[r-cran-zelig] 01/102: Import Upstream version 2.0-11
Andreas Tille
tille at debian.org
Sun Jan 8 16:58:08 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-zelig.
commit 0bb09379dfa65207b4dc7818ad851c5d80b2b8c8
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:38:56 2017 +0100
Import Upstream version 2.0-11
---
COPYING | 278 ++++
DESCRIPTION | 25 +
INDEX | 9 +
LICENSE | 27 +
NAMESPACE | 76 +
R/MIsimulation.R | 34 +
R/beta.regression.R | 22 +
R/bootfn.default.R | 13 +
R/cmvglm.R | 45 +
R/coef.BetaReg.R | 2 +
R/define.data.R | 34 +
R/define.par.R | 20 +
R/gsource.R | 15 +
R/help.zelig.R | 65 +
R/load.first.R | 10 +
R/misc.vglm.R | 36 +
R/model.end.R | 24 +
R/names.relogit.R | 6 +
R/names.summary.vglm.R | 5 +
R/names.summary.zelig.R | 11 +
R/names.summary.zelig.relogit.R | 11 +
R/names.vglm.R | 2 +
R/names.zelig.R | 11 +
R/names.zelig.relogit.R | 11 +
R/param.R | 2 +
R/param.default.R | 7 +
R/param.glm.R | 44 +
R/param.lm.R | 16 +
R/param.multinom.R | 12 +
R/param.polr.R | 14 +
R/param.relogit.R | 140 ++
R/param.survreg.R | 19 +
R/param.vglm.R | 7 +
R/parse.par.R | 17 +
R/plot.ci.R | 82 +
R/plot.zelig.R | 11 +
R/plot.zelig.blogit.R | 36 +
R/plot.zelig.bprobit.R | 36 +
R/plot.zelig.exp.R | 12 +
R/plot.zelig.gamma.R | 12 +
R/plot.zelig.logit.R | 20 +
R/plot.zelig.lognorm.R | 12 +
R/plot.zelig.ls.R | 12 +
R/plot.zelig.mlogit.R | 49 +
R/plot.zelig.negbin.R | 13 +
R/plot.zelig.normal.R | 12 +
R/plot.zelig.ologit.R | 49 +
R/plot.zelig.oprobit.R | 49 +
R/plot.zelig.poisson.R | 16 +
R/plot.zelig.probit.R | 20 +
R/plot.zelig.relogit.R | 42 +
R/plot.zelig.weibull.R | 12 +
R/print.BetaReg.R | 18 +
R/print.names.relogit.R | 10 +
R/print.names.zelig.R | 3 +
R/print.relogit.R | 26 +
R/print.setx.R | 7 +
R/print.summary.MI.R | 28 +
R/print.summary.strata.R | 16 +
R/print.summary.zelig.R | 68 +
R/print.summary.zelig.strata.R | 14 +
R/print.zelig.R | 12 +
R/qi.BetaReg.R | 29 +
R/qi.R | 2 +
R/qi.glm.R | 74 +
R/qi.lm.R | 24 +
R/qi.multinom.R | 35 +
R/qi.polr.R | 77 +
R/qi.relogit.R | 79 +
R/qi.survreg.R | 92 ++
R/qi.vglm.R | 228 +++
R/relogit.R | 47 +
R/repl.R | 2 +
R/repl.default.R | 5 +
R/repl.zelig.R | 20 +
R/rocplot.R | 38 +
R/setx.MI.R | 31 +
R/setx.R | 3 +
R/setx.default.R | 176 +++
R/setx.noX.R | 24 +
R/setx.strata.R | 47 +
R/sim.R | 2 +
R/sim.cond.R | 74 +
R/sim.counter.R | 21 +
R/sim.default.R | 57 +
R/sim.setx.MI.R | 71 +
R/sim.setx.strata.R | 40 +
R/summarize.R | 2 +
R/summarize.array.R | 31 +
R/summarize.default.R | 32 +
R/summarize.matrix.R | 20 +
R/summary.BetaReg.R | 10 +
R/summary.MI.R | 62 +
R/summary.relogit.R | 41 +
R/summary.setx.R | 8 +
R/summary.setx.cond.R | 8 +
R/summary.strata.R | 27 +
R/summary.zelig.R | 62 +
R/summary.zelig.strata.R | 86 ++
R/terms.vglm.R | 2 +
R/ternarypoints.R | 10 +
R/user.prompt.R | 2 +
R/vcov.BetaReg.R | 2 +
R/vcov.lm.R | 4 +
R/vcov.relogit.R | 22 +
R/vcov.survreg.R | 1 +
R/zelig.R | 68 +
R/zelig2beta.R | 6 +
R/zelig2blogit.R | 18 +
R/zelig2bprobit.R | 17 +
R/zelig2exp.R | 8 +
R/zelig2gamma.R | 7 +
R/zelig2logit.R | 7 +
R/zelig2lognorm.R | 8 +
R/zelig2ls.R | 6 +
R/zelig2mlogit.R | 23 +
R/zelig2mloglm.R | 7 +
R/zelig2negbin.R | 6 +
R/zelig2normal.R | 7 +
R/zelig2ologit.R | 7 +
R/zelig2oprobit.R | 27 +
R/zelig2poisson.R | 7 +
R/zelig2probit.R | 8 +
R/zelig2relogit.R | 8 +
R/zelig2weibull.R | 8 +
README | 68 +
data/MatchIt.url.tab | 7 +
data/Zelig.url.tab | 36 +
data/coalition.tab | 315 ++++
data/immi1.tab | 2486 +++++++++++++++++++++++++++++++
data/immi2.tab | 2486 +++++++++++++++++++++++++++++++
data/immi3.tab | 2486 +++++++++++++++++++++++++++++++
data/immi4.tab | 2486 +++++++++++++++++++++++++++++++
data/immi5.tab | 2486 +++++++++++++++++++++++++++++++
data/immigration.tab | 2486 +++++++++++++++++++++++++++++++
data/macro.tab | 351 +++++
data/mexico.tab | 1360 +++++++++++++++++
data/mid.tab | 3127 +++++++++++++++++++++++++++++++++++++++
data/sanction.tab | 79 +
data/turnout.tab | 2001 +++++++++++++++++++++++++
demo/00Index | 23 +
demo/blogit.R | 90 ++
demo/bprobit.R | 92 ++
demo/conditional.R | 13 +
demo/exp.R | 33 +
demo/gamma.R | 27 +
demo/logit.R | 69 +
demo/lognorm.R | 25 +
demo/ls.R | 51 +
demo/match.R | 42 +
demo/mi.R | 16 +
demo/mlogit.R | 27 +
demo/negbin.R | 13 +
demo/normal.R | 55 +
demo/ologit.R | 67 +
demo/oprobit.R | 63 +
demo/poisson.R | 16 +
demo/probit.R | 69 +
demo/relogit.R | 28 +
demo/roc.R | 8 +
demo/vertci.R | 44 +
demo/weibull.R | 39 +
man/MatchIt.url.Rd | 15 +
man/Zelig.Rd | 89 ++
man/Zelig.url.Rd | 15 +
man/coalition.Rd | 38 +
man/gsource.Rd | 51 +
man/help.zelig.Rd | 47 +
man/house.Rd | 30 +
man/immigration.Rd | 35 +
man/macro.Rd | 37 +
man/match.data.Rd | 41 +
man/mexico.Rd | 28 +
man/mid.Rd | 35 +
man/plot.ci.Rd | 76 +
man/plot.zelig.Rd | 49 +
man/repl.Rd | 89 ++
man/rocplot.Rd | 72 +
man/sanction.Rd | 27 +
man/setx.Rd | 111 ++
man/sim.Rd | 143 ++
man/summary.zelig.Rd | 56 +
man/ternarypoints.Rd | 52 +
man/turnout.Rd | 28 +
man/user.prompt.Rd | 27 +
185 files changed, 28111 insertions(+)
diff --git a/COPYING b/COPYING
new file mode 100644
index 0000000..727ef8f
--- /dev/null
+++ b/COPYING
@@ -0,0 +1,278 @@
+ GNU GENERAL PUBLIC LICENSE
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diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..4fb69fe
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,25 @@
+Package: Zelig
+Version: 2.0-11
+Date: 2005-01-14
+Title: Zelig: Everyone's Statistical Software
+Author: Kosuke Imai <kimai at Princeton.Edu>,
+ Gary King <king at harvard.edu>,
+ Olivia Lau <olau at fas.harvard.edu>
+Maintainer: Olivia Lau <olau at fas.harvard.edu>
+Depends: R (>= 1.9.0), MASS, boot
+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.
+License: GPL version 2 or newer
+URL: http://gking.harvard.edu/zelig
+Packaged: Fri Jan 14 15:04:05 2005; king
diff --git a/INDEX b/INDEX
new file mode 100644
index 0000000..792f393
--- /dev/null
+++ b/INDEX
@@ -0,0 +1,9 @@
+Zelig "Zelig: Everyone's Statistical Software" package.
+ Refer to http://gking.harvard.edu/zelig for full
+ documentation.
+zelig Estimate statistitcal models
+setx Set values of explanatory variables
+sim Simulate quantities of interest
+help.zelig Get html help for functions and models in the Zelig
+ package.
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..0851d70
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,27 @@
+Zelig: Everyone's Statistical Software (Zelig)
+Copyright (C) 2004. Kosuke Imai, Gary King and Olivia Lau.
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program; if not, write to the Free Software
+Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
+
+Please contact:
+
+Kosuke Imai
+Corwin Hall 041
+Department of Politics
+Princeton University
+Princeton, NJ 08544
+Email: kimai at princeton.edu
+
+With any problems or questions.
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..bd15706
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,76 @@
+import(MASS)
+import(boot)
+
+export( help.zelig,
+ zelig,
+ help.zelig,
+ gsource,
+ setx,
+ sim,
+ plot.ci,
+ plot.zelig,
+ rocplot,
+ ternarypoints,
+ user.prompt
+)
+
+
+S3method(names, relogit)
+S3method(names, summary.vglm)
+S3method(names, summary.zelig.relogit)
+S3method(names, vglm)
+S3method(names, zelig)
+S3method(names, zelig.relogit)
+S3method(plot, zelig)
+S3method(plot.zelig, beta)
+S3method(plot.zelig, blogit)
+S3method(plot.zelig, bprobit)
+S3method(plot.zelig, exp)
+S3method(plot.zelig, gamma)
+S3method(plot.zelig, logit)
+S3method(plot.zelig, lognorm)
+S3method(plot.zelig, ls)
+S3method(plot.zelig, mlogit)
+S3method(plot.zelig, negbin)
+S3method(plot.zelig, normal)
+S3method(plot.zelig, ologit)
+S3method(plot.zelig, oprobit)
+S3method(plot.zelig, probit)
+S3method(plot.zelig, poisson)
+S3method(plot.zelig, relogit)
+S3method(plot.zelig, weibull)
+S3method(print, BetaReg)
+S3method(print, names.relogit)
+S3method(print, names.zelig)
+S3method(print, relogit)
+S3method(print, setx)
+S3method(print, summary.MI)
+S3method(print, summary.strata)
+S3method(print, summary.zelig.strata)
+S3method(print, summary.zelig)
+S3method(print, zelig)
+S3method(repl, zelig)
+S3method(sim, default)
+S3method(sim, cond)
+S3method(sim, counter)
+S3method(sim, setx.MI)
+S3method(sim, setx.strata)
+S3method(setx, default)
+S3method(setx, noX)
+S3method(setx, MI)
+S3method(setx, strata)
+S3method(summarize, array)
+S3method(summary, BetaReg)
+S3method(summary, MI)
+S3method(summary, strata)
+S3method(summary, relogit)
+S3method(summary, setx.cond)
+S3method(summary, setx)
+S3method(summary, zelig.strata)
+S3method(summary, zelig)
+S3method(terms, vglm)
+S3method(vcov, BetaReg)
+S3method(vcov, lm)
+S3method(vcov, relogit)
+
+
diff --git a/R/MIsimulation.R b/R/MIsimulation.R
new file mode 100644
index 0000000..429d4dd
--- /dev/null
+++ b/R/MIsimulation.R
@@ -0,0 +1,34 @@
+MIsimulation <- function (object, num = c(1000, 100), prev = NULL,
+ bootstrap = FALSE, bootfn = NULL, ...) {
+ M <- length(object)
+ simpar <- simqi <- res <- list()
+ if (is.null(prev)) {
+ numM <- round(num/M)
+ if (!bootstrap) {
+ for (i in 1:M)
+ simpar[[i]] <- param(object[[i]], num = numM, bootstrap =
+ bootstrap)
+ }
+ else {
+ tt <- terms(object[[1]])
+ if (missing(bootfn))
+ bootfn <- bootfn.default
+ for (i in 1:M) {
+ dta <- eval(object[[i]]$data, sys.parent())
+ res <- boot(dta, bootfn, R = num, object = object[[i]], ...)
+ colnames(res$t) <- names(res$t0)
+ simpar[[i]] <- res$t
+ }
+ }
+ params <- as.matrix(simpar[[1]])
+ for (j in 2:M)
+ params <- rbind(params, as.matrix(simpar[[j]]))
+ }
+ else {
+ if (bootstrap)
+ stop("Error: Choosing 'bootstrap = TRUE' generates new parameters. \nIf you wish to use previously generated parameters, \nplease specify only 'prev'.")
+ else params <- prev
+ }
+ params
+}
+
diff --git a/R/beta.regression.R b/R/beta.regression.R
new file mode 100644
index 0000000..3118cf2
--- /dev/null
+++ b/R/beta.regression.R
@@ -0,0 +1,22 @@
+beta.regression <- function(formula, data, start = NULL, ...) {
+ kall <- match.call()
+ Terms <- define.data(formula, data)
+ if (is.null(start))
+ start <- c(lm(y ~ x - 1)$coef, 1)
+ start.val <- define.par(Terms, coef.name = "beta", ancillary = "phi",
+ start = start)
+ llik.beta <- function(par, X, Y, Terms) {
+ parse.par(par, X, Terms)
+ Xbeta <- X %*% beta
+ mu <- exp(Xbeta) / (1 + exp(Xbeta))
+ sum(lgamma(phi) - lgamma(mu * phi) - lgamma((1 - mu) * phi) +
+ (mu * phi - 1) * log(Y) + ((1 - mu) * phi - 1) * log(1 - y))
+ }
+ res <- optim(start.val, llik.beta, method = "BFGS",
+ control = list(fnscale = -1),
+ hessian = TRUE, X = x, Y = y,
+ Terms = Terms, ...)
+ fit <- model.end(Terms, res)
+ class(fit) <- "BetaReg"
+ fit
+}
diff --git a/R/bootfn.default.R b/R/bootfn.default.R
new file mode 100644
index 0000000..32faa27
--- /dev/null
+++ b/R/bootfn.default.R
@@ -0,0 +1,13 @@
+bootfn.default <- function(data, i, object) {
+ d <- data[i,]
+ object$call$data <- d
+ fit <- eval(object$call, sys.parent())
+ l <- length(param(object, bootstrap = TRUE))
+ l1 <- length(param(fit, bootstrap = TRUE))
+ while (l > l1) {
+ object$call$data <- data[sample(nrow(data), replace=TRUE),]
+ fit <- eval(object$call, sys.parent())
+ l1 <- length(param(fit, bootstrap = TRUE))
+ }
+ param(fit, bootstrap = TRUE)
+}
diff --git a/R/cmvglm.R b/R/cmvglm.R
new file mode 100644
index 0000000..dfb564c
--- /dev/null
+++ b/R/cmvglm.R
@@ -0,0 +1,45 @@
+cmvglm <- function(formula, model, constrain, omit, constant, ndim){
+ tt<-terms(formula)
+ vars<-c("(Intercept)", attr(tt, "term.labels"))
+ cm<-vector("list", length(vars))
+ names(cm)<-vars
+ for(i in 1:length(cm))
+ cm[[i]]<-diag(1, ndim)
+ if(!is.null(constrain)){
+ tmp <- sort(names(constrain))
+ for (i in 1:length(constrain[[tmp[1]]])) {
+ ci <- NULL
+ counter <- c("1", "2", "3")
+ for (j in 1:3)
+ if (is.null(constrain[[counter[j]]]))
+ ci <- c(ci, NA)
+ else
+ ci <- c(ci, constrain[[counter[j]]][i])
+ if (is.null(na.omit(ci)) || length(unique(na.omit(ci)))!=1)
+ stop("invalid input for constrain")
+ minj <- match(FALSE, is.na(ci))
+ whatvar <- pmatch(unique(na.omit(ci)), names(cm))
+ for (j in 1:3)
+ if (!is.na(ci[j])) {
+ cm[[whatvar]][j,j]<-0
+ cm[[whatvar]][j,minj]<-1
+ }
+ }
+ }
+ if(!is.null(omit)){
+ tmp <- sort(names(omit))
+ for(i in 1:length(omit)){
+ omiti <- omit[[tmp[i]]]
+ for (j in 1:length(omiti))
+ cm[[pmatch(omiti[j], names(cm))]][i,i]<-0
+ }
+ }
+ if(!is.null(constant))
+ for(i in 1:length(constant))
+ for(j in 1:length(cm))
+ if(names(cm)[j]!="(Intercept)")
+ cm[[j]][constant[i],]<-matrix(0, ncol=ncol(cm[[j]]))
+ for(i in 1:length(cm))
+ cm[[i]]<-as.matrix(cm[[i]][,apply(cm[[i]], 2, sum)!=0])
+ list("formula"=formula, "constraints"=cm)
+}
diff --git a/R/coef.BetaReg.R b/R/coef.BetaReg.R
new file mode 100644
index 0000000..f67524e
--- /dev/null
+++ b/R/coef.BetaReg.R
@@ -0,0 +1,2 @@
+coef.BetaReg <- function(object)
+ object$coef
diff --git a/R/define.data.R b/R/define.data.R
new file mode 100644
index 0000000..50245ce
--- /dev/null
+++ b/R/define.data.R
@@ -0,0 +1,34 @@
+define.data <- function(formula, data, x.name = "x",
+ y.name = "y", mf.name = "mf",
+ xlev.name = "xlev", lev.name = "lev") {
+ mc <- match.call()
+ mc$x.name <- mc$y.name <- mc$t.name <- mc$xlev.name <- mc$l.name <- NULL
+
+ mc[[1]] <- as.name("model.frame")
+ mf <- eval.parent(mc)
+ assign(mf.name, mf, envir = parent.frame())
+
+ Terms <- attr(mf, "terms")
+
+ x <- model.matrix(Terms, mf)
+ assign(x.name, x, envir = parent.frame())
+
+ yvar <- attr(Terms, "response")
+ xvars <- as.character(attr(Terms, "response"))[-1]
+ xlev <- if (length(xvars) > 0) {
+ xlev <- lapply(mf[xvars], levels)
+ xlev[!sapply(xlev, is.null)]
+ }
+ assign(xlev.name, xlev, envir = parent.frame())
+
+ y <- model.response(mf)
+ assign(y.name, y, envir = parent.frame())
+
+ lev <- levels(y)
+ assign(lev.name, lev, envir = parent.frame())
+
+ attr(Terms, "items") <- list(x = x.name, y = y.name,
+ mf = mf.name, xlev =
+ xlev.name, lev = lev.name)
+ Terms
+}
diff --git a/R/define.par.R b/R/define.par.R
new file mode 100644
index 0000000..101a43d
--- /dev/null
+++ b/R/define.par.R
@@ -0,0 +1,20 @@
+define.par <- function(object, coef.name = "beta",
+ ancillary = NULL, start = NULL){
+ mc <- match.call()
+ x <- eval(as.name(attr(object, "items")$x), parent.frame())
+ coef <- colnames(x)
+ if (!is.null(ancillary))
+ coef <- c(coef, ancillary)
+
+ attr(object, "items")$ancillary <- ancillary
+ attr(object, "items")$coef <- coef.name
+ assign(as.character(mc$object), object, envir = parent.frame())
+
+ if (is.null(start))
+ start.val <- vector(mode = "numeric", length = length(coef))
+ else
+ start.val <- start
+
+ names(start.val) <- coef
+ start.val
+}
diff --git a/R/gsource.R b/R/gsource.R
new file mode 100644
index 0000000..b4d8e7c
--- /dev/null
+++ b/R/gsource.R
@@ -0,0 +1,15 @@
+gsource <- function(var.names= NULL, variables) {
+ if (is.null(var.names)) {
+ cat(variables, file = ".Rtmp.dat", sep = "\n")
+ out <- read.table(".Rtmp.dat")
+ }
+ else {
+ cat(var.names, variables, file = ".Rtmp.dat", sep = "\n")
+ out <- read.table(".Rtmp.dat", header = TRUE)
+ }
+ unlink(".Rtmp.dat")
+ return(as.data.frame(out))
+}
+
+
+
diff --git a/R/help.zelig.R b/R/help.zelig.R
new file mode 100644
index 0000000..06bbc34
--- /dev/null
+++ b/R/help.zelig.R
@@ -0,0 +1,65 @@
+help.zelig <- function(object) {
+ # Elements of this function use functions from the Hmisc library for
+ # R by Frank E Harrell Jr, distributed under the GNU GPL v.2.
+ under.unix <- !(version$os=='Microsoft Windows' ||
+ version$os=='Win32' || version$os=='mingw32')
+ sys <- function (command, text = NULL) {
+ cmd <- if (length(text))
+ paste(command, text)
+ else command
+ if (under.unix)
+ system(cmd)
+ else shell(cmd, wait = TRUE)
+ }
+ browser <- .Options$help.browser
+ if(!length(browser)) browser <- .Options$browser
+ if(!length(browser)) browser <- getOption("browser")
+ url <- NULL
+ if (missing(object))
+ url <- c("http://gking.harvard.edu/zelig/docs/")
+ else if (is.character(object)) {
+ z <- .libPaths()
+ n <- urls <- array()
+ for (i in 1:length(z))
+ n[i] <- file.path(z[i], "Zelig", fsep = .Platform$file.sep)
+ check <- file.exists(n)
+ if (sum(check) > 1) {
+ Zdir <- n[check][1]
+ warning(paste("library Zelig found in two locations. Using",
+Zdir))
+ }
+ else
+ Zdir <- n[which(check)]
+ Zdata <- file.path(Zdir, "data", fsep = .Platform$file.sep)
+ files <- list.files(Zdata, pattern = "url")
+ for (i in 1:length(files))
+ urls[i] <- file.path(Zdata, files[i], fsep = .Platform$file.sep)
+ data.path <- read.table(urls[1], header = FALSE)
+ if (length(urls) > 1) {
+ for (i in 2:length(urls)) {
+ tmp <- read.table(urls[i], header = FALSE)
+ data.path <- rbind(data.path, tmp)
+ }
+ }
+ url <- data.path[which(as.character(data.path[,1]) == object), 2]
+ }
+ else
+ stop("Please enclose the requested topic \n in quotes and try again.")
+ if (is.null(url)) {
+ cat("Warning: Requested topic not found in Zelig help. \n If you are sure the topic exists, please check \n the full documentation at http://gking.harvard.edu/zelig. \n Now searching R-help.\n\n")
+ topic <- as.name(object)
+ do.call("help", list(topic, htmlhelp = TRUE))
+ }
+ else {
+ if (under.unix) {
+ sys(paste(browser, as.character(url), '&'))
+ invisible()
+ }
+ if (!under.unix) {
+ browseURL(as.character(url), browser = browser)
+ invisible("")
+ }
+ }
+}
+
+
diff --git a/R/load.first.R b/R/load.first.R
new file mode 100644
index 0000000..52a3c9b
--- /dev/null
+++ b/R/load.first.R
@@ -0,0 +1,10 @@
+.onAttach <- function(...) {
+ cat("\nPlease refer to http://gking.harvard.edu/zelig for full documentation \n",
+ "or help.zelig() for help with commands and models supported by Zelig.\n\n",
+ sep='')
+ if(!any(search()=="package:MASS"))
+ require(MASS)
+ if(!any(search()=="package:boot"))
+ require(boot)
+ options(digits = 4)
+}
diff --git a/R/misc.vglm.R b/R/misc.vglm.R
new file mode 100644
index 0000000..6d5f3ab
--- /dev/null
+++ b/R/misc.vglm.R
@@ -0,0 +1,36 @@
+"$.vglm"<-"$.summary.vglm"<-function(a, b){
+ if(is.na(pmatch(b, slotNames(a)))){
+ pmt<-pmatch(b, names.default(a))
+ if(is.na(pmt))
+ stop(paste("no such object exists in", a))
+ else
+ a[[pmt]]
+ }
+ else
+ slot(a, b)
+}
+
+"$<-.vglm"<-"$<-summary.vglm"<-function(a, b, value){
+ if(is.na(pmatch(b, slotNames(a)))){
+ tmp<-class(a)
+ class(a)<-NULL
+ if(length(a)==0){
+ a[[1]]<-value
+ names(a)[1]<-b
+ }
+ else{
+ pmt<-pmatch(b, names(a))
+ if(is.na(pmt)){
+ n<-length(names(a))
+ a[[n+1]]<-value
+ names(a)[n+1]<-b
+ }
+ else
+ a[[pmt]]<-value
+ }
+ class(a)<-tmp
+ }
+ else
+ slot(a, b)<-value
+ a
+}
diff --git a/R/model.end.R b/R/model.end.R
new file mode 100644
index 0000000..fb38309
--- /dev/null
+++ b/R/model.end.R
@@ -0,0 +1,24 @@
+model.end <- function(Terms, res) {
+
+ variance <- -solve(res$hessian)
+
+ lev <- eval(as.name(attr(Terms, "items")$lev), envir = parent.frame())
+ kall <- eval(as.name("kall"), envir = parent.frame())
+ xlev <- eval(as.name(attr(Terms, "items")$xlev), envir = parent.frame())
+ mf <- eval(as.name(attr(Terms, "items")$mf), envir = parent.frame())
+ x <- eval(as.name(attr(Terms, "items")$x), envir = parent.frame())
+
+ names(res$par) <- c(colnames(x), attr(Terms, "items")$ancillary)
+ colnames(variance) <- rownames(variance) <- names(res$par)
+
+ fit <- list(coefficients = res$par, variance = variance,
+ lev = lev, terms = Terms, call = kall,
+ convergence = res$convergence, xlevels = xlev)
+
+ attr(fit, "na.message") <- attr(mf, "na.message")
+
+ if (!is.null(attr(mf, "na.action")))
+ fit$na.action <- attr(mf, "na.action")
+
+ fit
+}
diff --git a/R/names.relogit.R b/R/names.relogit.R
new file mode 100644
index 0000000..38c6025
--- /dev/null
+++ b/R/names.relogit.R
@@ -0,0 +1,6 @@
+names.relogit <- function(x,...){
+ res <- list(default=names.default(x, ...),
+ estimate = names.default(x$lower.estimate, ...))
