solely as a backup when an author does not contribute a 'describe' function for their model} \usage{ \method{describe}{default}(...) } \arguments{ \item{...}{dummy parameters purely to cast the correct object. That is, the parameters of the function should not BE referenced specifically} } \value{ a list to be processed by \code{as.description} } \description{ Default describe function for an arbitrary model This method exists solely as a backup when an author does not contribute a 'describe' function for their model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/GetSlot.zelig.Rd0000644000176000001440000000120312061700507015102 0ustar ripleyusers\name{GetSlot.zelig} \alias{GetSlot.zelig} \title{Return a Value from a \code{zelig} Fitted Model} \usage{ \method{GetSlot}{zelig}(obj, key, ...) } \arguments{ \item{obj}{a \code{zelig} object} \item{key}{a character-string specifying the which value to extract from the fitted model object} \item{...}{subsequent values to extract from the fitted model object} } \value{ values of the specified keys } \description{ Returns a value from the result of a model fitting function } \note{ This function is primarily used by Zelig developers within \code{qi} functions } \author{ Matt Owen \emph{mowen@iq.harvard.edu} } Zelig/man/print.summary.pooled.sim.Rd0000644000176000001440000000140312061700507017312 0ustar ripleyusers\name{print.summary.pooled.sim} \alias{print.summary.pooled.sim} \title{Print a Summary of a Set of Pooled Simulated Interests} \usage{ \method{print}{summary.pooled.sim}(x, ...) } \arguments{ \item{x}{a ``summary.pooled.sim'' object, containing summarized information about simulated quantities of interest} \item{...}{Optional parameters that will be passed onward to ``print.matrix'' (the matrix printing function)} } \value{ a ``summary.pooled.sim'' object storing the quantities of interest } \description{ Prints the summary information from a set of pooled simulated interests. This method assumes that quantities of interest are kept in a data type which can be used with ``rbind''. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/names.qi.Rd0000644000176000001440000000132312061700507014126 0ustar ripleyusers\name{names.qi} \alias{names.qi} \title{The Names of a 'qi' Object} \usage{ \method{names}{qi}(x) } \arguments{ \item{x}{a 'qi' object} } \value{ a character-vector containing the names of the Quantities of Interest } \description{ Function to get the names of a 'qi' object. This function does not entirely parallel the functionality of traditional 'names' methods; this is because the \code{$} operator has been overloaded to support a unique style of value extraction. For technical details, please see the source code. } \note{ No method exists to set the names of a 'qi' object, once it is constructed. This will be a feature added later. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/approval.Rd0000644000176000001440000000062112061700507014237 0ustar ripleyusers\name{approval} \alias{approval} \title{U.S. Presidential Approval Data} \description{ Monthy public opinion data for 2001-2006. } \usage{data(approval)} \format{ A table containing 8 variables ("month", "year", "approve", "disapprove", "unsure", "sept.oct.2001", "iraq.war", and "avg.price") and 65 observations. } \source{ICPSR} \references{ Stuff here } \keyword{datasets} Zelig/man/as.data.frame.setx.Rd0000644000176000001440000000136512061700507016007 0ustar ripleyusers\name{as.data.frame.setx} \alias{as.data.frame.setx} \title{Coerce a \code{setx} Object into a \code{data.frame}} \usage{ \method{as.data.frame}{setx}(x, row.names=NULL, optional=FALSE, ...) } \arguments{ \item{x}{a \code{setx} object} \item{row.names}{ignored parameter} \item{optional}{ignored parameter} \item{...}{ignored parameters} } \value{ the \code{setx} object interpretted as a \code{data.frame}. The column-names of the resulting \code{data.frame} are specified by the names of the \code{setx} object. The row-names are typically unlabeled. } \description{ Coerce a \code{setx} Object into a \code{data.frame} } \note{ In subsequent versions of Zelig, this version is expected to undergo minor modifications. } Zelig/man/makeZeligObject.Rd0000644000176000001440000000166112061700507015457 0ustar ripleyusers\name{makeZeligObject} \alias{makeZeligObject} \title{Make an Individual Zelig Object} \usage{ makeZeligObject(object, model, call, zelig_call, data, label, env, package.name = NULL) } \arguments{ \item{object}{a fitted statistical model} \item{model}{a character-string specifying the name of the model} \item{call}{The call that produced the fitted model} \item{zelig_call}{The call made to the original zelig function} \item{data}{the data.frame used to fit the model} \item{label}{a character-string or symbol used as a human-readable label for the data-set} \item{env}{an environment variable that contains all variables to evaluate the call ``zelig_call''} \item{package.name}{a character-string specifyign the name of the package that is the source of the model used to fit this object} } \value{ A ``zelig'' object } \description{ Returns a ``zelig'' object with the proper specifications } Zelig/man/plot.sim.Rd0000644000176000001440000000062412061700507014163 0ustar ripleyusers\name{plot.sim} \alias{plot.sim} \title{Method for plotting simulations} \usage{ \method{plot}{sim}(x, ...) } \arguments{ \item{x}{a `sim' object} \item{...}{parameters to be passed to the `truehist' function which is implicitly called for numeric simulations} } \value{ nothing } \description{ Plot simulated quantities of interest. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.summarized.list.Rd0000644000176000001440000000065312061700507016154 0ustar ripleyusers\name{as.summarized.list} \alias{as.summarized.list} \title{list -> summarized.qi Convert a list into a ``summarized.qi'' object} \usage{ \method{as.summarized}{list}(x, ...) } \arguments{ \item{x}{a list} \item{...}{ignored parameters} } \value{ a ``summarized.qi'' object } \description{ list -> summarized.qi Convert a list into a ``summarized.qi'' object } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.summary.relogit.Rd0000644000176000001440000000101012061700507016700 0ustar ripleyusers\name{print.summary.relogit} \alias{print.summary.relogit} \title{Print Summary of a Rare-event Logistic Model} \usage{ \method{print}{summary.relogit}(x, digits = max(3, getOption("digits") - 3), ...) } \arguments{ \item{x}{an ``relogit.summary'' object produced by the ``summary'' method.} \item{digits}{an integer specifying the number of digits of precision to specify} \item{...}{parameters passed forward to the ``print.glm'' function} } \value{ x (invisibly) } \description{ Prints the } Zelig/man/describe.tobit.Rd0000644000176000001440000000052412061700507015315 0ustar ripleyusers\name{describe.tobit} \alias{describe.tobit} \title{Describe a ``tobit'' model to Zelig} \usage{ \method{describe}{tobit}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe a ``tobit'' model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/bootfn.default.Rd0000644000176000001440000000141012061700507015322 0ustar ripleyusers\name{bootfn.default} \alias{bootfn.default} \title{Default Boot-strapping procedure} \usage{ bootfn.default(data, i, object, bootstrapfn = NULL, num, ...) } \arguments{ \item{data}{a data.frame} \item{i}{an integer or chacter-string specifying the index of the row to be used in the bootstrapping procedure.} \item{object}{the fitted model object} \item{bootstrapfn}{a function used to bootstrap the object} \item{num}{an integer specifying the number of samples to simulate} \item{...}{unspecified parameters} } \value{ a list of paramters } \description{ The default procedure for extracting bootstrap information. Note that this method re-fits the data and resamples the data frequently. This is a good candidate for fixing-up. } Zelig/man/summary.Relogit2.Rd0000644000176000001440000000053512061700507015602 0ustar ripleyusers\name{summary.Relogit2} \alias{summary.Relogit2} \title{Summary for ``Relogit2'' Fitted Model} \usage{ \method{summary}{Relogit2}(object, ...) } \arguments{ \item{object}{a ``Relogit2'' object} \item{...}{other parameters} } \value{ a ``summary.relogit2'' object } \description{ Summarize important components of the ``relogit'' model } Zelig/man/summary.zelig.Rd0000644000176000001440000000060712061700507015225 0ustar ripleyusers\name{summary.zelig} \alias{summary.zelig} \title{Zelig Object Summaries} \usage{ \method{summary}{zelig}(object, ...) } \arguments{ \item{object}{a zelig object} \item{...}{parameters forwarded to the generic summary object} } \value{ the summary of the fitted model } \description{ Compute summary data for zelig objects } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.description.list.Rd0000644000176000001440000000054012061700507016312 0ustar ripleyusers\name{as.description.list} \alias{as.description.list} \title{list -> description} \usage{ \method{as.description}{list}(descr, ...) } \arguments{ \item{descr}{a list} \item{...}{ignored} } \value{ an object of type 'description' } \description{ Convert list into a description object. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Mode.Rd0000644000176000001440000000060212061700507013276 0ustar ripleyusers\name{Mode} \alias{Mode} \title{Compute the Statistical Mode of a Vector} \usage{ Mode(x) } \arguments{ \item{x}{a vector of numeric, factor, or ordered values} } \value{ the statistical mode of the vector. If two modes exist, one is randomly selected (by design) } \description{ Compute the Statistical Mode of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Weimar.Rd0000644000176000001440000000157612061700507013651 0ustar ripleyusers\name{Weimar} \alias{Weimar} \title{1932 Weimar election data} \description{ This data set contains election results for 10 kreise (equivalent to precincts) from the 1932 Weimar (German) election. } \usage{data(Weimar)} \format{A table containing 11 variables and 10 observations. The variables are \describe{ \item{Nazi}{Number of votes for the Nazi party} \item{Government}{Number of votes for the Government} \item{Communists}{Number of votes for the Communist party} \item{FarRight}{Number of votes for far right parties} \item{Other}{Number of votes for other parties, and non-voters} \item{shareunemployed}{Proportion unemployed} \item{shareblue}{Proportion working class} \item{sharewhite}{Proportion white-collar workers} \item{sharedomestic}{Proportion domestic servants} \item{shareprotestants}{Proportion Protestant} } } \source{ICPSR} %\references{ %} \keyword{datasets} Zelig/man/parameters.Rd0000644000176000001440000000142712061700507014563 0ustar ripleyusers\name{parameters} \alias{parameters} \title{Constructor for `parameters' class} \usage{ parameters(simulations, alpha, fam = NULL, link = NULL, linkinv = NULL) } \arguments{ \item{simulations}{a vector or matrix containing simulated values} \item{alpha}{ancillary parameters for the Zelig statistical model} \item{fam}{a family object which implicitly specifies the link and link-inverse functions for the} \item{link}{the link function of the specified statistical model. The `linkinv' parameter is implicitly defined by by the `link' parameter, when `linkinv' is omitted} \item{linkinv}{the inverse link function} } \value{ a `parameters' object } \description{ Constructor for `parameters' class } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.default.Rd0000644000176000001440000000063012061700507015136 0ustar ripleyusers\name{param.default} \alias{param.default} \title{Default Method for ``param''} \usage{ \method{param}{default}(obj, num, ...) } \arguments{ \item{obj}{ignored parameter} \item{num}{ignored parameter} \item{...}{ignored parameters} } \description{ If no \code{param} function is set for a Zelig model, then this function will return NULL. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zelig2.Rd0000644000176000001440000000401112061700507013604 0ustar ripleyusers\name{zelig2} \alias{zelig2} \title{Interface Between Zelig Models and External Functions} \value{ The main purpose of the \code{zelig2} function is to return a list of key-value pairs, specifying how Zelig should interface with the external method. This list has the following format: \item{\code{.function}}{specifies the name of the external method to be called by \code{zelig} function. Subsequent parameters, are called and evaluated as a function call to the function of the named string.} \item{\code{.hook}}{specifies the name of a hook function as a string. The hook function is only evaluated on zelig object once the external method fits the statistical model} \item{...}{any parameters aside from \code{.function} and \code{.hook} is as part of the function call to the external model} } \description{ The \code{zelig2} function acts as a simple interface between a user's call to the \code{zelig} function and the zelig functions subsequent call to the pre-existing external model. The external model varies based on which model is being called. } \note{ Writing \code{zelig2} functions is required of Zelig developers. In particular, \code{zelig2} functions act as an interface between external models (models not included in the Zelig package) and the \code{zelig} function which must use that model. \code{zelig2} is not an actual function. Rather, } \examples{ zelig2some.model <- function (formula, weights, verbose, ..., data) { list( .function = 'some.other.method', .hook = NULL, formula = formula, weights = 2 * weights, data = data ) } ## This \\code{zelig2} function equates the following function call: ## zelig(formula, weights = weights, verbose = TRUE, data = data, model="some.model") ## ## with: ## some.other.method(formula = formula, weights = 2 * weights, data=data) ## Note that the 'verbose' parameter is ignored, since the ## 'zelig2some.model' does not include the 'verbose' parameter in its return ## value. } Zelig/man/describe.exp.Rd0000644000176000001440000000051212061700507014765 0ustar ripleyusers\name{describe.exp} \alias{describe.exp} \title{Describe a ``exp'' model to Zelig} \usage{ \method{describe}{exp}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe a ``exp'' model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/store.object.Rd0000644000176000001440000000224512061700507015020 0ustar ripleyusers\name{store.object} \alias{store.object} \title{Store Object in Environment with a Fake Name} \usage{ store.object(obj, envir, name = NULL, prefix = ".") } \arguments{ \item{obj}{any object} \item{envir}{an environment object, which will contain the object with the assigned name} \item{name}{a character-string specifying the name that the object will be stored as in the specified environment} \item{prefix}{a character string specifying the prefixes to append to names that already have matches in the destination environment} } \value{ a character-string specifying the name of the object in the destination environment } \description{ This function takes the value of an object and stores it within a specified environment. This is similar to simply using the \code{assign} function, but will not overwrite existing values in the specified environment. It accomplishes this by appending a prefix to the name of the variable until the name becomes unique. } \note{ This method does not correct invalid names. That is, there is no test to determine whether the submitted name is valid. