[r-cran-zelig] 46/102: Import Upstream version 2.8-4
Andreas Tille
tille at debian.org
Sun Jan 8 16:58:21 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 2fbaee40d6a4d69668d3568098b5de68923add37
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:39:16 2017 +0100
Import Upstream version 2.8-4
---
DESCRIPTION | 6 +-
R/ate/sate.R | 53 -
R/gee/param.gee.R | 34 -
R/gee/qi.gee.R | 69 -
R/gee/vcov.gee.R | 3 -
R/gee/zelig2gamma.gee.R | 8 -
R/gee/zelig2logit.gee.R | 8 -
R/gee/zelig2normal.gee.R | 8 -
R/gee/zelig2poisson.gee.R | 8 -
R/help.zelig.R | 107 +-
R/load.first.R | 4 +
R/systemfit/2sls.tex | 1 -
R/systemfit/3sls.tex | 1 -
R/systemfit/callsystemfit.R | 7 -
R/systemfit/cmsystemfit.R | 19 -
R/systemfit/describe.2sls.R | 18 -
R/systemfit/describe.3sls.R | 18 -
R/systemfit/param.multiple.R | 19 -
R/systemfit/plot.zelig.2sls.R | 14 -
R/systemfit/plot.zelig.3sls.R | 14 -
R/systemfit/plot.zelig.sur.R | 14 -
R/systemfit/plot.zelig.w2sls.R | 14 -
R/systemfit/qi.multiple.R | 60 -
R/systemfit/sur.tex | 1 -
R/systemfit/zelig22sls.R | 25 -
R/systemfit/zelig23sls.R | 25 -
R/systemfit/zelig2sur.R | 16 -
R/systemfit/zelig2w2sls.R | 15 -
R/vignettesMenu.R | 28 +
R/zvcClient.R | 4 +-
cleanup | 18 +
inst/doc/Makefile | 28 +-
inst/doc/Rd.sty | 363 +
inst/doc/Rplots.ps | 40000 ++++++++++++
inst/doc/arima.Rnw | 329 +
inst/doc/arima.pdf | Bin 0 -> 111103 bytes
inst/doc/arima.tex | 349 +
inst/doc/blogit.Rnw | 359 +
inst/doc/blogit.pdf | Bin 0 -> 137905 bytes
inst/doc/blogit.tex | 375 +
inst/doc/bprobit.Rnw | 391 +
inst/doc/bprobit.pdf | Bin 0 -> 152500 bytes
inst/doc/bprobit.tex | 410 +
inst/doc/coda_diag.tex | 32 +
inst/doc/commands/model.end.aux | 25 +-
inst/doc/commands/model.frame.multiple.aux | 27 +-
inst/doc/commands/model.matrix.multiple.aux | 27 +-
inst/doc/commands/network.aux | 41 -
inst/doc/commands/parse.formula.aux | 31 +-
inst/doc/commands/parse.par.aux | 27 +-
inst/doc/commands/plot.ci.aux | 43 -
inst/doc/commands/plot.zelig.aux | 42 -
inst/doc/commands/print.aux | 21 +-
inst/doc/commands/put.start.aux | 25 +-
inst/doc/commands/replicate.aux | 42 -
inst/doc/commands/rocplot.aux | 43 -
inst/doc/commands/set.start.aux | 27 +-
inst/doc/commands/sim.aux | 41 -
inst/doc/commands/summary.aux | 43 -
inst/doc/commands/tag.aux | 27 +-
inst/doc/commands/ternaryplot.aux | 42 -
inst/doc/commands/ternarypoints.aux | 42 -
inst/doc/commandsRd/CVS.tex | 2 +
inst/doc/commandsRd/MatchIt.url.tex | 6 +
inst/doc/commandsRd/PErisk.tex | 67 +
inst/doc/commandsRd/SupremeCourt.tex | 26 +
inst/doc/commandsRd/Weimar.tex | 26 +
inst/doc/commandsRd/Zelig.tex | 62 +
inst/doc/commandsRd/Zelig.url.tex | 6 +
inst/doc/commandsRd/approval.tex | 20 +
inst/doc/commandsRd/coalition.tex | 31 +
inst/doc/commandsRd/current.packages.tex | 35 +
inst/doc/commandsRd/dims.tex | 37 +
inst/doc/commandsRd/eidat.tex | 14 +
inst/doc/commandsRd/friendship.tex | 15 +
inst/doc/commandsRd/gsource.tex | 49 +
inst/doc/commandsRd/help.zelig.tex | 29 +
inst/doc/commandsRd/hoff.tex | 24 +
inst/doc/commandsRd/immigration.tex | 28 +
inst/doc/commandsRd/macro.tex | 28 +
inst/doc/commandsRd/match.data.tex | 43 +
inst/doc/commandsRd/mexico.tex | 24 +
inst/doc/{commands => commandsRd}/mi.aux | 21 +-
inst/doc/commandsRd/mi.tex | 39 +
inst/doc/commandsRd/mid.tex | 28 +
inst/doc/commandsRd/model.end.tex | 44 +
inst/doc/commandsRd/model.frame.multiple.tex | 56 +
inst/doc/commandsRd/model.matrix.multiple.tex | 83 +
inst/doc/commandsRd/network.aux | 38 +
inst/doc/commandsRd/network.tex | 42 +
inst/doc/commandsRd/newpainters.tex | 32 +
inst/doc/commandsRd/parse.formula.tex | 77 +
inst/doc/commandsRd/parse.par.tex | 82 +
inst/doc/commandsRd/plot.ci.aux | 38 +
inst/doc/commandsRd/plot.ci.tex | 68 +
inst/doc/commandsRd/plot.zelig.aux | 38 +
inst/doc/commandsRd/plot.zelig.tex | 48 +
inst/doc/commandsRd/put.start.tex | 33 +
inst/doc/commandsRd/repl.aux | 38 +
inst/doc/commandsRd/repl.tex | 89 +
inst/doc/commandsRd/rocplot.aux | 38 +
inst/doc/commandsRd/rocplot.tex | 87 +
inst/doc/commandsRd/sanction.tex | 24 +
inst/doc/commandsRd/set.start.tex | 42 +
inst/doc/{commands => commandsRd}/setx.aux | 20 +-
inst/doc/commandsRd/setx.tex | 113 +
inst/doc/commandsRd/sim.aux | 38 +
inst/doc/commandsRd/sim.tex | 132 +
inst/doc/commandsRd/sna.ex.tex | 15 +
.../summary.aux} | 31 +-
inst/doc/commandsRd/summary.zelig.tex | 51 +
inst/doc/commandsRd/swiss.tex | 40 +
inst/doc/commandsRd/ternaryplot.aux | 38 +
inst/doc/commandsRd/ternaryplot.tex | 77 +
inst/doc/commandsRd/ternarypoints.aux | 38 +
inst/doc/commandsRd/ternarypoints.tex | 39 +
inst/doc/commandsRd/tobin.tex | 23 +
inst/doc/commandsRd/turnout.tex | 25 +
inst/doc/commandsRd/user.prompt.tex | 25 +
inst/doc/commandsRd/zeligVDC.tex | 62 +
inst/doc/contributors.tex | 15 +
inst/doc/contributors2.tex | 11 +
inst/doc/ei.RxC.Rnw | 234 +
inst/doc/ei.RxC.pdf | Bin 0 -> 86751 bytes
inst/doc/ei.RxC.tex | 238 +
inst/doc/ei.dynamic.Rnw | 309 +
inst/doc/ei.dynamic.pdf | Bin 0 -> 95055 bytes
inst/doc/ei.dynamic.tex | 306 +
inst/doc/ei.hier.Rnw | 311 +
inst/doc/ei.hier.pdf | Bin 0 -> 92998 bytes
inst/doc/ei.hier.tex | 309 +
inst/doc/exp.Rnw | 302 +
inst/doc/exp.pdf | Bin 0 -> 123848 bytes
inst/doc/exp.tex | 304 +
inst/doc/factor.bayes.Rnw | 293 +
inst/doc/factor.bayes.pdf | Bin 0 -> 87105 bytes
inst/doc/factor.bayes.tex | 283 +
inst/doc/factor.mix.Rnw | 299 +
inst/doc/factor.mix.pdf | Bin 0 -> 91656 bytes
inst/doc/factor.mix.tex | 295 +
inst/doc/factor.ord.Rnw | 276 +
inst/doc/factor.ord.pdf | Bin 0 -> 90074 bytes
inst/doc/factor.ord.tex | 274 +
inst/doc/figs/increase.eps | 12290 ----
inst/doc/figs/increase.pdf | Bin 30671 -> 0 bytes
inst/doc/figs/roc.eps | 396 -
inst/doc/figs/roc.pdf | 401 -
inst/doc/figs/sample1.eps | 914 -
inst/doc/figs/sample1.pdf | 568 -
inst/doc/figs/sample2.eps | 695 -
inst/doc/figs/sample2.pdf | 709 -
inst/doc/figs/ternary.eps | 2182 -
inst/doc/figs/ternary.pdf | 2220 -
inst/doc/figs/vertci.eps | 962 -
inst/doc/figs/vertci.pdf | 820 -
inst/doc/forMake.sh | 93 +
inst/doc/gamma.Rnw | 264 +
inst/doc/gamma.pdf | Bin 0 -> 118924 bytes
inst/doc/gamma.tex | 266 +
inst/doc/index.shtml | 2 +-
inst/doc/install.R | 13 -
inst/doc/irt1d.Rnw | 279 +
inst/doc/irt1d.pdf | Bin 0 -> 90626 bytes
inst/doc/irt1d.tex | 274 +
inst/doc/irtkd.Rnw | 281 +
inst/doc/irtkd.pdf | Bin 0 -> 93380 bytes
inst/doc/irtkd.tex | 279 +
inst/doc/logit.Rnw | 310 +
inst/doc/logit.bayes.Rnw | 275 +
inst/doc/logit.bayes.pdf | Bin 0 -> 91888 bytes
inst/doc/logit.bayes.tex | 288 +
inst/doc/logit.pdf | Bin 0 -> 140338 bytes
inst/doc/logit.tex | 323 +
inst/doc/lognorm.Rnw | 291 +
inst/doc/lognorm.pdf | Bin 0 -> 123488 bytes
inst/doc/lognorm.tex | 293 +
inst/doc/ls.Rnw | 274 +
inst/doc/ls.pdf | Bin 0 -> 130081 bytes
inst/doc/ls.tex | 285 +
inst/doc/mlogit.Rnw | 285 +
inst/doc/mlogit.bayes.Rnw | 307 +
inst/doc/mlogit.bayes.pdf | Bin 0 -> 94347 bytes
inst/doc/mlogit.bayes.tex | 316 +
inst/doc/mlogit.pdf | Bin 0 -> 144664 bytes
inst/doc/mlogit.tex | 296 +
inst/doc/mloglm.Rnw | 129 +
inst/doc/mloglm.pdf | Bin 0 -> 43976 bytes
inst/doc/mloglm.tex | 116 +
inst/doc/models/arima.aux | 42 -
inst/doc/models/blogit.aux | 47 -
inst/doc/models/bprobit.aux | 47 -
inst/doc/models/ei.dynamic.aux | 47 -
inst/doc/models/ei.hier.aux | 46 -
inst/doc/models/eiRxC.aux | 42 -
inst/doc/models/exp.aux | 46 -
inst/doc/models/factor.bayes.aux | 46 -
inst/doc/models/factor.mix.aux | 46 -
inst/doc/models/factor.ord.aux | 46 -
inst/doc/models/gamma.aux | 50 -
inst/doc/models/irt1d.aux | 46 -
inst/doc/models/irtkd.aux | 46 -
inst/doc/models/logit.aux | 52 -
inst/doc/models/logit.bayes.aux | 45 -
inst/doc/models/lognormal.aux | 46 -
inst/doc/models/ls.aux | 51 -
inst/doc/models/mlogit.aux | 45 -
inst/doc/models/mlogit.bayes.aux | 45 -
inst/doc/models/negbin.aux | 50 -
inst/doc/models/netlogit.aux | 42 -
inst/doc/models/netls.aux | 42 -
inst/doc/models/normal.aux | 50 -
inst/doc/models/normal.bayes.aux | 45 -
inst/doc/models/ologit.aux | 45 -
inst/doc/models/oprobit.aux | 44 -
inst/doc/models/oprobit.bayes.aux | 45 -
inst/doc/models/poisson.aux | 50 -
inst/doc/models/poisson.bayes.aux | 45 -
inst/doc/models/probit.aux | 50 -
inst/doc/models/probit.bayes.aux | 45 -
inst/doc/models/relogit.aux | 52 -
inst/doc/models/tobit.aux | 46 -
inst/doc/models/tobit.bayes.aux | 47 -
inst/doc/models/weibull.aux | 46 -
inst/doc/negbin.Rnw | 265 +
inst/doc/negbin.pdf | Bin 0 -> 87261 bytes
inst/doc/negbin.tex | 264 +
inst/doc/netlogit.Rnw | 199 +
inst/doc/netlogit.pdf | Bin 0 -> 115142 bytes
inst/doc/netlogit.tex | 206 +
inst/doc/netls.Rnw | 190 +
inst/doc/netls.pdf | Bin 0 -> 100596 bytes
inst/doc/netls.tex | 182 +
inst/doc/normal.Rnw | 308 +
inst/doc/normal.bayes.Rnw | 266 +
inst/doc/normal.bayes.pdf | Bin 0 -> 95205 bytes
inst/doc/normal.bayes.tex | 277 +
inst/doc/normal.pdf | Bin 0 -> 118002 bytes
inst/doc/normal.tex | 299 +
inst/doc/ologit.Rnw | 267 +
inst/doc/ologit.pdf | Bin 0 -> 89133 bytes
inst/doc/ologit.tex | 273 +
inst/doc/oprobit.Rnw | 285 +
inst/doc/oprobit.bayes.Rnw | 303 +
inst/doc/oprobit.bayes.pdf | Bin 0 -> 97739 bytes
inst/doc/oprobit.bayes.tex | 309 +
inst/doc/oprobit.pdf | Bin 0 -> 159009 bytes
inst/doc/oprobit.tex | 291 +
inst/doc/poisson.Rnw | 251 +
inst/doc/poisson.bayes.Rnw | 264 +
inst/doc/poisson.bayes.pdf | Bin 0 -> 90909 bytes
inst/doc/poisson.bayes.tex | 273 +
inst/doc/poisson.pdf | Bin 0 -> 95705 bytes
inst/doc/poisson.tex | 253 +
inst/doc/probit.Rnw | 248 +
inst/doc/probit.bayes.Rnw | 289 +
inst/doc/probit.bayes.pdf | Bin 0 -> 92535 bytes
inst/doc/probit.bayes.tex | 300 +
inst/doc/probit.pdf | Bin 0 -> 83930 bytes
inst/doc/probit.tex | 247 +
inst/doc/refman.tex | 95 +-
inst/doc/relogit.Rnw | 433 +
inst/doc/relogit.pdf | Bin 0 -> 132238 bytes
inst/doc/relogit.tex | 453 +
inst/doc/runSweave.sh | 11 +
inst/doc/tobit.Rnw | 232 +
inst/doc/tobit.bayes.Rnw | 295 +
inst/doc/tobit.bayes.pdf | Bin 0 -> 95686 bytes
inst/doc/tobit.bayes.tex | 306 +
inst/doc/tobit.pdf | Bin 0 -> 81296 bytes
inst/doc/tobit.tex | 235 +
inst/doc/weibull.Rnw | 296 +
inst/doc/weibull.pdf | Bin 0 -> 126082 bytes
inst/doc/weibull.tex | 298 +
inst/doc/zelig.aux | 361 -
inst/doc/zelig.bbl | 218 -
inst/doc/zelig.blg | 105 -
inst/doc/zelig.log | 4338 --
inst/doc/zelig.out | 133 -
inst/doc/zelig.pdf | 62304 ++++++++++++++-----
inst/doc/zelig.tex | 5 +-
inst/doc/zelig.toc | 562 -
inst/doc/zinput.tex | 38 +
inst/unitTests/runit.multiple.R | 4 +-
inst/zideal/zideal.RData | Bin 417 -> 1263 bytes
inst/zideal/zvcServer.R | 7 +
tests/check.describe.R | 4 +-
286 files changed, 110641 insertions(+), 46096 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 18f8d50..8a84358 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: Zelig
-Version: 2.8-3
-Date: 2007-05-29
+Version: 2.8-4
+Date: 2007-06-01
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
@@ -22,4 +22,4 @@ Description: Zelig is an easy-to-use program that can estimate, and
translates them into quantities of direct interest.