+ class(res) <- "names.relogit"
+ res
+}
diff --git a/R/names.summary.vglm.R b/R/names.summary.vglm.R
new file mode 100644
index 0000000..0802cdd
--- /dev/null
+++ b/R/names.summary.vglm.R
@@ -0,0 +1,5 @@
+names.summary.vglm <- function(x, ...){
+ res <- list(default = slotNames(x, ...))
+ class(res) <- "names.summary.vglm"
+ res[[1]]
+}
diff --git a/R/names.summary.zelig.R b/R/names.summary.zelig.R
new file mode 100644
index 0000000..c737bfc
--- /dev/null
+++ b/R/names.summary.zelig.R
@@ -0,0 +1,11 @@
+names.summary.zelig <- function(x, ...) {
+ res <- names.default(x, ...)[1:4]
+ qi.names <- names.default(x$qi.stats, ...)
+ for (i in 1:length(qi.names)) {
+ qi.names[i] <- paste("qi.stats$", qi.names[i], sep = "")
+ }
+ res <- c(res, qi.names)
+ res <- list(default = res)
+ class(res)<-"names.zelig"
+ res
+}
diff --git a/R/names.summary.zelig.relogit.R b/R/names.summary.zelig.relogit.R
new file mode 100644
index 0000000..005e2fb
--- /dev/null
+++ b/R/names.summary.zelig.relogit.R
@@ -0,0 +1,11 @@
+names.summary.zelig.relogit <- function(x, ...) {
+ res <- names.default(x, ...)[1:5]
+ qi.names <- names.default(x$qi.stats, ...)
+ for (i in 1:length(qi.names)) {
+ qi.names[i] <- paste("qi.stats$", qi.names[i], sep = "")
+ }
+ res <- c(res, qi.names)
+ res <- list(default = res)
+ class(res)<-"names.zelig"
+ res
+}
diff --git a/R/names.vglm.R b/R/names.vglm.R
new file mode 100644
index 0000000..9151d36
--- /dev/null
+++ b/R/names.vglm.R
@@ -0,0 +1,2 @@
+names.vglm <- function(x, ...)
+ c(slotNames(x, ...), names.default(x, ...))
diff --git a/R/names.zelig.R b/R/names.zelig.R
new file mode 100644
index 0000000..0915a1b
--- /dev/null
+++ b/R/names.zelig.R
@@ -0,0 +1,11 @@
+names.zelig <- function (x, ...) {
+ res <- names.default(x, ...)[1:5]
+ qi.names <- names.default(x$qi, ...)
+ for (i in 1:length(qi.names)) {
+ qi.names[i] <- paste("qi$", qi.names[i], sep = "")
+ }
+ res <- c(res, qi.names)
+ res <- list(default = res)
+ class(res) <- "names.zelig"
+ res
+}
diff --git a/R/names.zelig.relogit.R b/R/names.zelig.relogit.R
new file mode 100644
index 0000000..a307e45
--- /dev/null
+++ b/R/names.zelig.relogit.R
@@ -0,0 +1,11 @@
+names.zelig.relogit <- function(x, ...) {
+ res <- names.default(x, ...)[1:5]
+ qi.names <- names.default(x$qi, ...)
+ for (i in 1:length(qi.names)) {
+ qi.names[i] <- paste("qi$", qi.names[i], sep = "")
+ }
+ res <- c(res, qi.names)
+ res <- list(default = res)
+ class(res) <- "names.zelig"
+ res
+}
diff --git a/R/param.R b/R/param.R
new file mode 100644
index 0000000..989e180
--- /dev/null
+++ b/R/param.R
@@ -0,0 +1,2 @@
+param<-function(object, ...)
+ UseMethod("param")
diff --git a/R/param.default.R b/R/param.default.R
new file mode 100644
index 0000000..89b15ac
--- /dev/null
+++ b/R/param.default.R
@@ -0,0 +1,7 @@
+param.default <- function(object, num, bootstrap = FALSE) {
+ if (!bootstrap)
+ res <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ else
+ res <- coef(object)
+ res
+}
diff --git a/R/param.glm.R b/R/param.glm.R
new file mode 100644
index 0000000..473f7e4
--- /dev/null
+++ b/R/param.glm.R
@@ -0,0 +1,44 @@
+param.glm <- function(object, num = NULL, bootstrap = FALSE) {
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ if (object$zelig == "normal") {
+ df <- object$df.residual
+ sig2 <- summary(object)$dispersion
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ res <- cbind(coef, alpha)
+ }
+ else if (object$zelig == "gamma") {
+ rate <- gamma.shape(object)
+ alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
+ res <- cbind(coef, alpha)
+ }
+ else if (object$zelig == "negbin") {
+ alpha <- object$theta
+ res <- cbind(coef, c(alpha))
+ }
+ else
+ res <- coef
+ }
+ else {
+ coef <- coef(object)
+ if (object$family$family == "gaussian") {
+ alpha <- sum(object$residuals^2)/length(object$residuals)
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "Gamma") {
+ alpha <- gamma.dispersion(object)
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "neg.bin") {
+ alpha <- object$theta
+ res <- c(coef, alpha)
+ }
+ else
+ res <- coef
+ }
+ res
+}
+
+
+
+
diff --git a/R/param.lm.R b/R/param.lm.R
new file mode 100644
index 0000000..4df93f2
--- /dev/null
+++ b/R/param.lm.R
@@ -0,0 +1,16 @@
+param.lm <-function(object, num, bootstrap = FALSE) {
+ if (num < 1) num <- 1
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov.lm(object))
+ df <- object$df.residual
+ sig2 <- summary(object)$sigma^2
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ res <- cbind(coef, alpha)
+ }
+ else {
+ coef <- coef(object)
+ alpha <- summary(object)$sigma
+ res <- c(coef, alpha)
+ }
+ res
+}
diff --git a/R/param.multinom.R b/R/param.multinom.R
new file mode 100644
index 0000000..c968e30
--- /dev/null
+++ b/R/param.multinom.R
@@ -0,0 +1,12 @@
+param.multinom <- function(object, num, bootstrap = FALSE) {
+ k <- length(object$lev)
+ coef <- NULL
+ tmp <- coef(object)
+ for (i in 1:(k-1))
+ coef <- c(coef, tmp[i,])
+ if (!bootstrap)
+ sim.coef <- mvrnorm(num, mu=coef, Sigma=vcov(object))
+ else
+ sim.coef <- coef
+ sim.coef
+}
diff --git a/R/param.polr.R b/R/param.polr.R
new file mode 100644
index 0000000..183c94f
--- /dev/null
+++ b/R/param.polr.R
@@ -0,0 +1,14 @@
+param.polr <- function(object, num, bootstrap=FALSE) {
+ coef <- object$coefficients
+ zeta <- object$zeta
+ k <- length(coef)
+ if (!bootstrap) {
+ par <- mvrnorm(num, mu=c(coef,zeta), Sigma=vcov(object))
+ coef <- par[,1:k]
+ alpha <- par[,(k+1):ncol(par)]
+ res <- as.matrix(cbind(coef, alpha))
+ }
+ else
+ res <- c(coef, zeta)
+ res
+}
diff --git a/R/param.relogit.R b/R/param.relogit.R
new file mode 100644
index 0000000..44a949f
--- /dev/null
+++ b/R/param.relogit.R
@@ -0,0 +1,140 @@
+param.relogit <- function(object, num, x, bootstrap = FALSE, bootfn = NULL, ...) {
+ tau <- eval(object$call$tau, sys.parent())
+ if (is.null(object$call$bias.correct))
+ object$call$bias.correct <- TRUE
+ if (length(tau) == 2) { # without full population information
+ tmp0 <- tmp1 <- object
+ tmp0$coefficients["(Intercept)"] <- object$correct[1]
+ tmp1$coefficients["(Intercept)"] <- object$correct[2]
+ pping <- function(object, x, x1, num, bootstrap, bootfn,...) {
+ if(!bootstrap) {
+ par0 <- param.default(tmp0, num=num, bootstrap=bootstrap)
+ par1 <- param.default(tmp1, num=num, bootstrap=bootstrap)
+ }
+ else {
+ dta <- eval(object$data, sys.parent())
+ dta <- dta[complete.cases(model.frame(object, dta)),]
+ if (is.null(bootfn)) {
+ bootfn <- function(data, i, obj) {
+ d <- data[i,]
+ obj$call$data <- d
+ fit <- eval(obj$call, sys.parent())
+ fit$coefficients["(Intercept)"] <- fit$correct[1]
+ coef0 <- fit$coefficients
+ fit$coefficients["(Intercept)"] <- fit$correct[2]
+ coef1 <- fit$coefficients
+ return(c(coef0, coef1))
+ }
+ }
+ pars <- boot(dta, bootfn, R=num, obj = object, ...)
+ colnames(pars$t) <- names(pars$t0)
+ par0 <- pars$t[, 1:length(object$coef)]
+ par1 <- pars$t[, (length(object$coef)+1):nrow(pars$t)]
+ }
+ sim00 <- qi.glm(tmp0, par0, x = x)
+ P00 <- as.matrix(sim00$qi$ev)
+ sim10 <- qi.glm(tmp1, par1, x = x)
+ P10 <- as.matrix(sim10$qi$ev)
+ test <- P00[,1] < P10[,1]
+ par0 <- as.matrix(par0[test,])
+ par1 <- as.matrix(par1[test,])
+ list(par0 = par0, par1 = par1)
+ }
+ tmp <- pping(object, bootstrap=bootstrap, bootfn=bootfn, x=x, x1=x1, num=num, ...)
+ par0 <- tmp$par0
+ par1 <- tmp$par1
+ while (nrow(par0) < num) {
+ tmp <- pping(object, bootstrap=bootstrap, bootfn=bootfn, x=x, x1=x1, num=num,...)
+ par0 <- rbind(par0, tmp$par0)
+ par1 <- rbind(par1, tmp$par1)
+ }
+ if (nrow(par0) > num) {
+ par0 <- par0[1:num,]
+ par1 <- par1[1:num,]
+ }
+ par0 <- as.matrix(par0)
+ par1 <- as.matrix(par1)
+ rownames(par0) <- 1:nrow(par0)
+ rownames(par1) <- 1:nrow(par1)
+ return(list(par0 = par0, par1 = par1))
+ }
+ else { # with precise population info, or no population info
+ object$coefficients["(Intercept)"] <- object$correct
+ if (!bootstrap)
+ simpar <- param.default(object, num=num, bootstrap = bootstrap)
+ else {
+ tt <- terms(object)
+ dta <- eval(object$data, sys.parent())
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ if(is.null(bootfn)) {
+ bootfn <- function(data, i, object) {
+ d <- data[i,]
+ object$call$data <- d
+ fit <- eval(object$call, sys.parent())
+ fit$coefficients["(Intercept)"] <- correct
+ fit$coefficients
+ }
+ }
+ res <- boot(dta, bootfn, R = num, object = object, ...)
+ colnames(res$t) <- names(res$t0)
+ simpar <- res$t
+ }
+ return(simpar)
+ }
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
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+
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+
diff --git a/R/param.survreg.R b/R/param.survreg.R
new file mode 100644
index 0000000..32f4f0e
--- /dev/null
+++ b/R/param.survreg.R
@@ -0,0 +1,19 @@
+param.survreg <- function(object, num, bootstrap = FALSE) {
+ cov <- vcov(object)
+ coef <- object$coefficients
+ log.scale <- log(object$scale)
+ k <- length(coef)
+ if(!bootstrap) {
+ if(ncol(cov)==k)
+ res <- mvrnorm(num, mu=coef, Sigma=cov)
+ else
+ res <- mvrnorm(num, mu=c(coef, log.scale), Sigma=cov)
+ }
+ else {
+ if (ncol(cov) == k)
+ res <- c(coef)
+ else
+ res <- c(coef, log.scale)
+ }
+ res
+}
diff --git a/R/param.vglm.R b/R/param.vglm.R
new file mode 100644
index 0000000..99c987b
--- /dev/null
+++ b/R/param.vglm.R
@@ -0,0 +1,7 @@
+param.vglm <- function(object, num, bootstrap = FALSE) {
+ cov <- vcov(object)
+ res <- object at coefficients
+ if (!bootstrap)
+ res <- mvrnorm(num, mu=res, Sigma=cov)
+ res
+}
diff --git a/R/parse.par.R b/R/parse.par.R
new file mode 100644
index 0000000..68ae68d
--- /dev/null
+++ b/R/parse.par.R
@@ -0,0 +1,17 @@
+parse.par <- function(par, x, terms) {
+ k <- ncol(x)
+ beta <- par[1:k]
+ assign(attr(terms, "items")$coef, beta, envir = parent.frame())
+ ancil.names <- attr(terms, "items")$ancillary
+ j <- 0
+ if (!is.null(ancil.names)) {
+ j <- length(ancil.names)
+ for (i in 1:j) {
+ tmp <- par[(k + i)]
+ assign(ancil.names[i], tmp, envir = parent.frame())
+ }
+ }
+ if (length(par) > (k + j))
+ return(par[(k + j):length(par)])
+}
+
diff --git a/R/plot.ci.R b/R/plot.ci.R
new file mode 100644
index 0000000..1a57b7a
--- /dev/null
+++ b/R/plot.ci.R
@@ -0,0 +1,82 @@
+plot.ci <- function(x, CI=95, qi = "ev", main = "",
+ ylab = NULL, xlab = NULL, xlim = NULL,
+ ylim = NULL, col = c("red", "blue"), ...) {
+ if (class(x) != "zelig")
+ stop(" plot.ci() works only for sim() output.")
+ if (!(x$zelig.call$model) %in% c("ls", "logit", "probit", "exp", "gamma", "lognorm",
+ "weibull", "normal", "poisson", "relogit",
+ "negbin", "MCMCregress", "MCMClogit", "MCMCprobit",
+ "MCMCpoisson", "MCMCregress"))
+ stop("\n plot.ci() is valid only for non-categorical, univariate response models.")
+ var <- var1 <- list()
+ idx <- array()
+ cip <- c((100-CI)/200, 1-(100-CI)/200)
+ summarize <- function(z, cip){
+ res <- NULL
+ res <- cbind(res, apply(z, 2, quantile, prob=cip[1]))
+ res <- cbind(res, apply(z, 2, quantile, prob=cip[2]))
+ res
+ }
+ for (i in 1:ncol(x$x)) {
+ var[[i]] <- unique(x$x[,i])
+ if (length(var[[i]]) > 1)
+ idx[i] <- i
+ else
+ idx[i] <- NA
+ }
+ if (!is.null(x$x1)) {
+ idx1 <- NULL
+ for (i in 1:ncol(x$x1)) {
+ var1[[i]] <- unique(x$x1[,i])
+ if (length(var1[[i]]) > 1)
+ idx1[i] <- i
+ else
+ idx1[i] <- NA
+ }
+ idx1 <- na.omit(idx1)
+ }
+ idx <- na.omit(idx)
+ if (!is.null(idx1))
+ if (!identical(idx, idx1))
+ stop(" x and x1 in vary on different dimensions.")
+ var <- var[[idx[1]]]
+ q <- pmatch(qi, names.default(x$qi))
+ qofi <- x$qi[[q]]
+ sum.qi <- summarize(qofi, cip)
+ if (!is.null(x$x1) && qi == "ev") {
+ fd <- x$qi$fd
+ ev1 <- fd + qofi
+ sum.qi1 <- summarize(ev1, cip)
+ }
+ else
+ sum.qi1 <- NULL
+ if (is.null(ylab))
+ ylab <- x$qi.name[[q]]
+ if (is.null(xlab))
+ xlab <- paste("Range of", colnames(x$x)[idx[1]])
+ if (is.null(ylim)) {
+ if (is.null(sum.qi1))
+ ylim <- c(min(sum.qi), max(sum.qi))
+ else
+ ylim <- c(min(sum.qi, sum.qi1), max(sum.qi, sum.qi1))
+ }
+ if (is.null(xlim))
+ xlim <- c(min(var), max(var))
+ plot.default(var, type = "n", ylab = ylab, main = main, xlab = xlab,
+ xlim = xlim, ylim = ylim)
+ for (i in 1:length(var)) {
+ lines(c(var[i], var[i]), c(sum.qi[i,1], sum.qi[i,2]), col = col[1], ...)
+ if (!is.null(x$x1) && qi == "ev")
+ lines(c(var[i], var[i]), c(sum.qi1[i,1], sum.qi1[i,2]), col = col[2], ...)
+ }
+}
+
+
+
+
+
+
+
+
+
+
diff --git a/R/plot.zelig.R b/R/plot.zelig.R
new file mode 100644
index 0000000..dc72f32
--- /dev/null
+++ b/R/plot.zelig.R
@@ -0,0 +1,11 @@
+plot.zelig <- function(x, xlab = "", user.par = FALSE, ...) {
+ if (dim(x$x)[1] > 1)
+ plot.ci(x, xlab = "", ...)
+ else {
+ class(x) <- x$zelig.call$model
+ if (exists(paste("plot.zelig", x$zelig.call$model, sep = ".")))
+ UseMethod("plot.zelig", x)
+ else
+ stop(paste(x$zelig.call$model, "does not have an applicable plot method for sim() output."))
+ }
+}
diff --git a/R/plot.zelig.blogit.R b/R/plot.zelig.blogit.R
new file mode 100644
index 0000000..2fed8cd
--- /dev/null
+++ b/R/plot.zelig.blogit.R
@@ -0,0 +1,36 @@
+plot.zelig.blogit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ if ("rr" %in% names(x$qi)) {
+ x$qi$rr <- x$qi.name$rr <- NULL
+ k <- k - 1
+ }
+ main <- x$qi.name
+ par(mfrow = c(k, 1))
+ for (i in 1:k) {
+ qi <- as.matrix(x$qi[[i]])
+ if (is.character(qi[1])) {
+ total <- sum(as.integer(qi))
+ y00 <- 100 * sum(as.integer(qi[,1]))/total
+ y01 <- 100 * sum(as.integer(qi[,2]))/total
+ y10 <- 100 * sum(as.integer(qi[,3]))/total
+ y11 <- 100 * sum(as.integer(qi[,4]))/total
+ xmax <- max(y00, y01, y10, y11)
+ labels <- c("(0,0)", "(0,1)","(1,0)", "(1,1)")
+ barplot(c(y00, y01, y10, y11), horiz = TRUE, col = alt.col,
+ names.arg = labels, xpd = TRUE, main = main[[i]],
+ xlim = c(0, min(100, 1.25*xmax)),
+ xlab = "Percentage of Simulations")
+ }
+ else if (is.numeric(qi[1])) {
+ y1 <- qi[, 3] + qi[, 4]
+ y2 <- qi[, 2] + qi[, 4]
+ contour(kde2d(y1, y2), xlab = "Pr(Y1 = 1)",
+ ylab = "Pr(Y2 = 1)", main = main[[i]], ...)
+ }
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.bprobit.R b/R/plot.zelig.bprobit.R
new file mode 100644
index 0000000..728828e
--- /dev/null
+++ b/R/plot.zelig.bprobit.R
@@ -0,0 +1,36 @@
+plot.zelig.bprobit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ if ("rr" %in% names(x$qi)) {
+ x$qi$rr <- x$qi.name$rr <- NULL
+ k <- k - 1
+ }
+ main <- x$qi.name
+ par(mfrow = c(k, 1))
+ for (i in 1:k) {
+ qi <- as.matrix(x$qi[[i]])
+ if (is.character(qi[1])) {
+ total <- sum(as.integer(qi))
+ y00 <- 100 * sum(as.integer(qi[,1]))/total
+ y01 <- 100 * sum(as.integer(qi[,2]))/total
+ y10 <- 100 * sum(as.integer(qi[,3]))/total
+ y11 <- 100 * sum(as.integer(qi[,4]))/total
+ xmax <- max(y00, y01, y10, y11)
+ labels <- c("(0,0)", "(0,1)","(1,0)", "(1,1)")
+ barplot(c(y00, y01, y10, y11), horiz = TRUE, col = alt.col,
+ names.arg = labels, xpd = TRUE, main = main[[i]],
+ xlim = c(0, min(100, 1.25*xmax)),
+ xlab = "Percentage of Simulations")
+ }
+ else if (is.numeric(qi[1])) {
+ y1 <- qi[, 3] + qi[, 4]
+ y2 <- qi[, 2] + qi[, 4]
+ contour(kde2d(y1, y2), xlab = "Pr(Y1 = 1)",
+ ylab = "Pr(Y2 = 1)", main = main[[i]], ...)
+ }
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.exp.R b/R/plot.zelig.exp.R
new file mode 100644
index 0000000..e12de93
--- /dev/null
+++ b/R/plot.zelig.exp.R
@@ -0,0 +1,12 @@
+plot.zelig.exp <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.gamma.R b/R/plot.zelig.gamma.R
new file mode 100644
index 0000000..8f8627e
--- /dev/null
+++ b/R/plot.zelig.gamma.R
@@ -0,0 +1,12 @@
+plot.zelig.gamma <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$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))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.logit.R b/R/plot.zelig.logit.R
new file mode 100644
index 0000000..35032aa
--- /dev/null
+++ b/R/plot.zelig.logit.R
@@ -0,0 +1,20 @@
+plot.zelig.logit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$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$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.lognorm.R b/R/plot.zelig.lognorm.R
new file mode 100644
index 0000000..cb9c5a2
--- /dev/null
+++ b/R/plot.zelig.lognorm.R
@@ -0,0 +1,12 @@
+plot.zelig.lognorm <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$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))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.ls.R b/R/plot.zelig.ls.R
new file mode 100644
index 0000000..bdfb6a3
--- /dev/null
+++ b/R/plot.zelig.ls.R
@@ -0,0 +1,12 @@
+plot.zelig.ls <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.mlogit.R b/R/plot.zelig.mlogit.R
new file mode 100644
index 0000000..99d25a1
--- /dev/null
+++ b/R/plot.zelig.mlogit.R
@@ -0,0 +1,49 @@
+plot.zelig.mlogit <- function(x, xlab = "", user.par = FALSE, alt.col = NULL, ...){
+ k <- length(x$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 (!is.null(x$qi$rr)) {
+ k <- k - 1
+ x$qi$rr <- x$qi.name$rr <- NULL
+ }
+ par(mfrow = c(k,1))
+ pr <- x$qi$pr
+ spr <- array()
+ lev <- sort(unique(pr))
+ K <- length(lev)
+ if (is.null(alt.col))
+ alt.col <- rainbow(K)
+ total <- length(pr)
+ for (i in 1:K)
+ spr[i] <- 100 * sum(as.character(pr) == lev[i])/total
+ xmax <- max(spr)
+ labels <- paste("Y=", lev, sep = "")
+ barplot(spr, horiz = TRUE, col = alt.col, names.arg = labels,
+ las = 1, main = x$qi.name$pr,
+ xlim = c(0, min(100, 1.15*xmax)),
+ xlab = "Percentage of Simulations")
+ x$qi$pr <- x$qi.name$pr <- NULL
+ main <- x$qi.name
+ for (i in 1:(k-1)) {
+ qi <- x$qi[[i]]
+ if (length(dim(qi)) == 3 && dim(qi)[3] == 1)
+ qi <- qi[,,1]
+ dens <- list()
+ xmax <- ymax <- array()
+ for (j in 1:ncol(qi)) {
+ dens[[j]] <- density(qi[,j])
+ xmax[j] <- max(dens[[j]]$x)
+ ymax[j] <- max(dens[[j]]$y)
+ }
+ plot(dens[[1]], col = alt.col[1],
+ xlim = c(min(min(qi), 0), max(xmax)),
+ xlab = xlab, main = "",
+ ylim = c(0, max(ymax)), ...)
+ for (j in 2:ncol(qi))
+ lines(dens[[j]], col = alt.col[j])
+ title(main = x$qi.name[[i]][1])
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.negbin.R b/R/plot.zelig.negbin.R
new file mode 100644
index 0000000..240a56c
--- /dev/null
+++ b/R/plot.zelig.negbin.R
@@ -0,0 +1,13 @@
+plot.zelig.negbin <- function(x, xlab = "", user.par = FALSE, ...){
+ k <- length(x$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))
+ truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
+ xlab = xlab, ylab = "Probability", ...)
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ if (k == 3)
+ plot(density(x$qi$fd), main = x$qi$fd, xlab = xlab, ...)
+ par(op)
+}
diff --git a/R/plot.zelig.normal.R b/R/plot.zelig.normal.R
new file mode 100644
index 0000000..c172af6
--- /dev/null
+++ b/R/plot.zelig.normal.R
@@ -0,0 +1,12 @@
+plot.zelig.normal <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.ologit.R b/R/plot.zelig.ologit.R
new file mode 100644
index 0000000..d80eafe
--- /dev/null
+++ b/R/plot.zelig.ologit.R
@@ -0,0 +1,49 @@
+plot.zelig.ologit <- function(x, xlab = "", user.par = FALSE, alt.col = NULL, ...){
+ k <- length(x$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 (!is.null(x$qi$rr)) {
+ k <- k - 1
+ x$qi$rr <- x$qi.name$rr <- NULL
+ }
+ par(mfrow = c(k,1))
+ pr <- x$qi$pr
+ spr <- array()
+ lev <- sort(unique(pr))
+ K <- length(lev)
+ if (is.null(alt.col))
+ alt.col <- rainbow(K)
+ total <- length(pr)
+ for (i in 1:K)
+ spr[i] <- 100 * sum(as.character(pr) == lev[i])/total
+ xmax <- max(spr)
+ labels <- paste("Y=", lev, sep = "")
+ barplot(spr, horiz = TRUE, col = alt.col, names.arg = labels,
+ las = 1, main = x$qi.name$pr,
+ xlim = c(0, min(100, 1.15*xmax)),
+ xlab = "Percentage of Simulations")
+ x$qi$pr <- x$qi.name$pr <- NULL
+ main <- x$qi.name
+ for (i in 1:(k-1)) {
+ qi <- x$qi[[i]]
+ if (length(dim(qi)) == 3 && dim(qi)[3] == 1)
+ qi <- qi[,,1]
+ dens <- list()
+ xmax <- ymax <- array()
+ for (j in 1:ncol(qi)) {
+ dens[[j]] <- density(qi[,j])
+ xmax[j] <- max(dens[[j]]$x)
+ ymax[j] <- max(dens[[j]]$y)
+ }
+ plot(dens[[1]], col = alt.col[1],
+ xlim = c(min(min(qi), 0), max(xmax)),
+ xlab = xlab, main = "",
+ ylim = c(0, max(ymax)), ...)