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zelig.skeleton.Rd0000644000176000001440000000354212061700507015355 0ustar ripleyusers\name{zelig.skeleton} \alias{zelig.skeleton} \title{Creates a Skeleton for a New Zelig package} \usage{ zelig.skeleton(pkg, models = c(), author = "UNKNOWN AUTHOR", path = ".", force = FALSE, email = "maintainer@software-project.org", depends = c(), ..., .gitignore = TRUE, .Rbuildignore = TRUE) } \arguments{ \item{pkg}{a character-string specifying the name of the Zelig package} \item{models}{a vector of strings specifying models to be included in the package} \item{author}{a vector of strings specifying contributors to the package} \item{path}{a character-string specifying the path to the package} \item{force}{a logical specifying whether to overwrite files and create necessary directories} \item{email}{a string specifying the email address of the package's maintainer} \item{depends}{a vector of strings specifying package dependencies} \item{...}{ignored parameters} \item{.gitignore}{a logical specifying whether to include a copy of a simple \code{.gitignore} in the appropriate folders (\code{inst/doc} and the package root} \item{.Rbuildignore}{a logical specifying whether to include a copy of a simple \code{.Rbuildignore} in the appropriate folders (\code{inst/doc} and the package root} } \value{ nothing } \description{ 'zelig.skeleton' generates the necessary files used to create a Zelig package. Based on (and using) R's 'package.skeleton' it removes some of the monotony of building statistical packages. In particular, 'zelig.skeleton' produces templates for the \code{zelig2}, \code{describe}, \code{param}, and \code{qi} methods. For more information about creating these files on an individual basis, please refer to the tech manuals, which are available by typing: \code{?zelig2}, \code{?param}, or \code{?qi}. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.qi.default.Rd0000644000176000001440000000037712061700507015061 0ustar ripleyusers\name{as.qi.default} \alias{as.qi.default} \title{??? -> qi} \usage{ as.qi.default(s) } \arguments{ \item{s}{any unsupported object} } \value{ an object of type `qi' } \description{ ??? -> qi } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/terms.vglm.Rd0000644000176000001440000000060212061700507014510 0ustar ripleyusers\name{terms.vglm} \alias{terms.vglm} \title{Model Terms for 'vglm' Models} \usage{ \method{terms}{vglm}(x, ...) } \arguments{ \item{x}{a fitted model object from the VGAM library} \item{...}{ignored parameters} } \value{ the models terms of this fitted model object } \description{ Model Terms for 'vglm' Models } \author{ Ferdinand Alimadhi, Kosuke Imai and Olivia Lau } Zelig/man/alpha.Rd0000644000176000001440000000056712061700507013511 0ustar ripleyusers\name{alpha} \alias{alpha} \title{Extract ancillary parameters from `parameters' objects} \usage{ alpha(param) } \arguments{ \item{param}{a `parameters' object} } \value{ the ancillary parameters \emph{specified} for the statistical model } \description{ Extract ancillary parameters from `parameters' objects } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/special_print_LIST.Rd0000644000176000001440000000101212061700507016075 0ustar ripleyusers\name{special_print_LIST} \alias{.print.qi.summarized.LIST} \alias{special_print_LIST} \title{Method for Printing Summarized QI's in a List Form} \usage{ .print.qi.summarized.LIST(x, ...) } \arguments{ \item{x}{a 'summarized.qi' object} \item{...}{additional parameters to be used by the 'print.matrix' method} } \value{ x (invisibly) } \description{ Method for Printing Summarized QI's in a List Form } \note{ This function is used internall by Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Min.Rd0000644000176000001440000000053012061700507013135 0ustar ripleyusers\name{Min} \alias{Min} \title{Compute the Minumum Value of a Vector} \usage{ Min(x, na.rm = NULL) } \arguments{ \item{x}{a vector of numeric or ordered values} \item{na.rm}{ignored} } \value{ the minimum value of the vector } \description{ Compute the Minumum Value of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.description.description.Rd0000644000176000001440000000061112061700507017661 0ustar ripleyusers\name{as.description.description} \alias{as.description.description} \title{description -> description} \usage{ \method{as.description}{description}(descr, ...) } \arguments{ \item{descr}{an object of type 'description'} \item{...}{ignored} } \value{ the same object } \description{ Identity operation on a description object. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/parse.formula.Rd0000644000176000001440000000063412061700507015175 0ustar ripleyusers\name{parse.formula} \alias{parse.formula} \title{Parse Formulas for Zelig Models} \usage{ parse.formula(formula, model, data = NULL) } \arguments{ \item{formula}{a formula} \item{model}{a Zelid model} \item{data}{a data-frame} } \description{ Parse Formulas for Zelig Models } \note{ This is used typically in multinomial and multivariate Zelig models } \author{ Kosuke Imai and Olivia Lau } Zelig/man/special_print_MATRIX.Rd0000644000176000001440000000075412061700507016342 0ustar ripleyusers\name{special_print_MATRIX} \alias{.print.qi.summarized.MATRIX} \alias{special_print_MATRIX} \title{Method for Printing Summarized QI's in a Matrix Form} \usage{ .print.qi.summarized.MATRIX(x, ...) } \arguments{ \item{x}{a 'summarized.qi' object} \item{...}{additional parameters} } \value{ x (invisibly) } \description{ Method for Printing Summarized QI's in a Matrix Form } \note{ This function is used internall by Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/constructDataFrame.Rd0000644000176000001440000000123312061700507016204 0ustar ripleyusers\name{constructDataFrame} \alias{constructDataFrame} \title{Construct Data Frame Construct and return a tiny (single-row) data-frame from a larger data-frame, a list of specified values, and a formula} \usage{ constructDataFrame(data, specified) } \arguments{ \item{data}{a ``data.frame'' that will be used to create a small design matrix} \item{specified}{a list with key-value pairs that will be used to explicitly set several values} } \value{ a ``data.frame'' containing a single row } \description{ Construct Data Frame Construct and return a tiny (single-row) data-frame from a larger data-frame, a list of specified values, and a formula } Zelig/man/simulations.parameters.Rd0000644000176000001440000000122012061700507017120 0ustar ripleyusers\name{simulations.parameters} \alias{simulations.parameters} \title{Return Simulations of Parameter Coefficients} \usage{ \method{simulations}{parameters}(object, ...) } \arguments{ \item{object}{a 'parameters' object} \item{\dots}{ignored} } \value{ simulations, specified by the Zelig model, of the ancillary parameters } \description{ Returns simulated parameters of coefficients for use in statistical simulation. The values are set by the model-fitting function and the developer of the qi. method. } \note{ This function does not differ at all from coef.default } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/mid.Rd0000644000176000001440000000165412061700507013173 0ustar ripleyusers\name{mid} \alias{mid} \title{Militarized Interstate Disputes} \description{ A small sample from the militarized interstate disputes database, available at \url{http://pss.la.psu.edu/MID_DATA.HTM}. } \usage{data(mid)} \format{ A table containing 6 variables ("conflict", "major", "contig", "power", "maxdem", "mindem", and "years") and 3,126 observations. For full variable descriptions, please see King and Zeng, 2001. } \source{Militarized Interstate Disputes database} \references{ King, Gary, and Lanche Zeng (2001). ``Explaining Rare Events in International Relations,'' \emph{International Organization}, vol. 55, no. 3, pp. 693-715. Jones, Daniel M., Stuart A. Bremer and David Singer (1996). ``Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,'' \emph{Conflict Management and Peace Science}, vol. 15, no. 2, pp. 163-213. } \keyword{datasets} Zelig/man/param.normal.Rd0000644000176000001440000000100412061700507014776 0ustar ripleyusers\name{param.normal} \alias{param.normal} \title{Param Method for the 'normal' Zelig Model} \usage{ \method{param}{normal}(obj, num=1000, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the 'normal' Zelig Model } \note{ This method is used by the 'normal' Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.qi.Rd0000644000176000001440000000061112061700507014156 0ustar ripleyusers\name{print.qi} \alias{print.qi} \title{Print a Quantity of Interest in Human-Readable Form} \usage{ \method{print}{qi}(x, ...) } \arguments{ \item{x}{a qi object} \item{...}{ignored parameters} } \value{ the object that was printed (invisibly) } \description{ Print simulated quantities of interest in a human-readable form } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/summary.MI.Rd0000644000176000001440000000072412142745743014433 0ustar ripleyusers\name{summary.MI} \alias{summary.MI} \title{Summarry of Multiply Imputed Statistical Models} \usage{ \method{summary}{MI}(object, subset = NULL, ...) } \arguments{ \item{object}{a set of fitted statistical models} \item{subset}{an integer vector, specifying the indices of the data.frames to be used in the subset} \item{...}{parameters to forward} } \value{ a list of summaries } \description{ ... } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/list.zelig.dependent.packages.Rd0000644000176000001440000000103012061700507020214 0ustar ripleyusers\name{list.zelig.dependent.packages} \alias{list.zelig.dependent.packages} \title{Get a List of Packages Installed on the Current Machine that Depend on Zelig} \usage{ list.zelig.dependent.packages() } \value{ a character-vector of all zelig-dependent packages on the current machine } \description{ Get a List of Packages Installed on the Current Machine that Depend on Zelig } \note{ This function is used primarily internally to determine whether a a package is contributing a function to the Zelig software suite } Zelig/man/name.object.Rd0000644000176000001440000000134112061700507014600 0ustar ripleyusers\name{name.object} \alias{name.object} \title{Name Elements of an Object} \usage{ name.object(obj, names) } \arguments{ \item{obj}{a vector or matrix} \item{names}{a character-vector specifying names} } \value{ the original object, with a "colnames" or "names" equal to the parameter "names". If "names" is larger than "obj", the "names" parameter is truncated appropriately. If it is smaller, then the latter part of "obj" is replaced with a numbered generic column name } \description{ Returns an object } \note{ This method is used internally by Zelig to name the columns and elements of matrices and vectors for simulations and bootstrapped parameters. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.setx.Rd0000644000176000001440000000053712061700507014537 0ustar ripleyusers\name{print.setx} \alias{print.setx} \title{Print values of `setx' objects} \usage{ \method{print}{setx}(x, ...) } \arguments{ \item{x}{a `setx' object} \item{...}{ignored parameters} } \value{ the value of x (invisibly) } \description{ Print a ``setx'' object in human-readable form. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/bootstrap.negbinom.Rd0000644000176000001440000000116412061700507016230 0ustar ripleyusers\name{bootstrap.negbinom} \alias{bootstrap.negbinom} \title{Bootstrap Parameters for Zelig ``negbinom'' GLM} \usage{ \method{bootstrap}{negbinom}(obj, ...) } \arguments{ \item{obj}{a ``zelig'' object that will be used to produce boot-strapped parameters} \item{...}{extra parameters to be passed to the ``boot'' method. These are typically ignored, but is included for further expansion.} } \value{ a list containing information concerning link, link-inverses, etc. } \description{ Returns bootstrapped parameter estimates for a negative-binomial GLM. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/terms.multiple.Rd0000644000176000001440000000067712061700507015412 0ustar ripleyusers\name{terms.multiple} \alias{terms.multiple} \title{Extract Terms from a \code{multiple} Object} \usage{ \method{terms}{multiple}(x, data=NULL,...) } \arguments{ \item{x}{a Zelig v3.5 formula} \item{data}{a \code{data.frame}} \item{...}{ignored parameters} } \description{ Extracts terms from Zelig-3.5-style formulae. This function is scheduled for removal. } \author{ Kosuke Imai, Olivia Lau, Gary King and Ferdinand Alimadhi } Zelig/man/ZeligListModels.Rd0000644000176000001440000000121012061700507015460 0ustar ripleyusers\name{ZeligListModels} \alias{ZeligListModels} \title{Get a Character-Vector of All Models with a 'zelig2' Function} \usage{ ZeligListModels(zelig.only = FALSE) } \arguments{ \item{zelig.only}{a boolean specifying whether we want to search only the Zelig namespace} } \value{ a character-vector of the Zelig models loaded on the user's machine } \description{ Get a Character-Vector of All Models with a 'zelig2' Function } \note{ In order for a Zelig model to either execute correctly or be listed as a legal Zelig model, the function name must be prefixed with 'zelig2'. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/table.levels.Rd0000644000176000001440000000115712061700507015000 0ustar ripleyusers\name{table.levels} \alias{table.levels} \title{Create a table, but ensure that the correct columns exist. In particular, this allows for entires with zero as a value, which is not the default for standard tables} \usage{ table.levels(x, levels, ...) } \arguments{ \item{x}{a vector} \item{levels}{a vector of levels} \item{...