License: GPL version 2 or newer
URL: http://gking.harvard.edu/zelig
-Packaged: Tue May 29 22:48:20 2007; king
+Packaged: Fri Jun 1 03:05:56 2007; king
diff --git a/R/ate/sate.R b/R/ate/sate.R
deleted file mode 100644
index c495dc3..0000000
--- a/R/ate/sate.R
+++ /dev/null
@@ -1,53 +0,0 @@
-sate <- function(formula, model, treat, data, sims, zARGS = NULL, sARGS = NULL) {
- tmp <- as.formula(paste(deparse(formula[[2]]), deparse(formula[[1]]),
- paste(treat, "+", deparse(formula[[3]]))))
- D <- model.frame(tmp, data = data)
- idx <- names(model.frame(formula, data = D))
- if (treat %in% idx)
- one.reg <- TRUE
- else
- one.reg <- FALSE
- tname <- treat
- treat <- D[[treat]]
- check <- unique(treat)
- if (length(na.omit(check)) > 2)
- stop("Treatment indicator must be binary.")
- treat <- as.numeric(as.factor(treat))
- if (!all(unique(treat) %in% c(0,1))) {
- treat[treat == min(treat)] <- 0
- treat[treat == max(treat)] <- 1
- }
- if (one.reg) {
- z.out <- zelig(formula, data = D, model = model, ... = zARGS)
- x.out <- setx(z.out, fn = NULL, cond = TRUE)
- tidx <- match(tname, colnames(x.out))
- x.out[, tidx] <- 1 - x.out[, tidx]
- x.all <- x.out[, 2:ncol(x.out)]
- x.all <- as.data.frame(x.all)
- s.out <- sim(z.out, x = x.all[, 2:nrow(x.all)], num = sims, ... = sARGS)
- pr.all <- matrix(as.numeric(s.out$qi$pr), nrow = sims, ncol = nrow(x.all))
-### replace with model.response
- y.all <- matrix(x.out[,1], nrow = sims, ncol nrow(x.out), byrow = TRUE)
- te.all <- y.all - pr.all
- te.all[, treat == 0] <- -1 * te.all[, treat == 0]
- s.out$qi <- s.out$qi.name <- NULL
- s.out$qi <- list(sate = apply(te.all, 1, mean),
- satt = apply(te.all[, treat == 1], 1, mean))
- s.out$qi.name <- list(sate = "Sample Average Treatment Effect for Everyone: E[Y(1)-Y(0)]",
- satt = "Sample Average Treatment Effect for Treated: E[Yobs(1) - Ymiss(1)]")
- return(s.out)
- }
- else {
- z0 <- zelig(formula, data = D[treat == 0,], model = model, ... = zARGS)
- z1 <- zelig(formula, data = D[treat == 1,], model = model, ... = zARGS)
- x0 <- setx(z0, fn = NULL, cond = TRUE)
- x1 <- setx(z1, fn = NULL, cond = TRUE)
- tidx <- match(tname, colnames(x0))
- x0[, tidx] <- 1 - x0[, tidx]
- x1[, tidx] <- 1 - x1[, tidx]
- y0 <-
-
- s0 <- sim(z0, x = x0, num = round(sims / 2), ... = sARGS)
- s1 <- sim(z1, x = x1, num = round(sims / 2), ... = sARGS)
- pr.all <- rbind(s0$qi$pr, s1$qi$pr)
- y.all <-
diff --git a/R/gee/param.gee.R b/R/gee/param.gee.R
deleted file mode 100644
index d0f4554..0000000
--- a/R/gee/param.gee.R
+++ /dev/null
@@ -1,34 +0,0 @@
-param.gee <- function(object, num = NULL, bootstrap = FALSE) {
- if (!bootstrap) {
- coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- if (object$zelig == "normal.gee") {
- sig2 <- object$scale
- alpha <- sqrt(sig2)
- res <- cbind(coef, alpha)
- }
- else if (object$zelig == "gamma.gee") {
- alpha<-1/object$scale
- res <- cbind(coef, 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 <- object$scale
- res <- c(coef, alpha)
- }
- else
- res <- coef
- }
- res
-}
-
-
-
-
diff --git a/R/gee/qi.gee.R b/R/gee/qi.gee.R
deleted file mode 100644
index 1137f84..0000000
--- a/R/gee/qi.gee.R
+++ /dev/null
@@ -1,69 +0,0 @@
-qi.gee <- function(object, simpar, x, x1 = NULL, y = NULL) {
- check <- FALSE
- model <- object$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$linkinv(eta), nrow = nrow(coef))
- pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
- dimnames(pr) <- dimnames(ev) <- dimnames(theta)
- if (model =="logit.gee") {
- check <- TRUE
- ev <- theta
- for (i in 1:ncol(theta))
- pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
- }
- else if (model == "normal.gee") {
- ev <- theta
- for (i in 1:nrow(ev))
- pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
- }
- else if (model == "gamma.gee") {
- ev <- theta * 1/alpha
- for (i in 1:nrow(ev))
- pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
- }
- else if (model == "poisson.gee") {
- ev <- theta
- for (i in 1:ncol(ev))
- pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
- }
- qi <- list(ev = ev, pr = pr)
- qi.name <- list(ev = "Expected Values: E(Y|X)",
- pr = "Predicted Values: Y|X")
- if (!is.null(x1)){
- theta1 <- matrix(object$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/gee/vcov.gee.R b/R/gee/vcov.gee.R
deleted file mode 100644
index c0b7543..0000000
--- a/R/gee/vcov.gee.R
+++ /dev/null
@@ -1,3 +0,0 @@
-vcov.gee <- function(object, ...) {
- return(object$robust.variance)
-}
diff --git a/R/gee/zelig2gamma.gee.R b/R/gee/zelig2gamma.gee.R
deleted file mode 100644
index 04dec9b..0000000
--- a/R/gee/zelig2gamma.gee.R
+++ /dev/null
@@ -1,8 +0,0 @@
-zelig2gamma.gee <- function(formula, model, data, M, ...) {
- require(gee) || stop("install gee using...")
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- mf[[1]] <- as.name("gee")
- mf$family <- as.name("Gamma")
- as.call(mf)
-}
diff --git a/R/gee/zelig2logit.gee.R b/R/gee/zelig2logit.gee.R
deleted file mode 100644
index 0932882..0000000
--- a/R/gee/zelig2logit.gee.R
+++ /dev/null
@@ -1,8 +0,0 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee) || stop("install gee using...")
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- mf[[1]] <- as.name("gee")
- mf$family <- as.name("binomial")
- as.call(mf)
-}
diff --git a/R/gee/zelig2normal.gee.R b/R/gee/zelig2normal.gee.R
deleted file mode 100644
index c156d78..0000000
--- a/R/gee/zelig2normal.gee.R
+++ /dev/null
@@ -1,8 +0,0 @@
-zelig2normal.gee <- function(formula, model, data, M, ...) {
- require(gee) || stop("install gee using...")
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- mf[[1]] <- as.name("gee")
- mf$family <- as.name("gaussian")
- as.call(mf)
-}
diff --git a/R/gee/zelig2poisson.gee.R b/R/gee/zelig2poisson.gee.R
deleted file mode 100644
index f0cd077..0000000
--- a/R/gee/zelig2poisson.gee.R
+++ /dev/null
@@ -1,8 +0,0 @@
-zelig2poisson.gee <- function(formula, model, data, M, ...) {
- require(gee) || stop("install gee using...")