+ for (j in 2:ncol(qi))
+ lines(dens[[j]], col = alt.col[j])
+ title(main = x$qi.name[[i]][1])
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.oprobit.R b/R/plot.zelig.oprobit.R
new file mode 100644
index 0000000..6943175
--- /dev/null
+++ b/R/plot.zelig.oprobit.R
@@ -0,0 +1,49 @@
+plot.zelig.oprobit <- function(x, xlab = "", user.par = FALSE, alt.col = NULL, ...){
+ k <- length(x$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 (!is.null(x$qi$rr)) {
+ k <- k - 1
+ x$qi$rr <- x$qi.name$rr <- NULL
+ }
+ par(mfrow = c(k,1))
+ pr <- x$qi$pr
+ spr <- array()
+ lev <- sort(unique(pr))
+ K <- length(lev)
+ if (is.null(alt.col))
+ alt.col <- rainbow(K)
+ total <- length(pr)
+ for (i in 1:K)
+ spr[i] <- 100 * sum(as.character(pr) == lev[i])/total
+ xmax <- max(spr)
+ labels <- paste("Y=", lev, sep = "")
+ barplot(spr, horiz = TRUE, col = alt.col, names.arg = labels,
+ las = 1, main = x$qi.name$pr,
+ xlim = c(0, min(100, 1.15*xmax)),
+ xlab = "Percentage of Simulations")
+ x$qi$pr <- x$qi.name$pr <- NULL
+ main <- x$qi.name
+ for (i in 1:(k-1)) {
+ qi <- x$qi[[i]]
+ if (length(dim(qi)) == 3 && dim(qi)[3] == 1)
+ qi <- qi[,,1]
+ dens <- list()
+ xmax <- ymax <- array()
+ for (j in 1:ncol(qi)) {
+ dens[[j]] <- density(qi[,j])
+ xmax[j] <- max(dens[[j]]$x)
+ ymax[j] <- max(dens[[j]]$y)
+ }
+ plot(dens[[1]], col = alt.col[1],
+ xlim = c(min(min(qi), 0), max(xmax)),
+ xlab = xlab, main = "",
+ ylim = c(0, max(ymax)), ...)
+ for (j in 2:ncol(qi))
+ lines(dens[[j]], col = alt.col[j])
+ title(main = x$qi.name[[i]][1])
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.poisson.R b/R/plot.zelig.poisson.R
new file mode 100644
index 0000000..cb5b861
--- /dev/null
+++ b/R/plot.zelig.poisson.R
@@ -0,0 +1,16 @@
+plot.zelig.poisson <- function(x, xlab = "", user.par = FALSE, ...){
+ k <- length(x$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))
+ truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
+ xlab = xlab, ylab = "Probability", ...)
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ if (k > 2) {
+ for (i in 3:k)
+ plot(density(x$qi[[i]]), main = x$qi.name[[i]],
+ xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.probit.R b/R/plot.zelig.probit.R
new file mode 100644
index 0000000..4bd6ed9
--- /dev/null
+++ b/R/plot.zelig.probit.R
@@ -0,0 +1,20 @@
+plot.zelig.probit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$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$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.relogit.R b/R/plot.zelig.relogit.R
new file mode 100644
index 0000000..7414a09
--- /dev/null
+++ b/R/plot.zelig.relogit.R
@@ -0,0 +1,42 @@
+plot.zelig.relogit <- function(x, xlab ="", user.par = FALSE, alt.col = "red",
+ ylab = NULL, samples = 100, ...){
+ k <- length(x$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$qi.name[i])
+ if (is.null(rownames(qi)))
+ rownames(qi) <- 1:dim(qi)[1]
+ idx <- as.integer(sample(rownames(qi), 100))
+ tmp <- qi[idx,,1]
+ xlim <- c(min(qi[,1,1]), max(qi[,2,1]))
+ if (is.null(ylab))
+ ylab <- paste("Observations (n = ", samples, ")", sep = "")
+ plot(xlim, type = "n", xlab = xlab, ylab = ylab,
+ main = main, ylim = c(0, 100), xlim = xlim, ...)
+ for (j in 1:nrow(tmp))
+ lines(c(tmp[j,1], tmp[j,2]), c(j,j), col = alt.col)
+ abline(v = mean(qi[,1,1]))
+ abline(v = mean(qi[,2,1]))
+ }
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.weibull.R b/R/plot.zelig.weibull.R
new file mode 100644
index 0000000..3d09774
--- /dev/null
+++ b/R/plot.zelig.weibull.R
@@ -0,0 +1,12 @@
+plot.zelig.weibull <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$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))
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/print.BetaReg.R b/R/print.BetaReg.R
new file mode 100644
index 0000000..bb176af
--- /dev/null
+++ b/R/print.BetaReg.R
@@ -0,0 +1,18 @@
+print.BetaReg <- function(x, digits = getOption("digits"), ...) {
+ cat("\nCall: ", deparse(x$call), "\n", fill = TRUE)
+ cat("Coefficients:\n")
+ if(is.matrix(x$coef)) {
+ print(x$coef[1:nrow(x$coef) - 1,], digits = digits, ...)
+ cat("\n")
+ phi <- c(x$coef[nrow(x$coef),])
+ names(phi) <- colnames(x$coef)
+ cat("Dispersion parameter (phi): \n")
+ print(phi, digits = digits, ...)
+ }
+ else {
+ print(x$coef[1:length(x$coef) - 1], digits = digits, ...)
+ cat("\n")
+ cat("Dispersion parameter (phi) = ", x$coef[length(x$coef)], "\n")
+ }
+ invisible(x)
+}
diff --git a/R/print.names.relogit.R b/R/print.names.relogit.R
new file mode 100644
index 0000000..5eb8d50
--- /dev/null
+++ b/R/print.names.relogit.R
@@ -0,0 +1,10 @@
+print.names.relogit<-function(x, ...){
+ if (length(x$tau) == 2) {
+ print(x$default, ...)
+ cat(paste("Additional objects available in lower.estimate and upper.estimate: \n"))
+ print(x$estimate)
+ }
+ else
+ print(x$default, ...)
+}
+
diff --git a/R/print.names.zelig.R b/R/print.names.zelig.R
new file mode 100644
index 0000000..d04555f
--- /dev/null
+++ b/R/print.names.zelig.R
@@ -0,0 +1,3 @@
+print.names.zelig <- function(x, ...){
+ print(x$default, ...)
+}
diff --git a/R/print.relogit.R b/R/print.relogit.R
new file mode 100644
index 0000000..d6b7944
--- /dev/null
+++ b/R/print.relogit.R
@@ -0,0 +1,26 @@
+print.relogit <-function (x, digits = max(3, getOption("digits") - 3),
+ ...) {
+ if (is.null(x$call$bias.correct))
+ x$call$bias.correct <- TRUE
+ cat("\nCall: ", deparse(x$call), "\n\n")
+ cat("Coefficients")
+ if (is.character(co <- x$contrasts))
+ cat(" [contrasts: ", apply(cbind(names(co), co),
+ 1, paste, collapse = "="), "]")
+ cat(":\n")
+ print.default(format(x$coefficients, digits = digits),
+ print.gap = 2, quote = FALSE)
+ if (!is.null(x$call$tau)) {
+ cat("\nIntercepts with prior correction for: \n")
+ print.default(x$correct, digits = digits,
+ print.gap = 1, quote = FALSE)
+ }
+ cat("\nDegrees of Freedom:", x$df.null, "Total (i.e. Null); ",
+ x$df.residual, "Residual\n")
+ cat("Null Deviance:\t ", format(signif(x$null.deviance,
+ digits)), "\nResidual Deviance:", format(signif(x$deviance,
+ digits)), "\tAIC:", format(signif(x$aic, digits)), "\n")
+ if (x$call$bias.correct)
+ cat("Rare events bias correction performed.")
+ invisible(x)
+}
diff --git a/R/print.setx.R b/R/print.setx.R
new file mode 100644
index 0000000..9fd17bb
--- /dev/null
+++ b/R/print.setx.R
@@ -0,0 +1,7 @@
+print.setx<-function(x, digits=getOption("digits"),
+ ...){
+ attributes(x)$class<-NULL
+ attributes(x)$assign<-NULL
+ attributes(x)$contrasts<-NULL
+ print.matrix(x, digits=digits, ...)
+}
diff --git a/R/print.summary.MI.R b/R/print.summary.MI.R
new file mode 100644
index 0000000..7c202d7
--- /dev/null
+++ b/R/print.summary.MI.R
@@ -0,0 +1,28 @@
+print.summary.MI <- 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$all[[i]], ...)
+ }
+ }
+}
+
diff --git a/R/print.summary.strata.R b/R/print.summary.strata.R
new file mode 100644
index 0000000..fbe5d84
--- /dev/null
+++ b/R/print.summary.strata.R
@@ -0,0 +1,16 @@
+print.summary.strata <- function(x, subset = NULL, ...){
+ if (is.null(subset))
+ m <- length(x$M)
+ else if (any(subset > max(m)))
+ stop("the subset selected lies outside the range of available \n sets of regression output.")
+ else
+ m <- subset
+ cat("\n Model:", x$call$model)
+ cat("\n Number of subsets evaluated:", m, "\n")
+ for (i in 1:m) {
+ cat(paste("\nResults for", x$by[1], "=", x$lev[i], "\n"))
+ print(x[[i]])
+ cat("\n")
+ }
+}
+
diff --git a/R/print.summary.zelig.R b/R/print.summary.zelig.R
new file mode 100644
index 0000000..7efee16
--- /dev/null
+++ b/R/print.summary.zelig.R
@@ -0,0 +1,68 @@
+print.summary.zelig <- function(x, digits=getOption("digits"),
+ print.x=FALSE, ...){
+ cat("\n Model:", x$zelig, "\n")
+ if (!is.null(x$num))
+ cat(" Number of simulations:", x$num, "\n")
+ if (!is.null(x$x)) {
+ if (print.x || nrow(x$x) == 1 || is.null(dim(x$x))) {
+ if (any(class(x$x) == "cond"))
+ cat("\nObserved Data \n")
+ else
+ cat("\nValues of X \n")
+ print.setx(x$x, digits=digits, ...)
+ if(!is.null(x$x1)){
+ cat("\nValues of X1 \n")
+ print.setx(x$x1, digits=digits, ...)
+ }
+ }
+ else {
+ if (any(class(x$x) == "cond"))
+ cat("\nMean Values of Observed Data (n = ", nrow(x$x), ") \n", sep = "")
+ else
+ cat("\nMean Values of X (n = ", nrow(x$x), ") \n", sep = "")
+ print.setx(apply(x$x, 2, mean), digits=digits, ...)
+ if (!is.null(x$x1)) {
+ cat("\nMean Values of X1 (n = ", nrow(x$x1), ") \n", sep = "")
+ print.setx(apply(x$x1, 2, mean), digits=digits, ...)
+ }
+ }
+ }
+ for (i in 1:length(x$qi.name)){
+ indx <- pmatch(names(x$qi.name[i]), names(x$qi.stats))
+ tmp <- x$qi.stats[[indx]]
+ if (names(x$qi.name)[indx] == "pr" && colnames(tmp)[1] != "mean")
+ lab <- paste(x$qi.name[[i]], "(percentage of simulations)", sep = " ")
+ else
+ lab <- x$qi.name[[i]]
+ cat("\n", lab, "\n", sep = "")
+ if (length(dim(tmp)) == 3) {
+ for (j in 1:dim(tmp)[3]){
+ cat("\n Observation", dimnames(tmp)[[3]][j], "\n")
+ if (is.null(rownames(tmp[,,j])))
+ rownames(tmp[,,j]) <- 1:nrow(tmp[,,j])
+ if (!is.null(names(tmp[,,j])))
+ names(tmp[,,j]) <- NULL
+ print.matrix(tmp[,,j], digits=digits, ...)
+ }
+ }
+ else {
+ if (is.null(rownames(tmp)))
+ rownames(tmp) <- 1:nrow(tmp)
+ if (!is.null(names(tmp)))
+ names(tmp) <- NULL
+ print.matrix(tmp, digits=digits, ...)
+ }
+ }
+}
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/print.summary.zelig.strata.R b/R/print.summary.zelig.strata.R
new file mode 100644
index 0000000..a7c9c21
--- /dev/null
+++ b/R/print.summary.zelig.strata.R
@@ -0,0 +1,14 @@
+print.summary.zelig.strata <- function(x, subset = NULL, ...){
+ if (is.null(subset))
+ m <- length(x)
+ else if (any(subset > max(m)))
+ stop("the subset selected lies outside the range of available \n sets of regression output.")
+ else
+ m <- subset
+ for (i in 1:m) {
+ cat(paste("\nResults for", names(x)[i], "\n"))
+ print(x[[i]])
+ cat("\n")
+ }
+}
+
diff --git a/R/print.zelig.R b/R/print.zelig.R
new file mode 100644
index 0000000..1fd6a5d
--- /dev/null
+++ b/R/print.zelig.R
@@ -0,0 +1,12 @@
+print.zelig <- function (x, digits = max(3, getOption("digits") - 3), ...)
+{
+ cat("\nModel:", x$zelig.call$model, "\n", sep = " ")
+ cat("Number of simulations:", x$call$num, "\n\n", sep = " ")
+ idx <- unlist(x$qi.name)
+ cat("Available Quantities of Interest: \n")
+ for (i in 1:length(x$qi.name)){
+ cat(paste(" qi$", names(x$qi)[i], " = ", idx[[i]], "\n", sep = ""))
+ }
+ cat("\nPlease use summary() to obtain more information. \n")
+ invisible(x)
+}
diff --git a/R/qi.BetaReg.R b/R/qi.BetaReg.R
new file mode 100644
index 0000000..229fd14
--- /dev/null
+++ b/R/qi.BetaReg.R
@@ -0,0 +1,29 @@
+qi.BetaReg <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ k <- ncol(x)
+ coef <- simpar[,1:k]
+ phi <- simpar[,(k+1):ncol(simpar)]
+ eta <- coef %*% t(x)
+ ev <- exp(eta) / (1 + exp(eta))
+ a <- ev * phi
+ b <- phi - ev * phi
+ pr <- matrix(NA, ncol = ncol(ev), nrow = nrow(ev))
+ for (i in 1:ncol(pr))
+ pr[,i] <- sapply(a[,i], rbeta, n = 1, shape2 = b[,i])
+ qi <- list(ev = ev, pr = pr)
+ qi.name <- list(ev = "Expected Values: E(Y|X)",
+ pr = "Predicted Values: Y|X")
+ if(!is.null(x1)){
+ eta1 <- coef %*% t(x1)
+ ev1 <- exp(eta1) / (1 + exp(eta1))
+ qi$fd <- ev1 - ev
+ qi.name$fd <-
+ "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
diff --git a/R/qi.R b/R/qi.R
new file mode 100644
index 0000000..4e531c8
--- /dev/null
+++ b/R/qi.R
@@ -0,0 +1,2 @@
+qi<-function(object, ...)
+ UseMethod("qi")
diff --git a/R/qi.glm.R b/R/qi.glm.R
new file mode 100644
index 0000000..7ad0d8e
--- /dev/null
+++ b/R/qi.glm.R
@@ -0,0 +1,74 @@
+qi.glm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ check <- FALSE
+ model <- object$zelig
+ k <- length(object$coef)
+ coef <- simpar[,1:k]
+ if (k < ncol(simpar))
+ alpha <- simpar[,(k+1):ncol(simpar)]
+ eta <- coef %*% t(x)
+ theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
+ pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
+ dimnames(pr) <- dimnames(ev) <- dimnames(theta)
+ if (model %in% c("logit", "probit", "relogit")) {
+ check <- TRUE
+ ev <- theta
+ for (i in 1:ncol(theta))
+ pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
+ }
+ else if (model == "normal") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
+ }
+ else if (model == "gamma") {
+ ev <- theta * 1/alpha
+ for (i in 1:nrow(ev))
+ pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
+ }
+ else if (model == "poisson") {
+ ev <- theta
+ for (i in 1:ncol(ev))
+ pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
+ }
+ else if (model == "negbin") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnegbin(length(ev[i,]), mu = ev[i,], theta = alpha[i])
+ }
+ qi <- list(ev = ev, pr = pr)
+ qi.name <- list(ev = "Expected Values: E(Y|X)",
+ pr = "Predicted Values: Y|X")
+ if (!is.null(x1)){
+ theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
+ nrow = nrow(coef))
+ if (model == "gamma")
+ ev1 <- theta1 * 1/alpha
+ else
+ ev1 <- theta1
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ if (model %in% c("logit", "probit", "relogit")) {
+ qi$rr <- ev1/ev
+ qi.name$rr <- "Risk Ratios: P(Y=1|X1)/P(Y=1|X)"
+ }
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ if (check)
+ tmp.pr <- yvar - as.integer(qi$pr)
+ else
+ tmp.pr <- yvar - qi$pr
+ qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
diff --git a/R/qi.lm.R b/R/qi.lm.R
new file mode 100644
index 0000000..0b29a3b
--- /dev/null
+++ b/R/qi.lm.R
@@ -0,0 +1,24 @@
+qi.lm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ k <- length(object$coef)
+ coef <- simpar[,1:k]
+ alpha <- simpar[,(k+1):ncol(simpar)]
+ ev <- coef %*% t(x)
+ qi <- list(ev=ev)
+ qi.name <- list(ev="Expected Values: E(Y|X)")
+ if(!is.null(x1)){
+ ev1 <- coef %*% t(x1)
+ qi$fd <- ev1-ev
+ qi.name$fd <-
+ "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
diff --git a/R/qi.multinom.R b/R/qi.multinom.R
new file mode 100644
index 0000000..797e03d
--- /dev/null
+++ b/R/qi.multinom.R
@@ -0,0 +1,35 @@
+qi.polr <- function(object, simpar, x = x, x1 = NULL) {
+ m <- ncol(x)
+ lev <- object$lev
+ k <- length(lev)
+ ev.multinom<-function(num, sims, x, lev, k, m){
+ eta <- NULL
+ for (i in 1:(k-1))
+ eta <- cbind(eta, sims[,(m*(i-1)+1):(m*i)]%*%t(x))
+ eta <- exp(cbind(rep(0, num), eta))
+ ev <- eta/apply(eta, 1, sum)
+ colnames(ev) <- lev
+ ev
+ }
+ ev <- ev.multinom(num, simpar, x, lev, k, m)
+ Ipr <- sim.cut <- matrix(NA, nrow = nrow(ev), ncol = ncol(ev))
+ colnames(Ipr) <- colnames(sim.cut) <- lev
+ sim.cut[,1] <- ev[,1]
+ for (j in 2:k)
+ sim.cut[,j] <- sim.cut[,(j-1)] + ev[,j]
+ tmp <- runif(nrow(ev), 0, 1)
+ for (l in 1:k)
+ Ipr[,l] <- tmp > sim.cut[,l]
+ pr <- NULL
+ for (m in 1:nrow(Ipr))
+ pr[m] <- 1 + sum(Ipr[m,])
+ res$qi <- list(ev=ev, pr=pr)
+ res$qi.name <- list(ev = "Predicted Probabilities: P(Y=j|X)",
+ pr = "Predicted Values: Y|X")
+ if(!is.null(x1)){
+ ev1 <- ev.multinom(num, simpar, x1, lev, k, m)
+ res$qi$fd <- ev1-ev
+ res$qi.name$fd <- "First Differences: P(Y=j|X1)-P(Y=j|X)"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
diff --git a/R/qi.polr.R b/R/qi.polr.R
new file mode 100644
index 0000000..9467f71
--- /dev/null
+++ b/R/qi.polr.R
@@ -0,0 +1,77 @@
+qi.polr <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ m <- length(coef(object))
+ sim.coef <- simpar[,1:m]
+ sim.zeta <- simpar[,(m+1):ncol(simpar)]
+ k <- length(object$zeta) + 1
+ lev <- object$lev
+ eta <- x[,-1] %*% t(sim.coef)
+ ev.polr <- function(ev, eta, sim.zeta) {
+ for (j in 1:ncol(sim.zeta))
+ ev[,j,] <- exp(sim.zeta[,j] - t(eta)) / (1 + exp(sim.zeta[,j] - t(eta)))
+ for (j in 0:(ncol(sim.zeta)-1))
+ ev[,dim(ev)[2]-j,] <- ev[,dim(ev)[2]-j,]-ev[,(dim(ev)[2]-j-1),]
+ ev
+ }
+ ev <- array(1, dim = c(nrow(sim.coef), length(lev), nrow(x)),
+ dimnames = list(NULL, lev, rownames(x)))
+ ev <- ev.polr(ev, eta, sim.zeta)
+ sim.cut <- Ipr <- array(1, dim = dim(ev), dimnames = dimnames(ev))
+ pr <- matrix(NA, nrow = nrow(sim.coef), ncol = nrow(x))
+ sim.cut[,1,] <- ev[,1,]
+ tmp <- matrix(runif(length(sim.cut[,1,]), 0, 1), nrow =
+ dim(sim.cut)[1], ncol = dim(sim.cut)[3])
+ for (j in 2:length(lev))
+ sim.cut[,j,] <- sim.cut[,(j-1),] + ev[,j,]
+ for (k in 1:length(lev))
+ Ipr[,k,] <- as.integer(tmp > sim.cut[,k,])
+ for (n in 1:dim(Ipr)[3])
+ pr[,n] <- 1 + rowSums(Ipr[,,n])
+ pr <- matrix(factor(pr, labels = lev, ordered = TRUE),
+ nrow = nrow(sim.coef), ncol = nrow(x))
+ colnames(pr) <- rownames(x)
+ qi <- list(ev=ev, pr=pr)
+ qi.name <- list(ev="Expected Values: P(Y=j|X)",
+ pr="Predicted Values: Y|X")
+ if(!is.null(x1)){
+ ev1 <- array(1, dim = c(nrow(sim.coef), length(lev), nrow(x)),
+ dimnames = list(NULL, lev, rownames(x)))
+ eta1 <- x1[,-1] %*% t(sim.coef)
+ ev1 <- ev.polr(ev1, eta1, sim.zeta)
+ qi$fd <- ev1 - ev
+ qi$rr <- ev1 / ev
+ qi.name$fd <- "First Differences: P(Y=j|X1)-P(Y=j|X)"
+ qi.name$rr <- "Risk Ratio: P(Y=j|X1)-P(Y=j|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(NA, nrow = length(y), ncol = length(lev))
+ tmp.ev <- tmp.pr <- array(NA, dim = dim(qi$ev))
+ pr.idx <- array(NA, dim = c(nrow(pr), length(lev), nrow(x)))
+ qi$ate.ev <- qi$ate.pr <- matrix(NA, dim(qi$ev)[1], dim(qi$ev)[2])
+ for (i in 1:length(lev)) {
+ yvar[,i] <- as.integer(y == lev[i])
+ pr.idx[,i,] <- as.integer(pr[,i] == lev[i])
+ }
+ colnames(yvar) <- lev
+ for (j in 1:ncol(yvar)) {
+ tmp.ev[,j,] <- yvar[,j] - qi$ev[,j,]
+ tmp.pr[,j,] <- yvar[,j] - pr.idx[,j,]
+ qi$ate.ev[,j] <- apply(tmp.ev[,j,], 1, mean)
+ qi$ate.pr[,j] <- apply(tmp.pr[,j,], 1, mean)
+ }
+ colnames(qi$ate.ev) <- colnames(qi$ate.pr) <- lev
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/qi.relogit.R b/R/qi.relogit.R
new file mode 100644
index 0000000..59bdb40
--- /dev/null
+++ b/R/qi.relogit.R
@@ -0,0 +1,79 @@
+qi.relogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ tau <- eval(object$call$tau, sys.parent())
+ if (is.null(object$call$bias.correct))
+ object$call$bias.correct <- TRUE
+ num <- nrow(simpar$par0)
+ tmp0 <- tmp1 <- object
+ tmp0$coefficients["(Intercept)"] <- object$correct[1]
+ tmp1$coefficients["(Intercept)"] <- object$correct[2]
+ if (length(tau) == 2){
+ low <- qi.glm(tmp0, simpar$par0, x, x1)
+ up <- qi.glm(tmp1, simpar$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.glm(tmp0, par0, x = x1, x1 = NULL)
+ sim11 <- qi.glm(tmp1, par1, x = x1, x1 = NULL)
+ tau0 <- object$call$tau[1]
+ tau1 <- object$call$tau[2]
+ 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$y)
+ beta0.e <- tmp0$coefficients
+ beta1.e <- tmp1$coefficients
+ ## evaluating RD at tau0 and tau1
+ RD0.p <- 1/(1+exp(-t(beta0.e) %*% t(x1))) - 1/(1+exp(-t(beta0.e) %*% t(x)))
+ RD1.p <- 1/(1+exp(-t(beta1.e) %*% t(x1))) - 1/(1+exp(-t(beta1.e) %*% t(x)))
+ ## evaluating RD at tau0+e and tau1+e
+ e <- 0.001
+ beta0.e["(Intercept)"] <- beta0.e["(Intercept)"]+log(1-tau0)-log(tau0) -
+ log(1-tau0-0.001)+log(tau0+0.001)
+ beta1.e["(Intercept)"] <- beta1.e["(Intercept)"]+log(1-tau1)-log(tau1) -
+ log(1-tau1-e)+log(tau1+e)
+ RD0.e <- 1/(1+exp(-t(beta0.e) %*% t(x1))) - 1/(1+exp(-t(beta0.e) %*% t(x)))
+ RD1.e <- 1/(1+exp(-t(beta1.e) %*% t(x1))) - 1/(1+exp(-t(beta1.e) %*% t(x)))
+ ## checking the sign and computing the bounds
+ check <- sum((RD1.e-RD1.p) * (RD0.e-RD0.p))
+ if (check > 0) {
+ FD[,1,] <- pmin(RD0, RD1)
+ FD[,2,] <- pmax(RD0, RD1)
+ }
+ else {
+ FD[,1,] <- pmin(RD0, RD1, RD)
+ FD[,2,] <- pmax(RD0, RD1, RD)
+ }
+ qi$fd <- FD
+ qi$rr <- RR
+ qi.name$fd <- "First Differences: P(Y=1|X1) - P(Y=1|X)"
+ qi.name$rr <- "Risk Ratios: P(Y=1|X1) / P(Y=1|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, num), nrow = num, byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ tmp.pr <- yvar - as.integer(qi$pr)
+ qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = num)
+ qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = num)
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+ }
+ out <- list(qi = qi, qi.name = qi.name)
+ }
+ else
+ out <- qi.glm(object = object, simpar = simpar, x = x, x1 = x1, y = y)
+ out
+}
diff --git a/R/qi.survreg.R b/R/qi.survreg.R
new file mode 100644
index 0000000..fea4e78
--- /dev/null
+++ b/R/qi.survreg.R
@@ -0,0 +1,92 @@
+qi.survreg <- function(object, simpar, x, x1 = NULL, y = NULL, cond.data = NULL) {
+ model <- object$zelig
+ k <- length(object$coef)
+ sim.coef <- as.matrix(simpar[,1:k])
+ if (model %in% c("weibull", "Weibull", "lognorm")) #{
+ sim.scale <- simpar[,(k+1):ncol(simpar)]
+ if (!is.null(y)) {
+ status <- x[,1]
+ x <- as.data.frame(x[,2:ncol(x)])
+ }
+ link <- survreg.distributions[[object$dist]]$itrans
+ ev.pr.surv <- function(sim.coef, sim.scale, x, link) {
+ eta <- sim.coef %*% t(x)
+ theta <- as.matrix(apply(eta, 2, link))
+ ev <- pr <- matrix(NA, ncol=ncol(theta), nrow=nrow(theta))
+ dimnames(pr) <- dimnames(ev) <- dimnames(theta)
+ if (model == "exp") {
+ ev <- theta
+ for (i in 1:nrow(pr))
+ pr[i,] <- rexp(length(ev[i,]), rate = 1/ev[i,])
+ }
+ else if (model == "weibull" || model == "Weibull") {
+ ev <- theta * gamma(1 + exp(sim.scale))
+ for (i in 1:nrow(pr))
+ pr[i,] <- rweibull(length(ev[i,]), shape=1/exp(sim.scale[i]),
+ scale=theta[i,])
+ }
+ else if (model == "lognorm") {
+ ev <- exp(log(theta) + 0.5*(1/exp(sim.scale))^2)
+ for (i in 1:nrow(pr))
+ pr[i,] <- rlnorm(length(ev[i,]), meanlog = log(theta[i,]),
+ sdlog = 1/exp(sim.scale[i]))
+ }
+ list(ev=ev, pr=pr)
+ }
+ qi <- ev.pr.surv(sim.coef, sim.scale, x, link)
+ if (!is.null(y)) {
+ if (is.null(cond.data))
+ stop("`cond.data' is required for the exponential, Weibull, and lognormal models.")