}{parameters for table} } \value{ a table } \description{ Create a table, but ensure that the correct columns exist. In particular, this allows for entires with zero as a value, which is not the default for standard tables } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zeligBuildWeights.Rd0000644000176000001440000000413412216625034016046 0ustar ripleyusers\name{zeligBuildWeights} \alias{zeligBuildWeights} \title{Developer Utility Function for Dealing with Observation Weighting} \usage{ zeligBuildWeights(weights=NULL, repweights=NULL, zeros="zeros", rebuild=FALSE, allowweights=TRUE, allowrepweights=TRUE, data=NULL) } \arguments{ \item{weights}{A set of non-negative value weights. Overrides repweights if defined.} \item{repweights}{A set of whole number (non-negative integer) weights. Useful if weights are just for making copies of or deleting certain observations or for frequency weights.} \item{zeros}{An option on how to deal with zero valued user supplied weights. Default of "zero" allows zero weights, "epsilon" changes zeroes to 1e-08, "remove" removes those observations from the dataset.} \item{rebuild}{An option to allow specified repweights to reconfigure the rows of the dataset to rebuild a corresponding dataset where every row is of weight 1. Useful if analysis model does not accept weights.} \item{allowweights}{Defines if weights are allowed in model.} \item{allowrepweights}{Defines if repweights are allowed in model. Overridden if \code{useweights=TRUE}.} \item{data}{Dataset, required if weights are defined by variable name, or if dataset is to be reconfigured (by \code{rebuild} or \code{zeros} options)} } \value{ \item{weights}{A vector of weights of the structure defined by the developer and required by the analysis model. Or NULL if certain checks are failed.} \item{data}{A reconfigured dataset, if modified.} } \description{ The \code{zeligBuildWeights} utility allows developers building models or modules for Zelig, to easily define what types of weights can be set by the user and passed to estimation function. In some cases it can reconfigure the dataset by duplication to functionally invoke discrete weighting by replication of observations, when the estimator itself can not utilize weights. } \author{ James Honaker \email{jhonaker@iq.harvard.edu} } \seealso{ The full Zelig developer manual may be accessed online at \url{http://gking.harvard.edu/zelig} } \keyword{weights} Zelig/man/PErisk.Rd0000644000176000001440000000555712061700507013625 0ustar ripleyusers\name{PErisk} \alias{PErisk} \title{Political Economic Risk Data from 62 Countries in 1987} \description{ Political Economic Risk Data from 62 Countries in 1987. } \usage{data(PErisk)} \format{ A data frame with 62 observations on the following 6 variables. All data points are from 1987. See Quinn (2004) for more details. country: a factor with levels 'Argentina' 'Australia' 'Austria' 'Bangladesh' 'Belgium' 'Bolivia' 'Botswana' 'Brazil' 'Burma' 'Cameroon' 'Canada' 'Chile' 'Colombia' 'Congo-Kinshasa' 'Costa Rica' 'Cote d'Ivoire' 'Denmark' 'Dominican Republic' 'Ecuador' 'Finland' 'Gambia, The' 'Ghana' 'Greece' 'Hungary' 'India' 'Indonesia' 'Iran' 'Ireland' 'Israel' 'Italy' 'Japan' 'Kenya' 'Korea, South' 'Malawi' 'Malaysia' 'Mexico' 'Morocco' 'New Zealand' 'Nigeria' 'Norway' 'Papua New Guinea' 'Paraguay' 'Philippines' 'Poland' 'Portugal' 'Sierra Leone' 'Singapore' 'South Africa' 'Spain' 'Sri Lanka' 'Sweden' 'Switzerland' 'Syria' 'Thailand' 'Togo' 'Tunisia' 'Turkey' 'United Kingdom' 'Uruguay' 'Venezuela' 'Zambia' 'Zimbabwe' courts: an ordered factor with levels '0' < '1'.'courts' is an indicator of whether the country in question is judged to have an independent judiciary. From Henisz (2002). barb2: a numeric vector giving the natural log of the black market premium in each country. The black market premium is coded as the black market exchange rate (local currency per dollar) divided by the official exchange rate minus 1. From Marshall, Gurr, and Harff (2002). prsexp2: an ordered factor with levels '0' < '1' < '2' < '3' < '4' < '5', giving the lack of expropriation risk. From Marshall, Gurr, and Harff (2002). prscorr2: an ordered factor with levels '0' < '1' < '2' < '3' < '4' < '5', measuring the lack of corruption. From Marshall, Gurr, and Harff (2002). gdpw2: a numeric vector giving the natural log of real GDP per worker in 1985 international prices. From Alvarez et al. (1999). } \source{ Mike Alvarez, Jose Antonio Cheibub, Fernando Limongi, and Adam Przeworski. 1999. ``ACLP Political and Economic Database.'' . Witold J. Henisz. 2002. ``The Political Constraint Index (POLCON) Dataset.'' \ . Monty G. Marshall, Ted Robert Gurr, and Barbara Harff. 2002. ``State Failure Task Force Problem Set.'' . } \references{ Kevin M. Quinn. 2004. ``Bayesian Factor Analysis for Mixed Ordinal and Continuous Response.'' \emph{Political Analyis}. Vol. 12, pp.338--353. } \keyword{datasets} Zelig/man/as.parameters.parameters.Rd0000644000176000001440000000104512061700507017323 0ustar ripleyusers\name{as.parameters.parameters} \alias{as.parameters.parameters} \title{parameters -> parameters This is merely an identity function when casting 'parameters' objects into 'parameters'.} \usage{ as.parameters.parameters(params, ...) } \arguments{ \item{params}{a parameters object} \item{...}{ignored parameters} } \value{ the same parameter object } \description{ parameters -> parameters This is merely an identity function when casting 'parameters' objects into 'parameters'. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zelig2-survey.Rd0000644000176000001440000001010312061700507015136 0ustar ripleyusers\name{zelig2-survey} \alias{zelig2gamma.survey} \alias{zelig2logit.survey} \alias{zelig2normal.survey} \alias{zelig2poisson.survey} \alias{zelig2probit.survey} \title{Interface between \code{Zelig} and \code{svyglm}} \usage{ zelig2gamma.survey(formula, weights = NULL, ids = NULL, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates = FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, link = "inverse", data, ...) zelig2logit.survey(formula, weights = NULL, ids = NULL, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates = FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, data) zelig2normal.survey(formula, weights = NULL, ids = NULL, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates = FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, data) zelig2poisson.survey(formula, weights = NULL, ids = NULL, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates = FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, data) zelig2probit.survey(formula, weights = NULL, ids = NULL, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", return.replicates = FALSE, na.action = "na.omit", start = NULL, etastart = NULL, mustart = NULL, offset = NULL, model1 = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, design = NULL, data) } \arguments{ \item{formula}{a \code{formula}} \item{weights}{...} \item{ids}{...} \item{probs}{...} \item{strata}{...} \item{fpc}{...} \item{nest}{...} \item{check.strata}{...} \item{repweights}{...} \item{type}{...} \item{combined.weights}{...} \item{rho}{...} \item{bootstrap.average}{...} \item{scale}{...} \item{rscales}{...} \item{fpctype}{...} \item{return.replicates}{...} \item{na.action}{...} \item{start}{...} \item{etastart}{...} \item{mustart}{...} \item{offset}{...} \item{model1}{...} \item{method}{...} \item{x}{...} \item{y}{...} \item{contrasts}{...} \item{design}{...} \item{link}{an object specifying a link function between the predictor and response variables} \item{data}{a \code{data.frame}} \item{\dots}{ Additional parameters passed to teh ``gamma.survey'' model fitting function } } \value{ a \code{list} used to construct parameters for the \code{svyglm} function } \description{ Interface between \code{zelig} and \code{svyglm} for the \code{logit.survey} } \note{ This manual file is largely incomplete, and needs a significant amount of filling out. This, in itself, might be motivation to divide this package into more models with more specific function. } Zelig/man/describe.negbinom.Rd0000644000176000001440000000065412061700507015776 0ustar ripleyusers\name{describe.negbinom} \alias{describe.negbinom} \title{Describe the \code{negbinom} model to Zelig} \usage{ \method{describe}{negbinom}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by \code{as.description} } \description{ Describe the \code{negbinom} model to Zelig } \note{ \code{negbinom} stands for "negative binomial" } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/mi.Rd0000644000176000001440000000113212061700507013016 0ustar ripleyusers\name{mi} \alias{mi} \title{Bundle Data-sets for Multiple Imputation} \usage{ mi(...) } \arguments{ \item{...}{a set of \code{data.frame}'s} } \value{ an \code{almost.mi} object, which contains the important internals of a valid, useful \code{mi} object } \description{ This object prepares data-sets for processing with multiple imputation. } \note{ This function is largely identical to simply creating a list object, with the exception that any unnamed data-sets are automatically labeled via the \code{substitute} function } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/replace.call.Rd0000644000176000001440000000104012061700507014734 0ustar ripleyusers\name{replace.call} \alias{replace.call} \title{Hook to Update the Zelig Call with the Appropriate Call Object} \usage{ replace.call(zobj, call1, call2) } \arguments{ \item{zobj}{a 'zelig' object} \item{call1}{the original call to Zelig} \item{call2}{the manuafactured call to the model fitting function} } \value{ the 'zelig' object with a modified 'call' slot } \description{ Hook to Update the Zelig Call with the Appropriate Call Object } \note{ This function is used internally by Zelig, and currently deprecated. } Zelig/man/coef.parameters.Rd0000644000176000001440000000117212061700507015473 0ustar ripleyusers\name{coef.parameters} \alias{coef.parameters} \title{Return Simulations of Parameter Coefficients} \usage{ \method{coef}{parameters}(object, ...) } \arguments{ \item{object}{a 'parameters' object} \item{\dots}{ignored} } \value{ simulations, specified by the Zelig model, of the ancillary parameters } \description{ Returns simulated parameters of coefficients for use in statistical simulation. The values are set by the model-fitting function and the developer of the qi. method. } \note{ This function may not differ at all from coef.default } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/zelig2-core.Rd0000644000176000001440000000327012061700507014540 0ustar ripleyusers\name{zelig2-core} \alias{zelig2exp} \alias{zelig2gamma} \alias{zelig2logit} \alias{zelig2lognorm} \alias{zelig2ls} \alias{zelig2negbinom} \alias{zelig2normal} \alias{zelig2poisson} \alias{zelig2probit} \alias{zelig2relogit} \alias{zelig2tobit} \alias{zelig2twosls} \title{ Zelig to Basic GLM Fitting Functions } \usage{ zelig2exp(formula, ..., robust = FALSE, cluster = NULL, data) zelig2gamma(formula, ..., data) zelig2logit(formula, weights = NULL, robust = F, ..., data) zelig2lognorm(formula, ..., robust = FALSE, cluster = NULL, data) zelig2ls(formula, ..., data, weights = NULL) zelig2negbinom(formula, weights = NULL, ..., data) zelig2normal(formula, weights = NULL, ..., data) zelig2poisson(formula, weights = NULL, ..., data) zelig2probit(formula, weights = NULL, ..., data) zelig2relogit(formula, ..., tau = NULL, bias.correct = NULL, case.control = NULL, data) zelig2tobit(formula, ..., below = 0, above = Inf, robust = FALSE, cluster = NULL, data) zelig2twosls(formula, ..., data) } \arguments{ \item{formula}{a formula} \item{...}{additonal parameters} \item{weights}{a numeric vector} \item{robust}{a boolean specifying whether to use robust error estimates} \item{cluster}{a vector describing the clustering of the data} \item{data}{a data.frame} \item{tau}{...} \item{bias.correct}{...} \item{case.control}{...} \item{below}{a numeric or infinite specifying a lower boundary for censored responses} \item{above}{a numeric or infinite specifying an upper boundary for censored responses} } \value{ a list used by Zelig to call the model-fitting function } \description{ Interface between Zelig and Basic GLM Fitting Functions } Zelig/man/constructDesignMatrix.Rd0000644000176000001440000000114012061700507016753 0ustar ripleyusers\name{constructDesignMatrix} \alias{constructDesignMatrix} \title{Construct Design Matrix from Construct and return a design matrix based on a tiny data-frame (single-row).} \usage{ constructDesignMatrix(data, formula) } \arguments{ \item{data}{a ``data.frame'' (preferably single-rowed) that will be used to create a small design matrix} \item{formula}{a formula, whose predictor variables will be used to create a design matrix} } \value{ a design (model) matrix } \description{ Construct Design Matrix from Construct and return a design matrix based on a tiny data-frame (single-row). } Zelig/man/loadDependencies.Rd0000644000176000001440000000146212061700507015645 0ustar ripleyusers\name{loadDependencies} \alias{loadDependencies} \alias{load.dependencies} \title{Load External Dependencies Safely and Dynamically} \usage{ loadDependencies(..., character.only = FALSE) } \arguments{ \item{\ldots}{ A collection of packages to load. If ``character.only''=FALSE, these can be entered symbolically (e.g. loadDependencies(MASS)). Otherwise, these arguments are character-strings. } \item{character.only}{ A boolean specifying whether the arguments are strictly character-strings. } } \value{ TRUE (invisibly) if successful. Otherwise the script is stopped. } \description{ ``loadDependencies'' is a helper function for loading external dependencies at runtime. } \note{ This is used by Zelig developers to dynamically load ``dependent'' pacakges at runtime. } Zelig/man/makeModelMatrix.Rd0000644000176000001440000000067512061700507015507 0ustar ripleyusers\name{makeModelMatrix} \alias{makeModelMatrix} \title{Make a Model Matrix from a Zelig-Style Formula} \usage{ makeModelMatrix(formula, data) } \arguments{ \item{formula}{a Zelig-style formula} \item{data}{a \code{data.frame}} } \value{ a design (or model) matrix } \description{ This is a helper function that creates a \code{model.matrix} like object of Zelig-style formulae. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/grunfeld.Rd0000644000176000001440000000122112061700507014216 0ustar ripleyusers\name{grunfeld} \alias{grunfeld} \title{Simulation Data for model Seemingly Unrelated Regression (sur) that corresponds to method SUR of systemfit} \description{ Dataframe contains 20 annual observations from 1935 to 1954 of 7 variables for two firms General Electric and Westinghouse. Columns are Year; Ige and Iw = Gross investment for GE and W,respectively; Fge and Fw=Market value of Firm as of begin of the year; Cge and Cw= Capital stock measure as of begin of the year. } \usage{data(grunfeld)} \format{ A table containing 7 variables ("Year", "Ige", "Fge", "Cge","Iw", "Fw","Cw") and 20 observations. } \keyword{datasets} Zelig/man/cmvglm.Rd0000644000176000001440000000057612061700507013711 0ustar ripleyusers\name{cmvglm} \alias{cmvglm} \title{cmvglm} \usage{ cmvglm(formula, model, ndim, data = NULL, fact = NULL) } \arguments{ \item{formula}{a formula} \item{model}{the names of the Zelig model} \item{ndim}{the number of dimensions in the statistical model} \item{data}{a data-frame} \item{fact}{???