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- mf[[1]] <- as.name("gee")
- mf$family <- as.name("poisson")
- as.call(mf)
-}
diff --git a/R/help.zelig.R b/R/help.zelig.R
index fcaab99..296ed2a 100644
--- a/R/help.zelig.R
+++ b/R/help.zelig.R
@@ -1,53 +1,58 @@
help.zelig <- function (...) {
- zipped <- FALSE
- loc <- NULL
- name <- c(as.character(substitute(list(...))[-1]), list)[[1]]
- if (length(name) == 0)
- loc <- "http://gking.harvard.edu/zelig"
- paths <- .find.package("Zelig")
- if (length(paths) > 1)
- warning(paste("Zelig installed in", length(paths), "locations. Using\n ", paths[1]))
- path <- paths[1]
- path <- file.path(path, "data")
- if (file_test("-f", file.path(path, "Rdata.zip"))) {
- zipped <- TRUE
- if (tools::file_test("-f", fp <- file.path(path, "filelist")))
- files <- file.path(path, scan(fp, what = "", quiet = TRUE))
- else
- stop(gettextf("file 'filelist' is missing for directory '%s'",
- path), domain = NA)
- }
- else
- files <- list.files(path, full = TRUE)
- files <- files[which(regexpr("url", files) > 0)]
- if (length(files) == 0)
- loc <- "http://gking.harvard.edu/zelig"
- else {
- zfile <- array()
- for (f in 1:length(files)) {
- if (zipped)
- zfile[f] <- zip.file.extract(files[f], "Rdata.zip")
- else
- zfile <- files
- }
- tab <- read.table(zfile[1], header = FALSE, as.is = TRUE)
- if (length(zfile) > 1) {
- for (i in 2:length(zfile))
- tab <- rbind(tab, read.table(zfile[i], header = FALSE, as.is = TRUE))
- }
- loc <- tab[which(as.character(tab[, 1]) == name), 2]
- }
- if (is.null(loc)) {
- 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(name)
- do.call("help", list(topic, htmlhelp = TRUE))
- }
- else {
- browseURL(loc)
- invisible(name)
- }
- if (zipped) {
- for (i in 1:length(zfile))
- on.exit(unlink(zfile[i]))
- }
+
+
+### system(paste(getOption("pdfviewer"), system.file("doc/models/", "graphs.pdf",package="Zelig", lib.loc="~/.R/library")))
+### oprsys <- .Platform$OS.type
+### showpdf <- getOption("pdfviewer") unix
+### showpdf <- shell.exec() windows
+ driver <- match.call()
+ driver <- as.character(driver)
+ name <- NULL
+ if(length(driver) > 1) name <- driver[2]
+
+ filesPDf <- NULL
+
+ helpfile <- try(system.file("Meta", "vignette.rds", package="Zelig"))
+
+ if(helpfile!=""){
+ helpMtrx <- .readRDS(helpfile)
+ ix <- grep("[pP][dD][fF]", colnames(helpMtrx))
+ if(length(ix)) filesPDF <- helpMtrx[,ix]
+
+ if(length(filesPDF) && length(name))
+ {
+ fl <- paste("^",name,".pdf$",sep="")
+ ix <- grep(fl,filesPDF)
+ if(length(ix)){
+ file <- filesPDF[ix]
+ print(do.call("vignette", c(list(topic=name),list(package="Zelig"))))
+ return(list())
+ }
+ }
+ }
+ helpfile <- try(system.file("Meta", "hsearch.rds", package="Zelig"))
+
+ fileshtml <- NULL
+ if(helpfile != "")
+ {
+ helpMtrx <- .readRDS(helpfile)
+ fileshtml <- helpMtrx[[2]][,"Aliases"]
+ ix <- grep("url$", fileshtml)
+ if(length(ix))
+ fileshtml <- fileshtml[-ix]
+ if(length(fileshtml) && length(name))
+ {
+ ix <- grep(name, fileshtml)
+
+ if(length(ix)){
+
+ print(do.call("help", c(list(as.name(name)), list(package="Zelig")), envir=parent.frame()))
+ return(NULL)
+ }
+
+ }
+ }
+ message("Not valid input...Showing package description")
+ do.call("help", c(list("zelig"), list(package="Zelig")), envir=parent.frame())
}
+
diff --git a/R/load.first.R b/R/load.first.R
index eec9c42..223073b 100644
--- a/R/load.first.R
+++ b/R/load.first.R
@@ -12,4 +12,8 @@
require(boot)
options(digits = 4)
library.dynam("stats")
+
+ ## add viggnettes menu
+ addVigs2WinMenu("Zelig")
+
}
diff --git a/R/systemfit/2sls.tex b/R/systemfit/2sls.tex
deleted file mode 100644
index 890ad21..0000000
--- a/R/systemfit/2sls.tex
+++ /dev/null
@@ -1 +0,0 @@
-\documentclass[12pt]{book}%
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{graphicx}
\usepackage{fullpage}
\usepackage{multirow}
\usepackage{hyperref}%
\usepackage{bibentry}
\begin{document}
\subsection{\texttt{2sls}: Two Stage Least Squares}
\label{2sls}
\texttt{2sls} provides consistent estimates for linear regression models with
some explanatory variable (the instrumental variable)
correlated with the error term.
In this situation, ordinary least squares fails to provide consistent
estimates. The name two-stage least squares stems from the two regressions
in the estimation procedure. In stage one, an ordinary least squares
prediction of the instrumental variable is obtained from regressing it on
the instrument variables. In stage two, the coefficients of interst are
estimated using ordinary least square after substituting the instrumental
variable by its predictions from stage one.
\subsubsection{Syntax}
\begin{verbatim}
> fml <- list ("mu" = Y ~ X + Z,
"inst" = Z ~ W + X)
> z.out <- zelig(formula = fml, model = "2sls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
\subsubsection{Inputs}
\texttt{2sls} regression take the following inputs:
\begin{itemize}
\item \texttt{formula}:a list of the main equation and instrumental variable
equation. The first object in the list \texttt{mu} corresponds to the
regression model needs to be estimated. The second list object \texttt{inst}
specifies the regression model for the instrumental variable \texttt{Z}.
For example:
\begin{verbatim}
> fml <- list ("mu" = Y ~ X + Z,
"inst" = Z ~ W + X)
\end{verbatim}
\begin{itemize}
\item \texttt{Y}: the dependent variable of interest.
\item \texttt{Z}: the instrumental variable.
\item \texttt{W}: exogenous instrument variables.
\end{itemize}
\end{itemize}
\subsubsection{Additional Inputs}
\texttt{2sls} takes the following additional inputs for model
specifications:
\begin{itemize}
\item \texttt{TX}: an optional matrix to transform the regressor
matrix and, hence, also the coefficient vector (see \ref{details}). Default is \texttt{NULL}.
\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
matrix (see \ref{details}). Default is equal to 1.
\item \texttt{probdfsys}: use the degrees of freedom of the whole system
(in place of the degrees of freedom of the single equation to calculate probability
values for the t-test of individual parameters.
\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
equation to calculate the covariance matrix and the standard errors of the coefficients.
\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
not crucial for the basic estimate (e.g McElroy's $R^2$).
\end{itemize}
\subsubsection{Details}
\label{details}
\begin{itemize}
\item \texttt{TX}: The matrix \texttt{TX} transforms the regressor matrix
($X$) by $X\ast=X \times TX$. Thus,
the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the
original(stacked)
vector of all coefficients and $b\ast$ is the new coefficient vector
that is estimated instead.
Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
less columns than it has rows).
\item \texttt{rcovformula}: The formula to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
of the following (see Judge et al., 1955, p.469):
if \texttt{rcovformula}= 0:
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
\end{eqnarray*}
if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
\end{eqnarray*}
if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
\end{eqnarray*}
if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
\end{eqnarray*}
If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
\end{itemize}
\subsubsection{Examples}
\subsubsection{Model}
Let's consider the following regression model,
\begin{eqnarray*}
Y_i=X_i\beta + Z_i\gamma + \epsilon_i, \quad i=1,\ldots,N
\end{eqnarray*}
where $Y_i$ is the dependent variable,
$X_i = (X_{1i},\ldots, X_{Ni})$ is the vector of explanatory variables,
$\beta$ is the vector of coefficients of the explanatory variables $X_i$,
$Z_i$
is the problematic explanatory variable, and $\gamma$ is the coefficient
of $Z_i$. In the equation, there is a direct dependence of $Z_i$
on the structural disturbances of $\epsilon$.
\begin{itemize}
\item The \emph{stochastic component} is given by
\begin{eqnarray*}
\epsilon_i & \sim & {\cal N}(0, \sigma^2), \quad {\rm and} \quad
{\rm cov}(Z_i, \epsilon_i) \ne 0,
\end{eqnarray*}
\item The \emph{systematic component} is given by:
\begin{eqnarray*}
\mu_{i}= E(Y_i)= X_{i}\beta + Z_i\gamma,
\end{eqnarray*}
\end{itemize}
\noindent To correct the problem caused by the correlation of $Z_i$ and $\epsilon$,
two stage least squares utilizes two steps:
\begin{itemize}
\item \emph{Stage 1}: A new instrumental variable $\hat{Z}$ is created
for $Z_i$ which is the ordinary least squares predictions from regressing
$Z_i$ on a set of exogenous instruments $W$ and $X$.
\begin{eqnarray*}
\widehat{Z_i} = \widetilde{W}_i[(\widetilde{W}^\top\widetilde{W})^{-1}\widetilde{W}^\top Z]
\end{eqnarray*}
where $\widetilde{W} = (W,X)$
\item \emph{Stage 2}: Substitute for $\hat{Z}_i$ for $Z_i$ in the original
equation, estimate $\beta$ and $\gamma$ by ordinary least squares regression
of $Y$ on $X$ and $\hat{Z}$ as in the following equation.
\begin{eqnarray*}
Y_i=X_i\beta + \widehat{Z_i}\gamma + \epsilon_i, \quad {\rm for}
\quad i=1,\ldots,N
\end{eqnarray*}
\end{itemize}
\subsubsection{See Also}
For information about three stage least square regression, see
\Sref{3sls} and \texttt{help(3sls)}.
For information about seemingly unrelated regression, see
\Sref{sur} and \texttt{help(sur)}.
\subsubsection{Quantities of Interest}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you may
view. For example, if you run:
\begin{verbatim}
z.out <- zelig(formula=fml, model = "2sls", data)
\end{verbatim}
\noindent then you may examine the available information in \texttt{z.out} by
using \texttt{names(z.out)}, see the draws from the posterior distribution of
the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
summary of information through \texttt{summary(z.out)}. Other elements
available through the \texttt{\$} operator are listed below:
\begin{itemize}
\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
\item \texttt{single.eq.sigma}: different $\sigma^2$s for each single equation?.
\end{itemize}
\input{systemfit_output}
\begin{itemize}
\item \texttt{inst*}: instruments of the ith equation.
\item \texttt{h*}: matrix of instrumental variables of the ith equation.
\end{itemize}
\subsubsection{Contributors}
The \texttt{2sls} function is adapted from \input{contributors_systemfit}
\end{document}
\ No newline at end of file
diff --git a/R/systemfit/3sls.tex b/R/systemfit/3sls.tex
deleted file mode 100644
index bb266b3..0000000
--- a/R/systemfit/3sls.tex
+++ /dev/null
@@ -1 +0,0 @@
-\documentclass[12pt]{book}%
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{graphicx}
\usepackage{fullpage}
\usepackage{multirow}
\usepackage{hyperref}%
\usepackage{bibentry}
\begin{document}
\subsection{\texttt{3sls}: Three Stage Least Squares}
\label{3sls}
\texttt{3sls} is a combination of two stage least squares
and seemingly unrelated regression. It provides consistent estimates for linear regression models with
explanatory variables correlated with the error term. It also extends ordinary least squares
analysis to estimate system of linear equations with correlated error terms
\subsubsection{Syntax}
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ X1 + Z1,
"mu2" = Y2 ~ X2 + Z2,
"inst1" = Z1 ~ W1 + X1,
"inst2" = Z2 ~ W2 + X2)
> z.out <- zelig(formula = fml, model = "3sls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
\subsubsection{Inputs}
\texttt{3sls} regression specification requires at least two sets of equations. The first set of $M$ euqations
corresponds to the $M$ dependent variables ($Y_1,\ldots,Y_M$) to be estimated. The second set of equations ($Z$)
corresponds to the instrumental variables in the $M$ equations.
\begin{itemize}
\item \texttt{formula}:a list of the system of equations and instrumental variable
equations. The system of equations is listed first as \texttt{mu}s. The equations
for the instrumental variables are listed next as \texttt{inst}s.
For example:
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ X1 + Z1,
"mu2" = Y2 ~ X2 + Z2,
"inst1" = Z1 ~ W1 + X1,
"inst2" = Z2 ~ W2 + X2)
\end{verbatim}
\texttt{"mu1"} is the first equation in the two equation model with \texttt{Y1}
as the dependent variable and \texttt{X1} and \texttt{Z1} as
the explanatory variables. \texttt{"mu2"} is the second equation with
\texttt{Y2} as the dependent variable
and \texttt{X2} and \texttt{Z2} as the explanatory variables.
\texttt{Z1} and \texttt{Z2} are also problematic endogenous variables, so
they are estimated through instruments in the \texttt{"inst1"}
and \texttt{"inst2"} equations.
\item \texttt{Y}: dependent variables of interest in the system of equations.
\item \texttt{Z}: the problematic explanatory variables correlated with
the error term.
\item \texttt{W}: exogenous instrument variables used to estimate the
problematic explanatory variables (\texttt{Z})
\end{itemize}
\subsubsection{Additional Inputs}
\texttt{3sls} takes the following additional inputs for model
specifications:
\begin{itemize}
\item \texttt{TX}: an optional matrix to transform the regressor
matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
\item \texttt{maxiter}: maximum number of iterations.
\item \texttt{tol}: tolerance level indicating when to stop the iteration.
\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
matrix (see details). Default is equal to 1.
\item \texttt{formula3sls}: formula for calculating the 3sls estimator, one of ``GLS'',
``IV'', ``GMM'', ``Schmidt'', or ``Eviews'' (see details.)
\item \texttt{probdfsys}: use the degrees of freedom of the whole system
(in place of the degrees of freedom of the single equation to calculate probability
values for the t-test of individual parameters.
\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
equation to calculate the covariance matrix and the standard errors of the coefficients.
\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
not crucial for the basic estimate (e.g McElroy's $R^2$).