+ call <- object$call
+ call$by <- NULL
+ call$data <- cond.data
+ est0 <- eval(call, sys.frame())
+ par0 <- matrix(param(est0, bootstrap = TRUE), nrow = 1)
+ coef0 <- matrix(par0[,1:k], nrow = 1)
+ if (model %in% c("weibull", "Weibull", "lognorm"))
+ scale0 <- rep(par0[,(k+1):ncol(par0)], nrow(x))
+ qi0 <- ev.pr.surv(coef0, scale0, x, link)
+ y1 <- y
+ tmp <- status
+ y[status == 0] <- qi0$pr[(status == 0), 1]
+ status[status == 0] <- as.integer(y1[status == 0] < y[status == 0])
+ while (sum(status) < length(status)) {
+ qi0 <- ev.pr.surv(coef0, scale0, x, link)
+ y[status == 0] <- qi0$pr[status == 0, 1]
+ status[status == 0] <- as.integer(y1[status == 0] < y[status == 0])
+ }
+ }
+ qi.name <- list(ev="Expected Values: E(Y|X)",
+ pr="Predicted Values: Y|X")
+ if (!is.null(x1)) {
+ eta1.sim <- sim.coef %*% t(x1)
+ ev1 <- apply(eta1.sim, 2, link)
+ qi$fd <- ev1 - qi$ev
+ qi.name$fd <- "First Differences: E(Y|X1)-E(Y|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ tmp.pr <- yvar - qi$pr
+ qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/qi.vglm.R b/R/qi.vglm.R
new file mode 100644
index 0000000..6eacacd
--- /dev/null
+++ b/R/qi.vglm.R
@@ -0,0 +1,228 @@
+qi.vglm <- function (object, simpar, x, x1=NULL, y = NULL) {
+ model <- object$zelig
+ cm <- object at constraints
+ if (model=="mlogit" || model=="oprobit")
+ ndim <- (ncol(object at y)-1)
+ else if (model=="blogit" || model=="bprobit")
+ ndim <- 3
+ else if (model=="sur")
+ ndim <- ncol(object at y)
+ v <- rep(list(NULL), ndim)
+ for(i in 1:length(cm)) {
+ if(ncol(cm[[i]])==1){
+ for(j in 1:ndim)
+ if(sum(cm[[i]][j,])==1)
+ v[[j]] <- c(v[[j]], names(cm)[i])
+ }
+ else {
+ for (j in 1:ndim)
+ if (sum(cm[[i]][j,])==1)
+ v[[j]] <- c(v[[j]], paste(names(cm)[i], ":", j, sep=""))
+ }
+ }
+ all.coef <- NULL
+ for(i in 1:ndim)
+ all.coef <- c(all.coef, list(simpar[,v[[i]]]))
+ if (model=="mlogit" || model=="oprobit"){
+ if(is.null(colnames(object at y)))
+ cnames <- ynames <- seq(1,ndim+1,1)
+ else
+ cnames <- ynames <- colnames(object at y)
+ for(i in 1:(ndim+1))
+ cnames[i] <- paste("Pr(Y=", ynames[i],")",sep="")
+ }
+ else if(model=="blogit" || model=="bprobit")
+ cnames <- c("Pr(Y1=0, Y2=0)", "Pr(Y1=0, Y2=1)",
+ "Pr(Y1=1, Y2=0)", "Pr(Y1=1, Y2=1)")
+ else if(model=="sur"){
+ if(is.null(colnames(object at y)))
+ cnames <- ynames <- seq(1,ndim,1)
+ else
+ ynames <- cnames <- colnames(object at y)
+ for(i in 1:ndim)
+ cnames[i] <- paste("E(Y_", ynames[i],"|X)",sep="")
+ }
+ else
+ stop(paste(model, "is not supported"))
+ pp.vglm <- function(object, cm, all.coef, x, ndim, cnames){
+ xm <- rep(list(NULL), ndim)
+ for (i in 1:length(cm))
+ for (j in 1:length(xm))
+ if (sum(cm[[i]][j,]) == 1)
+ xm[[j]] <- c(xm[[j]], x[,names(cm)[i]])
+ sim.eta <- NULL
+ for (i in 1:ndim)
+ sim.eta <- cbind(sim.eta, all.coef[[i]] %*% as.matrix(xm[[i]]))
+ ev <- object at family@inverse(sim.eta)
+ colnames(ev) <- cnames
+ ev
+ }
+ pr.vglm <- function(object, ev, ynames) { # To assign predicted values.
+ model <- object$zelig
+ if (model == "mlogit" || model == "oprobit") {
+ k <- ncol(ev)
+ Ipr <- sim.cut <- matrix(NA, nrow = nrow(ev), ncol = ncol(ev))
+ colnames(Ipr) <- colnames(sim.cut) <- colnames(ev)
+ sim.cut[,1] <- ev[,1]
+ for (j in 2:k)
+ sim.cut[,j] <- sim.cut[,(j-1)] + ev[,j]
+ tmp <- runif(nrow(ev), 0, 1)
+ for (l in 1:k)
+ Ipr[,l] <- tmp > sim.cut[,l]
+ pr <- NULL
+ for (m in 1:nrow(Ipr))
+ pr[m] <- 1 + sum(Ipr[m,])
+ pr <- factor(pr, levels = sort(unique(pr)), labels = ynames)
+ if (model == "mlogit")
+ pr <- factor(pr, ordered = FALSE)
+ pr <- matrix(pr, nrow = dim(ev)[1])
+ }
+ else if (model == "blogit" || model == "bprobit") {
+ mpr <- cbind((ev[,3] + ev[,4]), (ev[,2] + ev[,4]))
+ index <- matrix(NA, ncol = 2, nrow = nrow(mpr))
+ for (i in 1:2)
+ index[,i] <- rbinom(length(mpr[,i]), 1, mpr[,i])
+ pr <- matrix(NA, nrow(simpar), 4)
+ pr[,1] <- as.character(as.integer(index[,1] == 0 & index[,2] == 0))
+ pr[,2] <- as.character(as.integer(index[,1] == 0 & index[,2] == 1))
+ pr[,3] <- as.character(as.integer(index[,1] == 1 & index[,2] == 0))
+ pr[,4] <- as.character(as.integer(index[,1] == 1 & index[,2] == 1))
+ colnames(pr) <- c("(Y1=0, Y2=0)", "(Y1=0, Y2=1)", "(Y1=1, Y2=0)",
+ "(Y1=1, Y2=1)")
+ }
+# else if (model == "sur") { # Need to test/add pr.
+# pr[i,] <- mvrnorm(length(ev[i,], mu = ev[i,], vcov = vcov(object)))
+# }
+ pr
+ }
+ if (nrow(x) == 1) {
+ ev <- pp.vglm(object, cm, all.coef, x, ndim, cnames)
+ pr <- pr.vglm(object, ev, ynames)
+ }
+ else {
+ ev <- array(dim = c(nrow(simpar), if(model=="sur") ndim else (ndim+1), nrow(x)))
+ if (model == "mlogit" || model == "oprobit")
+ pr <- matrix(nrow=nrow(simpar), ncol=nrow(x))
+ else if (model == "blogit" || model == "bprobit")
+ pr <- array(dim = c(nrow(simpar), 4, nrow(x)))
+ else if (model == "sur") #### Need to change this
+ pr <- NULL
+ for (i in 1:nrow(x)){
+ tmp <- matrix(x[i,], nrow=1)
+ colnames(tmp) <- colnames(x)
+ rownames(tmp) <- rownames(x)[i]
+ tmp.ev <- pp.vglm(object, cm, all.coef, tmp, ndim, cnames)
+ tmp.pr <- pr.vglm(object, tmp.ev, ynames)
+ ev[,,i] <- tmp.ev
+ if (model == "blogit" || model == "bprobit")
+ pr[,,i] <- tmp.pr
+ else if (model == "mlogit" || model == "oprobit")
+ pr[,i] <- tmp.pr
+ else if (model == "sur") ##### Placeholder NEED TO FIX!!!!
+ pr <- NULL
+ }
+ dimnames(ev) <- list(rownames(tmp.ev), colnames(tmp.ev), NULL)
+ if (model == "blogit" || model == "bprobit")
+ dimnames(pr) <- list(rownames(tmp.pr), colnames(tmp.pr), NULL)
+ else if (model == "mlogit" || model == "oprobit")
+ dimnames(pr) <- list(c(1:nrow(simpar)), NULL)
+ else if (model == "sur") ### Need to change.
+ dimnames(pr) <- NULL
+ }
+ if (model=="mlogit" || model=="oprobit") {
+ qi <- list(ev=ev, pr=pr)
+ qi.name <- list(ev="Predicted Probabilities: Pr(Y=k|X)",
+ pr="Predicted Values: Y=k|X")
+ }
+ else if (model=="blogit" || model=="bprobit") {
+ qi <- list(ev=ev, pr=pr)
+ qi.name <- list(ev="Predicted Probabilities: Pr(Y1=k,Y2=l|X)",
+ pr="Predicted Values: (Y1,Y2)|X")
+ }
+ else if (model=="sur") {
+ qi <- list(ev=ev, pr=pr)
+ qi.name <- list(ev="Expected Values: E(Yj|X)",
+ pr="Predicted Values: Yj|X")
+ }
+ if (!is.null(x1)) {
+ if (nrow(x1) == 1)
+ ev1 <- pp.vglm(object, cm, all.coef, x1, ndim, cnames)
+ else {
+ ev1 <- array(dim = c(nrow(simpar), if(model=="sur") ndim else (ndim+1), nrow(x1)))
+ for (i in 1:nrow(x1)) {
+ tmp <- matrix(x1[i,], nrow=1)
+ colnames(tmp) <- colnames(x1)
+ rownames(tmp) <- rownames(x1)[i]
+ tmp <- pp.vglm(object, cm, all.coef, tmp, ndim, cnames)
+ ev1[,,i] <- tmp
+ }
+ dimnames(ev1) <- list(rownames(tmp), colnames(tmp), NULL)
+ }
+ qi$fd <- ev1 - ev
+ if (model=="mlogit" || model=="oprobit") {
+ qi$rr <- ev1 / ev
+ qi.name$fd <- "First Differences: Pr(Y=k|X1) - Pr(Y=k|X)"
+ qi.name$rr <- "Risk Ratios: Pr(Y=k|X1) / Pr(Y=k|X)"
+ }
+ else if (model=="blogit" || model=="bprobit") {
+ qi$rr <- ev1/ev
+ qi.name$fd <- "First Differences: Pr(Y1=k,Y2=l|X1) - Pr(Y1=k,Y2=l|X)"
+ qi.name$rr <- "Risk Ratios: Pr(Y1=k,Y2=l|X1) / Pr(Y1=k,Y2=l|X)"
+ }
+ else if(model=="sur")
+ qi.name$fd <- "First Differences: E(Yj|X1j) - E(Yj|Xj)"
+ }
+ if (!is.null(y)) {
+ tmp.ev <- tmp.pr <- array(NA, dim = dim(qi$ev))
+ qi$ate.ev <- qi$ate.pr <- matrix(NA, dim(qi$ev)[1], dim(qi$ev)[2])
+ if (model=="mlogit" || model=="oprobit") {
+ yvar <- matrix(NA, nrow = length(y), ncol = length(ynames))
+ pr.idx <- array(NA, dim = c(nrow(pr), length(ynames), nrow(x)))
+ for (i in 1:length(ynames)) {
+ yvar[,i] <- as.integer(y == ynames[i])
+ pr.idx[,i,] <- as.integer(pr[,i] == ynames[i])
+ }
+ colnames(qi$ate.ev) <- colnames(qi$ate.pr) <- ynames
+ }
+ else if (model=="blogit" || model=="bprobit") {
+ yvar <- matrix(NA, nrow = nrow(y), ncol = 4)
+ yvar[,1] <- as.integer(y[,1] == 0 & y[,2] == 0)
+ yvar[,2] <- as.integer(y[,1] == 0 & y[,2] == 1)
+ yvar[,3] <- as.integer(y[,1] == 1 & y[,2] == 0)
+ yvar[,4] <- as.integer(y[,1] == 1 & y[,2] == 1)
+ pr.idx <- array(NA, dim = c(nrow(pr), 4, nrow(x)))
+ for (i in 1:4)
+ pr.idx[,i,] <- as.integer(pr[,i,])
+ colnames(qi$ate.ev) <- colnames(qi$ate.pr) <-
+ c("(Y1=0, Y2=0)", "(Y1=0, Y2=1)",
+ "(Y1=1, Y2=0)", "(Y1=1, Y2=1)")
+ }
+ for (j in 1:ncol(yvar)) {
+ for (i in 1:nrow(simpar)) {
+ tmp.ev[i,j,] <- yvar[,j] - qi$ev[i,j,]
+ tmp.pr[i,j,] <- yvar[,j] - pr.idx[i,j,]
+ }
+ qi$ate.ev[,j] <- apply(tmp.ev[,j,], 1, mean)
+ qi$ate.pr[,j] <- apply(tmp.pr[,j,], 1, mean)
+ }
+ qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+ qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/relogit.R b/R/relogit.R
new file mode 100644
index 0000000..bcb3ef8
--- /dev/null
+++ b/R/relogit.R
@@ -0,0 +1,47 @@
+relogit <- function(formula, data=sys.parent(), tau=NULL,
+ bias.correct=TRUE, ...){
+ mf <- match.call()
+ mf$tau <- mf$bias.correct <- NULL
+ if (!is.null(tau))
+ tau <- unique(tau)
+ if (length(tau) > 2)
+ stop("tau must be a vector of length less than or equal to 2")
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("binomial")
+ res <- eval(as.call(mf))
+ res$call <- match.call(expand.dots = TRUE)
+ ## prior correction
+ y <- res$y
+ ybar <- mean(y)
+ #w1 <- tau/ybar
+ #w0 <- (1-tau)/(1-ybar)
+ #wi <- w1*y + w0*(1-y)
+ X <- model.matrix(res)
+ if (bias.correct){
+ pihat <- fitted(res)
+ # if (is.null(tau))
+ # W <- pihat * (1 - pihat) * wi
+ # else
+ W <- pihat * (1 - pihat)
+ Qdiag <- lm.influence(glm(y ~ X-1, weights=W))$hat
+ #if (is.null(tau))
+ # xi <- 0.5 * Qdiag * ((1+w0)*pihat-w0)
+ #else
+ xi <- 0.5 * Qdiag * (2*pihat - 1)
+ res$coefficients <- res$coefficients -
+ glm(xi ~ X - 1, weights=W)$coefficients
+ }
+ if (!is.null(tau)){
+ if (tau <= 0 || tau >= 1)
+ stop("\ntau needs to be between 0 and 1.\n")
+ res$correct <- res$coefficients["(Intercept)"] -
+ log(((1-tau)/tau) * (ybar/(1-ybar)))
+ names(res$correct) <- paste("tau =", round(tau, 4))
+ }
+ res$linear.predictors <- t(res$coef) %*% t(X)
+ res$fitted.values <- 1/(1+exp(-res$linear.predictors))
+ res$zelig <- "relogit"
+ class(res) <- "relogit"
+ res
+}
+
diff --git a/R/repl.R b/R/repl.R
new file mode 100644
index 0000000..240ee64
--- /dev/null
+++ b/R/repl.R
@@ -0,0 +1,2 @@
+repl <- function(object, ...)
+ UseMethod("repl")
diff --git a/R/repl.default.R b/R/repl.default.R
new file mode 100644
index 0000000..74dd4e1
--- /dev/null
+++ b/R/repl.default.R
@@ -0,0 +1,5 @@
+repl.default <- function(object, data=NULL, ...) {
+ if (!is.null(data))
+ object$call$data <- data
+ eval(object$call, sys.parent())
+}
diff --git a/R/repl.zelig.R b/R/repl.zelig.R
new file mode 100644
index 0000000..d007c2d
--- /dev/null
+++ b/R/repl.zelig.R
@@ -0,0 +1,20 @@
+repl.zelig <- function(object, prev=NULL, data=NULL, x=NULL,
+ x1=NULL, bootfn=NULL, ...) {
+ rep.zelig <- eval(object$zelig.call, sys.parent())
+ object$call$object <- rep.zelig
+ if (is.null(x))
+ object$call$x <- object$x
+ else
+ object$call$x <- x
+ if (is.null(x1))
+ object$call$x1 <- object$x1
+ else
+ object$call$x1
+ if (!is.null(prev))
+ object$call$prev <- prev
+ eval(object$call, sys.parent())
+}
+
+
+
+
diff --git a/R/rocplot.R b/R/rocplot.R
new file mode 100644
index 0000000..57b3418
--- /dev/null
+++ b/R/rocplot.R
@@ -0,0 +1,38 @@
+rocplot <- function(y1, y2, fitted1, fitted2,
+ cutoff = seq(from=0, to=1, length=100), lty1="solid",
+ lty2="dashed", lwd1=par("lwd"), lwd2=par("lwd"),
+ col1=par("col"), col2=par("col"), main="ROC Curve",
+ xlab = "Proportion of 1's Correctly Predicted",
+ ylab="Proportion of 0's Correctly Predicted", ...) {
+ op <- par(no.readonly = TRUE)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ ones <- zeros <- matrix(NA, nrow = length(cutoff), ncol = 2)
+ for (i in 1:length(cutoff)) {
+ ones[i,1] <- mean(fitted1[y1==1] >= cutoff[i])
+ ones[i,2] <- mean(fitted2[y2==1] >= cutoff[i])
+ zeros[i,1] <- mean(fitted1[y1==0] < cutoff[i])
+ zeros[i,2] <- mean(fitted2[y2==0] < cutoff[i])
+ }
+ plot(0:1, 0:1, type = "n", xaxs = "i", yaxs = "i",
+ main=main, xlab=xlab, ylab=ylab, ...)
+ lines(cbind(ones[,1], zeros[,1]), lty = lty1, lwd = lwd1, col=col1)
+ lines(cbind(ones[,2], zeros[,2]), lty = lty2, lwd = lwd2, col=col2)
+ abline(1, -1, lty = "dotted")
+ par(op)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/setx.MI.R b/R/setx.MI.R
new file mode 100644
index 0000000..4180f2f
--- /dev/null
+++ b/R/setx.MI.R
@@ -0,0 +1,31 @@
+setx.MI <- function(object, fn = list(numeric = mean, ordered =
+ median, other = mode), data = NULL,
+ cond = FALSE, counter = NULL, ...) {
+ M <- length(object)
+ dta <- NULL
+ if (!cond) {# unconditional predition
+ obj <- object[[1]]
+ for (i in 1:M) {
+ if(is.null(data))
+ tmp <- as.data.frame(eval(object[[1]]$call$data,
+ sys.parent())[[i]])
+ else
+ tmp <- data[[i]]
+ dta <- rbind(dta, tmp)
+ }
+ X <- setx(object[[1]], fn = fn, data = dta, cond = FALSE,
+ counter = NULL, ...)
+ class(X) <- c("setx.MI", "setx")
+ }
+ else { # conditional prediction
+ X <- list()
+ if (is.null(data))
+ data <- eval(object[[1]]$call$data, sys.parent())
+ for (i in 1:M)
+ X[[i]] <- setx(object[[i]], fn = NULL, data = data[[i]], cond = TRUE,
+ counter = counter, ...)
+ class(X) <- c("setx.MI", "cond")
+ }
+ return(X)
+}
+
diff --git a/R/setx.R b/R/setx.R
new file mode 100644
index 0000000..006716c
--- /dev/null
+++ b/R/setx.R
@@ -0,0 +1,3 @@
+setx<-function(object, ...)
+ UseMethod("setx")
+
diff --git a/R/setx.default.R b/R/setx.default.R
new file mode 100644
index 0000000..daef0cc
--- /dev/null
+++ b/R/setx.default.R
@@ -0,0 +1,176 @@
+setx.default <- function(object, fn = list(numeric = mean, ordered =
+ median, other = mode), data = NULL,
+ cond = FALSE, counter = NULL, ...){
+ mf <- match.call()
+ mode <- function(x){
+ tb <- tapply(x, x, length)
+ if(is.factor(x))
+ value <- factor(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])),
+ levels=levels(x))
+ else if (is.logical(x))
+ value <- as.logical(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else if (is.character(x))
+ value <- as.character(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else
+ stop(paste(vars[i], "is not a supported variable type."))
+ if (length(value)>1) {
+ warning("There is more than one mode. The first level is selected.")
+ value <- sort(value)[1]
+ }
+ return(value)
+ }
+ median.default <- median
+ median <- function(x) {
+ if(is.numeric(x))
+ value <- median.default(x)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[median.default(as.integer(x))],
+ levels=levels(x))
+ else
+ stop("median cannot be calculated for this data type")
+ return(value)
+ }
+ max.default <- max
+ max <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- max.default(x, na.rm=na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[length(levels(x))], levels=levels(x))
+ else
+ stop("max cannot be calculated for this data type")
+ return(value)
+ }
+ min.default <- min
+ min <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- min.default(x, na.rm = na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[1], levels=levels(x))
+ else
+ stop("min cannot be calculated for this data type")
+ return(value)
+ }
+ if (any(class(object)=="vglm"))
+ tt <- object at terms$terms
+ else
+ tt <- terms(object)
+ if (is.null(data))
+ dta <- eval(object$call$data, sys.parent())
+ else
+ dta <- data
+ data <- dta <- dta[complete.cases(model.frame(tt, dta)), ]
+ vars <- names(dta)
+ if (!is.null(counter)) {
+ if (!any(counter == names(model.frame(tt, dta))))
+ stop(paste("the variable specified for counter is not used in the model"))
+ treat <- data[, names(data)==counter]
+ if(is.numeric(treat)) {
+ data[treat==1, names(data)==counter] <- 0
+ data[treat==0, names(data)==counter] <- 1
+ }
+ else if(is.factor(treat)) {
+ lev <- levels(treat)
+ if(length(lev)==2) {
+ treat <- as.numeric(treat) - 1
+ data[treat==1, names(data)==counter] <- lev[1]
+ data[treat==0, names(data)==counter] <- lev[2]
+ }
+ else
+ stop(paste("counter only takes a binary variable"))
+ }
+ else if(is.logical(treat)) {
+ treat <- as.numeric(treat)
+ data[treat==1, names(data)==counter] <- FALSE
+ data[treat==0, names(data)==counter] <- TRUE
+ }
+ else
+ stop(paste("not supported variable type for counter"))
+ if(!cond)
+ stop(paste("if counter is specified, cond must be TRUE"))
+ }
+ if (cond) {
+ if (is.null(data))
+ stop(paste("if cond = TRUE, you must specify the data frame."))
+ if (!is.null(fn)) {
+ warning(paste("when cond = TRUE, fn is coerced to NULL"))
+ fn <- NULL
+ }
+ if (class(object)[1] == "vglm")
+ Y <- model.response(model.frame(tt, data=dta), "any")
+ else
+ Y <- model.extract(model.frame(tt, data=dta), "response")
+ }
+ else if (!is.null(fn)) {
+ if (is.null(fn$numeric) || !is.function(fn$numeric)) {
+ warning("fn$numeric coerced to mean().")
+ fn$numeric <- mean
+ }
+ if (is.null(fn$ordered) || !is.function(fn$ordered) ||
+ identical(mean, fn$ordered)) {
+ warning("fn$ordered coreced to median().")
+ fn$ordered <- median
+ }
+ else if (identical(min.default, fn$ordered))
+ fn$ordered <- min
+ else if (identical(max.default, fn$ordered))
+ fn$ordered <- max
+ else if (identical(median.default, fn$ordered))
+ fn$ordered <- median
+ if (is.null(fn$other) || !is.function(fn$other)) {
+ warning("the only available fn for other is mode.")
+ fn$other <- mode
+ }
+ for (i in 1:ncol(dta)) {
+ v <- na.omit(dta[,i])
+ if (any(vars[i] == names(model.frame(tt, dta)))) {
+ if (is.numeric(v))
+ value <- lapply(list(v), fn$numeric)[[1]]
+ else if (is.ordered(v))
+ value <- lapply(list(v), fn$ordered)[[1]]
+ else
+ value <- lapply(list(v), fn$other)[[1]]
+ data[,i] <- value
+ }
+ }
+ opt <- vars[na.omit(pmatch(names(mf), vars))]
+ maxl <- 1
+ if (length(opt) > 0)
+ for (i in 1:length(opt)) {
+ value <- eval(mf[[opt[i]]], sys.parent())
+ lv <- length(value)
+ if (lv>1)
+ if (maxl==1 || maxl==lv) {
+ maxl <- lv
+ data <- data[1:lv,]
+ }
+ else
+ stop("vector inputs should have the same length.")