} } \description{ cmvglm } \author{ Kosuke Imai and Olivia Lau } Zelig/man/zelig2-bayes.Rd0000644000176000001440000000262112216135776014726 0ustar ripleyusers\name{zelig2-bayes} \alias{zelig2factor.bayes} \alias{zelig2logit.bayes} \alias{zelig2mlogit.bayes} \alias{zelig2normal.bayes} \alias{zelig2oprobit.bayes} \alias{zelig2poisson.bayes} \alias{zelig2probit.bayes} \title{Zelig Bridge Functions to Bayesian Models} \usage{ zelig2factor.bayes(formula, factors = 2, burnin = 1000, mcmc = 20000, verbose = 0, ..., data) zelig2logit.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) zelig2mlogit.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) zelig2normal.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) zelig2oprobit.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) zelig2poisson.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) zelig2probit.bayes(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data) } \arguments{ \item{formula}{a formula} \item{...}{additonal parameters} \item{data}{a data.frame} \item{factors}{a list of factors} \item{burnin}{a parameter corresponding to the 'burnin' paramater for the MCMCprobit function} \item{mcmc}{a parameter corresponding to the 'mcmc' paramater for the MCMCprobit function} \item{verbose}{a parameter corresponding to the 'verbose' paramater for the MCMCprobit function} } \value{ a list specifying '.function' } \description{ Interface between Zelig and the bayesian models. } Zelig/man/is.qi.Rd0000644000176000001440000000053712061700507013444 0ustar ripleyusers\name{is.qi} \alias{is.qi} \title{Test If Value is Interpretable as a QI} \usage{ is.qi(qi) } \arguments{ \item{qi}{a potential quantity of interest} } \value{ a logical specifying whether this value should or should-not be output } \description{ Test If Value is Interpretable as a QI } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/summary.sim.Rd0000644000176000001440000000062612061700507014704 0ustar ripleyusers\name{summary.sim} \alias{summary.sim} \title{Method for summarizing simulations of quantities of interest} \usage{ \method{summary}{sim}(object, ...) } \arguments{ \item{object}{a 'MI.sim' object} \item{...}{ignored parameters} } \value{ a 'summarized.MI.sim' object } \description{ Return a ``summary.sim'' object (typically for display) } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/describe.ls.Rd0000644000176000001440000000060412061700507014611 0ustar ripleyusers\name{describe.ls} \alias{describe.ls} \title{Describe a \code{ls} model to Zelig} \usage{ \method{describe}{ls}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by \code{as.description} } \description{ Describe a \code{ls} model to Zelig } \note{ \code{ls} stands for "least squares fit" } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.parameters.list.Rd0000644000176000001440000000101012061700507016123 0ustar ripleyusers\name{as.parameters.list} \alias{as.parameters.list} \title{list -> parameters} \usage{ as.parameters.list(params, num = NULL, ...) } \arguments{ \item{params}{a list object} \item{num}{an integer specifying the number of simulations to be taken} \item{...}{ignored parameters} } \value{ an object of type `parameters' } \description{ The list may contain: 'link', 'linkinv', 'family', 'alpha', and 'simulations' keys. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ as.parameters } Zelig/man/termsFromFormula.Rd0000644000176000001440000000052612061700507015723 0ustar ripleyusers\name{termsFromFormula} \alias{termsFromFormula} \title{Extract Terms from Zelig-style Formulae} \usage{ termsFromFormula(obj) } \arguments{ \item{obj}{a Zelig-style formula} } \description{ This method is a sugary function to extract terms from any type of Zelig-style formula. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.summarized.summarized.qi.Rd0000644000176000001440000000065112061700507017767 0ustar ripleyusers\name{as.summarized.summarized.qi} \alias{as.summarized.summarized.qi} \title{summarized.qi -> summarized.qi} \usage{ \method{as.summarized}{summarized.qi}(x, ...) } \arguments{ \item{x}{an object of type 'summarized.qi'} \item{...}{ignored parameters} } \value{ the same 'summarized.qi' object } \description{ Identity operation on ``summarized.qi'' objects } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/MCMChook.Rd0000644000176000001440000000143712110047000014004 0ustar ripleyusers\name{MCMChook} \alias{MCMChook} \title{Hook to Clean-up MCMC Objects} \usage{ MCMChook(obj, model.call, zelig.call, seed = NULL, ..., data = NULL) } \arguments{ \item{obj}{the fitted model object (in this case a \code{mcmc} object.} \item{model.call}{the call made to the external model} \item{zelig.call}{the actual call to zelig itself} \item{seed}{a seed for the MCMC algorithm} \item{...}{ignored parameters} \item{data}{the data.frame being used to fit the statistical model} } \value{ an object useable by Zelig } \description{ This method gives valid methods to the resulting MCMC object so that it can be used with Zelig. } \note{ This function is used internally by the ZeligBayesian package. } \author{ Olivia Lau, Kosuke Imai, Gary King and Matt Owen } Zelig/man/summary.MI.sim.Rd0000644000176000001440000000071012061700507015202 0ustar ripleyusers\name{summary.MI.sim} \alias{summary.MI.sim} \title{Method for summarizing simulations of multiply imputed quantities of interest} \usage{ \method{summary}{MI.sim}(object, ...) } \arguments{ \item{object}{a `MI.sim' object} \item{...}{ignored parameters} } \value{ a `summarized.MI.sim' object } \description{ Method for summarizing simulations of multiply imputed quantities of interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/MatchIt.url.Rd0000644000176000001440000000027712061700507014554 0ustar ripleyusers\name{MatchIt.url} \alias{MatchIt.url} \title{Table of links for Zelig} \description{ Table of linds for \code{help.zelig} for the companion MatchIt package. } \keyword{datasets} Zelig/man/t.setx.Rd0000644000176000001440000000074412061700507013646 0ustar ripleyusers\name{t.setx} \alias{t.setx} \title{Matrix Transpose of a ``setx'' Object} \usage{ \method{t}{setx}(x) } \arguments{ \item{x}{a `setx' object} } \value{ a transposed matrix } \description{ Returns a ``setx'' object as column vector. If multiple values for each explanatory term has been set, then return a NxM matrix where `N' is the number of explanatory terms and `M' is the number of values set for each term. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/plot.MI.sim.Rd0000644000176000001440000000064212215713204014466 0ustar ripleyusers\name{plot.MI.sim} \alias{plot.MI.sim} \title{Plot graphs of simulated multiply-imputed data} \usage{ \method{plot}{MI.sim}(x, ...) } \arguments{ \item{x}{A zelig `sim' object, with multiply imputed data} \item{...}{ignored parameters} } \value{ NULL (invisibly) } \description{ This function is currently unimplemented, and reserved for future use. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/coalition2.Rd0000644000176000001440000000221512061700507014457 0ustar ripleyusers\name{coalition2} \alias{coalition2} \docType{data} \title{Coalition Dissolution in Parliamentary Democracies, Modified Version} \description{ This data set contains survival data on government coalitions in parliamentary democracies (Belgium, Canada, Denmark, Finland, France, Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom) for the period 1945-1987. Country indicator variables are included in the sample data. } \usage{data(coalition2)} \format{ A data frame containing 8 variables ("duration", "ciep12", "invest", "fract", "polar", "numst2", "crisis", "country") and 314 observations. For variable descriptions, please refer to King, Alt, Burns and Laver (1990). } \source{ICPSR} \references{ King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990). ``A Unified Model of Cabinet Dissolution in Parliamentary Democracies,'' \emph{American Journal of Political Science}, vol. 34, no. 3, pp. 846-870. Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR Publication Related Archive, 1115. } \keyword{datasets}Zelig/man/ignore.Rd0000644000176000001440000000113712061700507013701 0ustar ripleyusers\name{ignore} \alias{ignore} \title{Constructor for the 'ignore' class This class is included for future use, and is currently not used in any Zelig model. It is designed for use with zelig2* functions} \usage{ ignore(default = NULL, type = "no pass") } \arguments{ \item{default}{default value} \item{type}{ignored parameter} } \value{ an 'ignore' object } \description{ Constructor for the 'ignore' class This class is included for future use, and is currently not used in any Zelig model. It is designed for use with zelig2* functions } \author{ Matt Owen \emph{mowen@iq.harvard.edu} } Zelig/man/getResponseTerms.Formula-not-formula.Rd0000644000176000001440000000127412061700507021576 0ustar ripleyusers\name{getResponseTerms.Formula} \alias{getResponse.Formula} \alias{getResponseTerms.Formula} \title{Get Response Terms from a ``Formula'' Object} \usage{ \method{getResponseTerms}{Formula}(x, ..., single.only=FALSE, duplicates=TRUE) } \arguments{ \item{x}{a formula} \item{...}{ignored parameters} \item{single.only}{a logical specifying whether 'cbind' or 'list' keywords are allowed} \item{duplicates}{a logical specifying whether the returned character-vector will only return duplicates.} } \value{ a character-vector specifying the response terms of the formula } \description{ This method gets the response terms from a ``Formula'' Object } \author{ Matt Owen } Zelig/man/repl.sim.Rd0000644000176000001440000000204312061700507014144 0ustar ripleyusers\name{repl.sim} \alias{repl.sim} \title{Method for Replicating Simulated Quantities of Interest} \usage{ \method{repl}{sim}(object, x=NULL, x1=NULL, y=NULL, num=1000, prev = NULL, bootstrap = FALSE, boot.fn=NULL, cond.data = NULL, ...) } \arguments{ \item{object}{a 'zelig' object} \item{x}{a 'setx' object} \item{x1}{a secondary 'setx' object used to perform particular computations of quantities of interest} \item{y}{a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter} \item{num}{an integer specifying the number of simulations to compute} \item{prev}{ignored} \item{bootstrap}{ignored} \item{boot.fn}{ignored} \item{cond.data}{ignored} \item{...}{special parameters which are reserved for future versions of Zelig} } \value{ a 'sim' object storing the replicated quantities of interest } \description{ Replicate simulated quantities of interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.summarized.Rd0000644000176000001440000000122412061700507015175 0ustar ripleyusers\name{as.summarized} \alias{as.summarized} \title{Generic Method for Casting Objectst as 'summarized' Objects} \usage{ as.summarized(x, ...) } \arguments{ \item{x}{an object} \item{...}{unspecified parameters} } \value{ a 'summarized.qi' object } \description{ This function is particularly for use by the 'summarize' method, which summarizes the simulations taken from the 'qi' method. The generic function 'summary' when applied to a Zelig Simulation implicitly uses this function. } \note{ This is made available on the Global namespace as a matter of potential future compliancy. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/make.parameters.Rd0000644000176000001440000000055612061700507015501 0ustar ripleyusers\name{make.parameters} \alias{make.parameters} \title{??? For use with cmvglm} \usage{ make.parameters(terms, shape = "vector", ancillary = TRUE, eqns = NULL) } \arguments{ \item{terms}{???} \item{shape}{???} \item{ancillary}{???} \item{eqns}{???} } \value{ ??? } \description{ ??? For use with cmvglm } \author{ Kosuke Imai and Olivia Lau } Zelig/man/param.gamma.Rd0000644000176000001440000000073612061700507014603 0ustar ripleyusers\name{param.gamma} \alias{param.gamma} \title{param method for the `gamma' Zelig model} \usage{ \method{param}{gamma}(obj, num, ...) } \arguments{ \item{obj}{a `zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored parameters} } \value{ a list to be cast as a `parameters' object } \description{ Return parameter estimates for the ``gamma'' GLM in Zelig. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/describe.zelig.Rd0000644000176000001440000000120212061700507015300 0ustar ripleyusers\name{describe.zelig} \alias{describe.zelig} \title{Get Description Object Used to Cite this Zelig Model} \usage{ \method{describe}{zelig}(object, ...) } \arguments{ \item{object}{a 'zelig' object} \item{...}{ignored parameters} } \value{ a 'description' object used internally to produce citation text } \description{ Get Description Object Used to Cite this Zelig Model } \note{ This function should be reevaluated in design, since 'description' objects are exclusively used internally. In particular, this method would be more useful to users as a 'cite' method. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/is.valid.qi.list.Rd0000644000176000001440000000053712061700507015514 0ustar ripleyusers\name{is.valid.qi.list} \alias{is.valid.qi.list} \title{Check If Object Is a List of Valid Quantities of Interest} \usage{ is.valid.qi.list(x) } \arguments{ \item{x}{an object to be tested} } \value{ TRUE or FALSE } \description{ Check If Object Is a List of Valid Quantities of Interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.qi.qi.Rd0000644000176000001440000000036012061700507014036 0ustar ripleyusers\name{as.qi.qi} \alias{as.qi.qi} \title{qi -> qi} \usage{ as.qi.qi(s) } \arguments{ \item{s}{an object of type `qi'} } \value{ s an object of type `qi' } \description{ qi -> qi } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.qi.summarized.Rd0000644000176000001440000000077612061700507016351 0ustar ripleyusers\name{print.qi.summarized} \alias{print.qi.summarized} \title{Print Method for Summarized Quantities of Interest} \usage{ \method{print}{qi.summarized}(x, \dots) } \arguments{ \item{x}{a 'summarized.qi' object} \item{...}{parameters to be passed to the specific print functions} } \value{ x (invisibly) } \description{ Print Method for Summarized Quantities of Interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ \link{special_print_MATRIX} and \link{special_print_LIST} } Zelig/man/plot.simulations.Rd0000644000176000001440000000132512061700507015741 0ustar ripleyusers\name{plot.simulations} \alias{plot.simulations} \title{Plot Any Simulation from the Zelig Core Package} \usage{ plot.simulations(x, ...) } \arguments{ \item{x}{an object} \item{...