\end{itemize}
\subsubsection{Details}
The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked)
vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
less columns than it has rows).
If iterated (with \texttt{maxit}>1), the covergence criterion is
\begin{eqnarray*}
\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}} < tol
\end{eqnarray*}
where $b_{i,g}$ is the ith coefficient of the gth iteration step.
The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
of the following (see Judge et al., 1955, p.469):
if \texttt{rcovformula}= 0:
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
\end{eqnarray*}
if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
\end{eqnarray*}
if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
\end{eqnarray*}
if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
\end{eqnarray*}
If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
(Theil, 1971, p.322).
The formulas to calculate the 3sls estimator lead to identical results
if the same instruments are used in all equations. If different instruments
are used in the different equations, only the GMM-3sls estimator (``GMM'')
and the 3sls estimator proposed by Schmidt (1990) (``Schmidt'') are consistent, whereas
``GMM'' is efficient relative to ``Schmidt'' (see Schmidt, 1990).
\subsubsection{Examples}
\subsubsection{Model}
\subsubsection{See Also}
For information about two stage least square regression, see
\Sref{2sls} and \texttt{help(2sls)}.
For information about seemingly unrelated regression, see
\Sref{sur} and \texttt{help(sur)}.
\subsubsection{Quantities of Interest}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you may
view. For example, if you run:
\begin{verbatim}
z.out <- zelig(formula=fml, model = "3sls", data)
\end{verbatim}
\noindent then you may examine the available information in \texttt{z.out} by
using \texttt{names(z.out)}, see the draws from the posterior distribution of
the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
summary of information through \texttt{summary(z.out)}. Other elements
available through the \texttt{\$} operator are listed below:
\begin{itemize}
\item \texttt{rcovest}: residual covariance matrix used for estimation.
\item \texttt{mcelr2}: McElroys R-squared value for the system.
\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
\item \texttt{formula3sls}: formula for calculating the 3sls estimator
\end{itemize}
\input{systemfit_output}
\begin{itemize}
\item \texttt{inst*}: instruments of the ith equation.
\item \texttt{h*}: matrix of instrumental variables of the ith equation.
\end{itemize}
\subsubsection{Contributors}
The \texttt{3sls} function is adapted from \input{contributors_systemfit}
\end{document}
\ No newline at end of file
diff --git a/R/systemfit/callsystemfit.R b/R/systemfit/callsystemfit.R
deleted file mode 100644
index 9697313..0000000
--- a/R/systemfit/callsystemfit.R
+++ /dev/null
@@ -1,7 +0,0 @@
-callsystemfit<-function(formula,data,method,inst=NULL,...){
- t<-terms.multiple(formula)
- out<-systemfit(data=data,eqns=formula,method=method,inst=inst,...)
- attr(out,"terms")<-t
- class(out)<-c(class(out),"multiple")
- return (out)
-}
diff --git a/R/systemfit/cmsystemfit.R b/R/systemfit/cmsystemfit.R
deleted file mode 100644
index 09b5de3..0000000
--- a/R/systemfit/cmsystemfit.R
+++ /dev/null
@@ -1,19 +0,0 @@
-cmsystemfit<-function(formu,omit=NULL,...){
- tr<-terms.multiple(formu,omit)
- ev<-attr(tr,"term.labels")
- dv<-all.vars(attr(tr,"variables")[[2]],unique=FALSE)
- om<-attr(tr,"omit")
- syst<-res<-list()
- for(i in 1:length(dv)){
- syst[[i]]<- ev[om[i,]==0]
- res[[i]]<-paste(dv[i],"~")
- for(j in 1:(length(syst[[i]])-1)){
- res[[i]]<-paste(res[[i]],syst[[i]][j])
- res[[i]]<-paste(res[[i]],"+")
- }
- res[[i]]<-paste(res[[i]],syst[[i]][length(syst[[i]])])
- res[[i]]<-as.formula(res[[i]])
-
- }
- return (res)
-}
diff --git a/R/systemfit/describe.2sls.R b/R/systemfit/describe.2sls.R
deleted file mode 100644
index 07c491f..0000000
--- a/R/systemfit/describe.2sls.R
+++ /dev/null
@@ -1,18 +0,0 @@
-describe.2sls<-function(){
-category <- "continuous"
-description <- "Two Stage Least Squares"
-package <-list( name ="systemfit",
- version ="0.8",
- )
-parameters<-list(mu="mu", inst="inst")
-parameters$mu<-list(equations=c(2,Inf),
- tagsAllowed=TRUE,
- depVar=TRUE,
- expVar=TRUE)
-
-parameters$inst<-list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=FALSE,
- expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
-}
diff --git a/R/systemfit/describe.3sls.R b/R/systemfit/describe.3sls.R
deleted file mode 100644
index 7809b12..0000000
--- a/R/systemfit/describe.3sls.R
+++ /dev/null
@@ -1,18 +0,0 @@
-describe.3sls<-function(){
-category <- "continuous"
-description <- "Three Stage Least Squares"
-package <-list( name ="systemfit",
- version ="0.8",
- )
-parameters<-list(mu="mu", inst="inst")
-parameters$mu<-list(equations=c(2,Inf),
- tagsAllowed=TRUE,
- depVar=TRUE,
- expVar=TRUE)
-
-parameters$inst<-list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=FALSE,
- expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
-}
diff --git a/R/systemfit/param.multiple.R b/R/systemfit/param.multiple.R
deleted file mode 100644
index 6b26b0a..0000000
--- a/R/systemfit/param.multiple.R
+++ /dev/null
@@ -1,19 +0,0 @@
-param.multiple <- function(object, num = NULL, bootstrap = FALSE) {
- if (!bootstrap) {
- coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- if (object$zelig %in% c("sur","2sls","w2sls","3sls")) {
- res <- coef
- }
- else
- res <- coef
- }
- else {
- coef <- coef(object)
- res <- coef
- }
- res
-}
-
-
-
-
diff --git a/R/systemfit/plot.zelig.2sls.R b/R/systemfit/plot.zelig.2sls.R
deleted file mode 100644
index 973ba9a..0000000
--- a/R/systemfit/plot.zelig.2sls.R
+++ /dev/null
@@ -1,14 +0,0 @@
-plot.zelig.2sls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/systemfit/plot.zelig.3sls.R b/R/systemfit/plot.zelig.3sls.R
deleted file mode 100644
index 8507059..0000000
--- a/R/systemfit/plot.zelig.3sls.R
+++ /dev/null
@@ -1,14 +0,0 @@
-plot.zelig.3sls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/systemfit/plot.zelig.sur.R b/R/systemfit/plot.zelig.sur.R
deleted file mode 100644
index 691e8e1..0000000
--- a/R/systemfit/plot.zelig.sur.R
+++ /dev/null
@@ -1,14 +0,0 @@
-plot.zelig.sur <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/systemfit/plot.zelig.w2sls.R b/R/systemfit/plot.zelig.w2sls.R
deleted file mode 100644
index 925a660..0000000
--- a/R/systemfit/plot.zelig.w2sls.R
+++ /dev/null
@@ -1,14 +0,0 @@
-plot.zelig.w2sls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/systemfit/qi.multiple.R b/R/systemfit/qi.multiple.R
deleted file mode 100644
index 7f69581..0000000
--- a/R/systemfit/qi.multiple.R
+++ /dev/null
@@ -1,60 +0,0 @@
-qi.multiple <- function(object, simpar, x, x1 = NULL, y = NULL) {
-
- check <- FALSE
- model <- object$zelig
- coef<-list()
- om<-attr(object$terms,"omit")
- nreq<-nrow(om)
-
- start<-1
- for(i in 1:nreq){
- eqni<-paste("eqn",i,sep="")
- coef[[i]]<-simpar[,start:(start+length(attr(x,eqni))-1)]
- start<-start+length(attr(x,eqni))
- }
-
- fillmatrix<-function(simpar,x,nreq){
- r<-list()
- eta<-list()
- if(nrow(x)==1)
- q<-array(NA,c(nrow(simpar),nreq))
- else
- q<- array(NA,c(nrow(simpar),nreq,nrow(x)))
-
- for(i in 1:nreq){
- eqn=paste("eqn",i,sep="")
- r[[i]]= as.matrix(x[,attr(x,eqn)])
- if(ncol(r[[i]])==1){
- eta[[i]] <- coef[[i]] %*% r[[i]]
- q[,i] <- eta[[i]]
- }
- else
- {
- eta[[i]] <- coef[[i]] %*% t(r[[i]])
- q[,i,] <- eta[[i]]
- }
- }
- return (q)
-
- }
- pr<-ev<-fillmatrix(simpar,x,nreq)
-
- qi <- list(ev = ev,pr=pr)
- qi.name <- list(ev = "Expected Values: E(Y|X)",pr = "Predicted Values: Y|X")
-
-
- if (!is.null(x1)){
- theta1<-fillmatrix(simpar,x1,nreq)
- ev1 <- theta1
- qi$fd <- ev1-ev
- qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
-
- }
- list(qi=qi, qi.name=qi.name)
-}
-
-
-
-
-
-
diff --git a/R/systemfit/sur.tex b/R/systemfit/sur.tex
deleted file mode 100644
index acf9d4b..0000000
--- a/R/systemfit/sur.tex
+++ /dev/null
@@ -1 +0,0 @@
-\documentclass[12pt]{book}%
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{graphicx}
\usepackage{fullpage}
\usepackage{multirow}
\usepackage{hyperref}%
\usepackage{bibentry}
\begin{document}
\subsection{\texttt{sur}: Seemingly Unrelated Regression}
\label{sur}
\texttt{sur} extends ordinary least squares analysis to estimate system of linear
equations with correlated error terms. The seemingly unrelated regression model can
be viewed as a special case of generalized least squares.
\subsubsection{Syntax}
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ X1,
"mu2" = Y2 ~ X2,
"mu3" = Y3 ~ X3)
> z.out<-zelig(formula = fml, model = "2sls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
\subsubsection{Inputs}
\texttt{sur} regression specification has at least $M$ equations
($M \ge 2$) corresponding to the dependent variables ($Y_1, Y_2, \ldots, Y_M$).
\begin{itemize}
\item \texttt{formula}:a list whose elements are formulae corresponding to
the $M$ equations and their respective dependent and explanatory variables.
For example, when there are no constraints on the coefficients:
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ X1,
"mu2" = Y2 ~ X2,
"mu3" = Y3 ~ X3)
\end{verbatim}
\texttt{"mu1"} is the label for the first equation with Y1 as the dependent variable
and X1 as the explanatory variable. Similarly \texttt{"mu2"} and \texttt{"mu3"} are the
labels for the Y2 and Y3 equations.
\item \texttt{tag}: Users can also put constraints on the coefficients by using
the special function \texttt{tag}. \texttt{tag} takes two parameters. The first
parameter is the variable whose coefficient needs to be constrained and the second
parameter is label for the constrained coefficient. Each label uniquely identifies
the constrained coefficient. For example:
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
"mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
"mu3" = Y3 ~ X3)
\end{verbatim}
\end{itemize}
\subsubsection{Additional Inputs}
\texttt{sur} takes the following additional inputs for model
specifications:
\begin{itemize}
\item \texttt{TX}: an optional matrix to transform the regressor
matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
\item \texttt{maxiter}: maximum number of iterations.
\item \texttt{tol}: tolerance level indicating when to stop the iteration.
\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
matrix (see details). Default is equal to 1.
\item \texttt{probdfsys}: use the degrees of freedom of the whole system
(in place of the degrees of freedom of the single equation to calculate probability
values for the t-test of individual parameters.
\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
inverting a matrix or calculating a determinant. Default is \texttt {solvetol}= \begin{verbatim}.Machine\$double.eps.\end{verbatim}
\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
not crucial for the basic estimate (e.g McElroy's $R^2$).
\end{itemize}
\subsubsection{Details}
The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked)
vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
less columns than it has rows).
If iterated (with \texttt{maxit}>1), the covergence criterion is
\begin{eqnarray*}
\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}}< tol
\end{eqnarray*}
where $b_{i,g}$ is the ith coefficient of the gth iteration step.
The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
of the following (see Judge et al., 1955, p.469):
if \texttt{rcovformula}= 0:
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
\end{eqnarray*}
if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
\end{eqnarray*}
if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
\end{eqnarray*}
if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
\begin{eqnarray*}
\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
\end{eqnarray*}
If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
(Theil, 1971, p.322).