+ if (is.factor(data[,opt[i]]))
+ data[,opt[i]] <- list(as.factor(value))
+ else if (is.numeric(data[,opt[i]]))
+ data[,opt[i]] <- list(as.numeric(value))
+ else if (is.logical(data[,opt[i]]))
+ data[,opt[i]] <- list(as.logical(value))
+ else
+ data[,opt[i]] <- list(value)
+ }
+ data <- data[1:maxl,]
+ }
+ X <- model.matrix(delete.response(tt), data = data, contrasts =
+ if (length(object$contrasts)>0) object$contrasts
+ else NULL, xlev = object$xlevels, ...)
+ row.names(X) <- 1:nrow(X)
+ X <- as.matrix(X)
+ class(X) <- "setx"
+ if (cond) {
+ X <- cbind(Y, X)
+ if (!is.null(counter)) {
+ X <- list(treat=X[treat==1,], control=X[treat==0,])
+ class(X$treat) <- class(X$control) <- c("setx", "cond")
+ class(X) <- "setx.counter"
+ }
+ else
+ class(X) <- c("setx", "cond")
+ }
+ return(X)
+}
diff --git a/R/setx.noX.R b/R/setx.noX.R
new file mode 100644
index 0000000..a8e3394
--- /dev/null
+++ b/R/setx.noX.R
@@ -0,0 +1,24 @@
+setx.noX <- function(object, data = NULL, fn = NULL, cond = TRUE, ...) {
+ if (!is.null(fn) || !cond)
+ stop(paste("\n", object$call$model, "is only appropriate in a CONDITIONAL prediction research design!"))
+ if (any(class(object) == "latent"))
+ x <- eval(object$call$formula[[2]])
+ else if (any(class(object) == "EI")) {
+ if (is.null(data))
+ data <- eval(object$call$data, sys.parent())
+ x <- model.frame(object, data = data, ...)
+ x1 <- x[[1]]
+ for (i in 2:length(x))
+ x1 <- cbind(x[[1]], x[[i]])
+ x1 <- data.frame(x1)
+ if (is.null(object$call$covar))
+ names(x1) <- c(colnames(x[[1]]), colnames(x[[2]]))
+ else
+ names(x1) <- c(colnames(x[[1]]), colnames(x[[2]]),
+ deparse(object$call$covar[[3]]))
+ rownames(x1) <- rownames(x)
+ x <- x1
+ }
+ class(x) <- c("cond", "data.frame")
+ x
+}
diff --git a/R/setx.strata.R b/R/setx.strata.R
new file mode 100644
index 0000000..ca02636
--- /dev/null
+++ b/R/setx.strata.R
@@ -0,0 +1,47 @@
+setx.strata <- function(object, fn = list(numeric = mean, ordered =
+ median, other = mode), data =
+ NULL, cond = FALSE, counter = NULL, ...) {
+ obj <- object[[1]]
+ x <- list()
+ if (any(class(obj)=="MI")) { # with multiple imputation
+ if (is.null(data))
+ dta <- eval(obj[[1]]$call$data, sys.parent())
+ else
+ dta <- data
+ by <- obj[[1]]$call$by
+ M <- length(dta)
+ d <- dta[[1]]
+ idx <- pmatch(by, names(d))[1]
+ lev <- sort(unique(eval(d[[idx]], sys.parent())))
+ for (i in 1:length(lev)) {
+ d <- list()
+ for (j in 1:M) {
+ dM <- dta[[j]]
+ d[[j]] <- as.data.frame(dM[dM[[idx]] == lev[i],])
+ }
+ x[[i]] <- setx(object[[i]], fn = fn, data = d, cond = cond,
+ counter = counter, ...)
+ }
+ }
+ else { # without multiple imputation
+ if (is.null(data))
+ dta <- eval(obj$call$data, sys.parent())
+ else
+ dta <- data
+ by <- obj$call$by
+ idx <- pmatch(by, names(dta))[1]
+ lev <- sort(unique(eval(dta[[idx]], sys.parent())))
+ for (i in 1:length(lev)) {
+ d <- as.data.frame(dta[dta[[idx]] == lev[i],])
+ x[[i]] <- setx(object[[i]], fn = fn, data = d, cond = cond,
+ counter = counter, ...)
+ }
+ }
+ names(x) <- names(object)
+ class(x) <- "setx.strata"
+ return(x)
+}
+
+
+
+
diff --git a/R/sim.R b/R/sim.R
new file mode 100644
index 0000000..8925ecd
--- /dev/null
+++ b/R/sim.R
@@ -0,0 +1,2 @@
+sim <- function(object, x, ...)
+ UseMethod("sim", x)
diff --git a/R/sim.cond.R b/R/sim.cond.R
new file mode 100644
index 0000000..376383e
--- /dev/null
+++ b/R/sim.cond.R
@@ -0,0 +1,74 @@
+sim.cond <- function(object, x, x1=NULL, num=c(1000, 100),
+ qoi = c("ev", "pr"), prev = NULL,
+ bootstrap = FALSE, bootfn=NULL,
+ cond.data = NULL, ...) {
+ if (!is.null(x1)) {
+ warning("First Differences are not calculated in conditional prediction models.")
+ x1 <- NULL
+ }
+ if (object$call$model %in% c("bprobit", "blogit")) {
+ yvar <- x[, 1:2]
+ x <- x[, 3:ncol(x)]
+ }
+ else if (any(class(object) == "EI")) {
+ yvar <- x[, 1:object$dims[2]]
+ x <- x[, (object$dims[2]+1):ncol(x)]
+ }
+ else if (any(class(object) == "latent"))
+ yvar <- NULL
+ else {
+ yvar <- x[,1]
+ x <- x[,2:ncol(x)]
+ }
+ class(x) <- c("cond")
+ if (any(class(object) == "MCMCZelig"))
+ num <- nrow(object$coefficients)
+ if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ if (is.null(prev)) {
+ if (!bootstrap & any(class(object) != "relogit"))
+ simpar <- param(object, num=num, bootstrap=bootstrap)
+ else if (any(class(object) == "relogit"))
+ simpar <- param.relogit(object, num=num, x=x,
+ bootstrap=bootstrap, bootfn=bootfn, ...)
+ else {
+ tt <- terms(object)
+ dta <- eval(object$data, sys.parent())
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ if (is.null(bootfn))
+ bootfn <- bootfn.default
+ res <- boot(dta, bootfn, R = num, object = object, ...)
+ colnames(res$t) <- names(res$t0)
+ simpar <- res$t
+ }
+ }
+ else {
+ if (bootstrap)
+ stop("Error: Choosing 'bootstrap = TRUE' generates new parameters. \nIf you wish to use previously generated parameters, \nplease specify only 'prev'.")
+ else
+ simpar <- prev
+ }
+ if (class(object)[1] == "survreg")
+ simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = yvar,
+ cond.data = cond.data)
+ else
+ simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = yvar)
+ class(x) <- "cond"
+ c <- match.call()
+ c$num <- num
+ res <- list(x=x, x1=x1, call = c, zelig.call = object$call,
+ par = simpar, qi=simqi$qi, qi.name=simqi$qi.name)
+ class(res) <- "zelig"
+ res
+}
+
+
+
+
+
+
+
diff --git a/R/sim.counter.R b/R/sim.counter.R
new file mode 100644
index 0000000..934b896
--- /dev/null
+++ b/R/sim.counter.R
@@ -0,0 +1,21 @@
+sim.counter <- function(object, x, x1=NULL, num = c(100, 1000), ...) {
+ if (any(class(object) == "MCMCZelig"))
+ num <- nrow(object$coefficients)
+ else if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ res <- list()
+ N <- 0
+ for (i in 1:length(x))
+ N <- N + nrow(x[[i]])
+ for (i in 1:length(x)) {
+ numX <- round((nrow(x[[i]]) / N) * num)
+ res[[i]] <- sim.default(object, x = x[[i]], x1 = x1[[i]], num =
+ numX, ...)
+ }
+ res
+}
+
diff --git a/R/sim.default.R b/R/sim.default.R
new file mode 100644
index 0000000..9af8614
--- /dev/null
+++ b/R/sim.default.R
@@ -0,0 +1,57 @@
+sim.default <- function(object, x, x1=NULL, num=c(1000, 100),
+ prev = NULL, bootstrap = FALSE, bootfn=NULL,
+ cond.data = NULL, ...) {
+ if (any(class(object) == "MCMCZelig"))
+ num <- nrow(object$coefficients)
+ else if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ if (is.null(prev)) {
+ if (!bootstrap & any(class(object) != "relogit"))
+ simpar <- param(object, num=num, bootstrap=bootstrap)
+ else if (any(class(object) == "relogit"))
+ simpar <- param.relogit(object, num=num, x=x, bootstrap=bootstrap, bootfn=bootfn, ...)
+ else {
+ tt <- terms(object)
+ dta <- eval(object$data, sys.parent())
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ if (is.null(bootfn))
+ bootfn <- bootfn.default
+ res <- boot(dta, bootfn, R = num, object = object, ...)
+ colnames(res$t) <- names(res$t0)
+ simpar <- res$t
+ }
+ }
+ else {
+ if (bootstrap)
+ stop("Error: Choosing 'bootstrap = TRUE' generates new parameters. \nIf you wish to use previously generated parameters, \nplease specify only 'prev'.")
+ else
+ simpar <- prev
+ }
+ simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = NULL)
+ c <- match.call()
+ c$num <- num
+ res <- list(x=x, x1=x1, call = c, zelig.call = object$call,
+ par = simpar, qi=simqi$qi, qi.name=simqi$qi.name)
+ class(res) <- "zelig"
+ res
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/sim.setx.MI.R b/R/sim.setx.MI.R
new file mode 100644
index 0000000..11144b0
--- /dev/null
+++ b/R/sim.setx.MI.R
@@ -0,0 +1,71 @@
+sim.setx.MI <- function(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
+ bootstrap = FALSE, bootfn = NULL, ...) {
+ if (any(class(object) == "MCMCZelig"))
+ num <- nrow(object[[1]]$coefficients) * length(object)
+ else if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ ca <- match.call()
+ if (!any(class(x) == "cond")) {
+ simpar <- MIsimulation(object, num, prev, bootstrap, bootfn, ...)
+ simqi <- qi(object[[1]], simpar = simpar, x = x, x1 = x1)
+ ca$num <- num
+ res <- list(x = x, x1 = x1, call = ca, zelig.call = object[[1]]$call,
+ par = simpar, qi = simqi$qi, qi.name = simqi$qi.name)
+ }
+ else {
+ simpar <- MIsimulation(object, num, prev, bootstrap, bootfn, ...)
+ tmp.qi <- list()
+ for (i in 1:length(x)) {
+ if (!is.null(x1)) {
+ warning("First Differences are not calculated in conditional prediction models.")
+ x1 <- NULL
+ }
+ if (object[[i]]$call$model %in% c("bprobit", "blogit")) {
+ yvar <- x[[i]][,1:2]
+ x[[i]] <- x[[i]][,3:ncol(x[[i]])]
+ }
+ else {
+ yvar <- x[[i]][,1]
+ x[[i]] <- x[[i]][,2:ncol(x[[i]])]
+ }
+ tmp.qi[[i]] <- qi(object[[1]], simpar = simpar, x = x[[i]], x1 =
+ x1[[i]], y = yvar)
+ }
+ simqi <- tmp.qi[[1]]
+ for (i in 2:length(tmp.qi)) {
+ for (j in 1:length(simqi)) {
+ if (length(dim(simqi$qi[[j]])) == 2)
+ simqi$qi[[j]] <- cbind(simqi$qi[[j]], tmp.qi[[i]]$qi[[j]])
+ else {
+ tmp <- array(NA, dim = c(dim(simqi$qi[[j]])[1],
+ dim(simqi$qi[[j]])[2],
+ (dim(simqi$qi[[j]])[3] +
+ dim(tmp.qi[[i]]$qi[[j]])[3])))
+ tmp[,, 1:dim(simqi$qi[[j]])[3]] <- simqi$qi[[j]]
+ tmp[,, (dim(simqi$qi[[j]])[3]+1):dim(tmp)[3]] <-
+ tmp.qi[[i]]$qi[[j]]
+ simqi$qi[[j]] <- tmp
+ }
+ }
+ print(object[[1]]$call)
+ ca$num <- num
+ res <- list(x = x, x1 = x1, call = ca,
+ zelig.call = object[[1]]$call,
+ par = simpar, qi = simqi$qi, qi.name =
+ simqi$qi.name)
+ }
+ }
+ class(res) <- "zelig"
+ res
+}
+
+
+
+
+
+
+
diff --git a/R/sim.setx.strata.R b/R/sim.setx.strata.R
new file mode 100644
index 0000000..6a76a33
--- /dev/null
+++ b/R/sim.setx.strata.R
@@ -0,0 +1,40 @@
+sim.setx.strata <- function(object, x, x1 = NULL, num = c(1000, 100),
+ bootstrap = FALSE, ...){
+ if (any(class(object) == "MCMCZelig"))
+ num <- nrow(object$coefficients)
+ if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ if(any(class(object[[1]])=="MI")) {
+ dta <- eval((object[[1]])[[1]]$call$data, sys.parent())[[1]]
+ by <- (object[[1]])[[1]]$call$by
+ }
+ else {
+ dta <- eval(object[[1]]$call$data, sys.parent())
+ by <- object[[1]]$call$by
+ }
+ N <- length(object)
+ res <- list()
+ idx <- match(by, names(dta))
+ lev <- names(object)
+ for (i in 1:length(lev)) {
+ dat <- dta[dta[[idx]] == lev[i],]
+ numN <- round(num/N)
+ res[[i]] <- sim(object[[i]], x = x[[i]], x1 = x1[[i]], num = numN,
+ bootstrap = bootstrap, ...)
+ res[[i]]$nx <- nrow(dat)/nrow(dta)
+ }
+ class(res) <- "zelig.strata"
+ names(res) <- names(object)
+ res
+}
+
+
+
+
+
+
+
diff --git a/R/summarize.R b/R/summarize.R
new file mode 100644
index 0000000..62fd6b5
--- /dev/null
+++ b/R/summarize.R
@@ -0,0 +1,2 @@
+summarize <- function(x, ...)
+ UseMethod("summarize", x)
diff --git a/R/summarize.array.R b/R/summarize.array.R
new file mode 100644
index 0000000..c7e363d
--- /dev/null
+++ b/R/summarize.array.R
@@ -0,0 +1,31 @@
+summarize.array <- function(x, cip, stats, model, object, subset = NULL) {
+ if (is.function(subset)){ # subset = all; all is class "function"
+ tmp <- summarize.default(x[,,1], cip, stats, model)
+ tmp <- array(NA, dim=c(nrow(tmp), ncol(tmp), dim(x)[3]),
+ dimnames=list(dimnames(x)[[2]], dimnames(tmp)[[2]],
+ rownames(object$x)))
+ for (j in 1:dim(x)[3])
+ tmp[,,j] <- summarize.default(x[,,j], cip, stats, model)
+ res <- tmp
+ }
+ if(is.null(subset)){# subset = NULL; summarizes all obs at once.
+ tmp <-NULL
+ for (j in 1:dim(x)[3])
+ tmp <- rbind(tmp, x[,,j])
+ res <- summarize.default(tmp, cip, stats, model)
+ }
+ if (is.numeric(subset)) { # subset=integer, summarizes identified obs
+ if (length(subset) > 1) {
+ tmp <- summarize.default(x[,,1], cip, stats, model)
+ tmp <- array(NA, dim=c(ncol(x), ncol(tmp), length(subset)),
+ dimnames=list(dimnames(x)[[2]],
+ dimnames(tmp)[[2]], as.character(subset)))
+ for (l in 1:length(subset))
+ tmp[,,l] <- summarize.default(x[,,subset[l]], cip, stats, model)
+ res <- tmp
+ }
+ else
+ res <- summarize.default(x[,,subset], cip, stats, model)
+ }
+ return(res)
+}
diff --git a/R/summarize.default.R b/R/summarize.default.R
new file mode 100644
index 0000000..ab5cd78
--- /dev/null
+++ b/R/summarize.default.R
@@ -0,0 +1,32 @@
+summarize.default <- function(x, cip, stats, model, object, subset = NULL) {
+
+ res <- NULL
+ if (!is.matrix(x))
+ x <- as.matrix(x)
+ if (is.numeric(x)) {
+ if (!is.null(stats))
+ for (i in 1:length(stats))
+ res <- cbind(res, apply(x, 2, stats[i]))
+ res <- cbind(res, apply(x, 2, quantile, prob=cip[1]))
+ res <- cbind(res, apply(x, 2, quantile, prob=cip[2]))
+ colnames(res) <- c(stats, paste(cip[1]*100, "%", sep=""),
+ paste(cip[2]*100, "%", sep=""))
+ }
+ else {
+ res <- t(apply(if(is.matrix(x)) x else as.matrix(x), 2, table))
+ if (length(unique(x))==1) {
+ colnames(res) <- unique(x)
+ if(model=="relogit")
+ if(colnames(res)==1) {
+ res <- cbind(0, res)
+ colnames(res)[1] <-0
+ }
+ else {
+ res <- cbind(res, 0)
+ colnames(res)[2] <-1
+ }
+ }
+ res <- res/nrow(x) * 100
+ }
+ return(res)
+}
diff --git a/R/summarize.matrix.R b/R/summarize.matrix.R
new file mode 100644
index 0000000..747ddb2
--- /dev/null
+++ b/R/summarize.matrix.R
@@ -0,0 +1,20 @@
+summarize.matrix <- function(x, cip, stats, model, object, subset = NULL) {
+ res <- NULL
+ if (dim(x)[2]==1)
+ res <- summarize.default(x, cip, stats, model)
+ else {
+ if (is.function(subset))
+ res <- summarize.default(x, cip, stats, model)
+ if (is.null(subset)) {
+ tmp <- matrix(c(x), ncol=1)
+ tmp <- summarize.default(tmp, cip, stats, model)
+ res <- as.matrix(tmp)
+ names(res) <- colnames(tmp)
+ }
+ if (is.numeric(subset)) {
+ res <- summarize.default(as.matrix(x[,subset]), cip, stats, model)
+ rownames(res) <- subset
+ }
+ }
+ return(res)
+}
diff --git a/R/summary.BetaReg.R b/R/summary.BetaReg.R
new file mode 100644
index 0000000..c218b69
--- /dev/null
+++ b/R/summary.BetaReg.R
@@ -0,0 +1,10 @@
+summary.BetaReg <- function(object, digits = getOption("digits"), ...) {
+ summ <- matrix(NA, nrow = length(object$coef), ncol = 3)
+ colnames(summ) <- c("Estimate", "SD", "t-stat")
+ rownames(summ) <- names(object$coef)
+ summ[,1] <- object$coef
+ summ[,2] <- sqrt(diag(object$variance))
+ summ[,3] <- summ[,1] / summ[,2]
+ object$coefficients <- summ
+ object
+}
diff --git a/R/summary.MI.R b/R/summary.MI.R
new file mode 100644
index 0000000..0427a84
--- /dev/null
+++ b/R/summary.MI.R
@@ -0,0 +1,62 @@
+summary.MI <- function(object, subset = NULL, ...){
+ res <- list()
+ if(is.null(subset))
+ M <- 1:length(object)
+ else
+ M <- c(subset)
+ for (i in M)
+ res[[i]] <- summary(object[[i]])
+ ans <- res[[1]]
+ ans$zelig <- object[[1]]$zelig
+ ans$call <- object[[1]]$call
+ if (length(M) > 1) {
+ ans$all <- res
+ coef <- se <- NULL
+ for (i in M){
+ if (any(class(object[[1]]) == "vglm")) {
+ coef <- cbind(coef, res[[i]]@coef3[,1])
+ se <- cbind(se, res[[i]]@coef3[,2])
+ }
+ else {
+ coef <- cbind(coef, coef(res[[i]])[,1])
+ se <- cbind(se, coef(res[[i]])[,2])
+ }
+ }
+ Q <- apply(coef, 1, mean)
+ U <- apply(se^2, 1, mean)
+ B <- apply((coef-Q)^2, 1, sum)/(length(M)-1)
+ var <- U+(1+1/length(M))*B
+ nu <- (length(M)-1)*(1+U/((1+1/length(M))*B))^2
+ if (any(class(object[[1]]) == "vglm")) {
+ coef.table <- matrix(NA, nrow(ans at coef3), 4)
+ dimnames(coef.table) <- list(c(dimnames(ans at coef3)[[1]]),
+ c("Value", "Std. Error", "t-stat", "p-value"))
+ }
+ else {
+ coef.table <- matrix(NA, nrow = nrow(coef(ans)), ncol = 4)
+ dimnames(coef.table) <- list(rownames(coef(ans)),
+ c("Value", "Std. Error", "t-stat", "p-value"))
+ }
+ coef.table[,1] <- Q
+ coef.table[,2] <- sqrt(var)
+ coef.table[,3] <- Q/sqrt(var)
+ coef.table[,4] <- pt(abs(Q/sqrt(var)), df=nu, lower.tail=F)*2
+ ans$coefficients <- coef.table
+ ans$cov.scaled <- ans$cov.unscaled <- NULL
+ if (!any(class(object[[1]]) == "vglm")) {
+ for (i in 1:length(ans)) {
+ if (is.numeric(ans[[i]]) && !names(ans)[i] %in% c("coefficients")){
+ tmp <- NULL
+ for (j in M)
+ tmp <- cbind(tmp, res[[j]][[pmatch(names(ans)[i], names(res[[j]]))]])
+ ans[[i]] <- apply(tmp, 1, mean)
+ }
+ }
+ }
+ class(ans) <- "summary.MI"
+ }
+ else
+ ans <- summary(object[[M]])
+ ans
+}
+
diff --git a/R/summary.relogit.R b/R/summary.relogit.R
new file mode 100644
index 0000000..32deddb
--- /dev/null
+++ b/R/summary.relogit.R
@@ -0,0 +1,41 @@
+summary.relogit <- function(object, ...) {
+ if (!is.numeric(object$call$tau))
+ tau <- eval(object$call$tau, sys.frame(sys.parent()))
+ else
+ tau <- object$call$tau
+ if (is.null(object$call$bias.correct))
+ object$call$bias.correct <- TRUE
+ dta <- eval(object$call$data, sys.parent())
+ tt <- terms(object)
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ n <- nrow(dta)
+ k <- ncol(dta)
+ bias.cor <- function(res.obj, bc, n, k){
+ if (bc) {
+ res.obj$cov.unscaled <- res.obj$cov.unscaled * (n/(n+k))^2
+ res.obj$cov.scaled <- res.obj$cov.unscaled * res.obj$dispersion
+ res.obj$coef[,2] <- sqrt(diag(res.obj$cov.scaled))
+ res.obj$coef[,3] <- res.obj$coef[,1] / res.obj$coef[,2]
+ res.obj$coef[,4 ] <- 2*pt(-abs(res.obj$coef[,3]), res.obj$df.residual)
+ }
+ res.obj
+ }
+ res <- summary.glm(object, ...)
+ res <- bias.cor(res, object$call$bias.correct, n, k)
+ res$call <- object$call
+ res$correct <- object$correct
+ class(res) <- "relogit"
+ res
+}
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/summary.setx.R b/R/summary.setx.R
new file mode 100644
index 0000000..336a96a
--- /dev/null
+++ b/R/summary.setx.R
@@ -0,0 +1,8 @@
+summary.setx <- function(object, ...) {
+ res <- matrix(NA, nrow(object), ncol(object))
+ for (i in 1:ncol(object))
+ res[,i] <- object[,i]
+ colnames(res) <- colnames(object)
+ res <- as.data.frame(res)
+ summary(res, ...)
+}
diff --git a/R/summary.setx.cond.R b/R/summary.setx.cond.R
new file mode 100644
index 0000000..95c0bf0
--- /dev/null
+++ b/R/summary.setx.cond.R
@@ -0,0 +1,8 @@
+summary.setx.cond <- function(object, ...) {
+ res <- matrix(NA, nrow(object), ncol(object))
+ for (i in 1:ncol(object))
+ res[,i] <- object[,i]
+ colnames(res) <- colnames(object)
+ res <- as.data.frame(res)
+ summary(res, ...)