}{parameters passed to the ``plot'' and ``barplot'' functions} } \value{ the original graphical parameters } \description{ Plots any simulation from the core package. In general, this function can \emph{neatly} plot simulations containing five of the popular ``quantities of interest'' - ``Expected Values: E(Y|X)'', ``Predicted Values: Y|X'', ``Expected Values (for X1): E(Y|X1)'', ``Predicted Values (for X1): Y|X1'' and ``First Differences: E(Y|X1) - E(Y|X)''. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/getResponseTerms.list.Rd0000644000176000001440000000065112061700507016701 0ustar ripleyusers\name{getResponseTerms.list} \alias{getResponseTerms.list} \title{Get Response Terms from a List-style Formula} \usage{ \method{getResponseTerms}{list}(x, ...) } \arguments{ \item{x}{a list of formulae} \item{...}{ignored parameters} } \value{ a character-vector specifying the response terms of the formula } \description{ This method gets the response terms from a standard formula } \author{ Matt Owen } Zelig/man/describe.poisson.Rd0000644000176000001440000000053612061700507015671 0ustar ripleyusers\name{describe.poisson} \alias{describe.poisson} \title{Describe the `poisson' model to Zelig} \usage{ \method{describe}{poisson}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe the `poisson' model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.poisson.Rd0000644000176000001440000000101212061700507015177 0ustar ripleyusers\name{param.poisson} \alias{param.poisson} \title{Param Method for the 'poisson' Zelig Model} \usage{ \method{param}{poisson}(obj, num=1000, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the 'poisson' Zelig Model } \note{ This method is used by the 'poisson' Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/describe.normal.Rd0000644000176000001440000000054312061700507015465 0ustar ripleyusers\name{describe.normal} \alias{describe.normal} \title{Describe the \code{normal} model to Zelig} \usage{ \method{describe}{normal}(...) } \arguments{ \item{...}{ignored parameters} } \value{ a list to be processed by `as.description' } \description{ Describe the \code{normal} model to Zelig } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.summary.MCMCZelig.Rd0000644000176000001440000000075612061700507016765 0ustar ripleyusers\name{print.summary.MCMCZelig} \alias{print.summary.MCMCZelig} \title{Print a Summary MCMCZelig Object} \usage{ \method{print}{summary.MCMCZelig}(x, digits=max(3, getOption("digits") - 3), ...) } \arguments{ \item{x}{an "MCMCZelig" object} \item{digits}{a numeric specifying the precision of the summary object} \item{...}{ignored parameters} } \value{ a \code{summary.MCMCZelig} object } \description{ This method prints a summary object for \code{MCMCZelig} objects } Zelig/man/zelig.Rd0000644000176000001440000000534512061700507013535 0ustar ripleyusers\name{zelig} \alias{zelig} \title{Estimating a Statistical Model} \usage{ zelig(formula, model, data, ..., by = NULL, cite = T) } \arguments{ \item{formula}{a symbolic representation of the model to be estimated, in the form \code{y \~\, x1 + x2}, where \code{y} is the dependent variable and \code{x1} and \code{x2} are the explanatory variables, and \code{y}, \code{x1}, and \code{x2} are contained in the same dataset. (You may include more than two explanatory variables, of course.) The \code{+} symbol means ``inclusion'' not ``addition.'' You may also include interaction terms and main effects in the form \code{x1*x2} without computing them in prior steps; \code{I(x1*x2)} to include only the interaction term and exclude the main effects; and quadratic terms in the form \code{I(x1^2)}} \item{model}{the name of a statistical model. Type \code{help.zelig("models")} to see a list of currently supported models} \item{data}{the name of a data frame containing the variables referenced in the formula, or a list of multiply imputed data frames each having the same variable names and row numbers (created by \code{mi})} \item{...}{additional arguments passed to \code{zelig}, depending on the model to be estimated} \item{by}{a factor variable contained in \code{data}. Zelig will subset the data frame based on the levels in the \code{by} variable, and estimate a model for each subset. This a particularly powerful option which will allow you to save a considerable amount of effort. For example, to run the same model on all fifty states, you could type: \code{z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")} You may also use \code{by} to run models using MatchIt subclass} \item{cite}{If is set to "TRUE" (default), the model citation will be} } \value{ Depending on the class of model selected, \code{zelig} will return an object with elements including \code{coefficients}, \code{residuals}, and \code{formula} which may be summarized using \code{summary(z.out)} or individually extracted using, for example, \code{z.out\$coefficients}. See the specific models listed above for additional output values, or simply type \code{names(z.out)}. } \description{ The zelig command estimates a variety of statistical models. Use \code{zelig} output with \code{setx} and \code{sim} to compute quantities of interest, such as predicted probabilities, expected values, and first differences, along with the associated measures of uncertainty (standard errors and confidence intervals). } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Kosuke Imai, Olivia Lau, and Gary King Maintainer: Matt Owen \email{mowen@iq.harvard.edu} } \keyword{package} Zelig/man/zelig2-gee.Rd0000644000176000001440000000251712061700507014353 0ustar ripleyusers\name{zelig2-gee} \alias{zelig2gamma.gee} \alias{zelig2logit.gee} \alias{zelig2normal.gee} \alias{zelig2poisson.gee} \alias{zelig2probit.gee} \title{Bridge between Zelig and the GEE Model Fitting Functions} \usage{ zelig2gamma.gee(formula, id, robust, ..., R, corstr = "independence", data) zelig2logit.gee(formula, id, robust, ..., R, corstr = "independence", data) zelig2normal.gee(formula, id, robust, ..., R, corstr = "independence", data) zelig2poisson.gee(formula, id, robust, ..., R, corstr = "independence", data) zelig2probit.gee(formula, id, robust, ..., R, corstr = "independence", data) } \arguments{ \item{formula}{a formula} \item{id}{a character-string specifying the column of the data-set to use for clustering} \item{robust}{a logical specifying whether to robustly or naively compute the covariance matrix. This parameter is ignore in the \code{zelig2} method, and instead used in the \code{robust.hook} function, which executes after the call to the \code{gee} function} \item{...}{ignored parameters} \item{R}{a square-matrix specifying the correlation} \item{corstr}{a character-string specifying the correlation structure} \item{data}{a data.frame} } \value{ a list specifying the call to the external model } \description{ Bridge between Zelig and the GEE Model Fitting Functions } Zelig/man/newpainters.Rd0000644000176000001440000000222112061700507014750 0ustar ripleyusers\name{newpainters} \alias{newpainters} \title{The Discretized Painter's Data of de Piles} \description{ The original painters data contain the subjective assessment, on a 0 to 20 integer scale, of 54 classical painters. The newpainters data discretizes the subjective assessment by quartiles with thresholds 25\%, 50\%, 75\%. The painters were assessed on four characteristics: composition, drawing, colour and expression. The data is due to the Eighteenth century art critic, de Piles. } \usage{data(newpainters)} \format{A table containing 5 variables ("Composition", "Drawing", "Colour", "Expression", and "School") and 54 observations.} \source{ A. J. Weekes (1986).``A Genstat Primer''. Edward Arnold. M. Davenport and G. Studdert-Kennedy (1972). ``The statistical analysis of aesthetic judgement: an exploration.'' \emph{Applied Statistics}, vol. 21, pp. 324--333. I. T. Jolliffe (1986) ``Principal Component Analysis.'' Springer. } \references{ Venables, W. N. and Ripley, B. D. (2002) ``Modern Applied Statistics with S,'' Fourth edition. Springer. } \keyword{datasets} Zelig/man/user.prompt.Rd0000644000176000001440000000053312061700507014713 0ustar ripleyusers\name{user.prompt} \alias{user.prompt} \title{Prompt User} \usage{ user.prompt(msg = NULL) } \arguments{ \item{msg}{a character-string, specifying a message to be displayed} } \value{ This function is used for its side effects } \description{ Prompts user to hit enter } \note{ This function is primarily used by Zelig demo scripts } Zelig/man/hoff.Rd0000644000176000001440000000135712061700507013344 0ustar ripleyusers\name{hoff} \alias{hoff} \title{Social Security Expenditure Data} \description{ This data set contains annual social security expenditure (as percent of budget lagged by two years), the relative frequency of mentions social justice received in the party's platform in each year, and whether the president is Republican or Democrat. } \usage{data(hoff)} \format{A table containing 5 variables ("year", "L2SocSec", "Just503D", "Just503R", "RGovDumy") and 36 observations.} \source{ICPSR (replication dataset s1109)} \references{ Gary King and Michael Laver. ``On Party Platforms, Mandates, and Government Spending,'' \emph{American Political Science Review}, Vol. 87, No. 3 (September, 1993): pp. 744-750. } \keyword{datasets} Zelig/man/print.summary.sim.Rd0000644000176000001440000000070712061700507016037 0ustar ripleyusers\name{print.summary.sim} \alias{print.summary.sim} \title{Print Values of a Summarized ``sim'' Object} \usage{ \method{print}{summary.sim}(x, ...) } \arguments{ \item{x}{a 'summary.sim' object} \item{...}{ignored parameters} } \value{ the value of the `summary.sim' object (invisibly) } \description{ Print values of simulated quantities of interest (stored in a ``summary.sim'' object. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/print.setx.mi.Rd0000644000176000001440000000054712061700507015144 0ustar ripleyusers\name{print.setx.mi} \alias{print.setx.mi} \title{Print a Bundle of Data-sets} \usage{ \method{print}{setx.mi}(x, ...) } \arguments{ \item{x}{a \code{setx} object to print} \item{...}{ignored parameters} } \value{ the \code{setx} object (invisibly) } \description{ Print a Bundle of Data-sets } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.matrix.pooled.setx.Rd0000644000176000001440000000166412061700507016574 0ustar ripleyusers\name{as.matrix.pooled.setx} \alias{as.matrix.pooled.setx} \title{Convert a ``pooled.setx'' Object to a Matrix} \usage{ \method{as.matrix}{pooled.setx}(x, ...) } \arguments{ \item{x}{a setx object} \item{...}{ignored parameters} } \value{ a matrix containing columns and rows corrseponding to the explanatory variables specified in the call to the 'setx' function } \description{ The setx object is, in its most basic form, a list of column names and values specified for each of these column names. This function simply converts the key-value pairs of column-name and specified value into a matrix. } \note{ This method allows basic matrix arithmetic operations on data objects, which mirror values stored within setx objects. In many scenarios, simulations require matrix-multiplication, etc. to be performed on a data-set. This function faciliates that need. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/getResponseTerms.formula.Rd0000644000176000001440000000123412061700507017371 0ustar ripleyusers\name{getResponseTerms.formula} \alias{getResponseTerms.formula} \title{Get Response Terms from a Standard Formula} \usage{ \method{getResponseTerms}{formula}(x, ..., single.only=FALSE, duplicates=TRUE) } \arguments{ \item{x}{a formula} \item{...}{ignored parameters} \item{single.only}{a logical specifying whether 'cbind' or 'list' keywords are allowed} \item{duplicates}{a logical specifying whether the returned character-vector will only return duplicates.} } \value{ a character-vector specifying the response terms of the formula } \description{ This method gets the response terms from a standard formula } \author{ Matt Owen } Zelig/man/summary.relogit.Rd0000644000176000001440000000052712061700507015561 0ustar ripleyusers\name{summary.Relogit} \alias{summary.Relogit} \title{Summary for ``Relogit'' Fitted Model} \usage{ \method{summary}{Relogit}(object, ...) } \arguments{ \item{object}{a ``Relogit'' object} \item{...}{other parameters} } \value{ a ``summary.relogit'' object } \description{ Summarize important components of the ``relogit'' model } Zelig/man/friendship.Rd0000644000176000001440000000150012216135102014536 0ustar ripleyusers\name{friendship} \alias{friendship} \title{Simulated Example of Schoolchildren Friendship Network} \description{ This data set contains six sociomatrices of simulated data on friendship ties among schoolchildren.} \usage{data(friendship)} \format{ Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", "perpower" and "per". The sociomatrices were combined into the friendship dataset using the format.network.data function from the netglm package by Skyler Cranmer as shown in the example. } \source{fictitious} \examples{ \dontrun{ friendship <- format.network.data(friends, advice, prestige, authority, perpower, per) }} \keyword{datasets} Zelig/man/get.package.Rd0000644000176000001440000000111612061700507014564 0ustar ripleyusers\name{get.package} \alias{get.package} \title{Find the Zelig package that a particular model belong to} \usage{ get.package(model, quiet = TRUE, ...) } \arguments{ \item{model}{a character-string specifying a Zelig model} \item{quiet}{a logical indicating whether to display messages and warnings} \item{...}{ignored parameters} } \value{ NA or a character-string specifying the name of the package which contains a specific model } \description{ This method is used to help transition Zelig v3.5 users to Zelig v4 } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/param.logit.Rd0000644000176000001440000000102312061700507014625 0ustar ripleyusers\name{param.logit} \alias{param.logit} \title{Param Method for the \code{logit} Zelig Model} \usage{ \method{param}{logit}(obj, num, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{num}{an integer specifying the number of simulations to sample} \item{...