\subsubsection{Examples}
\subsubsection{Model}
The basic seemingly unrelated regression model assumes that for each individual
observation $i$ there are $M$ dependent variables ($Y_{ij}, j=1,\ldots,M$)
each with its own regression equation:
\begin{eqnarray*}
Y_{ij} = X_{ij}'\beta_j + \epsilon_{ij}, \quad {\rm for} \quad i=1,\ldots,N \quad {\rm and} \quad j=1,\ldots,M
\end{eqnarray*}
when $X_{ij}$ is a k-vector of explanatory variables, $\beta_j$
is the coefficients of the explanatory variables,
\begin{itemize}
\item The \emph{stochastic component} is:
\begin{eqnarray*}
\epsilon_{ij} & \sim & {\cal N}(0, \sigma_{ij})
\end{eqnarray*}
where within each $j$ equation, $epsilon_{ij}$ is identically
and independently distributed for $i=1,\ldots,M$,
\begin{eqnarray*}
{\rm Var}(\epsilon_{ij})=\sigma_j \quad {\rm and} \quad {\rm Cov}(\epsilon_{ij}, \epsilon_{i\prime j})= 0, \quad {\rm for} \quad i \neq i\prime,\quad {\rm and} \quad j=1,\ldots,M
\end{eqnarray*}
However, the error terms for the \emph{ith} observation can be correlated across equations
\begin{eqnarray*}
{\rm Cov}(\epsilon_{ij}, \epsilon_{ij\prime})\neq 0, \quad {\rm for} \quad j \neg j\prime, \quad {\rm and} \quad i=1,\ldots,N
\end{eqnarray*}
\item The \emph{systematic component} is:
\begin{eqnarray*}
\mu_{ij}= E(Y_ij)= X_{ij}\beta_j, \quad {\rm for} \quad i=1,\ldots,N, \quad {\rm and} \quad j=1,\ldots,M
\end{eqnarray*}
\end{itemize}
\subsubsection{See Also}
For information about two stage least squares regression, see
\Sref{2sls} and \texttt{help(2sls)}.
For information about three stage least squares regression, see
\Sref{3sls} and \texttt{help(3sls)}.
\subsubsection{Quantities of Interest}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you may
view. For example, if you run:
\begin{verbatim}
z.out <- zelig(formula=fml, model = "sur", data)
\end{verbatim}
\noindent then you may examine the available information in \texttt{z.out} by
using \texttt{names(z.out)}, see the draws from the posterior distribution of
the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
summary of information through \texttt{summary(z.out)}. Other elements
available through the \texttt{\$} operator are listed below:
\begin{itemize}
\item \texttt{rcovest}: residual covariance matrix used for estimation.
\item \texttt{mcelr2}: McElroys R-squared value for the system.
\end{itemize}
\input{systemfit_output}
\begin{itemize}
\item \texttt{maxiter}: maximum number of iterations.
\item \texttt{tol}: tolerance level indicating when to stop the iteration.
\end{itemize}
\subsubsection{Contributors}
The \texttt{sur} function is adapted from \input{contributors_systemfit}
\end{document}
\ No newline at end of file
diff --git a/R/systemfit/zelig22sls.R b/R/systemfit/zelig22sls.R
deleted file mode 100644
index 0295315..0000000
--- a/R/systemfit/zelig22sls.R
+++ /dev/null
@@ -1,25 +0,0 @@
-zelig22sls <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- "%w/o%" <- function(x,y) x[!x %in% y]
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- formula<-parse.formula(formula,model)
- tt<-terms(formula)
- ins<-names(tt) %w/o% names(attr(tt,"depVars"))
- if(length(ins)!=0)
- if(length(ins)==1)
- inst<-formula[[ins]]
- else inst<-formula[ins]
- else
- stop("2sls model requires instrument!!\n")
- mf$method<-"2SLS"
- mf$inst<-inst
- mf$model<- mf$M<-NULL
- mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
- as.call(mf)
-}
diff --git a/R/systemfit/zelig23sls.R b/R/systemfit/zelig23sls.R
deleted file mode 100644
index 8fb3d8c..0000000
--- a/R/systemfit/zelig23sls.R
+++ /dev/null
@@ -1,25 +0,0 @@
-zelig23sls <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- "%w/o%" <- function(x,y) x[!x %in% y]
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- formula<-parse.formula(formula,model)
- tt<-terms(formula)
- ins<-names(tt) %w/o% names(attr(tt,"depVars"))
- if(length(ins)!=0)
- if(length(ins)==1)
- inst<-formula[[ins]]
- else inst<-formula[ins]
- else
- stop("2sls model requires instrument!!\n")
- mf$method<-"3SLS"
- mf$inst<-inst
- mf$model<- mf$M<-NULL
- mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
- as.call(mf)
-}
diff --git a/R/systemfit/zelig2sur.R b/R/systemfit/zelig2sur.R
deleted file mode 100644
index 8210245..0000000
--- a/R/systemfit/zelig2sur.R
+++ /dev/null
@@ -1,16 +0,0 @@
-zelig2sur <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- formula<-parse.formula(formula,model)
- print(formula)
- tt<-terms(formula)
- mf$method<-"SUR"
- mf$model<- mf$M<-NULL
- mf$formula<-formula
- as.call(mf)
-}
diff --git a/R/systemfit/zelig2w2sls.R b/R/systemfit/zelig2w2sls.R
deleted file mode 100644
index 438f507..0000000
--- a/R/systemfit/zelig2w2sls.R
+++ /dev/null
@@ -1,15 +0,0 @@
-zelig2w2sls <- function(formula, model, data, M,
- omit = NULL, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- tmp <- cmsystemfit(formula, omit)
- mf$eqns <- tmp
- mf$method<-"3SLS"
- mf$model<- mf$M<-NULL
- as.call(mf)
-}
diff --git a/R/vignettesMenu.R b/R/vignettesMenu.R
new file mode 100644
index 0000000..067bc89
--- /dev/null
+++ b/R/vignettesMenu.R
@@ -0,0 +1,28 @@
+## adds a vignette menu for zelig packages (hacked from Seth's code )
+
+addVigs2WinMenu <- function(pkgName) {
+ if ((.Platform$OS.type == "windows") && (.Platform$GUI == "Rgui")
+ && interactive()) {
+ vigFile <- system.file("Meta", "vignette.rds", package=pkgName)
+ if (!file.exists(vigFile)) {
+ warning(sprintf("%s contains no vignette, nothing is added to the menu bar", pkgName))
+ } else {
+ vigMtrx <- .readRDS(vigFile)
+ vigs <- file.path(.find.package(pkgName), "doc", vigMtrx[,"PDF"])
+ names(vigs) <- vigMtrx[,"Title"]
+
+ if (!"Vignettes" %in% winMenuNames())
+ winMenuAdd("Vignettes")
+ pkgMenu <- paste("Vignettes", pkgName, sep="/")
+ winMenuAdd(pkgMenu)
+ for (i in vigs) {
+ item <- sub(".pdf", "", basename(i))
+ winMenuAddItem(pkgMenu, item, paste("shell.exec(\"", as.character(i), "\")", sep = ""))
+ }
+ } ## else
+ ans <- TRUE
+ } else {
+ ans <- FALSE
+ }
+ ans
+}
diff --git a/R/zvcClient.R b/R/zvcClient.R
index 960341f..224fa6d 100644
--- a/R/zvcClient.R
+++ b/R/zvcClient.R
@@ -733,7 +733,7 @@ check.advance.versions <- function(mat){
### evillalon at iq.harvard.edu
###
-zelig.packages.update <- function(destdir=NULL,installWithVers=FALSE,lib.loc=NULL, repos="http://cran.r-project.org")
+zeligDepUpdate <- function(destdir=NULL,installWithVers=FALSE,lib.loc=NULL, repos="http://cran.r-project.org")
{
####Input arguments################
@@ -1005,7 +1005,7 @@ matrixDependencies <- function(file=system.file("zideal", "zideal.RData", packag
### Elena Villalon
### evillalon at iq.harvard.edu
###
-zelig.packages.status <- function(lib.loc=NULL)
+zeligDepStatus <- function(lib.loc=NULL)
{
####Hiden input arguments################
### zmat is the matrix of dependencies stored in system files
diff --git a/cleanup b/cleanup
new file mode 100755
index 0000000..a3dc9d1
--- /dev/null
+++ b/cleanup
@@ -0,0 +1,18 @@
+#/bin/sh
+
+#
+# when R CMD build, this will create zideal.RData
+#
+
+# I think cleanup is ALSO run during R CMD INSTALL. We do not want this
+# to run on the user computer.
+# so check if host is part of hmdc.harvard.edu
+#
+
+H=`hostname`
+ if echo $H | grep "w[0-9].hmdc.harvard.edu" > /dev/null
+then
+ R --vanilla --slave < inst/zideal/zvcServer.R
+fi
+
+
diff --git a/inst/doc/Makefile b/inst/doc/Makefile
index b9d129e..85d8b2d 100644
--- a/inst/doc/Makefile
+++ b/inst/doc/Makefile
@@ -1,16 +1,18 @@
-
-# stub makefile, does nothing, but causes R CMD build not to force rebuild pdf file
+##
+## Zelig doc Makefile
+##
+## If you invoke make from command line, run ./runSweave.sh first
+##
+##
+## make command will be issued in two possible ways:
+## 1- from command line
+## 2- during R CMD build
+## -- R CMD build first run Sweave on all the vignettes in inst/doc folder and
+## then if a Makefile is found, run that Makefile
+##
-REVAL=TRUE
+all: zelig.pdf
-all: Zelig/inst/doc/zelig.pdf
-%.tex: %.Rnw
- echo "Sweave("$<", debug=TRUE, eval=$(REVAL))" | R --slave
-
-%.pdf: %.tex
- pdflatex $*
- bibtex $*
- pdflatex $*
- pdflatex $*
- pdflatex $*
+%.pdf:
+ /bin/sh ./forMake.sh
diff --git a/inst/doc/Rd.sty b/inst/doc/Rd.sty
new file mode 100644
index 0000000..99e97cc
--- /dev/null
+++ b/inst/doc/Rd.sty
@@ -0,0 +1,363 @@
+%%% Rd.sty ... Style for printing the R manual
+%%%
+%%% Modified 1998/01/05 by Friedrich.Leisch at ci.tuwien.ac.at
+%%% Modified 1998/07/07 by Martin Maechler
+%%% Modified 1999/11/20 by Brian Ripley
+%%% Modified 1999/12/26 by Kurt Hornik
+%%% and so on.
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{Rd}{}
+
+\RequirePackage{ifthen}
+\newboolean{Rd at has@ae}
+\newboolean{Rd at use@ae}
+\newboolean{Rd at use@hyper}
+\newboolean{Rd at has@times}
+\newboolean{Rd at use@times}
+\newboolean{Rd at use@cm-super}
+\newboolean{Rd at has@lm}
+\newboolean{Rd at use@lm}
+\DeclareOption{ae}{\setboolean{Rd at use@ae}{true}}
+\DeclareOption{hyper}{\setboolean{Rd at use@hyper}{true}}
+\DeclareOption{times}{\setboolean{Rd at use@times}{true}}
+\DeclareOption{lm}{\setboolean{Rd at use@lm}{true}}
+\DeclareOption{cm-super}{\setboolean{Rd at use@cm-super}{true}}
+\ProcessOptions
+\RequirePackage{longtable}
+\setcounter{LTchunksize}{250}
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\IfFileExists{hyperref.sty}{}{\setboolean{Rd at use@hyper}{false}
+ \message{package hyperref not found}}}
+{}
+
+\RequirePackage{bm} % standard boldsymbol
+\RequirePackage{alltt} % {verbatim} allowing \..
+\RequirePackage{verbatim} % small example code
+\RequirePackage{url} % set urls
+
+%% See 'upquote.sty' for details.
+%% We use \pkg{verbatim} for our ExampleCode environment, which in its
+%% \verbatim at font has an explicit \let\do\do at noligs\verbatim at nolig@list
+%% rather than (the identical) \@noligs from the LaTeX2e kernel.
+%% Hence, we add to \verbatim at font ... suggestion by Bernd Raichle
+%% <raichle at Informatik.Uni-Stuttgart.DE>.