+}
diff --git a/R/summary.strata.R b/R/summary.strata.R
new file mode 100644
index 0000000..ff89c12
--- /dev/null
+++ b/R/summary.strata.R
@@ -0,0 +1,27 @@
+summary.strata <- function(object, subset = NULL, ...) {
+ res <- list()
+ if(is.null(subset))
+ M <- 1:length(object)
+ else
+ M <- c(subset)
+ for (i in 1:length(M))
+ res[[i]] <- summary(object[[i]])
+ if (any(class(object[[1]]) == "MI")) {
+ by <- object[[1]][[1]]$call$by
+ call <- object[[1]][[1]]$call
+ }
+ else {
+ by <- object[[1]]$call$by
+ call <- object[[1]]$call
+ }
+ names(res) <- paste("summary.", by, names(object)[M], sep = "")
+ res$call <- call
+ res$by <- by
+ res$lev <- names(object)[M]
+ res$M <- M
+ class(res) <- "summary.strata"
+ res
+}
+
+
+
diff --git a/R/summary.zelig.R b/R/summary.zelig.R
new file mode 100644
index 0000000..4f28841
--- /dev/null
+++ b/R/summary.zelig.R
@@ -0,0 +1,62 @@
+summary.zelig<-function(object, subset = NULL, CI=95,
+ stats=c("mean", "sd", "min", "max"), ...){
+ cip <- c((100-CI)/200, 1-(100-CI)/200)
+ model <- object$zelig.call$model
+ qi.stats<-list()
+ X <- object$x
+ X1 <- object$x1
+ if (any(class(X)=="setx.MI") & any(class(X)=="cond")) {
+ X <- NULL
+ for (i in 1:length(object$x))
+ X <- rbind(X, object$x[[i]])
+ }
+ if (any(class(X1)=="setx.MI")) {
+ X1 <- NULL
+ for (i in 1:length(object$x1))
+ X1 <- rbind(X1, object$x1[[i]])
+ }
+ if (is.null(dim(X))) {
+ X <- matrix(X, ncol = 1)
+ colnames(X) <- "(Intercept)"
+ if (!is.null(X1)) {
+ X1 <- matrix(X1, ncol = 1)
+ colnames(X1) <- "(Intercept)"
+ }
+ }
+ for (i in 1:length(object$qi)) {
+ qi.stats[[i]] <- summarize(object$qi[[i]], cip, stats, model,
+ object, subset)
+ if (is.null(subset) & dim(object$qi[[i]])[2]!=1)
+ object$qi.name[i] <- paste("Pooled", object$qi.name[[i]])
+ }
+ if (is.numeric(subset)) {
+ X <- X[subset,]
+ if (!is.null(X1))
+ X1 <- X1[subset,]
+ }
+ names(qi.stats) <- names(object$qi)
+ res <- list(zelig=model, num=object$call$num, x=X, x1=X1,
+ qi.stats=qi.stats, qi.name=object$qi.name)
+ class(res)<-"summary.zelig"
+ return(res)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/summary.zelig.strata.R b/R/summary.zelig.strata.R
new file mode 100644
index 0000000..71c3d8e
--- /dev/null
+++ b/R/summary.zelig.strata.R
@@ -0,0 +1,86 @@
+summary.zelig.strata <-function(object, subset = NULL, CI=95,
+ stats=c("mean", "sd", "min", "max"), ...){
+ m <- length(object)
+ if(is.null(subset)) { # summary for all strata together
+ qi <- object[[1]]$qi
+ if(length(dim(qi[[1]]))==3) {
+ w <- NULL
+ for (i in 1:length(object))
+ w <- c(w, dim(object[[i]]$qi[[1]])[3])
+ }
+ for (i in 1:length(qi)){
+ qi1i <- object[[1]]$qi[[i]]
+ if(any(class(object[[1]]$x)=="cond")) {# conditional prediction
+ if(length(dim(qi1i))==3){
+ tmp <- array(NA, dim=c(dim(qi1i)[1:2], sum(w)))
+ tmp[,1:dim(qi1i)[2],1:dim(qi1i)[3]] <- qi1i
+ }
+ else
+ tmp <- as.matrix(qi1i)
+ }
+ else { # unconditional prediction
+ if(length(dim(qi1i))==3){
+ tmp <- array(NA, dim=c(dim(qi1i)[1:2], sum(w)))
+ tmp[,,1:w[1]] <- qi1i[,,1:w[1]]
+ }
+ else
+ tmp <- as.matrix(qi1i)
+ }
+ for (j in 2:m) {
+ qiji <- object[[j]]$qi[[i]]
+ if(any(class(object[[j]]$x)=="cond")) # conditional prediction
+ if(length(dim(qi1i))==3)
+ tmp[,,(sum(w[1:(m-1)])+1):sum(w[1:m])] <- qiji
+ else
+ tmp <- cbind(tmp, qiji)
+ else{ # unconditional prediction
+ if(length(dim(qi1i))==3)
+ tmp[(sum(w[1:(j-1)])+1):sum(w[1:j]),,] <- qiji[1:w[j],,]
+ else
+ tmp <- rbind(tmp, as.matrix(qiji))
+ }
+ }
+ qi[[i]] <- tmp
+ }
+ c <- match.call()
+ c$num <- object[[1]]$call$num
+ res <- list(qi=qi, qi.name=object[[1]]$qi.name,
+ x=object[[1]]$x, x1=NULL, call=c,
+ zelig.call=object[[1]]$zelig.call)
+ return(summary.zelig(res, ...))
+ }
+ else { # summary for each strata
+ res <- list()
+ if(is.function(subset)){
+ m <- length(object)
+ subset <- 1:m
+ }
+ else
+ m <- length(subset)
+ for (i in 1:m) {
+ res[[i]] <- summary.zelig(object[[subset[i]]], subset=NULL, ...)
+ names(res)[i] <- names(object)[i]
+ }
+ class(res) <- "summary.zelig.strata"
+ return(res)
+ }
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/terms.vglm.R b/R/terms.vglm.R
new file mode 100644
index 0000000..be245cb
--- /dev/null
+++ b/R/terms.vglm.R
@@ -0,0 +1,2 @@
+terms.vglm <- function(x, ...)
+ x at terms$terms
diff --git a/R/ternarypoints.R b/R/ternarypoints.R
new file mode 100644
index 0000000..804100c
--- /dev/null
+++ b/R/ternarypoints.R
@@ -0,0 +1,10 @@
+ternarypoints <- function(object, pch = 19, col = "blue", ...){
+ s <- rowSums(object)
+ if (any(s <= 0))
+ stop("each row of the input `object' must have a positive sum")
+ object <- object/s
+ top <- sqrt(3)/2
+ xp <- object[, 2] + object[, 3]/2
+ yp <- object[, 3] * top
+ points(xp, yp, pch = pch, col = col, ...)
+}
diff --git a/R/user.prompt.R b/R/user.prompt.R
new file mode 100644
index 0000000..ca31067
--- /dev/null
+++ b/R/user.prompt.R
@@ -0,0 +1,2 @@
+user.prompt <- function()
+ silent <- readline("\nPress <return> to continue: ")
diff --git a/R/vcov.BetaReg.R b/R/vcov.BetaReg.R
new file mode 100644
index 0000000..a606926
--- /dev/null
+++ b/R/vcov.BetaReg.R
@@ -0,0 +1,2 @@
+vcov.BetaReg <- function(object, ...)
+ object$variance
diff --git a/R/vcov.lm.R b/R/vcov.lm.R
new file mode 100644
index 0000000..e313688
--- /dev/null
+++ b/R/vcov.lm.R
@@ -0,0 +1,4 @@
+vcov.lm <- function(object, ...) {
+ so <- summary.lm(object, corr = FALSE, ...)
+ so$sigma^2 * so$cov.unscaled
+}
diff --git a/R/vcov.relogit.R b/R/vcov.relogit.R
new file mode 100644
index 0000000..5598e34
--- /dev/null
+++ b/R/vcov.relogit.R
@@ -0,0 +1,22 @@
+vcov.relogit <- function(object, ...) {
+ if (is.null(object$call$bias.correct))
+ object$call$bias.correct <- TRUE
+ dta <- eval(object$call$data, sys.parent())
+ tt <- terms(object)
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ n <- nrow(dta)
+ k <- ncol(dta)
+ bias.cor <- function(res.obj, bc, n, k){
+ if (bc) {
+ res.obj$cov.unscaled <- res.obj$cov.unscaled * (n/(n+k))^2
+ res.obj$cov.scaled <- res.obj$cov.unscaled * res.obj$dispersion
+ res.obj$coef[,2] <- sqrt(diag(res.obj$cov.scaled))
+ res.obj$coef[,3] <- res.obj$coef[,1] / res.obj$coef[,2]
+ res.obj$coef[,4 ] <- 2*pt(-abs(res.obj$coef[,3]), res.obj$df.residual)
+ }
+ res.obj
+ }
+ res <- summary.glm(object, ...)
+ res <- bias.cor(res, object$call$bias.correct, n, k)
+ res$cov.scaled
+}
diff --git a/R/vcov.survreg.R b/R/vcov.survreg.R
new file mode 100644
index 0000000..be335e6
--- /dev/null
+++ b/R/vcov.survreg.R
@@ -0,0 +1 @@
+vcov.survreg <- function(object, ...) object$var
diff --git a/R/zelig.R b/R/zelig.R
new file mode 100644
index 0000000..e471db2
--- /dev/null
+++ b/R/zelig.R
@@ -0,0 +1,68 @@
+zelig <- function(formula, model, data, by = NULL, ...) {
+ fn <- paste("zelig2", model, sep = "")
+ if (!exists(fn))
+ stop(model, "not supported. Type help.zelig(\"models\") to list supported models.")
+ mf <- match.call(expand.dots = TRUE)
+ if (missing(by))
+ by <- NULL
+ N <- M <- 1
+ object <- list()
+ if (!is.data.frame(data))
+ M <- length(data)
+ if (M > 1)
+ dat <- data[[1]]
+ else
+ dat <- data
+ if (!is.null(by)) {
+ if (any(as.character(by) %in% c(formula[[2]], formula[[3]])))
+ stop("the variable selected for subsetting cannot be called in the formula.")
+ idx <- dat[,by]
+ mf$by <- NULL
+ lev <- sort(unique(idx))
+ N <- length(lev)
+ }
+ mf <- do.call(fn, list(formula, model, dat, N, ...))
+ for (i in 1:N) {
+ if (N > 1) {
+ dat <- list()
+ if (M > 1) {
+ for (j in 1:M)
+ dat[[j]] <- data[[j]][idx == lev[i],]
+ }
+ else
+ dat <- data[idx == lev[i],]
+ }
+ else
+ dat <- data
+ obj <- list()
+ for (j in 1:M) {
+ if (M > 1)
+ d <- dat[[j]]
+ else
+ d <- dat
+ if (is.data.frame(d)) {
+ d <- d[complete.cases(model.frame(as.formula(formula), d)),]
+ mf$data <- d
+ res <- eval(as.call(mf))
+ res$call <- match.call()
+ res$data <- res$call$data
+ res$zelig <- model
+ if (M > 1)
+ obj[[j]] <- res
+ else
+ obj <- res
+ }
+ }
+ if (M > 1)
+ class(obj) <- "MI"
+ if (N > 1)
+ object[[i]] <- obj
+ else
+ object <- obj
+ }
+ if (N > 1) {
+ class(object) <- "strata"
+ names(object) <- lev
+ }
+ object
+}
diff --git a/R/zelig2beta.R b/R/zelig2beta.R
new file mode 100644
index 0000000..cbd5836
--- /dev/null
+++ b/R/zelig2beta.R
@@ -0,0 +1,6 @@
+zelig2beta <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("beta.regression")
+ as.call(mf)
+}
diff --git a/R/zelig2blogit.R b/R/zelig2blogit.R
new file mode 100644
index 0000000..2e42c4a
--- /dev/null
+++ b/R/zelig2blogit.R
@@ -0,0 +1,18 @@
+zelig2blogit <- function(formula, model, data, M, constrain = NULL,
+ omit = NULL, constant = 3, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "VGAM"))
+ require(VGAM)
+ else
+ stop("Please install VGAM using \n install.packages(\"VGAM\", CRAN = \"http://www.stat.auckland.ac.nz/~yee\")")
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("vglm")
+ mf$family <- as.name("blogit")
+ mf$... <- NULL
+ tmp <- cmvglm(formula, model, constrain, omit, constant, 3)
+ mf$formula <- tmp$formula
+ mf$constraints <- tmp$constraints
+ blogit <<- function() binom2.or(zero=NULL)
+ mf$model <- mf$constrain <- mf$omit <- mf$constant <- mf$M <- NULL
+ as.call(mf)
+}
diff --git a/R/zelig2bprobit.R b/R/zelig2bprobit.R
new file mode 100644
index 0000000..25baf89
--- /dev/null
+++ b/R/zelig2bprobit.R
@@ -0,0 +1,17 @@
+zelig2bprobit <- function(formula, model, data, M, constrain = NULL,
+ omit = NULL, constant = 3, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "VGAM"))
+ require(VGAM)
+ else
+ stop("Please install VGAM using \n install.packages(\"VGAM\", CRAN = \"http://www.stat.auckland.ac.nz/~yee\")")
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("vglm")
+ mf$family <- as.name("bprobit")
+ tmp <- cmvglm(formula, model, constrain, omit, constant, 3)
+ mf$formula <- tmp$formula
+ mf$constraints <- tmp$constraints
+ bprobit <<- function() binom2.rho(zero=NULL)
+ mf$model <- mf$constrain <- mf$omit <- mf$constant <- mf$M <- NULL
+ as.call(mf)
+}
diff --git a/R/zelig2exp.R b/R/zelig2exp.R
new file mode 100644
index 0000000..7a36c62
--- /dev/null
+++ b/R/zelig2exp.R
@@ -0,0 +1,8 @@
+zelig2exp <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ require(survival)
+ mf[[1]] <- as.name("survreg")
+ mf$dist <- as.character("exponential")
+ as.call(mf)
+}
diff --git a/R/zelig2gamma.R b/R/zelig2gamma.R
new file mode 100644
index 0000000..f9150a7
--- /dev/null
+++ b/R/zelig2gamma.R
@@ -0,0 +1,7 @@
+zelig2gamma <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("Gamma")
+ as.call(mf)
+}
diff --git a/R/zelig2logit.R b/R/zelig2logit.R
new file mode 100644
index 0000000..7cd2612
--- /dev/null
+++ b/R/zelig2logit.R
@@ -0,0 +1,7 @@
+zelig2logit <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("binomial")
+ as.call(mf)
+}
diff --git a/R/zelig2lognorm.R b/R/zelig2lognorm.R
new file mode 100644
index 0000000..b8f3714
--- /dev/null
+++ b/R/zelig2lognorm.R
@@ -0,0 +1,8 @@
+zelig2lognorm <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ require(survival)
+ mf[[1]] <- as.name("survreg")
+ mf$dist <- as.character("lognormal")
+ as.call(mf)
+}
diff --git a/R/zelig2ls.R b/R/zelig2ls.R
new file mode 100644
index 0000000..8e4b22f
--- /dev/null
+++ b/R/zelig2ls.R
@@ -0,0 +1,6 @@
+zelig2ls <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("lm")
+ as.call(mf)
+}
diff --git a/R/zelig2mlogit.R b/R/zelig2mlogit.R
new file mode 100644
index 0000000..0f79ce1
--- /dev/null
+++ b/R/zelig2mlogit.R
@@ -0,0 +1,23 @@
+zelig2mlogit <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "VGAM"))
+ require(VGAM)
+ else
+ stop("Please install VGAM using \n install.packages(\"VGAM\", CRAN = \"http://www.stat.auckland.ac.nz/~yee\")")
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("vglm")
+ mf$family <- as.name("multinomial")
+ if (mf$M == 1)
+ ndim <- length(unique(na.omit((eval(mf$formula[[2]], mf$data))))) - 1
+ if (mf$M > 1) {
+ ntmp <- array()
+ for (i in 1:mf$M)
+ ntmp[i] <- length(unique(na.omit((eval(mf$formula[[2]], mf$data[[i]]))))) $
+ ndim <- max(ntmp)
+ }
+ tmp <- cmvglm(mf$formula, mf$model, mf$equal, mf$zeros, mf$ones, ndim)
+ mf$formula <- tmp$formula
+ mf$constraints <- tmp$constraints
+ mf$model <- mf$equal <- mf$ones <- mf$zeros <- mf$M <- NULL
+ as.call(mf)
+}
diff --git a/R/zelig2mloglm.R b/R/zelig2mloglm.R
new file mode 100644
index 0000000..3068456
--- /dev/null
+++ b/R/zelig2mloglm.R
@@ -0,0 +1,7 @@
+zelig2mloglm <- function(formula, model, data, M, ...) {
+ mf$model <- mf$M <- mf$... <- NULL
+ require(nnet)
+ mf[[1]] <- as.name("multinom")
+ mf$Hess <- TRUE
+ as.call(mf)
+}
diff --git a/R/zelig2negbin.R b/R/zelig2negbin.R
new file mode 100644
index 0000000..6725da6
--- /dev/null
+++ b/R/zelig2negbin.R
@@ -0,0 +1,6 @@
+zelig2negbin <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm.nb")
+ as.call(mf)
+}
diff --git a/R/zelig2normal.R b/R/zelig2normal.R
new file mode 100644
index 0000000..f1a3bb5
--- /dev/null
+++ b/R/zelig2normal.R
@@ -0,0 +1,7 @@
+zelig2normal <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("gaussian")
+ as.call(mf)
+}
diff --git a/R/zelig2ologit.R b/R/zelig2ologit.R
new file mode 100644
index 0000000..2360654
--- /dev/null
+++ b/R/zelig2ologit.R
@@ -0,0 +1,7 @@
+zelig2ologit <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("polr")
+ mf$Hess <- TRUE
+ as.call(mf)
+}
diff --git a/R/zelig2oprobit.R b/R/zelig2oprobit.R
new file mode 100644
index 0000000..d50a89e
--- /dev/null
+++ b/R/zelig2oprobit.R
@@ -0,0 +1,27 @@
+zelig2oprobit <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "VGAM"))
+ require(VGAM)
+ else
+ stop("Please install VGAM using \n install.packages(\"VGAM\", CRAN = \"http://www.stat.auckland.ac.nz/~yee\")")
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$equal))
+ ordinal.probit <<- function() cumulative(link=probit, parallel=T)
+ else
+ ordinal.probit <<- function() cumulative(link=probit)
+ mf[[1]] <- as.name("vglm")
+ mf$family <- as.name("ordinal.probit")
+ if (mf$M == 1)
+ ndim <- length(unique(na.omit((eval(mf$formula[[2]], mf$data))))) - 1
+ if (mf$M > 1) {
+ ntmp <- array()
+ for (i in 1:mf$M)
+ ntmp[i] <- length(unique(na.omit((eval(mf$formula[[2]], mf$data[[i]]))))) - 1
+ ndim <- max(ntmp)
+ }
+ tmp <- cmvglm(mf$formula, mf$model, mf$equal, mf$zeros, mf$ones, ndim)
+ mf$formula <- tmp$formula
+ mf$constraints <- tmp$constraints
+ mf$model <- mf$equal <- mf$ones <- mf$zeros <- mf$M <- NULL
+ as.call(mf)
+}
diff --git a/R/zelig2poisson.R b/R/zelig2poisson.R
new file mode 100644
index 0000000..4edabba
--- /dev/null
+++ b/R/zelig2poisson.R
@@ -0,0 +1,7 @@
+zelig2poisson <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("poisson")
+ as.call(mf)
+}
diff --git a/R/zelig2probit.R b/R/zelig2probit.R
new file mode 100644
index 0000000..be5a03e
--- /dev/null
+++ b/R/zelig2probit.R
@@ -0,0 +1,8 @@
+zelig2probit <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- as.name("glm")
+ mf$family <- as.name("binomial.probit")
+ binomial.probit <<- function() binomial(link="probit")
+ as.call(mf)
+}
diff --git a/R/zelig2relogit.R b/R/zelig2relogit.R
new file mode 100644
index 0000000..93fedc3
--- /dev/null
+++ b/R/zelig2relogit.R
@@ -0,0 +1,8 @@
+zelig2relogit <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ if (is.null(mf$bias.correct))
+ mf$bias.correct <- TRUE
+ mf[[1]] <- as.name("relogit")
+ as.call(mf)
+}
diff --git a/R/zelig2weibull.R b/R/zelig2weibull.R
new file mode 100644
index 0000000..a633439
--- /dev/null
+++ b/R/zelig2weibull.R
@@ -0,0 +1,8 @@
+zelig2weibull <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ require(survival)
+ mf[[1]] <- as.name("survreg")
+ mf$dist <- as.character("weibull")
+ as.call(mf)
+}
diff --git a/README b/README
new file mode 100644
index 0000000..c7a3e98
--- /dev/null
+++ b/README
@@ -0,0 +1,68 @@
+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.
diff --git a/data/MatchIt.url.tab b/data/MatchIt.url.tab
new file mode 100644
index 0000000..9cf2ee9
--- /dev/null
+++ b/data/MatchIt.url.tab
@@ -0,0 +1,7 @@
+matchit http://gking.harvard.edu/matchit/docs/Usage.html
+distance http://gking.harvard.edu/matchit/docs/_TT_distance_TT.html
+matchdef http://gking.harvard.edu/matchit/docs/_TT_matchdef_TT.html
+subclassify http://gking.harvard.edu/matchit/docs/_TT_subclassify_TT.html
+print.matchit http://gking.harvard.edu/matchit/docs/Print.html
+summary.matchit http://gking.harvard.edu/matchit/docs/Summary.html
+plot.matchit http://gking.harvard.edu/matchit/docs/Plot.html
diff --git a/data/Zelig.url.tab b/data/Zelig.url.tab
new file mode 100644
index 0000000..b6954bd
--- /dev/null
+++ b/data/Zelig.url.tab
@@ -0,0 +1,36 @@
+command http://gking.harvard.edu/zelig/docs/Main_Commands.html
+commands http://gking.harvard.edu/zelig/docs/Main_Commands.html
+model http://gking.harvard.edu/zelig/docs/Specific_Models.html
+models http://gking.harvard.edu/zelig/docs/Specific_Models.html
+zelig http://gking.harvard.edu/zelig/docs/_TT_zelig_TT__Estimati.html
+setx http://gking.harvard.edu/zelig/docs/_TT_setx_TT__Setting_E.html
+sim http://gking.harvard.edu/zelig/docs/_TT_sim_TT__Simulating.html
+matchit http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
+Matchit http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
+MatchIt http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
+summary http://gking.harvard.edu/zelig/docs/_TT_summary_TT__Summar.html
+print http://gking.harvard.edu/zelig/docs/_TT_print_TT__Printing.html
+plot http://gking.harvard.edu/zelig/docs/_TT_plot_TT__Graphing.html
+plot.ci http://gking.harvard.edu/zelig/docs/_TT_plot_ci_TT__Plotti.html
+ROC http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
+roc http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
+rocplot http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
+ternarypoints http://gking.harvard.edu/zelig/docs/_TT_ternarypoints_TT.html
+repl http://gking.harvard.edu/zelig/docs/_TT_repl_TT__Replicati.html
+blogit http://gking.harvard.edu/zelig/docs/_TT_blogit_TT__Bivaria.html
+bprobit http://gking.harvard.edu/zelig/docs/_TT_bprobit_TT__Bivari.html
+exp http://gking.harvard.edu/zelig/docs/_TT_exp_TT__Exponentia.html
+gamma http://gking.harvard.edu/zelig/docs/_TT_gamma_TT__Gamma_Re.html
+ls http://gking.harvard.edu/zelig/docs/_TT_ls_TT__Least_Squar.html
+logit http://gking.harvard.edu/zelig/docs/_TT_logit_TT__Logistic.html
+lognorm http://gking.harvard.edu/zelig/docs/_TT_lognorm_TT__Log_No.html
+mlogit http://gking.harvard.edu/zelig/docs/_TT_mlogit_TT__Multino.html
+mloglm http://gking.harvard.edu/zelig/docs/_TT_mloglm_TT__Multino.html
+negbin http://gking.harvard.edu/zelig/docs/_TT_negbin_TT__Negativ.html
+normal http://gking.harvard.edu/zelig/docs/_TT_normal_TT__Normal.html
+ologit http://gking.harvard.edu/zelig/docs/_TT_ologit_TT__Ordinal.html
+oprobit http://gking.harvard.edu/zelig/docs/_TT_oprobit_TT__Ordina.html
+poisson http://gking.harvard.edu/zelig/docs/_TT_poisson_TT__Poisso.html
+probit http://gking.harvard.edu/zelig/docs/_TT_probit_TT__Probit.html
+relogit http://gking.harvard.edu/zelig/docs/_TT_relogit_TT__Rare_E.html
+weibull http://gking.harvard.edu/zelig/docs/_TT_weibull_TT__Weibul.html
\ No newline at end of file
diff --git a/data/coalition.tab b/data/coalition.tab
new file mode 100644
index 0000000..9207699
--- /dev/null
+++ b/data/coalition.tab
@@ -0,0 +1,315 @@
+"duration" "ciep12" "invest" "fract" "polar" "numst2" "crisis"
+"1" 0.5 1 1 656 11 0 24
+"2" 3 1 1 656 11 1 10
+"3" 7 1 1 656 11 1 24
+"4" 20 1 1 656 11 1 7
+"5" 6 1 1 656 11 1 7
+"6" 7 1 1 634 6 1 45
+"7" 2 1 1 599 3 1 51
+"8" 17 1 1 599 3 1 4
+"9" 27 1 1 599 3 1 6
+"10" 49 0 1 620 2 1 10
+"11" 4 1 1 592 1 0 23
+"12" 29 1 1 592 1 1 2
+"13" 49 0 1 628 5 1 29
+"14" 6 1 1 719 11 1 65
+"15" 23 1 1 719 11 1 38
+"16" 41 0 1 757 18 1 132
+"17" 10 1 1 775 24 1 73
+"18" 12 1 1 775 24 1 61
+"19" 2 1 1 762 24 0 65
+"20" 33 1 1 762 24 1 0
+"21" 1 1 1 762 24 0 0
+"22" 16 1 1 735 17 1 46
+"23" 2 1 1 753 17 1 9
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diff --git a/data/immi1.tab b/data/immi1.tab
new file mode 100644
index 0000000..e2a0f1c
--- /dev/null
+++ b/data/immi1.tab
@@ -0,0 +1,2486 @@
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diff --git a/data/immi2.tab b/data/immi2.tab
new file mode 100644
index 0000000..c6ebb71
--- /dev/null
+++ b/data/immi2.tab
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diff --git a/data/immi3.tab b/data/immi3.tab
new file mode 100644
index 0000000..1ab2663
--- /dev/null
+++ b/data/immi3.tab
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diff --git a/data/immi4.tab b/data/immi4.tab
new file mode 100644
index 0000000..1cd426c
--- /dev/null
+++ b/data/immi4.tab
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diff --git a/data/immi5.tab b/data/immi5.tab
new file mode 100644
index 0000000..b3ef2fd
--- /dev/null
+++ b/data/immi5.tab
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diff --git a/data/immigration.tab b/data/immigration.tab
new file mode 100644
index 0000000..0d41343
--- /dev/null
+++ b/data/immigration.tab
@@ -0,0 +1,2486 @@
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diff --git a/data/macro.tab b/data/macro.tab
new file mode 100644
index 0000000..be8e6ce
--- /dev/null
+++ b/data/macro.tab
@@ -0,0 +1,351 @@
+"country" "year" "gdp" "unem" "capmob" "trade"
+"1" "United States" 1966 5.1111407 3.8000000 0 9.622906
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+"339" "Japan" 1979 5.2000000 2.1000000 -1 22.002180
+"340" "Japan" 1980 4.4000000 2.0000000 0 26.316120
+"341" "Japan" 1981 3.9000000 2.2000000 0 31.215320
+"342" "Japan" 1982 2.8000000 2.4000000 0 32.518820
+"343" "Japan" 1983 3.2000000 2.6000000 0 32.806080
+"344" "Japan" 1984 5.0000000 2.7000000 0 29.735800
+"345" "Japan" 1985 4.7000000 2.6000000 0 30.654100
+"346" "Japan" 1986 2.5000000 2.8000000 0 29.161590
+"347" "Japan" 1987 4.4000000 2.9000000 0 21.909870
+"348" "Japan" 1988 5.7000000 2.5000000 0 21.712350
+"349" "Japan" 1989 4.7000000 2.2647528 0 23.128797
+"350" "Japan" 1990 5.2000000 2.0989975 0 20.000304
diff --git a/data/mexico.tab b/data/mexico.tab
new file mode 100644
index 0000000..65a81cd
--- /dev/null
+++ b/data/mexico.tab
@@ -0,0 +1,1360 @@
+"vote88" "pri82" "pan82" "novote82" "deathok" "forinvok" "limimp" "paydebt" "keepind" "polint" "auth" "natecon" "futecok" "persecon" "futperok" "school" "age" "female" "prof" "working" "union" "townsize" "north" "south" "zmgm" "religion" "pristr" "othcok" "othsocok" "ratemdm" "traitmjc" "traitccs" "one"
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diff --git a/data/mid.tab b/data/mid.tab
new file mode 100644
index 0000000..a900207
--- /dev/null
+++ b/data/mid.tab
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diff --git a/data/sanction.tab b/data/sanction.tab
new file mode 100644
index 0000000..5157911
--- /dev/null
+++ b/data/sanction.tab
@@ -0,0 +1,79 @@
+"mil" "coop" "target" "import" "export" "cost" "num" "ncost"
+"1" 1 4 3 1 1 4 15 "major loss"
+"2" 0 2 3 0 1 3 4 "modest loss"
+"3" 0 1 3 1 0 2 1 "little effect"
+"4" 1 1 3 1 1 2 1 "little effect"
+"5" 0 1 3 1 1 2 1 "little effect"
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+"7" 1 2 2 0 1 2 3 "little effect"
+"8" 0 1 3 0 0 2 3 "little effect"
+"9" 0 2 1 0 0 1 2 "net gain"
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+"11" 1 1 2 0 0 1 1 "net gain"
+"12" 0 1 2 1 1 2 1 "little effect"
+"13" 0 3 1 1 1 2 8 "little effect"
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diff --git a/data/turnout.tab b/data/turnout.tab
new file mode 100644
index 0000000..e87b572
--- /dev/null
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diff --git a/demo/00Index b/demo/00Index
new file mode 100644
index 0000000..7ee10ce
--- /dev/null
+++ b/demo/00Index
@@ -0,0 +1,23 @@
+beta Beta regression and simulation
+blogit Bivariate Logit regression and simulation
+bprobit Bivariate Probit regression and simulation
+conditional Conditional prediction
+exp 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
+match Regression and simulation on a matched data set
+mi Regression and simulation on multiply imputed data sets
+mlogit Multinomial Logit regression and simulation
+negbin Negative Binomial regression and simulation
+normal Normal (Gaussian) regression and simulation
+ologit Ordinal Logit regression and simulation
+oprobit Ordinal Probit regression and simulation
+poisson Poisson regression and simulation
+probit Probit regression and simulation
+relogit Rare events logit regression and simulation
+strata Regression and simulation in a stratified model
+roc ROC plot
+vertci Vertical confidence interval plot
+weibull Weibull regression and simulation
diff --git a/demo/blogit.R b/demo/blogit.R
new file mode 100644
index 0000000..a12100d
--- /dev/null
+++ b/demo/blogit.R
@@ -0,0 +1,90 @@
+# Load the sample data:
+data(sanction)
+
+##### Example 1: Basics #####
+
+# Note that by default, zelig() estimates two parameter estimates
+# for explanatory variable as well as the correlation parameter; this
+# formulation is parametrically independent (estimating separate effects
+# for each explanatory variable), but stochastically dependent because
+# the models share an odds ratio. See Example 2 for a more constrained
+# form of stochastic dependence.a list of at most 3 elements (corresponding to the 3
+# equations). Each element consists of a character vector of the
+# variables omitted from each equation.