}{ignored parameters} } \value{ a list to be cast as a 'parameters' object } \description{ Param Method for the \code{logit} Zelig Model } \note{ This method is used by the \code{logit} Zelig model } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/swiss.Rd0000644000176000001440000000272512061700507013572 0ustar ripleyusers\name{swiss} \alias{swiss} \title{Swiss Fertility and Socioeconomic Indicators (1888) Data} \description{ Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888. } \usage{data(swiss)} \format{ A data frame with 47 observations on 6 variables, each of which is in percent, i.e., in [0,100]. [,1] Fertility Ig, "common standardized fertility measure" [,2] Agriculture % of males involved in agriculture as occupation [,3] Examination % "draftees" receiving highest mark on army exami nation [,4] Education % education beyond primary school for "draftees". [,5] Catholic % catholic (as opposed to "protestant"). [,6] Infant.Mortality live births who live less than 1 year. All variables but 'Fert' give proportions of the population. } \source{ Project "16P5", pages 549-551 in Mosteller, F. and Tukey, J. W. (1977) ``Data Analysis and Regression: A Second Course in Statistics''. Addison-Wesley, Reading Mass. indicating their source as "Data used by permission of Franice van de Walle. Office of Population Research, Princeton University, 1976. Unpublished data assembled under NICHD contract number No 1-HD-O-2077." } \references{ Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) ``The New S Language''. Wadsworth & Brooks/Cole. } \keyword{datasets} Zelig/man/qi.Rd0000644000176000001440000000264512061700507013034 0ustar ripleyusers\name{qi} \alias{qi} \alias{qi.exp.Rd} \alias{qi.logit.Rd} \alias{qi.negbinom.Rd} \alias{qi.normal.survey.Rd} \alias{qi.poisson.survey.Rd} \alias{qi.relogit.Rd} \alias{qi.gamma.Rd} \alias{qi.ls.Rd} \alias{qi.normal.Rd} \alias{qi.poisson.Rd} \alias{qi.probit.Rd} \alias{qi.relogit2.Rd} \alias{qi.tobit.Rd} \title{Generic Method for Computing Quantities of Interest} \usage{ qi(obj, x = NULL, x1 = NULL, y = NULL, num, param = NULL) } \arguments{ \item{obj}{a \code{zelig} object} \item{x}{a \code{setx} object or NULL} \item{x1}{an optional \code{setx} object} \item{y}{this parameter is reserved for simulating average treatment effects, though this feature is currentlysupported by only a handful of models} \item{num}{an integer specifying the number of simulations to compute} \item{param}{a parameters object} } \value{ a list of key-value pairs specifying pairing titles of quantities of interest with their simulations } \description{ The \code{qi} function is used by developers to simulated quantities of interest. This method, as a result, is the most significant method of any Zelig statistical model. } \note{ Run \code{example(qi)} to see a trivial version of } \examples{ qi.some.model <- function(obj, x=NULL, x1=NULL, y=NULL, param=NULL) { list( "Expected Values: E(Y|X)" = NA, "Predicted Values: Y|X" = NA ) } } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/ZeligListTitles.Rd0000644000176000001440000000040312061700507015504 0ustar ripleyusers\name{ZeligListTitles} \alias{ZeligListTitles} \title{List the Titles of the Zelig Statistical Models} \usage{ ZeligListTitles() } \value{ a list of manual titles for the Zelig software } \description{ List the Titles of the Zelig Statistical Models } Zelig/man/turnout.Rd0000644000176000001440000000145312061700507014137 0ustar ripleyusers\name{turnout} \alias{turnout} \title{Turnout Data Set from the National Election Survey} \description{ This data set contains individual-level turnout data. It pools several American National Election Surveys conducted during the 1992 presidential election year. Only the first 2,000 observations (from a total of 15,837 observations) are included in the sample data. } \usage{data(turnout)} \format{A table containing 5 variables ("race", "age", "educate", "income", and "vote") and 2,000 observations.} \source{National Election Survey} \references{ King, Gary, Michael Tomz, Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp.341--355. } \keyword{datasets} Zelig/man/as.qi.Rd0000644000176000001440000000244512061700507013434 0ustar ripleyusers\name{as.qi} \alias{as.qi} \title{Generic Method for Converting Various Objects into 'qi' Objects 'qi' objects are list-style objects used by the 'summarize' function to compute simple summaries about the simulated data. For readability and and simplicity purposes, the 'qi' function typically returns a list of named simulations. This list is converted internally by Zelig into a 'qi' object so that several methods can be easily applied to the Quantities of Interest: plot, summarize, and print} \usage{ as.qi(s) } \arguments{ \item{s}{the object to be casted} } \value{ an object of type `qi' } \description{ Generic Method for Converting Various Objects into 'qi' Objects 'qi' objects are list-style objects used by the 'summarize' function to compute simple summaries about the simulated data. For readability and and simplicity purposes, the 'qi' function typically returns a list of named simulations. This list is converted internally by Zelig into a 'qi' object so that several methods can be easily applied to the Quantities of Interest: plot, summarize, and print } \note{ These functions are primarily used internall by Zelig and should not be used in the Global namespace. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } \seealso{ as.qi.default as.qi.qi as.qi.list } Zelig/man/callToString.Rd0000644000176000001440000000065512061700507015027 0ustar ripleyusers\name{callToString} \alias{callToString} \title{Convert \code{call} Object to a String} \usage{ callToString(x, ...) } \arguments{ \item{x}{a \code{call} object} \item{...}{ignored parameters} } \value{ a character-string representing the \code{call} object } \description{ This method concerts \code{call} objects into a simple, intuitive human-readable form. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/attach.env.Rd0000644000176000001440000000227712061700507014457 0ustar ripleyusers\name{attach.env} \alias{attach.env} \title{Attach Variables to a Function} \usage{ attach.env(f, env = NULL, ...) } \arguments{ \item{f}{a function which will be modified} \item{env}{an environment variable which will be attached to the function being returned} \item{...}{arbitrary key-value paired parameters which will be assigned to the environment of the function being returned} } \value{ the original function ``f'' with a different environment attached to it. } \description{ Returns a function, specified by the user, with the variables of a specified environment attached. This, in essence, allows programmers to write functions that have forms of private memory. This makes the function behave similarly to an object. } \note{ This function is used by Zelig to ensure that particular method calls - param, qi, bootstap - will contain the private variables: ``.fitted'', ``.model'', ``.call'' and ``.env'' which respectively contain the fitted model object, the name of the zelig model being invoked, the original call to the model-fitting function and the environment in which to call the function call. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/as.bootvector.Rd0000644000176000001440000000146312061700507015210 0ustar ripleyusers\name{as.bootvector} \alias{as.bootvector} \title{Convert Boot Object to a Vector} \usage{ as.bootvector(obj) } \arguments{ \item{obj}{a list with two slots: ``alpha'' and ``beta''. Respectively, these represent bootstrap samples for ancillary parameters and systematic component of the bootstrapped GLM.} } \value{ a list containing the resulting vector, as well as an object used to reverse-build the list (``obj'') from the resulting call to ``bootstrap''. } \description{ Receives a list with 2 slots as its input, and returns a vector of the two smashed together alongwith the offsets used to reverse-construct the object. } \note{ This method is used internally by Zelig to allow an intuitive, ``param''-like API for bootstrapping. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/bootstrap.normal.Rd0000644000176000001440000000126412061700507015723 0ustar ripleyusers\name{bootstrap.normal} \alias{bootstrap.normal} \title{Bootstrap Parameters for Zelig ``normal'' GLM} \usage{ \method{bootstrap}{normal}(obj, num, ...) } \arguments{ \item{obj}{a ``zelig'' object that will be used to produce boot-strapped parameters} \item{num}{an integer specifying the number of simulations to produce} \item{...}{extra parameters to be passed to the ``boot'' method. These are typically ignored, but is included for further expansion.} } \value{ a list containing information concerning link, link-inverses, etc. } \description{ Returns bootstrapped parameter estimates for a Gaussian GLM. } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/plot.pooled.sim.Rd0000644000176000001440000000400412216135466015450 0ustar ripleyusers\name{plot.pooled.sim} \alias{plot.pooled.sim} \title{Method for plotting pooled simulations by confidence intervals} \usage{ \method{plot}{pooled.sim}(x, qi="ev", var=NULL, ..., main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, legcol="gray20", col=NULL, leg=1, legpos=NULL, ci=c(80,95,99.9)) } \arguments{ \item{x}{A `sim' object} \item{qi}{a character-string specifying the quantity of interest to plot} \item{var}{The variable to be used on the x-axis. Default is the variable across all the chosen values with smallest nonzero variance} \item{...}{Parameters to be passed to the `truehist' function which is implicitly called for numeric simulations} \item{main}{A character-string, specifying the main title of the plot} \item{sub}{A character-string, specifying the sub-title of the plot} \item{xlab}{A character-string, specifying the label for the x-axis} \item{ylab}{A character-string, specifying the label for the y-axis} \item{xlim}{A vector of length 2, specifying the left-most and right-most values for the plot} \item{ylim}{A vector of length 2, specifying the bottom-most and top-most values for the plot} \item{legcol}{``legend color'', an valid color used for plotting the line colors in the legend} \item{col}{a valid vector of colors of at least length 3 to use to color the confidence intervals} \item{leg}{``legend position'', an integer from 1 to 4, specifying the position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and ``NE'' respectively} \item{legpos}{``legend type'', exact coordinates and sizes for legend. Overrides argment ``leg.type''} \item{ci}{A numeric triple, specifying the three levels to plot confidence intervals for, scaled 0 to 100} } \value{ the current graphical parameters. This is subject to change in future implementations of Zelig } \description{ Plot pooled simulated quantities of interest. } \author{ James Honaker, adapted by Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/immigration.Rd0000644000176000001440000000156212061700507014737 0ustar ripleyusers\name{immigration} \alias{immigration} \alias{immi1} \alias{immi2} \alias{immi3} \alias{immi4} \alias{immi5} \title{Individual Preferences Over Immigration Policy} \description{These five datasets are part of a larger set of 10 multiply imputed data sets describing individual preferences toward immigration policy. Imputation was performed via Amelia. } \format{ Each multiply-inputed data set consists of a table with 7 variables ("ipip", "wage1992", "prtyid", "ideol", "gender") and 2,485 observations. For variable descriptions, please refer to Scheve and Slaugher, 2001. } \source{National Election Survey} \references{ Scheve, Kenneth and Matthew Slaughter (2001). ``Labor Market Competition and Individual Preferences Over Immigration Policy,'' \emph{The Review of Economics and Statistics}, vol. 83, no. 1, pp. 133-145. } \keyword{datasets} Zelig/man/sim.Rd0000644000176000001440000001276612061700507013220 0ustar ripleyusers\name{sim} \alias{sim} \title{Generic Method for Computing and Organizing Simulated Quantities of Interest Simulate quantities of interest from the estimated model output from \code{zelig()} given specified values of explanatory variables established in \code{setx()}. For classical \emph{maximum likelihood} models, \code{sim()} uses asymptotic normal approximation to the log-likelihood. For \emph{Bayesian models}, Zelig simulates quantities of interest from the posterior density, whenever possible. For \emph{robust Bayesian models}, simulations are drawn from the identified class of Bayesian posteriors. Alternatively, you may generate quantities of interest using bootstrapped parameters.} \usage{ sim(obj, x = NULL, x1 = NULL, y = NULL, num = 1000, bootstrap = F, bootfn = NULL, cond.data = NULL, ...) } \arguments{ \item{obj}{the output object from zelig} \item{x}{values of explanatory variables used for simulation, generated by setx} \item{x1}{optional values of explanatory variables (generated by a second call of setx) particular computations of quantities of interest} \item{y}{a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter} \item{num}{an integer specifying the number of simulations to compute} \item{bootstrap}{currently unsupported} \item{bootfn}{currently unsupported} \item{cond.data}{currently unsupported} \item{...}{arguments reserved future versions of Zelig} } \value{ The output stored in \code{s.out} varies by model. Use the \code{names} command to view the output stored in \code{s.out}. Common elements include: \item{x}{the \code{\link{setx}} values for the explanatory variables, used to calculate the quantities of interest (expected values, predicted values, etc.). } \item{x1}{the optional \code{\link{setx}} object used to simulate first differences, and other model-specific quantities of interest, such as risk-ratios.} \item{call}{the options selected for \code{\link{sim}}, used to replicate quantities of interest. } \item{zelig.call}{the original command and options for \code{\link{zelig}}, used to replicate analyses. } \item{num}{the number of simulations requested. } \item{par}{the parameters (coefficients, and additional model-specific parameters). You may wish to use the same set of simulated parameters to calculate quantities of interest rather than simulating another set.} \item{qi\$ev}{simulations of the expected values given the model and \code{x}. } \item{qi\$pr}{simulations of the predicted values given by the fitted values. } \item{qi\$fd}{simulations of the first differences (or risk difference for binary models) for the given \code{x} and \code{x1}. The difference is calculated by subtracting the expected values given \code{x} from the expected values given \code{x1}. (If do not specify \code{x1}, you will not get first differences or risk ratios.) } \item{qi\$rr}{simulations of the risk ratios for binary and multinomial models. See specific models for details.