+\RequirePackage{upquote}
+\g at addto@macro\verbatim at font\@noligs
+
+\addtolength{\textheight}{12mm}
+\addtolength{\topmargin}{-9mm} % still fits on US paper
+\addtolength{\textwidth}{24mm} % still fits on US paper
+\setlength{\oddsidemargin}{10mm}
+\setlength{\evensidemargin}{\oddsidemargin}
+
+\newenvironment{display}[0]%
+ {\begin{list}{}{\setlength{\leftmargin}{30pt}}\item}%
+ {\end{list}}
+\newcommand{\HTML}{{\normalfont\textsc{html}}}
+\newcommand{\R}{{\normalfont\textsf{R}}{}}
+\newcommand{\Rdash}{-}
+
+\def\href#1#2{\special{html:<a href="#1">}{#2}\special{html:</a>}}
+
+\newcommand{\vneed}[1]{%
+ \penalty-1000\vskip#1 plus 10pt minus #1\penalty-1000\vspace{-#1}}
+
+\newcommand{\Rdcontents}[1]{% modified \tableofcontents -- not \chapter
+\section*{{#1}\@mkboth{\MakeUppercase#1}{\MakeUppercase#1}}
+ \@starttoc{toc}}
+
+\newcommand{\Header}[2]{%
+ \vneed{1ex}
+ \markboth{#1}{#1}
+ \noindent
+ \nopagebreak
+ \begin{center}
+ \ifthenelse{\boolean{Rd at use@hyper}}%
+ {\def\@currentHref{page.\thepage}
+ \hypertarget{Rfn.#1}{\index{#1@\texttt{#1}}}%
+ \myaddcontentsline{toc}{subsection}{#1}%
+ \pdfbookmark[1]{#1}{Rfn.#1}}
+ {\addcontentsline{toc}{subsection}{#1}
+ \index{#1@\texttt{#1}|textbf}}
+ \hrule
+ \parbox{0.95\textwidth}{%
+ \begin{ldescription}[1.5in]
+ \item[\texttt{#1}] \emph{#2}
+ \end{ldescription}}
+ \hrule
+ \end{center}
+ \nopagebreak}
+%
+%
+%
+% \alias{<alias>}{<header>}
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\alias}[2]{\hypertarget{Rfn.#1}{\index{#1@\texttt{#1} \textit{(\texttt{#2})}}}}}
+{\newcommand{\alias}[2]{\index{#1@\texttt{#1} \textit{(\texttt{#2})}}}}
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\methalias}[2]{\hypertarget{Rfn.#1}{\relax}}}
+{\newcommand{\methalias}[2]{}}
+% \keyword{<topic>}{<header>}
+\newcommand{\keyword}[2]{\index{$*$Topic{\large\ \textbf{#1}}!#2@\texttt{#2}}}
+%
+\newcommand{\Itemize}[1]{\begin{itemize}{#1}\end{itemize}}
+\newcommand{\Enumerate}[1]{\begin{enumerate}{#1}\end{enumerate}}
+\newcommand{\describe}[1]{\begin{description}{#1}\end{description}}
+
+\newcommand{\Tabular}[2]{%
+ \par\begin{longtable}{#1}
+ #2
+ \end{longtable}}
+
+\newlength{\ldescriptionwidth}
+\newcommand{\ldescriptionlabel}[1]{%
+ \settowidth{\ldescriptionwidth}{{#1}}%
+ \ifdim\ldescriptionwidth>\labelwidth
+ {\parbox[b]{\labelwidth}%
+ {\makebox[0pt][l]{#1}\\[1pt]\makebox{}}}%
+ \else
+ \makebox[\labelwidth][l]{{#1}}%
+ \fi
+ \hfil\relax}
+\newenvironment{ldescription}[1][1in]%
+ {\begin{list}{}%
+ {\setlength{\labelwidth}{#1}%
+ \setlength{\leftmargin}{\labelwidth}%
+ \addtolength{\leftmargin}{\labelsep}%
+ \renewcommand{\makelabel}{\ldescriptionlabel}}}%
+ {\end{list}}
+
+\newenvironment{Rdsection}[1]{%
+ \ifx\@empty#1\else\subsubsection*{#1}\fi
+ \begin{list}{}{\setlength{\leftmargin}{0.25in}}\item}
+ {\end{list}}
+
+\newenvironment{Arguments}{%
+ \begin{Rdsection}{Arguments}}{\end{Rdsection}}
+\newenvironment{Author}{%
+ \begin{Rdsection}{Author(s)}}{\end{Rdsection}}
+\newenvironment{Description}{%
+ \begin{Rdsection}{Description}}{\end{Rdsection}}
+\newenvironment{Details}{%
+ \begin{Rdsection}{Details}}{\end{Rdsection}}
+\newenvironment{Examples}{%
+ \begin{Rdsection}{Examples}}{\end{Rdsection}}
+\newenvironment{Note}{%
+ \begin{Rdsection}{Note}}{\end{Rdsection}}
+\newenvironment{References}{%
+ \begin{Rdsection}{References}}{\end{Rdsection}}
+\newenvironment{SeeAlso}{%
+ \begin{Rdsection}{See Also}}{\end{Rdsection}}
+\newenvironment{Format}{%
+ \begin{Rdsection}{Format}}{\end{Rdsection}}
+\newenvironment{Source}{%
+ \begin{Rdsection}{Source}}{\end{Rdsection}}
+\newenvironment{Section}[1]{%
+ \begin{Rdsection}{#1}}{\end{Rdsection}}
+\newenvironment{Usage}{%
+ \begin{Rdsection}{Usage}}{\end{Rdsection}}
+\newenvironment{Value}{%
+ \begin{Rdsection}{Value}}{\end{Rdsection}}
+
+\newenvironment{ExampleCode}{\small\verbatim}{\endverbatim}
+
+\ifx\textbackslash\undefined%-- e.g. for MM
+ \newcommand{\bsl}{\ifmmode\backslash\else$\backslash$\fi}
+\else
+ \newcommand{\bsl}{\ifmmode\backslash\else\textbackslash\fi}
+\fi
+%fails for index (but is not used there...)
+\newcommand{\SIs}{\relax\ifmmode\leftarrow\else$\leftarrow$\fi}
+\newcommand{\SIIs}{\relax\ifmmode<\leftarrow\else$<\leftarrow$\fi}
+\newcommand{\Sbecomes}{\relax\ifmmode\rightarrow\else$\rightarrow$\fi}
+%
+\newcommand{\deqn}[2]{\[#1\]}
+\newcommand{\eqn}[2]{$#1$}
+\newcommand{\bold}[1]{\ifmmode\bm{#1}\else\textbf{#1}\fi}
+\newcommand{\file}[1]{`\textsf{#1}'}
+
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\link}[1]{\hyperlink{Rfn.#1}{#1}\index{#1@\texttt{#1}}}}
+{\newcommand{\link}[1]{#1\index{#1@\texttt{#1}}}}
+
+\newcommand{\email}[1]{$\langle${#1}$\rangle$}
+
+%% \code without `-' ligatures
+{\catcode`\-=\active%
+ \global\def\code{\bgroup%
+ \catcode`\-=\active \let-\codedash%
+ \Rd at code}}
+\def\codedash{-\discretionary{}{}{}}
+\def\Rd at code#1{\texttt{#1}\egroup}
+
+\def\AsIs{\bgroup\let\do\@makeother\Rd at AsIs@dospecials\Rd at AsIsX}
+\def\Rd at AsIs@dospecials{\do\$\do\&\do\#\do\^\do\_\do\%\do\~}
+\def\Rd at AsIsX#1{\normalfont #1\egroup}
+\let\command=\code
+\let\env=\code
+
+\newcommand\samp{`\bgroup\@noligs\@sampx}
+\def\@sampx#1{{\normalfont\texttt{#1}}\egroup'}
+\let\option=\samp
+
+\newcommand{\var}[1]{{\normalfont\textsl{#1}}}
+
+\newcommand{\dfn}[1]{\textsl{#1}}
+\let\Cite=\dfn
+
+\newcommand{\acronym}[1]{\textsc{\lowercase{#1}}}
+\newcommand{\kbd}[1]{\texttt{\textsl{#1}}}
+
+\newcommand{\strong}[1]{{\normalfont\fontseries{b}\selectfont #1}}
+\let\pkg=\strong
+
+\newcommand{\sQuote}[1]{`#1'}
+\newcommand{\dQuote}[1]{``#1''}
+
+\IfFileExists{ae.sty}{\setboolean{Rd at has@ae}{true}}{}
+\ifthenelse{\boolean{Rd at use@ae}\and\boolean{Rd at has@ae}}{%
+ \usepackage[T1]{fontenc}
+ \usepackage{ae}
+ \input{t1aett.fd}
+ \DeclareFontShape{T1}{aett}{bx}{n}{<->ssub*aett/m/n}{}}
+ {\message{NOT loading ae}}
+\IfFileExists{times.sty}{\setboolean{Rd at has@times}{true}}{}
+\ifthenelse{\boolean{Rd at use@times}\and\boolean{Rd at has@times}}{%
+ \usepackage[T1]{fontenc}
+ \usepackage{times}}
+ {\message{NOT loading times}}
+\IfFileExists{lmodern.sty}{\setboolean{Rd at has@lm}{true}}{}
+\ifthenelse{\boolean{Rd at use@lm}\and\boolean{Rd at has@lm}}{%
+ \usepackage[T1]{fontenc}
+ \usepackage{lmodern}}
+ {\message{NOT loading lmodern}}
+\ifthenelse{\boolean{Rd at use@cm-super}}{%
+ \usepackage[T1]{fontenc}}{}
+
+\ifthenelse{\boolean{Rd at use@hyper}}{%
+ \RequirePackage{color}
+ \def\myaddcontentsline#1#2#3{%
+ \addtocontents{#1}{\protect\contentsline{#2}{#3}{\thepage}{page.\thepage}}}
+ \RequirePackage{hyperref}
+ \DeclareTextCommand{\Rpercent}{PD1}{\045} % percent
+ %% <NOTE>
+ %% Formerly in R's hyperref.cfg, possibly to be shared with Sweave.sty
+ %% as well (but without setting pagebackref as this can give trouble
+ %% for .bib entries containing URLs with '#' characters).
+ \definecolor{Blue}{rgb}{0,0,0.8}
+ \definecolor{Red}{rgb}{0.7,0,0}
+ \hypersetup{%
+ hyperindex,%
+ colorlinks,%
+ pagebackref,%
+ linktocpage,%
+ plainpages=false,%
+ linkcolor=Blue,%
+ citecolor=Blue,%
+ urlcolor=Red,%
+ pdfstartview=Fit,%
+ pdfview={XYZ null null null}%
+ }
+ %% </NOTE>
+ \renewcommand\tableofcontents{%
+ \if at twocolumn
+ \@restonecoltrue\onecolumn
+ \else
+ \@restonecolfalse
+ \fi
+ \chapter*{\contentsname
+ \@mkboth{%
+ \MakeUppercase\contentsname}{\MakeUppercase\contentsname}}%
+ \pdfbookmark{Contents}{contents}
+ \@starttoc{toc}%
+ \if at restonecol\twocolumn\fi
+ }
+ \renewenvironment{theindex}
+ {\if at twocolumn
+ \@restonecolfalse
+ \else
+ \@restonecoltrue
+ \fi
+ \columnseprule \z@
+ \columnsep 35\p@
+ \twocolumn[\@makeschapterhead{\indexname}]%
+ \@mkboth{\MakeUppercase\indexname}%
+ {\MakeUppercase\indexname}%
+ \pdfbookmark{Index}{index}
+ \myaddcontentsline{toc}{chapter}{Index}
+ \thispagestyle{plain}\parindent\z@
+ \parskip\z@ \@plus .3\p@\relax
+ \raggedright
+ \let\item\@idxitem}
+ {\if at restonecol\onecolumn\else\clearpage\fi}
+ }{
+ \renewenvironment{theindex}
+ {\if at twocolumn
+ \@restonecolfalse
+ \else
+ \@restonecoltrue
+ \fi
+ \columnseprule \z@
+ \columnsep 35\p@
+ \twocolumn[\@makeschapterhead{\indexname}]%
+ \@mkboth{\MakeUppercase\indexname}%
+ {\MakeUppercase\indexname}%
+ \addcontentsline{toc}{chapter}{Index}
+ \thispagestyle{plain}\parindent\z@
+ \parskip\z@ \@plus .3\p@\relax
+ \raggedright
+ \let\item\@idxitem}
+ {\if at restonecol\onecolumn\else\clearpage\fi}
+ }
+
+% new definitions for R >= 2.0.0
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\LinkA}[2]{\hyperlink{Rfn.#2}{#1}\index{#1@\texttt{#1}|textit}}}
+{\newcommand{\LinkA}[2]{#1\index{#1@\texttt{#1}|textit}}}
+%
+% \alias{<alias>}{<header>}
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\aliasA}[3]{\hypertarget{Rfn.#3}{\index{#1@\texttt{#1} \textit{(\texttt{#2})}}}}}
+{\newcommand{\aliasA}[3]{\index{#1@\texttt{#1} \textit{(\texttt{#2})}}}}
+\ifthenelse{\boolean{Rd at use@hyper}}
+{\newcommand{\methaliasA}[3]{\hypertarget{Rfn.#3}{\relax}}}
+{\newcommand{\methaliasA}[3]{}}
+\newcommand{\HeaderA}[3]{%
+ \vneed{1ex}
+ \markboth{#1}{#1}
+ \noindent
+ \nopagebreak
+ \begin{center}
+ \ifthenelse{\boolean{Rd at use@hyper}}%
+ {\def\@currentHref{page.\thepage}
+ \hypertarget{Rfn.#3}{\index{#1@\texttt{#1}}}%
+ \myaddcontentsline{toc}{subsection}{#1}%
+ \pdfbookmark[1]{#1}{Rfn.#3}}
+ {\addcontentsline{toc}{subsection}{#1}
+ \index{#1@\texttt{#1}|textbf}}
+ \hrule
+ \parbox{0.95\textwidth}{%
+ \begin{ldescription}[1.5in]
+ \item[\texttt{#1}] \emph{#2}
+ \end{ldescription}}
+ \hrule
+ \end{center}
+ \nopagebreak}
+\DeclareTextCommandDefault{\Rpercent}{\%{}}
+%% for use with the output of encoded_text_to_latex
+\ProvideTextCommandDefault{\textdegree}{\ensuremath{{^\circ}}}
+\ProvideTextCommandDefault{\textonehalf}{\ensuremath{\frac12}}
+\ProvideTextCommandDefault{\textonequarter}{\ensuremath{\frac14}}
+\ProvideTextCommandDefault{\textthreequarters}{\ensuremath{\frac34}}
+\ProvideTextCommandDefault{\textcent}{\TextSymbolUnavailable\textcent}
+\ProvideTextCommandDefault{\textyen}{\TextSymbolUnavailable\textyen}
+\ProvideTextCommandDefault{\textcurrency}{\TextSymbolUnavailable\textcurrency}
+\ProvideTextCommandDefault{\textbrokenbar}{\TextSymbolUnavailable\textbrokenbar}
+\ProvideTextCommandDefault{\texteuro}{\TextSymbolUnavailable\texteuro}
+\providecommand{\mathonesuperior}{\ensuremath{^1}}
+\providecommand{\mathtwosuperior}{\ensuremath{^2}}
+\providecommand{\maththreesuperior}{\ensuremath{^3}}
+
+\InputIfFileExists{Rd.cfg}{%
+ \typeout{Reading personal defaults ...}}{}
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+%%Trailer
+%%Pages: 26
+%%EOF
diff --git a/inst/doc/arima.Rnw b/inst/doc/arima.Rnw
new file mode 100644
index 0000000..9ba5180
--- /dev/null
+++ b/inst/doc/arima.Rnw
@@ -0,0 +1,329 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/arima}
+\include{zinput}
+%\VignetteIndexEntry{ARIMA Models for Time Series Data}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,time series}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{{\tt ARIMA}: ARIMA Models for Time Series Data}
+\label{ARIMA}
+
+Use auto-regressive, integrated, moving-average (ARIMA) models for
+time series data. A time series is a set of observations ordered
+according to the time they were observed. Because the value observed
+at time $t$ may depend on values observed at previous time points,
+time series data may violate independence assumptions. An ARIMA($p$,
+$d$, $q$) model can account for temporal dependence in several ways.