+z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
+ model = "blogit", data = sanction)
+user.prompt()
+summary(z.out1)
+user.prompt()
+# Generate baseline values for the explanatory variables (with cost set
+# to 1, net gain to sender) and alternative values (with cost set to 4,
+# major loss to sender):
+x.low <- setx(z.out1, cost = 1)
+x.high <- setx(z.out1, cost = 4)
+# Simulate fitted values and first differences:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out1)
+
+# Plot the s.out
+user.prompt()
+plot(s.out1)
+user.prompt()
+
+##### Example 2: Joint Estimation of a Model with #####
+##### Different Sets of Explanatory Variables #####
+
+# Estimate the statistical model, with import a function of coop
+# in the first equation and export a function of cost and target
+# in the second equation, by using the zeros argument:
+z.out2 <- zelig(cbind(import, export) ~ coop + cost + target,
+ omit = list("1" = "cost", "2" = c("coop", "target")),
+ model = "blogit", data = sanction)
+user.prompt()
+summary(z.out2)
+user.prompt()
+# Set the explanatory variables to their default values:
+x.out2 <- setx(z.out2)
+
+# Simulate draws from the posterior distribution:
+user.prompt()
+s.out2 <- sim(z.out2, x = x.out2)
+user.prompt()
+summary(s.out2)
+
+# Plotting marginal densities:
+user.prompt()
+plot(s.out2)
+
+##### Example 3: Joint Estimation of a Parametrically #####
+##### and Stochastically Dependent Model #####
+
+# A bivariate model is parametrically dependent if Y1 and Y2 share
+# some or all explanatory variables, {\it and} the effects of the shared
+# explanatory variables are jointly estimated. For example,
+user.prompt()
+z.out3 <- zelig(cbind(import, export) ~ coop + cost + target,
+ constrain = list("1" = c("coop", "cost", "target"),
+ "2" = c("coop", "cost", "target")),
+ model = "blogit", data = sanction)
+user.prompt()
+summary(z.out3)
+
+# Note that this model only returns one parameter estimate for each of
+# coop, cost, and target. Contrast this to Example 1 which returns two
+# parameter estimates for each of the explanatory variables.
+
+# Set values for the explanatory variables:
+user.prompt()
+x.out3 <- setx(z.out3, cost = 1:4)
+
+# Draw simulated expected values:
+user.prompt()
+s.out3 <- sim(z.out3, x = x.out3)
+user.prompt()
+summary(s.out3)
+
+
+
+
diff --git a/demo/bprobit.R b/demo/bprobit.R
new file mode 100644
index 0000000..f86c828
--- /dev/null
+++ b/demo/bprobit.R
@@ -0,0 +1,92 @@
+# Load the sample data:
+data(sanction)
+
+##### Example 1: Basics #####
+
+# Note that by default, zelig() estimates two parameter estimates
+# for explanatory variable as well as the correlation parameter; this
+# formulation is parametrically independent (estimating separate effects
+# for each explanatory variable), but stochastically dependent because
+# the models share an odds ratio. See Example 2 for a more constrained
+# form of stochastic dependence.
+user.prompt()
+z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
+ model = "bprobit", data = sanction)
+user.prompt()
+summary(z.out1)
+
+# Generate baseline values for the explanatory variables (with cost set
+# to 1, net gain to sender) and alternative values (with cost set to 4,
+# major loss to sender):
+user.prompt()
+x.low <- setx(z.out1, cost = 1)
+x.high <- setx(z.out1, cost = 4)
+
+# Simulate fitted values and first differences:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out1)
+
+# Plot the s.out
+user.prompt()
+plot(s.out1)
+
+
+##### Example 2: Joint Estimation of a Model with #####
+##### Different Sets of Explanatory Variables #####
+
+# Estimate the statistical model, with import a function of coop
+# in the first equation and export a function of cost and target
+# in the second equation, by using the zeros argument:
+user.prompt()
+z.out2 <- zelig(cbind(import, export) ~ coop + cost + target,
+ omit = list("1"="coop", "2"="cost", "3" = "target"),
+ model = "bprobit", data = sanction)
+user.prompt()
+summary(z.out2)
+
+# Set the explanatory variables to their default values:
+user.prompt()
+Xval2 <- setx(z.out2)
+
+# Simulate draws from the posterior distribution:
+user.prompt()
+s.out2 <- sim(z.out2, x = Xval2)
+user.prompt()
+summary(s.out2)
+
+# Plotting marginal densities:
+user.prompt()
+plot(s.out2)
+
+##### Example 3: Joint Estimation of a Parametrically #####
+##### and Stochastically Dependent Model #####
+
+# A bivariate model is parametrically dependent if Y1 and Y2 share
+# some or all explanatory variables, {\it and} the effects of the shared
+# explanatory variables are jointly estimated. For example,
+user.prompt()
+z.out3 <- zelig(cbind(import, export) ~ coop + cost + target,
+ constrain = list("1"=c("coop", "cost", "target"),
+ "2"=c("coop", "cost", "target")),
+ model = "bprobit", data = sanction)
+user.prompt()
+summary(z.out3)
+
+# Note that this model only returns one parameter estimate for each of
+# coop, cost, and target. Contrast this to Example 1 which returns two
+# parameter estimates for each of the explanatory variables.
+
+# Set values for the explanatory variables:
+user.prompt()
+x.out3 <- setx(z.out3, cost = 1:4)
+
+# Draw simulated expected values:
+user.prompt()
+s.out3 <- sim(z.out3, x = x.out3)
+user.prompt()
+summary(s.out3)
+
+
+
diff --git a/demo/conditional.R b/demo/conditional.R
new file mode 100644
index 0000000..84312a5
--- /dev/null
+++ b/demo/conditional.R
@@ -0,0 +1,13 @@
+data(turnout)
+z.out <- zelig(vote ~ age + educate + income, by = "race",
+ data = turnout, model = "probit")
+user.prompt()
+x.white <- setx(z.out$others, fn = NULL, data = turnout[turnout$race == "white",], cond = TRUE)
+user.prompt()
+s.others <- sim(z.out$others, x = x.white)
+summary(s.others)
+user.prompt()
+x.others <- setx(z.out$white, fn = NULL, data = turnout[turnout$race == "others",], cond = TRUE)
+s.others <- sim(z.out$white, x = x.others)
+summary(s.others)
+
diff --git a/demo/exp.R b/demo/exp.R
new file mode 100644
index 0000000..82dd4ec
--- /dev/null
+++ b/demo/exp.R
@@ -0,0 +1,33 @@
+data(coalition)
+
+## Creating a censored object for the dependent variable using
+## Surv(duration, ciep12), where duration is the dependent variable
+## (number of days alive during the observation period), and ciep12
+## is the censoring indicator (coded 0 if alive and 1 if dead at the
+## end of the observation period), and regressing this censored
+## object using the selected explanatory variables:
+user.prompt()
+z.out <- zelig(Surv(duration, ciep12) ~ invest + polar + numst2 + crisis,
+ model = "exp", data = coalition)
+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)
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
+plot(s.out)
+
+
diff --git a/demo/gamma.R b/demo/gamma.R
new file mode 100644
index 0000000..5412083
--- /dev/null
+++ b/demo/gamma.R
@@ -0,0 +1,27 @@
+data(coalition)
+user.prompt()
+z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
+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)
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
+plot(s.out)
+
+
+
+
diff --git a/demo/logit.R b/demo/logit.R
new file mode 100644
index 0000000..cdddde8
--- /dev/null
+++ b/demo/logit.R
@@ -0,0 +1,69 @@
+## Attaching the sample turnout dataset:
+data(turnout)
+
+##### Example 1: Simple Example
+
+## Generating empirical estimates:
+user.prompt()
+z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+user.prompt()
+## Viewing the regression output:
+summary(z.out1)
+
+## Using setx to generate baseline and alternative velus for the
+## explanatory variables.
+user.prompt()
+x.out1 <- setx(z.out1)
+
+## Simulating quantities of interest (predicted probabilites, risk
+## ratios, and risk differences):
+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$educate, prob = 0.25))
+
+user.prompt()
+s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+user.prompt()
+summary(s.out2)
+user.prompt()
+plot(s.out2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/lognorm.R b/demo/lognorm.R
new file mode 100644
index 0000000..824c9ae
--- /dev/null
+++ b/demo/lognorm.R
@@ -0,0 +1,25 @@
+# Load the sample data:
+data(coalition)
+
+# Estimate the model:
+user.prompt()
+z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
+ data = coalition)
+user.prompt()
+# View the regression output:
+summary(z.out)
+
+# Set the baseline values (with the ruling coalition in the minority)
+# and the alternative values (with the ruling coalition in the majority)
+# for X:
+user.prompt()
+x.low <- setx(z.out, numst2 = 0)
+x.high <- setx(z.out, numst2 = 1)
+
+# Simulate expected values qi$ev and first differences qi$fd:
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out)
+user.prompt()
+plot(s.out)
diff --git a/demo/ls.R b/demo/ls.R
new file mode 100644
index 0000000..8c5abbc
--- /dev/null
+++ b/demo/ls.R
@@ -0,0 +1,51 @@
+##### Example 1: Basic Example with First Differences #####
+
+# Attach sample data and variable names:
+data(macro)
+
+# Estimate model and present a summary:
+user.prompt()
+z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
+user.prompt()
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, with
+# high (80th percentile) and low (20th percentile) values:
+user.prompt()
+x.high<- setx(z.out1, trade = quantile(macro$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:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+user.prompt()
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+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.
+user.prompt()
+z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
+ model = "ls", data = macro)
+user.prompt()
+# Set values for the explanatory variables, using the default mean/mode
+# values, with country set to the United States and Japan, respectively:
+user.prompt()
+x.US <- setx(z.out2, country = "United States")
+x.Japan <- setx(z.out2, country = "Japan")
+
+# Simulate quantities of interest:
+user.prompt()
+s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+
+# Plot differences:
+user.prompt()
+plot(s.out2)
diff --git a/demo/match.R b/demo/match.R
new file mode 100644
index 0000000..788b8db
--- /dev/null
+++ b/demo/match.R
@@ -0,0 +1,42 @@
+library(MatchIt)
+data(lalonde)
+user.prompt()
+## an example for propensity score matching
+match.out1 <- matchit(treat ~ age + educ + black + hispan + married +
+ nodegree + re74 + re75, data = lalonde)
+user.prompt()
+
+z.out1 <- zelig(re78 ~ pscore, data = match.data(match.out1, "control"), model = "ls")
+user.prompt()
+
+x.out1 <- setx(z.out1, fn = NULL, data = match.data(match.out1, "treat"), cond = TRUE)
+user.prompt()
+
+s.out1 <- sim(z.out1, x = x.out1)
+user.prompt()
+summary(s.out1)
+user.prompt()
+
+## an example for subclassification
+match.out2 <- matchit(treat ~ age + educ + black + hispan + married +
+ nodegree + re74 + re75, data = lalonde,
+ replace = FALSE, subclass = 3)
+user.prompt()
+
+z.out2 <- zelig(re78 ~ pscore, data = match.data(match.out2, "control"),
+ model="ls", by="psclass")
+user.prompt()
+
+x.out2 <- setx(z.out2, fn = NULL, data = match.data(match.out2, "treat"), cond = TRUE)
+user.prompt()
+
+s.out2 <- sim(z.out2, x = x.out2)
+user.prompt()
+
+summary(s.out2) # overall results
+user.prompt()
+
+summary(s.out2, subset = 2) # subclass 2
+
+
+
diff --git a/demo/mi.R b/demo/mi.R
new file mode 100644
index 0000000..62127f1
--- /dev/null
+++ b/demo/mi.R
@@ -0,0 +1,16 @@
+data(immi1, immi2, immi3, immi4, immi5)
+user.prompt()
+
+z.out <- zelig(as.factor(ipip) ~ wage1992 + prtyid + ideol, model = "ologit",
+ data = list(immi1, immi2, immi3, immi4, immi5))
+user.prompt()
+summary(z.out)
+user.prompt()
+
+x.out <- setx(z.out)
+user.prompt()
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+summary(s.out)
+user.prompt()
+plot(s.out)
diff --git a/demo/mlogit.R b/demo/mlogit.R
new file mode 100644
index 0000000..a22c620
--- /dev/null
+++ b/demo/mlogit.R
@@ -0,0 +1,27 @@
+data(mexico)
+user.prompt()
+z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit",
+ data = mexico)
+user.prompt()
+summary(z.out)
+
+user.prompt()
+x.weak <- setx(z.out, pristr = 1)
+x.strong <- setx(z.out, pristr = 3)
+
+user.prompt()
+s.out <- sim(z.out, x = x.strong, x1 = x.weak)
+user.prompt()
+summary(s.out)
+
+user.prompt()
+ev.weak <- s.out$qi$ev + s.out$qi$fd
+
+user.prompt()
+library(vcd)
+ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
+ main = "1988 Mexican Presidential Election")
+user.prompt()
+ternarypoints(ev.weak, pch = ".", col = "red")
+
+
diff --git a/demo/negbin.R b/demo/negbin.R
new file mode 100644
index 0000000..87119a9
--- /dev/null
+++ b/demo/negbin.R
@@ -0,0 +1,13 @@
+data(sanction)
+user.prompt()
+z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
+user.prompt()
+summary(z.out)
+user.prompt()
+x.out <- setx(z.out)
+user.prompt()
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+summary(s.out)
+user.prompt()
+plot(s.out)
diff --git a/demo/normal.R b/demo/normal.R
new file mode 100644
index 0000000..42de1e0
--- /dev/null
+++ b/demo/normal.R
@@ -0,0 +1,55 @@
+##### Example 1: Basic Example with First Differences #####
+
+# Attach sample data and variable names:
+data(macro)
+
+# Estimate model and present a summary:
+user.prompt()
+z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal", data = macro)
+user.prompt()
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, with
+# high (80th percentile) and low (20th percentile) values:
+user.prompt()
+x.high <- setx(z.out1, trade = quantile(macro$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:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+user.prompt()
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+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.
+user.prompt()
+z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year)
+ + as.factor(country), model = "normal", data = macro)
+
+# Set values for the explanatory variables, using the default mean/mode
+# values, with country set to the United States and Japan, respectively:
+user.prompt()
+x.US <- setx(z.out2, country = "United States")
+x.Japan <- setx(z.out2, country = "Japan")
+
+# Simulate quantities of interest:
+user.prompt()
+s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+user.prompt()
+summary(s.out2)
+
+# Plot differences:
+user.prompt()
+plot(s.out2)
+
+
diff --git a/demo/ologit.R b/demo/ologit.R
new file mode 100644
index 0000000..96318a0
--- /dev/null
+++ b/demo/ologit.R
@@ -0,0 +1,67 @@
+##### Example 1: First Differences
+
+# Load the sample data:
+data(sanction)
+
+# Estimate the empirical model and returning the coefficients:
+user.prompt()
+z.out1 <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
+ data = sanction)
+user.prompt()
+summary(z.out1)
+
+# Set the explanatory variables to their means, with 'mil' set
+# to 0 (no military action in addition to sanctions) in the baseline
+# case and set to 1 (military action in addition to sanctions) in the
+# alternative case:
+user.prompt()
+x.low <- setx(z.out1, coop = 1)
+x.high <- setx(z.out1, coop = 4)
+
+# Generate simulated fitted values and first differences, and view
+# the results:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out1)
+user.prompt()
+plot(s.out1)
+
+##### Example 2: Creating An Ordered Dependent Variable #####
+
+# Create an ordered dependent variable:
+user.prompt()
+sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+
+# Estimate the model:
+user.prompt()
+z.out2 <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+user.prompt()
+summary(z.out2)
+
+# Set the explanatory variables to their observed values:
+user.prompt()
+x.out2 <- setx(z.out2, fn = NULL)
+
+# Simulate fitted values given Xval and view the results:
+user.prompt()
+s.out2 <- sim(z.out2, x = x.out2)
+user.prompt()
+summary(s.out2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/oprobit.R b/demo/oprobit.R
new file mode 100644
index 0000000..6d43f65
--- /dev/null
+++ b/demo/oprobit.R
@@ -0,0 +1,63 @@
+##### Example 1: First Differences
+
+# Load the sample data:
+data(sanction)
+
+# Estimate the empirical model and returning the coefficients:
+user.prompt()
+z.out1 <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
+ data = sanction)
+user.prompt()
+summary(z.out1)
+
+# Set the explanatory variables to their means, with 'mil' set
+# to 0 (no military action in addition to sanctions) in the baseline
+# case and set to 1 (military action in addition to sanctions) in the
+# alternative case:
+user.prompt()
+x.low <- setx(z.out1, mil = 0)
+x.high <- setx(z.out1, mil = 1)
+
+# Generate simulated fitted values and first differences, and view
+# the results:
+user.prompt()
+s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out1)
+
+
+##### Example 2: Creating An Ordered Dependent Variable #####
+
+# Create an ordered dependent variable:
+user.prompt()
+sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+
+# Z.Out the model:
+user.prompt()
+z.out2 <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
+user.prompt()
+summary(z.out2)
+
+# Set the explanatory variables to their observed values:
+user.prompt()
+x.out2 <- setx(z.out2, fn = NULL)
+
+# Simulate fitted values given Xval and view the results:
+user.prompt()
+s.out2 <- sim(z.out2, x = x.out2)
+user.prompt()
+summary(s.out2)
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/poisson.R b/demo/poisson.R
new file mode 100644
index 0000000..0c6e185
--- /dev/null
+++ b/demo/poisson.R
@@ -0,0 +1,16 @@
+data(sanction)
+user.prompt()
+
+z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
+user.prompt()
+summary(z.out)
+user.prompt()
+
+x.out <- setx(z.out)
+user.prompt()
+
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+summary(s.out)
+user.prompt()
+plot(s.out)
diff --git a/demo/probit.R b/demo/probit.R
new file mode 100644
index 0000000..2e27ab1
--- /dev/null
+++ b/demo/probit.R
@@ -0,0 +1,69 @@
+## Attaching the sample turnout dataset:
+data(turnout)
+
+##### Example 1: Simple Example
+
+## Generating empirical estimates:
+user.prompt()
+z.out1 <- zelig(vote ~ race + educate, model = "probit", data = turnout)
+## Viewing the regression output:
+user.prompt()
+summary(z.out1)
+
+## Using setx to generate baseline and alternative velus for the
+## explanatory variables.
+user.prompt()
+x.out1 <- setx(z.out1)
+
+## Simulating quantities of interest (predicted probabilites, risk
+## ratios, and risk differences):
+user.prompt()
+s.out1 <- sim(z.out1, x = x.out1)
+user.prompt()
+## Summarizing the simulated quantities of interest:
+summary(s.out1)
+
+## Diagnostic plot of the s.out:
+user.prompt()
+plot(s.out1)
+
+## Example 2: First Differences
+
+user.prompt()
+x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
+x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+
+user.prompt()
+s.out2 <- sim(z.out1, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out2)
+user.prompt()
+plot(s.out2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/relogit.R b/demo/relogit.R
new file mode 100644
index 0000000..6f41180
--- /dev/null
+++ b/demo/relogit.R
@@ -0,0 +1,28 @@
+data(mid)
+user.prompt()
+z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+ data = mid, model = "relogit", tau = 1042/303772)
+user.prompt()
+summary(z.out1)
+user.prompt()
+x.out1 <- setx(z.out1)
+user.prompt()
+s.out1 <- sim(z.out1, x = x.out1)
+user.prompt()
+summary(s.out1)
+user.prompt()
+plot(s.out1)
+user.prompt()
+z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+ data = mid, model = "relogit", tau = c(0.002, 0.005))
+user.prompt()
+summary(z.out2)
+user.prompt()
+x.out2 <- setx(z.out2)
+user.prompt()
+s.out2 <- sim(z.out2, x = x.out2)
+user.prompt()
+summary(s.out2)
+user.prompt()
+plot(s.out2)
+
diff --git a/demo/roc.R b/demo/roc.R
new file mode 100644
index 0000000..068750d
--- /dev/null
+++ b/demo/roc.R
@@ -0,0 +1,8 @@
+data(turnout)
+z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
+ data = turnout)
+user.prompt()
+z.out2 <- zelig(vote ~ race + educate, model = "logit",
+ data = turnout)
+user.prompt()
+rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
diff --git a/demo/vertci.R b/demo/vertci.R
new file mode 100644
index 0000000..144eb1b
--- /dev/null
+++ b/demo/vertci.R
@@ -0,0 +1,44 @@
+
+## 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()
+age.range <- 18:95
+user.prompt()
+x.low <- setx(z.out, educate = 12, age = age.range)
+x.high <- setx(z.out, educate = 16, age = age.range)
+
+## 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")
+user.prompt()
+legend(45, 0.55, legend = c("College Education (16 years)",
+ "High School Education (12 years)"), col = c("blue","red"),
+ lty = c("solid"))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/weibull.R b/demo/weibull.R
new file mode 100644
index 0000000..f3a4a8c
--- /dev/null
+++ b/demo/weibull.R
@@ -0,0 +1,39 @@
+data(coalition)
+
+## Creating a censored object for the dependent variable using
+## Surv(duration, ciep12), where duration is the dependent variable
+## (number of days alive during the observation period), and ciep12
+## is the censoring indicator (coded 0 if alive and 1 if dead at the
+## end of the observation period), and regressing this censored
+## object using the selected explanatory variables:
+user.prompt()
+z.out <- zelig(Surv(duration, ciep12) ~ invest + polar + numst2 + crisis,
+ model = "weibull", data = coalition)
+user.prompt()
+## Viewing the regression output. Note that the Weibull model
+## differs from the exponential model in that the Weibull has an
+## optional scale parameter. (The exponential is a special case of
+## the Weibull with scale set to 1.)
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables).
+user.prompt()
+x.out <- setx(z.out)
+
+## Simulating draws from the posterior distribution.
+user.prompt()
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+## Viewing the simulated quantities of interest (in this case, the
+## expected value is the ceteris paribus predicted duration):
+summary(s.out)
+
+## Plotting the differences in the expected values for the Weibull
+## predictions:
+user.prompt()
+plot(s.out)
+
+
+
+
diff --git a/man/MatchIt.url.Rd b/man/MatchIt.url.Rd
new file mode 100644
index 0000000..312c598
--- /dev/null
+++ b/man/MatchIt.url.Rd
@@ -0,0 +1,15 @@
+\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}
+
+
+
+
diff --git a/man/Zelig.Rd b/man/Zelig.Rd
new file mode 100644
index 0000000..7a33f92
--- /dev/null
+++ b/man/Zelig.Rd
@@ -0,0 +1,89 @@
+\name{zelig}
+
+\alias{zelig}
+\alias{Zelig}
+
+\title{Estimating a Statistical Model}
+
+\description{ The \code{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). }
+
+\usage{
+z.out <- zelig(formula, model, data, by, \dots)
+}
+
+\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, enclosed in \code{""}.
+ 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. }
+\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{\dots}{additional arguments passed to \code{zelig},
+ depending on the model to be estimated. }
+}
+
+\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)}.
+}
+
+\seealso{ The full Zelig manual is available at
+ \url{http://gking.harvard.edu/zelig}.