} \item{qi\$ate.ev}{simulations of the average expected treatment effect for the treatment group, using conditional prediction. Let \eqn{t_i} be a binary explanatory variable defining the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the average expected treatment effect for the treatment group is \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - E[Y_i(t_i=0)] \mid t_i=1 \,],} where \eqn{Y_i(t_i=1)} is the value of the dependent variable for observation \eqn{i} in the treatment group. Variation in the simulations are due to uncertainty in simulating \eqn{E[Y_i(t_i=0)]}, the counterfactual expected value of \eqn{Y_i} for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to \eqn{t_i=0}. } \item{qi\$ate.pr}{simulations of the average predicted treatment effect for the treatment group, using conditional prediction. Let \eqn{t_i} be a binary explanatory variable defining the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the average predicted treatment effect for the treatment group is \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - \widehat{Y_i(t_i=0)} \mid t_i=1 \,],} where \eqn{Y_i(t_i=1)} is the value of the dependent variable for observation \eqn{i} in the treatment group. Variation in the simulations are due to uncertainty in simulating \eqn{\widehat{Y_i(t_i=0)}}, the counterfactual predicted value of \eqn{Y_i} for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to \eqn{t_i=0}.} } \description{ Generic Method for Computing and Organizing Simulated Quantities of Interest Simulate quantities of interest from the estimated model output from \code{zelig()} given specified values of explanatory variables established in \code{setx()}. For classical \emph{maximum likelihood} models, \code{sim()} uses asymptotic normal approximation to the log-likelihood. For \emph{Bayesian models}, Zelig simulates quantities of interest from the posterior density, whenever possible. For \emph{robust Bayesian models}, simulations are drawn from the identified class of Bayesian posteriors. Alternatively, you may generate quantities of interest using bootstrapped parameters. } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Olivia Lau and Kosuke Imai } Zelig/man/bivariate.Rd0000644000176000001440000000106312061700507014362 0ustar ripleyusers\name{bivariate} \alias{bivariate} \title{Sample data for bivariate probit regression} \description{ Sample data for the bivariate probit regression. } \usage{data(bivariate)} \format{A table containing 6 variables ("y1", "y2", "x1", "x2", "x3", and "x4") and 78 observations.} \source{This is a cleaned and relabelled version of the sanction data set, available in Zelig.} \references{ Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral Economic Sanctions}, Princeton: Princeton University Press. } \keyword{datasets} Zelig/man/voteincome.Rd0000644000176000001440000000301612061700507014564 0ustar ripleyusers\name{voteincome} \alias{voteincome} \docType{data} \title{Sample Turnout and Demographic Data from the 2000 Current Population Survey} \description{ This data set contains turnout and demographic data from a sample of respondents to the 2000 Current Population Survey (CPS). The states represented are South Carolina and Arkansas. The data represent only a sample and results from this example should not be used in publication. } \usage{data(voteincome)} \format{ A data frame containing 7 variables ("state", "year", "vote", "income", "education", "age", "female") and 1500 observations. \describe{ \item{\code{state}}{a factor variable with levels equal to "AR" (Arkansas) and "SC" (South Carolina)} \item{\code{year}}{an integer vector} \item{\code{vote}}{an integer vector taking on values "1" (Voted) and "0" (Did Not Vote)} \item{\code{income}}{an integer vector ranging from "4" (Less than \$5000) to "17" (Greater than \$75000) denoting family income. See the CPS codebook for more information on variable coding} \item{\code{education}}{an integer vector ranging from "1" (Less than High School Education) to "4" (More than a College Education). See the CPS codebook for more information on variable coding} \item{\code{age}}{an integer vector ranging from "18" to "85"} \item{\code{female}}{an integer vector taking on values "1" (Female) and "0" (Male)} } } \source{Census Bureau Current Population Survey} \references{\url{http://www.census.gov/cps}} \keyword{datasets}Zelig/man/is.zelig.package.Rd0000644000176000001440000000076112061700507015536 0ustar ripleyusers\name{is.zelig.package} \alias{is.zelig.package} \title{Wether an Installed R-Pack Depends on Zelig} \usage{ is.zelig.package(package = "") } \arguments{ \item{package}{a character-string naming a package} } \value{ whether this package depends on Zelig } \description{ Wether an Installed R-Pack Depends on Zelig } \note{ This package was used internally to determine whether an R-package is Zelig compliant, but is now likely deprecated. This test is useless if not paired with } Zelig/man/sim.default.Rd0000644000176000001440000000177412061700507014640 0ustar ripleyusers\name{sim.default} \alias{sim.default} \title{Method for Simulating Quantities of Interest wrom 'zelig' Objects} \usage{ \method{sim}{default}(obj, x=NULL, x1=NULL, y=NULL, num=1000, bootstrap = FALSE, bootfn=NULL, cond.data = NULL, ...) } \arguments{ \item{obj}{a 'zelig' object} \item{x}{a 'setx' object} \item{x1}{a secondary 'setx' object used to perform particular computations of quantities of interest} \item{y}{a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter} \item{num}{an integer specifying the number of simulations to compute} \item{bootstrap}{ignored} \item{bootfn}{ignored} \item{cond.data}{ignored} \item{...}{parameters to be passed to the boot function, if one is supplied} } \value{ a 'sim' object storing the replicated quantities of interest } \description{ Simulate quantities of interest } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/GetSlot.Rd0000644000176000001440000000103712061700507013776 0ustar ripleyusers\name{GetSlot} \alias{GetSlot} \title{Generic method for extracting variables from both S3 and S4 fitted model object} \usage{ GetSlot(obj, key, ...) } \arguments{ \item{obj}{an object of type `zelig'} \item{key}{a character-string specifying the name of the variable to extract} \item{...}{typically ignored parameters} } \value{ the value of that extracted object or NULL } \description{ Generic method for extracting variables from both S3 and S4 fitted model object } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/list.zelig.models.Rd0000644000176000001440000000060312061700507015761 0ustar ripleyusers\name{list.zelig.models} \alias{list.zelig.models} \title{List Zelig Models Installed on the Current Machine} \usage{ list.zelig.models(with.namespace = TRUE) } \arguments{ \item{with.namespace}{a boolean specifying whether} } \value{ list of all zelig models } \description{ List Zelig Models Installed on the Current Machine } \note{ This list is not necessarily complete } Zelig/CHANGES0000644000176000001440000000244512061700507012352 0ustar ripleyusers4.0-6: * Improving parse.formula function * Renamed 'parse.formula' to 'parseFormula', to avoid name confusion and adhere to common naming standards in Zelig. 4.0-5 (August 25th, 2011): * Removed dependency on 'iterators' package * Updated NAMESPACE and DESCRIPTION files correspondingly * Restructured internals of 'zelig' function * Added CHANGES file to summarize detail-oriented code changes * Print methods for multiply-imputed data-sets have been improved * Print methods for setx objects have been improved * zelig now returns a specially constructed list when handling multiply- imputed data-sets * The 'state' variable has been added to the the zelig and MI objects. This variable stores shared information between an "MI" (zelig-list) object and all its children. * Beautified output of print.setx * Added terms.zelig as a method with documentation * Added a 'formula' index to the 'zelig' class. This contains a formula formula identical that contained in the result index (within the zelig object) * Added "old-formula" variable to the "state" attribute (this attribute is an environment. That is, the attribute "state", which is an environment, now contains a variable titled "old-formula". This specifies the original formula submitted to the 'zelig' function

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vignette source 'manual-gee.Rnw' ################################################### ### code chunk number 1: loadLibrary ################################################### library(Zelig) library(MCMCpack) ################################################### ### code chunk number 2: Example.data ################################################### data(coalition) ################################################### ### code chunk number 3: Example.cluster ################################################### coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4)) sorted.coalition <- coalition[order(coalition$cluster),] ################################################### ### code chunk number 4: Example.zelig ################################################### z.out <- zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition, robust=TRUE, corstr="exchangeable") summary(z.out) ################################################### ### code chunk number 5: Example.setx ################################################### x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) ################################################### ### code chunk number 6: Example.sim ################################################### s.out <- sim(z.out, x = x.low, x1 = x.high) summary(s.out) ################################################### ### code chunk number 7: ExamplePlot ################################################### plot(s.out) ################################################### ### code chunk number 8: Example.data ################################################### data(turnout) ################################################### ### code chunk number 9: Example.cluster ################################################### turnout$cluster <- rep(c(1:200),10) ################################################### ### code chunk number 10: Example.sort ################################################### sorted.turnout <- turnout[order(turnout$cluster),] ################################################### ### code chunk number 11: Example.zelig ################################################### z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3) ################################################### ### code chunk number 12: Example.setx ################################################### x.out1 <- setx(z.out1) ################################################### ### code chunk number 13: Example.sim ################################################### s.out1 <- sim(z.out1, x = x.out1) ################################################### ### code chunk number 14: Example.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 15: ExamplePlot ################################################### plot(s.out1) ################################################### ### code chunk number 16: FirstDifference.setx ################################################### x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) ################################################### ### code chunk number 17: FirstDifference.sim ################################################### s.out2 <- sim(z.out1, x = x.high, x1 = x.low) ################################################### ### code chunk number 18: FirstDifference.summary.sim ################################################### summary(s.out2) ################################################### ### code chunk number 19: FirstDifferencePlot ################################################### plot(s.out2) ################################################### ### code chunk number 20: Example2.corr ################################################### corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ################################################### ### code chunk number 21: Example2.zelig ################################################### z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat) ################################################### ### code chunk number 22: Example2.summary ################################################### summary(z.out2) ################################################### ### code chunk number 23: Example.data ################################################### data(macro) ################################################### ### code chunk number 24: Example.zelig ################################################### z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1) summary(z.out) ################################################### ### code chunk number 25: Example.setx ################################################### x.high <- setx(z.out, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out, trade = quantile(macro$trade, 0.2)) ################################################### ### code chunk number 26: Example.sim ################################################### s.out <- sim(z.out, x = x.high, x1 = x.low) ################################################### ### code chunk number 27: Example.summary.sim ################################################### summary(s.out) ################################################### ### code chunk number 28: ExamplePlot ################################################### plot(s.out) ################################################### ### code chunk number 29: Example.data ################################################### data(sanction) ################################################### ### code chunk number 30: Example.cluster ################################################### sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3)) ################################################### ### code chunk number 31: Example.sort ################################################### sorted.sanction <- sanction[order(sanction$cluster),] ################################################### ### code chunk number 32: Example.zelig ################################################### z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust=TRUE, corstr="exchangeable") summary(z.out) ################################################### ### code chunk number 33: Example.