+First, the time series is differenced to render it stationary, by
+taking $d$ differences. Second, the time dependence of the stationary
+process is modeled by including $p$ auto-regressive and $q$
+moving-average terms, in addition to any time-varying covariates. For
+a cyclical time series, these steps can be repeated according to the
+period of the cycle, whether quarterly or monthly or another time
+interval. ARIMA models are extremely flexible for continuous data.
+Common formulations include, ARIMA(0, 0, 0) for least squares
+regression (see \Sref{ls}), ARIMA(1, 0, 0), for an AR1 model, and
+ARIMA(0, 0, 1) for an MA1 model. For a more comprehensive review of
+ARIMA models, see \cite{Enders04}.
+
+\subsubsection*{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Diff(Y, d, ds=NULL, per=NULL) ~ lag.y(p, ps=NULL)
+ + lag.eps(q, qs=NULL) + X1 + X2,
+ model="arima", data=mydata, ...)
+> x.out <- setx(z.out, X1 = list(time, value), cond = FALSE)
+> s.out <- sim(z.out, x=x.out, x1=NULL)
+\end{verbatim}
+
+\subsubsection*{Inputs}
+In addition to independent variables, {\tt zelig()} accepts the
+following arguments to specify the {\tt ARIMA} model:
+\begin{itemize}
+\item {\tt Diff(Y, d, ds, per)} for a dependent variable Y sets the
+number of non-seasonal differences ({\tt d}), the number of seasonal differences ({\tt
+ds}), and the period of the season ({\tt per}).
+\item {\tt lag.y(p, ps)} sets the number of lagged observations of the
+dependent variable for non-seasonal ({\tt p}) and seasonal ({\tt ps})
+components.
+\item {\tt lag.eps(q, qs)} sets the number of lagged innovations, or
+differences between the observed value of the time series and the
+expected value of the time series for non-seasonal ({\tt q}) and
+seasonal ({\tt qs}) components.
+\end{itemize}
+In addition the user can control the estimation of the time
+series with the following terms:
+\begin{itemize}
+\item $\hdots$: Additional inputs. See {\tt help(arima)} in the stats
+library for further information.
+\end{itemize}
+
+\subsubsection*{Stationarity}
+A stationary time series has finite variance, correlations between
+observations that are not time-dependent, and a constant expected
+value for all components of the time series \citep[p.\ 12]{BroDav91}.
+Users should ensure that the time series being analyzed is stationary
+before specifying a model. The following commands provide diagnostics
+to determine if a time series {\tt Y} is stationary.
+\begin{itemize}
+\item {\tt pp.test(Y)}: Tests the null hypothesis that the time series
+ is non-stationary.
+\item {\tt kpss.test(Y)}: Tests the null hypothesis that the time
+ series model is stationary.
+\end{itemize}
+The following commands provide graphical means of diagnosing whether a
+given time series is stationary.
+\begin{itemize}
+\item {\tt ts.plot(Y)}: Plots the observed time series.
+\item {\tt acf(Y)}: Provides the sample auto-correlation function
+(correlogram) for the time series.
+\item {\tt pacf(Y)}: Provides the sample partial auto-correlation
+ function (PACF) for the time series. \\
+\end{itemize}
+These latter two plots are also useful in determining the $p$
+autoregressive terms and the $q$ lagged error terms. See
+\cite{Enders04} for a complete description of how to utilize ACF and
+PACF plots to determine the order of an ARIMA model.
+
+\subsubsection*{Examples}
+
+\begin{enumerate}
+
+\item No covariates\newline
+
+Estimate the ARIMA model, and summarize the results.
+<<Examples.data>>=
+ data(approval)
+@
+<<Example1.zelig>>=
+z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2),
+ data = approval, model = "arima")
+summary(z.out1)
+@
+Set the number of time periods (ahead) for the prediction to run.
+for which you would like the prediction to run:
+<<Example1.setx>>=
+x.out1 <- setx(z.out1, pred.ahead = 10)
+@
+Simulate the predicted quantities of interest:
+<<Example1.sim>>=
+s.out1 <- sim(z.out1, x = x.out1)
+@
+Summarize and plot the results:
+<<Example1.summary>>=
+summary(s.out1)
+@
+\begin{center}
+<<label=Example1Plot,fig=true>>=
+ plot(s.out1,lty.set=2)
+@
+\end{center}
+
+\item Calculating a treatment effect\newline
+
+Estimate an ARIMA model with exogenous regressors, in addition to
+lagged errors and lagged values of the dependent variable.
+
+<<Example2.zelig>>=
+z.out2<- zelig(Diff(approve, 1)~ iraq.war + sept.oct.2001 + avg.price + lag.eps(1) + lag.y(2),
+ data=approval, model="arima")
+@
+To calculate a treatment effect, provide one counterfactual value for
+one time period for one of the exogenous regressors (this is the
+counterfactual treatment).
+<<Example2.setx>>=
+x.out2<- setx(z.out2, sept.oct.2001=list(time=45, value=0), cond=T)
+
+@
+Simulate the quantities of interes
+<<Example2.sim>>=
+s.out2 <- sim(z.out2, x = x.out2)
+@
+Summarizing and plotting the quantities of interest.
+<<Example2.summary>>=
+summary(s.out2)
+@
+%the plot does not work
+\begin{center}
+<<label=Example2Plot,fig=true>>=
+ plot(s.out2)
+@
+\end{center}
+
+\item Calculating first differences\newline
+
+Continuing the example from above, calculate first differences by
+selecting several counterfactual values for one of the exogenous
+regressors.
+<<Example3.setx>>=
+x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value=0))
+x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value=1))
+@
+Simulating the quantities of interest
+<<Example3.sim>>=
+s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
+@
+Summarizing and plotting the quantities of interest. Choosing {\tt
+pred.se = TRUE} only displays the uncertainty resulting from parameter
+estimation.
+<<Example3.summary>>=
+summary(s.out3)
+@
+%the plot does not work
+\begin{center}
+<<label=Example3Plot,fig=true>>=
+plot(s.out3, pred.se = TRUE)
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection*{Model}
+Suppose we observe a time series $Y$, with observations $Y_i$ where
+$i$ denotes the time at which the observation was recorded. The first
+step in the ARIMA procedure is to ensure that this series is
+stationary. If initial diagnostics indicate non-stationarity, then we
+take additional differences until the diagnostics indicate
+stationarity. Formally, define the difference operator, $\nabla^
+{d}$, as follows. When $d = 1$, $\nabla^{1} Y = Y_i - Y_{i-1}$, for
+all observations in the series. When $d=2$, $\nabla^2 Y = (Y_{i} -
+Y_{i-1}) - (Y_{i-1} - Y_{i-2})$. This is analogous to a polynomial
+expansion, $Y_{i} - 2Y_{i-1} + Y_{i-2}$. Higher orders of
+differencing ($d > 2$) following the same function. Let $Y^*$
+represent the stationary time series derived from the initial time
+series by differencing $Y$ $d$ times. In the second step, lagged
+values of $Y^{*}$ and errors $\mu - Y^*_i$ are used to model the time
+series. ARIMA utilizes a state space representation of the ARIMA
+model to assemble the likelihood and then utilizes maximum likelihood
+to estimate the parameters of the model. See \cite{BroDav91} Chapter
+12 for further details.
+\begin{itemize}
+\item A stationary time series $Y_i^*$ that has been differenced $d$
+times has \textit{stochastic component}:
+\begin{equation*}
+Y_i^* \sim \text{Normal} (\mu_i, \sigma^2),
+\end{equation*}
+where $\mu_i$ and $\sigma^2$ are the mean and variance of the Normal
+distribution, respectively.
+\item The \textit{systematic component}, $\mu_i$ is modeled as
+\begin{equation*}
+\mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
++ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
+\end{equation*}
+where $x_i$ are the explanatory variables with associated parameter
+vector $\beta$; $Y^*$ the lag-$p$ observations from the stationary time
+series with associated parameter vector $\alpha$; and $\epsilon_i$ the
+lagged errors or innovations of order $q$, with associated parameter vector
+$\gamma$.
+\end{itemize}
+
+\subsubsection*{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component,
+\begin{equation*}
+\text{E(}Y_\text{i}) = \mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
++ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
+\end{equation*}
+given draws of $\beta$, $\alpha$, and $\gamma$ from their estimated
+distribution.
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+\text{FD}_i= E(Y | x_{1i}) - E(Y|x_{i})
+\end{equation*}
+\item The treatment effect ({\tt qi\$t.eff}), obtained with {\tt
+setx(\dots, cond = TRUE)}, represents the difference
+between the observed time series and the expected value of a time
+series with counterfactual values of the external regressors. Formally,
+\begin{equation*}
+\text{t.eff}_\text{i} = Y_i - E[Y_i | x_{i}]
+\end{equation*}
+Zelig will not estimate both first differences and treatment effects.
+\end{itemize}
+
+\subsubsection*{Output Values}
+The output of each Zelig command contains useful information which the
+user may view. For example, if the user runs {\tt z.out <-
+zelig(Diff(Y,1) + lag.y(1) + lag.eps(1) + X1, model = "arima", data)}
+then the user may examine the available information in {\tt z.out} by
+using {\tt names(z.out)}, see the coefficients by using {\tt
+z.out\$coef} and a default summary of information through {\tt
+summary(z.out)}. {\tt tsdiag(z.out)} returns a plot of the residuals,
+the ACF of the residuals, and a plot displaying the $p$-values for the
+Ljung-Box statistic. Other elements, available through the \$
+operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+\begin{itemize}
+\item {\tt coef}: parameter estimates for the explanatory variables,
+lagged observations of the time series, and lagged innovations.
+\item {\tt sigma2}: maximum likelihood estimate of the variance of
+the stationary time series.
+\item {\tt var.coef}: variance-covariance matrix for the parameters.
+\item {\tt loglik}: maximized log-likelihood.
+\item {\tt aic}: Akaike Information Criterion (AIC) for the maximized
+ log-likelihood.
+\item {\tt residuals}: Residuals from the fitted model.
+\item {\tt arma}: A vector with seven elements corresponding to the AR
+and MA, the seasonal AR and MA, the period of the seasonal component,
+and the number of non-seasonal and seasonal differences of the
+dependent variable.
+\item {\tt data}: the name of the input data frame.