+}
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{file}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/man/Zelig.url.Rd b/man/Zelig.url.Rd
new file mode 100644
index 0000000..be3c704
--- /dev/null
+++ b/man/Zelig.url.Rd
@@ -0,0 +1,15 @@
+\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}
+
+
+
+
diff --git a/man/coalition.Rd b/man/coalition.Rd
new file mode 100644
index 0000000..e1fc926
--- /dev/null
+++ b/man/coalition.Rd
@@ -0,0 +1,38 @@
+\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}
+
+
diff --git a/man/gsource.Rd b/man/gsource.Rd
new file mode 100644
index 0000000..1d92197
--- /dev/null
+++ b/man/gsource.Rd
@@ -0,0 +1,51 @@
+\name{gsource}
+
+\alias{gsource}
+
+\title{Read Data As a Space-Delimited Table}
+
+\description{
+ The \code{gsource} function allows you to read a space delimited table
+ as a data frame. Unlike \code{scan}, you may use \code{gsource} in a
+ \code{source}ed file, and unlike \code{read.table}, you may use
+ \code{gsource} to include a small (or large) data set in a file that
+ also contains other commands.
+}
+
+\usage{
+gsource(var.names = NULL, variables)
+}
+
+\arguments{
+ \item{var.names}{An optional vector of character strings representing
+ the column names. By default, \code{var.names = NULL}. }
+ \item{variables}{A single character string representing the data.}
+}
+
+\value{
+ The output from \code{gsource} is a data frame, which you may save to
+ an object in your workspace.
+}
+
+\seealso{\code{read.table}, \code{scan}}
+
+\author{ Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\examples{
+\dontrun{
+data <- gsource(variables = "
+ 1 2 3 4 5
+ 6 7 8 9 10
+ 3 4 5 1 3
+ 6 7 8 1 9 ")
+
+data <- gsource(var.names = "Vote Age Party", variables = "
+ 0 23 Democrat
+ 0 27 Democrat
+ 1 45 Republican
+ 1 65 Democrat ")
+}
+}
+
+\keyword{file}
diff --git a/man/help.zelig.Rd b/man/help.zelig.Rd
new file mode 100644
index 0000000..9b5c8d4
--- /dev/null
+++ b/man/help.zelig.Rd
@@ -0,0 +1,47 @@
+\name{help.zelig}
+
+\alias{help.zelig}
+
+\title{HTML Help for Zelig Commands and Models}
+
+\description{
+ The \code{help.zelig} command launches html help for Zelig commands
+ and supported models. The full manual is available online at
+ \url{http://gking.harvard.edu/zelig}.
+ }
+
+\usage{
+help.zelig(object)
+}
+
+\arguments{
+ \item{object}{a character string representing a Zelig command or model.
+ \code{help.zelig("command")} will take you to an index of Zelig
+ commands and \code{help.zelig("model")} will take you to a list of
+ models. }
+ }
+
+\seealso{The complete document is available online at
+ \url{http://gking.harvard.edu/zelig}.
+}
+
+\references{Gary King, 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.347--355.
+}
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau<\email{olau at fas.harvard.edu}>
+}
+
+\keyword{documentation}
+
+
+
+
+
+
+
+
diff --git a/man/house.Rd b/man/house.Rd
new file mode 100644
index 0000000..29d6e33
--- /dev/null
+++ b/man/house.Rd
@@ -0,0 +1,30 @@
+\name{house}
+
+\alias{house}
+
+\title{Elections to the United States House of Representatives}
+
+\description{
+ This data set contains data on the 1984 and 1986 elections for the US
+House of Representatives. }
+
+\usage{data(house)}
+
+\format{
+ A table containing 12 variables and 320 observations. Districts that
+were uncontested were omitted from the final data.
+}
+
+\source{ICPSR}
+
+\references{
+ Andrew Gelman and Gary King. ``Estimating Incumbency Advantage Without
+ Bias.'' American Journal of Political Science 34 (1990), 1142-1164.
+
+ King, Gary. ICPSR Publication Related Archive, 6311.
+
+}
+
+\keyword{datasets}
+
+
diff --git a/man/immigration.Rd b/man/immigration.Rd
new file mode 100644
index 0000000..c6b0418
--- /dev/null
+++ b/man/immigration.Rd
@@ -0,0 +1,35 @@
+\name{immigration}
+
+\alias{immigration}
+\alias{immi1}
+\alias{immi2}
+\alias{immi3}
+\alias{immi4}
+\alias{immi5}
+\alias{data}
+
+\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}
+
+
diff --git a/man/macro.Rd b/man/macro.Rd
new file mode 100644
index 0000000..61a7878
--- /dev/null
+++ b/man/macro.Rd
@@ -0,0 +1,37 @@
+\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}
+
+
+
+
diff --git a/man/match.data.Rd b/man/match.data.Rd
new file mode 100644
index 0000000..8309460
--- /dev/null
+++ b/man/match.data.Rd
@@ -0,0 +1,41 @@
+\name{match.data}
+
+\alias{match.data}
+
+\title{Output matched data sets}
+
+\description{The code \code{match.data} creates output data sets from the \code{matchit}
+matching algorithm.}
+
+\usage{
+match.data <- match.data(object, group = "all")
+}
+
+\arguments{
+\item{object}{Stored output from \code{matchit}.}
+
+\item{group}{Which units to output. Selecting "all" (default) gives all
+matched units (treated and control), "treat" gives just the matched
+treated units, and "control" gives just the matched control units.} }
+
+\value{ The \code{match.data} command generates a matched data set from
+the output of the \code{matchit} function, according to the options
+selected in the \code{group} argument. The matched data set contains
+the additional variables:
+ \item{pscore}{The propensity score for each unit.}
+ \item{psclass}{The subclass index for each unit (if applicable).}
+ \item{psweights}{The weight for each unit (generated from the matching
+ procedure).}
+See the \code{matchit} documentation for more details on these items.
+}
+
+\seealso{The complete documentation for \code{matchit} is available online at
+ \url{http://gking.harvard.edu/matchit}.}
+
+\author{
+ Daniel Ho <\email{deho at fas.harvard.edu}>; Kosuke Imai
+ <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Elizabeth Stuart<\email{stuart at stat.harvard.edu}>
+}
+
+\keyword{methods}
diff --git a/man/mexico.Rd b/man/mexico.Rd
new file mode 100644
index 0000000..de1b387
--- /dev/null
+++ b/man/mexico.Rd
@@ -0,0 +1,28 @@
+\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}
+
diff --git a/man/mid.Rd b/man/mid.Rd
new file mode 100644
index 0000000..1bc0f83
--- /dev/null
+++ b/man/mid.Rd
@@ -0,0 +1,35 @@
+\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}
+
+
diff --git a/man/plot.ci.Rd b/man/plot.ci.Rd
new file mode 100644
index 0000000..e855b0c
--- /dev/null
+++ b/man/plot.ci.Rd
@@ -0,0 +1,76 @@
+\name{plot.ci}
+
+\alias{plot.ci}
+
+\title{Plotting Vertical confidence Intervals}
+
+\description{ The \code{plot.ci} command generates vertical
+ confidence intervals for linear or generalized linear univariate
+ response models. }
+
+\usage{
+plot.ci(x, CI = 95, qi = "ev", main = "", ylab = NULL, xlab = NULL,
+ xlim = NULL, ylim = NULL, col = c("red", "blue"), ...)
+}
+
+\arguments{
+ \item{x}{stored output from \code{sim}. The \code{x$x} and optional
+ \code{x$x1} values used to generate the \code{sim} output object must
+ have more than one observation.}
+\item{CI}{the selected confidence interval. Defaults to 95
+ percent.}
+\item{qi}{the selected quantity of interest. Defaults to
+ expected values.}
+\item{main}{a title for the plot.}
+\item{ylab}{label for the y-axis.}
+\item{xlab}{label for the x-axis.}
+\item{xlim}{limits on the x-axis.}
+\item{ylim}{limits on the y-axis.}
+\item{col}{a vector of at most two colors for plotting the
+ expected value given by \code{x} and the alternative set of expected
+ values given by \code{x1} in \code{sim}. If the quantity of
+ interest selected is not the expected value, or \code{x1 = NULL},
+ only the first color will be used.}
+\item{\dots}{Additional parameters passed to \code{plot}.}
+}
+
+\value{
+For all univariate response models, {\tt plot.ci()} returns vertical
+confidence intervals over a specified range of one explanatory
+variable. You may save this plot using the commands described in the
+Zelig manual (\url{http://gking.harvard.edu/zelig}).
+}
+
+\example{
+data(turnout)
+z.out <- zelig(vote ~ race + educate + age + I(age^2) + income,
+ model = "logit", data = turnout)
+age.range <- 18:95
+x.low <- setx(z.out, educate = 12, age = age.range)
+x.high <- setx(z.out, educate = 16, age = age.range)
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+plot.ci(s.out, xlab = "Age in Years",
+ ylab = "Predicted Probability of Voting",
+ main = "Effect of Education and Age on Voting Behavior")
+legend(45, 0.52, legend = c("College Education (16 years)",
+ "High School Education (12 years)"), col = c("blue","red"),
+ lty = c("solid"))
+}
+
+\seealso{
+ The full Zelig manual is available at
+ \url{http://gking.harvard.edu/zelig}, and users may also wish to see
+ \code{plot}, \code{lines}.
+ }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{hplot}
+
+
+
+
+
diff --git a/man/plot.zelig.Rd b/man/plot.zelig.Rd
new file mode 100644
index 0000000..9abd49f
--- /dev/null
+++ b/man/plot.zelig.Rd
@@ -0,0 +1,49 @@
+\name{plot.zelig}
+
+\alias{plot.zelig}
+\alias{plot}
+
+\title{Graphing Quantities of Interest}
+
+\description{ The \code{zelig} method for the generic \code{plot}
+ command generates default plots for \code{\link{sim}} output with
+ one-observation values in \code{x} and \code{x1}. }
+
+\usage{
+\method{plot}{zelig}(x, xlab = "", user.par = FALSE, ...)
+}
+
+\arguments{
+\item{x}{stored output from \code{\link{sim}}. If the \code{x$x} or
+ \code{x$x1} values stored in the object contain more than one
+ observation, \code{plot.zelig} will return an error. For linear or
+ generalized linear models with more than one observation in \code{x$x}
+ and optionally \code{x$x1}, you may use \code{\link{plot.ci}}. }
+\item{xlab}{a character string for the x-axis label for all graphs.}
+\item{user.par}{a logical value indicating whether to use the default
+ Zelig plotting parameters (\code{user.par = FALSE}) or
+ user-defined parameters (\code{user.par = TRUE}), set using the
+ \code{par} function prior to plotting. }
+\item{\dots}{Additional parameters passed to \code{plot.default}.
+ Because \code{plot.zelig} primarily produces diagnostic plots, many
+ of these parameters are hard-coded for convenience and
+ presentation. }
+}
+
+\value{
+Depending on the class of model selected, \code{plot.zelig} will
+return an on-screen window with graphs of the various quantities of
+interest. You may save these plots using the commands described in
+the Zelig manual (available at \url{http://gking.harvard.edu/zelig}).
+}
+
+\seealso{ The full Zelig manual at
+ \url{http://gking.harvard.edu/zelig} and \code{plot}, \code{lines},
+ and \code{par}. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{hplot}
diff --git a/man/repl.Rd b/man/repl.Rd
new file mode 100644
index 0000000..f97fd0c
--- /dev/null
+++ b/man/repl.Rd
@@ -0,0 +1,89 @@
+\name{repl}
+
+\alias{repl}
+\alias{repl.default}
+\alias{repl.zelig}
+
+\title{Replicating Analyses}
+
+\description{ The generic function \code{repl} command takes
+\code{\link{zelig}} or
+ \code{\link{sim}} output objects and replicates (literally, re-runs)
+ the entire analysis. The results should be an output
+object
+ identical to the original input object in the case of
+ \code{\link{zelig}} output. In the case of \code{\link{sim}}
+ output, the replicated analyses may differ slightly due to
+ stochastic randomness in the simulation procedure. }
+
+\usage{
+repl(object, \dots)
+repl.default(object, data = NULL, \dots)
+repl.zelig(object, prev = NULL, data = NULL, x = NULL, x1 = NULL,
+ bootfn = NULL, \dots)
+}
+
+\arguments{
+\item{object}{Stored output from either \code{\link{zelig}} or
+ \code{\link{sim}}.}
+\item{data}{You may manually input the data frame name rather
+ than allowing replicate to draw the data frame name from the object
+ to be replicated.}
+\item{prev}{When replicating \code{\link{sim}} output, you may
+ optionally use the previously simulated parameters to calculate the
+ quantities of interest rather than simulating a new set of
+ parameters. For all models, this should produce identical
+ quantities of interest. In addition, for if the parameters were
+ bootstrapped in the original analysis, this will save a considerable
+ amount of time. }
+\item{x}{When replicating \code{\link{sim}} output, you may
+ optionally use an alternative \code{\link{setx}} value for the \code{x}
+ input. }
+\item{x1}{When replicating \code{\link{sim}} output, you may
+ optionally use an alternative \code{\link{setx}} object for the \code{x1}
+ input to replicating the \code{\link{sim}} object. }
+\item{bootfn}{When replicating \code{\link{sim}} output with
+ bootstrapped parameters, you should manually specify the
+ \code{bootfn} if a non-default option was used. }
+\item{\dots}{Additional arguments passed to either \code{\link{zelig}} or
+\code{\link{sim}}. }
+}
+
+\value{
+For \code{\link{zelig}} output, \code{repl} will create output that is in
+every way identical to the original input. You may check to see
+whether they are identical by using the \code{identical} command.
+
+For \code{\link{sim}} output, \code{repl} output will be will be
+identical to the original object if you choose not to simulate new
+parameters, and instead choose to calculate quantities of interest
+using the previously simulated parameters (using the \code{prev}
+option. If you choose to simulate new parameters, the summary
+statistics for each quantity of interest should be identical, up to a
+random approximation error. As the number of simulations increases,
+this error decreases.
+}
+
+\seealso{ \code{\link{zelig}}, \code{\link{setx}}, and
+ \code{\link{sim}}. In addition, the full Zelig manual may be
+ accessed online at \url{http://gking.harvard.edu/zelig}. }
+
+\example{
+data(turnout)
+z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout[1:1000,$
+x.out <- setx(z.out)
+s.out <- sim(z.out, x = x.out)
+z.rep <- repl(z.out)
+identical(z.out$coef, z.rep$coef)
+z.alt <- repl(z.out, data = turnout[1001:2000,])
+s.rep <- repl(s.out, prev = s.out$par)
+identical(s.out$ev, s.rep$ev)
+}
+
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{file}
diff --git a/man/rocplot.Rd b/man/rocplot.Rd
new file mode 100644
index 0000000..0f03e49
--- /dev/null
+++ b/man/rocplot.Rd
@@ -0,0 +1,72 @@
+\name{rocplot}
+
+\alias{rocplot}
+\alias{roc}
+\alias{ROC}
+\alias{ROCplot}
+
+\title{Receiver Operator Characteristic Plots}
+
+\description{
+The \code{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, xlab, ylab, ...)
+}
+
+\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, lty2}{The line type for the first model ({\tt
+ lty1}) and the second model (\code{lty2}), defaulting to solid and
+ dashed, respectively. }
+\item{lwd1, lwd2}{The width of the line for the first
+ model (\code{lwd1}) and the second model (\code{lwd2}), defaulting to
+ 1 for both. }
+\item{col1, col2}{The colors of the line for the first
+ model (\code{col1}) and the second model (\code{col2}), defaulting to
+ black for both. }
+\item{main}{a title for the plot. Defaults to "Roc Curve".}
+\item{xlab}{a label for the x-axis. Defaults to "Proportion of 1's
+Correctly Predicted".}
+\item{ylab}{a label for the y-axis. Defaults to Proportion of 0's
+Correctly Predicted".}
+\item{\dots}{Additional parameters passed to plot, including
+ \code{xlab}, \code{ylab}, and \code{main}. }
+}
+
+\value{
+For two logit or probit models, \code{rocplot} returns an on-screen
+window with the desired ROC plot.
+}
+
+\example{
+data(turnout)
+z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
+ data = turnout)
+z.out2 <- zelig(vote ~ race + educate, model = "logit",
+ data = turnout)
+rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
+}
+
+\seealso{ The full Zelig manual (available at
+ \url{http://gking.harvard.edu/zelig}), \code{plot}, \code{lines}. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{aplot}
diff --git a/man/sanction.Rd b/man/sanction.Rd
new file mode 100644
index 0000000..3707245
--- /dev/null
+++ b/man/sanction.Rd
@@ -0,0 +1,27 @@
+\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}
diff --git a/man/setx.Rd b/man/setx.Rd
new file mode 100644
index 0000000..9860462
--- /dev/null
+++ b/man/setx.Rd
@@ -0,0 +1,111 @@
+\name{setx}
+
+\alias{setx}
+
+\title{Setting Explanatory Variable Values}
+
+\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. }
+
+\usage{
+x.out <- setx(object, fn = list(numeric = mean, ordered = median,
+ others = mode),
+ data = NULL, cond = FALSE, \dots)
+}
+
+\arguments{
+ \item{object}{the saved output from \code{\link{zelig}}. }
+ \item{fn}{a list of functions to apply to three types of variables:
+ \item{numeric}{\code{numeric} variables are set to their mean by
+ default, but you may select any mathematical function to apply to
+ numeric variables.}
+ \item{ordered}{\code{ordered} factors are set to their meidan by
+ default, and most mathematical operations will work on them. If
+ you select \code{ordered = mean}, however, \code{setx} will
+ default to median with a warning.}
+ \item{other}{variables may consist of unordered factors, character
+ strings, or logical variables. The \code{other} variables may
+ only be set to their mode. If you wish to set one of the other
+ variables to a specific value, you may do so using \code{\dots}
+ below. }
+ In the special case \code{fn = NULL}, \code{setx} will return all
+ of the observations without applying any function to the data. }
+ \item{data}{a new data frame used to set the values of
+ explanatory variables. If \code{data = NULL} (the default), the
+ data frame called in \code{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{\dots}{user-defined values of specific variables
+ 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:
+\begin{verbatim}
+x.out <- setx(z.out[[5]])
+\end{verbatim}
+
+For conditional prediction, \code{x.out} includes the model matrix
+ and the dependent variables. For multiple analyses (when choosing
+ the \code{by} option in \code{zelig}), \code{setx} returns the
+ observed explanatory variables in each subset.
+}
+
+\example{
+# 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)
+
+# Unconditional prediction with all observations:
+x.out <- setx(z.out, fn = NULL)
+s.out <- sim(z.out, x = x.out)
+
+# Unconditional prediction with out of sample data:
+z.out <- zelig(vote ~ race + educate, model = "logit",
+ data = turnout[1:1000,])
+x.out <- setx(z.out, data = turnout[1001:2000,])
+s.out <- sim(z.out, x = x.out)
+
+# Using a user-defined function in fn:
+quants <- function(x)
+ quantile(x, 0.25)
+x.out <- setx(z.out, fn = list(numeric = quants))
+
+# Conditional prediction:
+data(lalonde)
+match.out <- matchit(treat ~ age + educ + black + hispan + married +
+ nodegree + re74 + re75, data = lalonde)
+z.out <- zelig(re78 ~ pscore, data = match.data(match, "control"),
+ model = "ls")
+x.out <- setx(z.out, fn = NULL, data = match.data(match.out, "treat"),
+ cond = TRUE)
+s.out <- sim(z.out, x = x.out)
+}
+
+\seealso{ The full Zelig manual may be accessed online at
+ \url{http://gking.harvard.edu/zelig}. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{file}
diff --git a/man/sim.Rd b/man/sim.Rd
new file mode 100644
index 0000000..0e1c4c6
--- /dev/null
+++ b/man/sim.Rd
@@ -0,0 +1,143 @@
+\name{sim}
+
+\alias{sim}
+
+\title{Simulating Quantities of Interest}
+
+\description{ 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{
+s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
+ bootstrap = FALSE, bootfn = NULL, cond.data = NULL, \dots)
+}
+
+\arguments{
+ \item{object}{the output object from \code{\link{zelig}}. }
+ \item{x}{values of explanatory variables used for simulation,
+ generated by \code{\link{setx}}. }
+ \item{x1}{optional values of explanatory variables (generated by a
+ second call of \code{\link{setx}}), used to simulate first
+ differences and risk ratios. (Not available for conditional
+ prediction.) }
+ \item{num}{the number of simulations, i.e., posterior draws. If the
+ \code{num} argument is omitted, \code{sim} draws 1,000
+ simulations by if \code{bootstrap = FALSE} (the default), or 100
+ simulations if \code{bootstrap = TRUE}. You may increase this
+ value to improve accuracy. (Not available for conditional
+ prediction.) }
+ \item{bootstrap}{a logical value indicating if parameters
+ should be generated by re-fitting the model for bootstrapped
+ data, rather than from the likelihood or posterior. (Not
+ available for conditional prediction.) }
+ \item{bootfn}{a function which governs how the data is
+ sampled, re-fits the model, and returns the bootstrapped model
+ parameters. If \code{bootstrap = TRUE} and \code{bootfn = NULL},
+ \code{\link{sim}} will sample observations from the original data
+ (with
+ replacement) until it creates a sampled dataset with the same
+ number of observations as the original data. Alternative
+ bootstrap methods include sampling the residuals rather than the
+ observations, weighted sampling, and parametric bootstrapping.
+ (Not available for conditional prediction.) }
+ \item{cond.data}{a data frame, identical to the \code{data}
+ argument in \code{\link{setx}}. Required for conditional prediction
+ with the exponential, Weibull, and lognormal models. }
+ \item{\dots}{additional optional arguments passed to
+ \code{boot}. }
+}
+
+\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}. }
+}
+
+In the case of censored $Y$ in the exponential, Weibull, and lognormal
+models, \code{sim} first imputes the uncensored values for $Y$ before
+calculating the ATE.
+
+You may use the \code{\$} operator to extract any of the
+above from \code{s.out}. For example, \code{s.out\$qi\$ev} extracts the
+simulated expected values.
+}
+
+\seealso{The full Zelig at \url{http://gking.harvard.edu/zelig}, and \code{boot}. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{file}
+
+
+
+
+
+
+
+
+
+
diff --git a/man/summary.zelig.Rd b/man/summary.zelig.Rd
new file mode 100644
index 0000000..91f41ba
--- /dev/null
+++ b/man/summary.zelig.Rd
@@ -0,0 +1,56 @@
+\name{summary.zelig}
+
+\alias{summary.zelig}
+\alias{summary}
+
+\title{Summary of Simulated Quantities of Interest}
+
+\description{Summarizes the object of class \code{\link{zelig}} (output
+ from \code{\link{sim}}) which contains simulated quantities of
+ interst.}
+
+\usage{
+\method{summary}{zelig}(object, subset = NULL, CI = 95, stats = c("mean", "sd", "min", "max"), \dots)
+}
+
+\arguments{
+ \item{object}{output object from \code{\link{sim}} (of class
+ \code{"zelig"}).}
+ \item{subset}{takes one of three values:
+ \item{NULL}{(default) for more than one observation, summarizes all the
+ observations at once for each quantity of interest.}
+ \item{a numeric vector}{indicates which observations to summarize,
+ and summarizes each one independently.}
+ \item{all}{summarizes all the observations independently.}
+ }
+ \item{stats}{summary statistics to be calculated.}
+ \item{CI}{a confidence interval to be calculated.}
+ \item{\ldots}{further arguments passed to or from other methods.}
+}
+
+\value{
+ \item{sim}{number of simulations, i.e., posterior draws.}
+ \item{x}{values of explanatory variables used for simulation.}
+ \item{x1}{values of explanatory variables used for simulation of first
+ differences etc.}
+ \item{qi.stats}{summary of quantities of interst. Use
+ \code{\link{names}} to view the model-specific items available in
+ \code{qi.stats}.}
+}
+
+\seealso{
+ \code{\link{zelig}}, \code{\link{setx}}, \code{\link{sim}},
+ and \code{\link{names}}, and the full Zelig manual at
+ \url{http://gking.harvard.edu/zelig}. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{file}
+
+
+
+
+
diff --git a/man/ternarypoints.Rd b/man/ternarypoints.Rd
new file mode 100644
index 0000000..0488891
--- /dev/null
+++ b/man/ternarypoints.Rd
@@ -0,0 +1,52 @@
+\name{ternarypoints}
+
+\alias{ternarypoints}
+
+\title{Adding Points to Ternary Diagrams}
+
+\description{
+Use \code{ternarypoints} to add points to a ternary diagram generated
+using the \code{ternaryplot} function in the vcd library. Use
+ternary diagrams to plot expected values for multinomial choice models
+with three categories in the dependent variable.
+}
+
+\usage{
+ternarypoints(object, pch = 19, col = "blue", ...)
+}
+
+\arguments{
+\item{object}{The input object must be a matrix with three
+ columns. }
+\item{pch}{The selected type of point. By default, \code{pch =
+ 19}, solid disks. }
+\item{col}{The color of the points. By default, \code{col =
+ "blue"}. }
+\item{\dots}{Additional parameters passed to \code{points}. }
+}
+
+\value{
+The \code{ternarypoints} command adds points to a previously existing
+ternary diagram. Use \code{ternaryplot} in the \code{vcd} library to
+generate the main ternary diagram.
+}
+
+\example{
+
+}
+
+\seealso{ The full Zelig manual at
+ \url{http://gking.harvard.edu/zelig}, \code{points}, and
+ \code{ternaryplot} in the \code{vcd} library. }
+
+\author{
+ Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+ <\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\keyword{aplot}
+
+
+
+
+
diff --git a/man/turnout.Rd b/man/turnout.Rd
new file mode 100644
index 0000000..3d9c30a
--- /dev/null
+++ b/man/turnout.Rd
@@ -0,0 +1,28 @@
+\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}
diff --git a/man/user.prompt.Rd b/man/user.prompt.Rd
new file mode 100644
index 0000000..277b4fb
--- /dev/null
+++ b/man/user.prompt.Rd
@@ -0,0 +1,27 @@
+\name{user.prompt}
+
+\alias{user.prompt}
+
+\title{Pause in demo files}
+
+\description{
+ Use \code{user.prompt} while writing demo files to force users to hit
+ return before continuing.
+}
+
+\usage{
+user.prompt()
+}
+
+\seealso{\code{readline}}
+
+\author{Olivia Lau <\email{olau at fas.harvard.edu}>
+}
+
+\examples{
+\dontrun{
+user.prompt()
+}
+}
+
+\keyword{file}
--
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