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 34: Example.sim ################################################### s.out <- sim(z.out, x = x.out) summary(s.out) ################################################### ### code chunk number 35: ExamplePlot ################################################### plot(s.out) ################################################### ### code chunk number 36: Example.data ################################################### data(turnout) ################################################### ### code chunk number 37: Example.cluster ################################################### turnout$cluster <- rep(c(1:200),10) ################################################### ### code chunk number 38: Example.sort ################################################### sorted.turnout <- turnout[order(turnout$cluster),] ################################################### ### code chunk number 39: Example.zelig ################################################### z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3) ################################################### ### code chunk number 40: Example.setx ################################################### x.out1 <- setx(z.out1) ################################################### ### code chunk number 41: Example.sim ################################################### s.out1 <- sim(z.out1, x = x.out1) ################################################### ### code chunk number 42: Example.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 43: ExamplePlot ################################################### plot(s.out1) ################################################### ### code chunk number 44: manual-gee.Rnw:1346-1347 ################################################### options(width=80) ################################################### ### code chunk number 45: FirstDifference.setx ################################################### x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25)) ################################################### ### code chunk number 46: FirstDifference.sim ################################################### s.out2 <- sim(z.out1, x = x.high, x1 = x.low) ################################################### ### code chunk number 47: FirstDifference.summary.sim ################################################### summary(s.out2) ################################################### ### code chunk number 48: FirstDifferencePlot ################################################### plot(s.out2) ################################################### ### code chunk number 49: manual-gee.Rnw:1373-1374 ################################################### options(width=75) ################################################### ### code chunk number 50: Example2.corr ################################################### corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10) diag(corr.mat) <- 1 ################################################### ### code chunk number 51: Example2.zelig ################################################### z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat) ################################################### ### code chunk number 52: Example2.summary ################################################### summary(z.out2) Zelig/inst/doc/manual.R0000644000176000001440000002657112217153457014520 0ustar ripleyusers### R code from vignette source 'manual.Rnw' ################################################### ### code chunk number 1: loadLibrary ################################################### library(Zelig) ################################################### ### code chunk number 2: Example.data ################################################### data(coalition) ################################################### ### code chunk number 3: Example.zelig ################################################### z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition) ################################################### ### code chunk number 4: Example.summary ################################################### summary(z.out) ################################################### ### code chunk number 5: Example.setx ################################################### x.low <- setx(z.out, numst2 = 0) x.high <- setx(z.out, numst2 = 1) ################################################### ### code chunk number 6: Example.sim ################################################### s.out <- sim(z.out, x = x.low, x1 = x.high) ################################################### ### code chunk number 7: Example.summary ################################################### summary(s.out) ################################################### ### code chunk number 8: gamma-ExamplePlot ################################################### plot(s.out) ################################################### ### code chunk number 9: Example.data ################################################### data(turnout) ################################################### ### code chunk number 10: Example.zelig ################################################### z.out1 <- zelig(vote ~ age + race, model = "logit", data = turnout) ################################################### ### code chunk number 11: Example.setx ################################################### x.out1 <- setx(z.out1, age = 36, race = "white") ################################################### ### code chunk number 12: Example.sim ################################################### s.out1 <- sim(z.out1, x = x.out1) ################################################### ### code chunk number 13: Example.summary ################################################### summary(s.out1) ################################################### ### code chunk number 14: logit-ExamplePlot ################################################### plot(s.out1) ################################################### ### code chunk number 15: FirstDifferences.setx ################################################### z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout) x.high <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.75)) x.low <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.25)) ################################################### ### code chunk number 16: FirstDifferences.sim ################################################### s.out2 <- sim(z.out2, x = x.high, x1 = x.low) ################################################### ### code chunk number 17: FirstDifferences.summary ################################################### summary(s.out2) ################################################### ### code chunk number 18: logit-FirstDifferencesPlot ################################################### plot(s.out2) ################################################### ### code chunk number 19: ROC.zelig ################################################### z.out1 <- zelig(vote ~ race + educate + age, model = "logit", data = turnout) z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout) ################################################### ### code chunk number 20: logit-ROCPlot ################################################### rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2)) ################################################### ### code chunk number 21: Examples.data ################################################### data(macro) ################################################### ### code chunk number 22: Examples.zelig ################################################### z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro) ################################################### ### code chunk number 23: Examples.summary ################################################### summary(z.out1) ################################################### ### code chunk number 24: Examples.setx ################################################### x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2)) ################################################### ### code chunk number 25: Examples.sim ################################################### s.out1 <- sim(z.out1, x = x.high, x1 = x.low) ################################################### ### code chunk number 26: Examples.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 27: Dummy.zelig ################################################### z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country), model = "ls", data = macro) ################################################### ### code chunk number 28: Dummy.setx ################################################### x.US <- setx(z.out2, country = "United States") x.Japan <- setx(z.out2, country = "Japan") ################################################### ### code chunk number 29: Dummy.sim ################################################### s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan) ################################################### ### code chunk number 30: Example.data ################################################### data(sanction) ################################################### ### code chunk number 31: Example.zelig ################################################### z.out <- zelig(num ~ target + coop, model = "negbinom", data = sanction) ################################################### ### code chunk number 32: Example.summary ################################################### summary(z.out) ################################################### ### code chunk number 33: Example.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 34: Example.sim ################################################### s.out <- sim(z.out, x = x.out) ################################################### ### code chunk number 35: Example.summary.sim ################################################### summary(s.out) ################################################### ### code chunk number 36: negbinom-Example1Plot ################################################### plot(s.out) ################################################### ### code chunk number 37: Examples.data ################################################### data(macro) ################################################### ### code chunk number 38: Examples.zelig ################################################### z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal", data = macro) ################################################### ### code chunk number 39: Examples.summary ################################################### summary(z.out1) ################################################### ### code chunk number 40: Examples.setx ################################################### x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8)) x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2)) ################################################### ### code chunk number 41: Examples.sim ################################################### s.out1 <- sim(z.out1, x = x.high, x1 = x.low) ################################################### ### code chunk number 42: Examples.summary.sim ################################################### summary(s.out1) ################################################### ### code chunk number 43: normal-ExamplesPlot ################################################### plot(s.out1) ################################################### ### code chunk number 44: Dummy.zelig ################################################### z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year) + as.factor(country), model = "normal", data = macro) ################################################### ### code chunk number 45: Dummy.setx ################################################### ### x.US <- try(setx(z.out2, country = "United States"),silent=T) ### x.Japan <- try(setx(z.out2, country = "Japan"),silent=T) ################################################### ### code chunk number 46: Dummy.sim ################################################### ### s.out2 <- try(sim(z.out2, x = x.US, x1 = x.Japan), silent=T) ################################################### ### code chunk number 47: Dummy.summary ################################################### ###try(summary(s.out2)) ################################################### ### code chunk number 48: Example.data ################################################### data(sanction) ################################################### ### code chunk number 49: Example.zelig ################################################### z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction) ################################################### ### code chunk number 50: Example.summary ################################################### summary(z.out) ################################################### ### code chunk number 51: Example.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 52: Example.sim ################################################### s.out <- sim(z.out, x = x.out) ################################################### ### code chunk number 53: Example.summary.sim ################################################### summary(s.out) ################################################### ### code chunk number 54: poisson-ExamplePlot ################################################### plot(s.out) ################################################### ### code chunk number 55: Examples.data ################################################### data(turnout) ################################################### ### code chunk number 56: Examples.zelig ################################################### z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout) ################################################### ### code chunk number 57: Examples.summary ################################################### summary(z.out) ################################################### ### code chunk number 58: Examples.setx ################################################### x.out <- setx(z.out) ################################################### ### code chunk number 59: Examples.sim ################################################### s.out <- sim(z.out, x = x.out) ################################################### ### code chunk number 60: Examples.summary.sim ################################################### summary(s.out) Zelig/inst/doc/logit.pdf0000644000176000001440000062053412217234502014717 0ustar ripleyusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 3966 /Filter /FlateDecode /N 75 /First 598 >> stream x[[s8~?o 멭%N8&).zT℥).BTA>)K-$z "Tb"SE!RϔJ.6h($SV"=TFQ2aYj:RGI0 ̠JA \3k aK2RcQ?ܡd̻D3S' uHg$0:Tt'B8P3Ct-R$ )ۃC AvfNeBJv0`R?*?]^К~dh21#'_1,D&/r2ڹum](*}ȋa^z%/&W2~8 YQ‰'/?%~t1f:˚#h۳0/7Uj ,x0˿^XwIzV<@CJ>Fey#RiK`T-`T {POB b ?@ QQ=$1&v?ibMćY7=:@'ŬSNyٟj2u?ťd$hD+}I1>W}x\%k_nf~vp;0Zeen5f7f3o=9$?'b|G6jC#;ʣ7Ƹ<<idks1(QDdtTMnP[6@Q|U ٤N0Iɯ_& >!,_^ 81yBmKG<8rw!q )UpN'Pq0D$b^/AUQ sy1˦Edt^=[G|9~¨ëlD~`/oL !bZo|0.eqq{' d(oI[?ReY0~7>y֟Ҽ_0xU ed,'B}r<V%=A~,fì?eh1pr7,\N~NvX(wGuqa%"mk$)}!v~ Co i1iբͱx#氵f YS[]MSuIia kg_t~کk Vk`Zûק/swk2@U_Gszr_0O63??z1||5;٭R~3o>^ߵ\cwc?xu=dxf7-U/ςXu8'1^]zܥܥ:~_˟+.z|WmiRףzJNIJ.ޟB yY۰TnŎQbqigW)\ҭ-pEBZs`,zaӭDYbeczn?Beχ#mۖ0/[+q+ 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