+\end{itemize}
+\item From the {\tt sim()} output object {\tt s.out} you may extract
+ quantities of interest arranged as matrices, with the rows
+ indicating the number of the simulations, and the columns
+ representing the simulated value of the dependent variable for the
+ counterfactual value at that time period. {\tt summary(s.out)}
+ provides a summary of the simulated values, while {\tt plot(s.out)}
+ provides a graphical representation of the simulations. Available
+ quantities are:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}.
+\item {\tt qi\$fd} : the simulated first difference for the values
+ that are specified in {\tt x} and {\tt x1}.
+\item{ \tt qi\$t.eff}: the simulated treatment effect, difference
+between the observed {\tt y} and the expected values given the
+counterfactual values specified in {\tt x}.
+\end{itemize}
+\end{itemize}
+
+\subsection*{See Also}
+
+Additional options for ARIMA models may be found using {\tt
+help(arima)}.
+
+For an accessible introduction to identifying the order of an ARIMA
+model consult:
+\begin{verse}
+\bibentry{Enders04}.
+\end{verse}
+In addition, advanced users may wish to become more familiar with the state-space
+representation of an ARIMA process:
+\begin{verse}
+\bibentry{BroDav91}.
+\end{verse}
+
+\subsubsection*{Contributors}
+The ARIMA function is part of the stats package by William Venables
+and Brian Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+Justin Grimmer added Zelig functionality.
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
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+++ b/inst/doc/arima.tex
@@ -0,0 +1,349 @@
+
+\include{zinput}
+%\VignetteIndexEntry{ARIMA Models for Time Series Data}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,time series}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{{\tt ARIMA}: ARIMA Models for Time Series Data}
+\label{ARIMA}
+
+Use auto-regressive, integrated, moving-average (ARIMA) models for
+time series data. A time series is a set of observations ordered
+according to the time they were observed. Because the value observed
+at time $t$ may depend on values observed at previous time points,
+time series data may violate independence assumptions. An ARIMA($p$,
+$d$, $q$) model can account for temporal dependence in several ways.
+First, the time series is differenced to render it stationary, by
+taking $d$ differences. Second, the time dependence of the stationary
+process is modeled by including $p$ auto-regressive and $q$
+moving-average terms, in addition to any time-varying covariates. For
+a cyclical time series, these steps can be repeated according to the
+period of the cycle, whether quarterly or monthly or another time
+interval. ARIMA models are extremely flexible for continuous data.
+Common formulations include, ARIMA(0, 0, 0) for least squares
+regression (see \Sref{ls}), ARIMA(1, 0, 0), for an AR1 model, and
+ARIMA(0, 0, 1) for an MA1 model. For a more comprehensive review of
+ARIMA models, see \cite{Enders04}.
+
+\subsubsection*{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Diff(Y, d, ds=NULL, per=NULL) ~ lag.y(p, ps=NULL)
+ + lag.eps(q, qs=NULL) + X1 + X2,
+ model="arima", data=mydata, ...)
+> x.out <- setx(z.out, X1 = list(time, value), cond = FALSE)
+> s.out <- sim(z.out, x=x.out, x1=NULL)
+\end{verbatim}
+
+\subsubsection*{Inputs}
+In addition to independent variables, {\tt zelig()} accepts the
+following arguments to specify the {\tt ARIMA} model:
+\begin{itemize}
+\item {\tt Diff(Y, d, ds, per)} for a dependent variable Y sets the
+number of non-seasonal differences ({\tt d}), the number of seasonal differences ({\tt
+ds}), and the period of the season ({\tt per}).
+\item {\tt lag.y(p, ps)} sets the number of lagged observations of the
+dependent variable for non-seasonal ({\tt p}) and seasonal ({\tt ps})
+components.
+\item {\tt lag.eps(q, qs)} sets the number of lagged innovations, or
+differences between the observed value of the time series and the
+expected value of the time series for non-seasonal ({\tt q}) and
+seasonal ({\tt qs}) components.
+\end{itemize}
+In addition the user can control the estimation of the time
+series with the following terms:
+\begin{itemize}
+\item $\hdots$: Additional inputs. See {\tt help(arima)} in the stats
+library for further information.
+\end{itemize}
+
+\subsubsection*{Stationarity}
+A stationary time series has finite variance, correlations between
+observations that are not time-dependent, and a constant expected
+value for all components of the time series \citep[p.\ 12]{BroDav91}.
+Users should ensure that the time series being analyzed is stationary
+before specifying a model. The following commands provide diagnostics
+to determine if a time series {\tt Y} is stationary.
+\begin{itemize}
+\item {\tt pp.test(Y)}: Tests the null hypothesis that the time series
+ is non-stationary.
+\item {\tt kpss.test(Y)}: Tests the null hypothesis that the time
+ series model is stationary.
+\end{itemize}
+The following commands provide graphical means of diagnosing whether a
+given time series is stationary.
+\begin{itemize}
+\item {\tt ts.plot(Y)}: Plots the observed time series.
+\item {\tt acf(Y)}: Provides the sample auto-correlation function
+(correlogram) for the time series.
+\item {\tt pacf(Y)}: Provides the sample partial auto-correlation
+ function (PACF) for the time series. \\
+\end{itemize}
+These latter two plots are also useful in determining the $p$
+autoregressive terms and the $q$ lagged error terms. See
+\cite{Enders04} for a complete description of how to utilize ACF and
+PACF plots to determine the order of an ARIMA model.
+
+\subsubsection*{Examples}
+
+\begin{enumerate}
+
+\item No covariates\newline
+
+Estimate the ARIMA model, and summarize the results.
+\begin{Schunk}
+\begin{Sinput}
+> data(approval)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2), data = approval,
++ model = "arima")
+> summary(z.out1)
+\end{Sinput}
+\end{Schunk}
+Set the number of time periods (ahead) for the prediction to run.
+for which you would like the prediction to run:
+\begin{Schunk}
+\begin{Sinput}
+> x.out1 <- setx(z.out1, pred.ahead = 10)
+\end{Sinput}
+\end{Schunk}
+Simulate the predicted quantities of interest:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.out1)
+\end{Sinput}
+\end{Schunk}
+Summarize and plot the results:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1, lty.set = 2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example1Plot}
+\end{center}
+
+\item Calculating a treatment effect\newline
+
+Estimate an ARIMA model with exogenous regressors, in addition to
+lagged errors and lagged values of the dependent variable.
+
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(Diff(approve, 1) ~ iraq.war + sept.oct.2001 +
++ avg.price + lag.eps(1) + lag.y(2), data = approval, model = "arima")
+\end{Sinput}
+\end{Schunk}
+To calculate a treatment effect, provide one counterfactual value for
+one time period for one of the exogenous regressors (this is the
+counterfactual treatment).
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2, sept.oct.2001 = list(time = 45, value = 0),
++ cond = T)
+\end{Sinput}
+\end{Schunk}
+Simulate the quantities of interes
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.out2)
+\end{Sinput}
+\end{Schunk}
+Summarizing and plotting the quantities of interest.
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+%the plot does not work
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example2Plot}
+\end{center}
+
+\item Calculating first differences\newline
+
+Continuing the example from above, calculate first differences by
+selecting several counterfactual values for one of the exogenous
+regressors.
+\begin{Schunk}
+\begin{Sinput}
+> x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 0))
+> x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 1))
+\end{Sinput}
+\end{Schunk}
+Simulating the quantities of interest
+\begin{Schunk}
+\begin{Sinput}
+> s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
+\end{Sinput}
+\end{Schunk}
+Summarizing and plotting the quantities of interest. Choosing {\tt
+pred.se = TRUE} only displays the uncertainty resulting from parameter
+estimation.
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out3)
+\end{Sinput}
+\end{Schunk}
+%the plot does not work
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out3, pred.se = TRUE)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example3Plot}
+\end{center}
+\end{enumerate}
+
+\subsubsection*{Model}
+Suppose we observe a time series $Y$, with observations $Y_i$ where
+$i$ denotes the time at which the observation was recorded. The first
+step in the ARIMA procedure is to ensure that this series is
+stationary. If initial diagnostics indicate non-stationarity, then we
+take additional differences until the diagnostics indicate
+stationarity. Formally, define the difference operator, $\nabla^
+{d}$, as follows. When $d = 1$, $\nabla^{1} Y = Y_i - Y_{i-1}$, for
+all observations in the series. When $d=2$, $\nabla^2 Y = (Y_{i} -
+Y_{i-1}) - (Y_{i-1} - Y_{i-2})$. This is analogous to a polynomial
+expansion, $Y_{i} - 2Y_{i-1} + Y_{i-2}$. Higher orders of
+differencing ($d > 2$) following the same function. Let $Y^*$
+represent the stationary time series derived from the initial time
+series by differencing $Y$ $d$ times. In the second step, lagged
+values of $Y^{*}$ and errors $\mu - Y^*_i$ are used to model the time
+series. ARIMA utilizes a state space representation of the ARIMA
+model to assemble the likelihood and then utilizes maximum likelihood
+to estimate the parameters of the model. See \cite{BroDav91} Chapter
+12 for further details.
+\begin{itemize}
+\item A stationary time series $Y_i^*$ that has been differenced $d$
+times has \textit{stochastic component}:
+\begin{equation*}
+Y_i^* \sim \text{Normal} (\mu_i, \sigma^2),
+\end{equation*}
+where $\mu_i$ and $\sigma^2$ are the mean and variance of the Normal
+distribution, respectively.
+\item The \textit{systematic component}, $\mu_i$ is modeled as
+\begin{equation*}
+\mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
++ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
+\end{equation*}
+where $x_i$ are the explanatory variables with associated parameter
+vector $\beta$; $Y^*$ the lag-$p$ observations from the stationary time
+series with associated parameter vector $\alpha$; and $\epsilon_i$ the
+lagged errors or innovations of order $q$, with associated parameter vector
+$\gamma$.
+\end{itemize}
+
+\subsubsection*{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component,
+\begin{equation*}
+\text{E(}Y_\text{i}) = \mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
++ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
+\end{equation*}
+given draws of $\beta$, $\alpha$, and $\gamma$ from their estimated
+distribution.
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+\text{FD}_i= E(Y | x_{1i}) - E(Y|x_{i})
+\end{equation*}
+\item The treatment effect ({\tt qi\$t.eff}), obtained with {\tt
+setx(\dots, cond = TRUE)}, represents the difference
+between the observed time series and the expected value of a time
+series with counterfactual values of the external regressors. Formally,
+\begin{equation*}
+\text{t.eff}_\text{i} = Y_i - E[Y_i | x_{i}]
+\end{equation*}
+Zelig will not estimate both first differences and treatment effects.
+\end{itemize}
+
+\subsubsection*{Output Values}
+The output of each Zelig command contains useful information which the
+user may view. For example, if the user runs {\tt z.out <-
+zelig(Diff(Y,1) + lag.y(1) + lag.eps(1) + X1, model = "arima", data)}
+then the user may examine the available information in {\tt z.out} by
+using {\tt names(z.out)}, see the coefficients by using {\tt
+z.out\$coef} and a default summary of information through {\tt
+summary(z.out)}. {\tt tsdiag(z.out)} returns a plot of the residuals,
+the ACF of the residuals, and a plot displaying the $p$-values for the
+Ljung-Box statistic. Other elements, available through the \$
+operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+\begin{itemize}
+\item {\tt coef}: parameter estimates for the explanatory variables,
+lagged observations of the time series, and lagged innovations.
+\item {\tt sigma2}: maximum likelihood estimate of the variance of
+the stationary time series.
+\item {\tt var.coef}: variance-covariance matrix for the parameters.
+\item {\tt loglik}: maximized log-likelihood.
+\item {\tt aic}: Akaike Information Criterion (AIC) for the maximized
+ log-likelihood.
+\item {\tt residuals}: Residuals from the fitted model.
+\item {\tt arma}: A vector with seven elements corresponding to the AR
+and MA, the seasonal AR and MA, the period of the seasonal component,
+and the number of non-seasonal and seasonal differences of the
+dependent variable.
+\item {\tt data}: the name of the input data frame.
+\end{itemize}
+\item From the {\tt sim()} output object {\tt s.out} you may extract
+ quantities of interest arranged as matrices, with the rows
+ indicating the number of the simulations, and the columns
+ representing the simulated value of the dependent variable for the
+ counterfactual value at that time period. {\tt summary(s.out)}
+ provides a summary of the simulated values, while {\tt plot(s.out)}
+ provides a graphical representation of the simulations. Available
+ quantities are:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}.
+\item {\tt qi\$fd} : the simulated first difference for the values
+ that are specified in {\tt x} and {\tt x1}.
+\item{ \tt qi\$t.eff}: the simulated treatment effect, difference
+between the observed {\tt y} and the expected values given the
+counterfactual values specified in {\tt x}.
+\end{itemize}
+\end{itemize}
+
+\subsection*{See Also}
+
+Additional options for ARIMA models may be found using {\tt
+help(arima)}.
+
+For an accessible introduction to identifying the order of an ARIMA
+model consult:
+\begin{verse}
+\bibentry{Enders04}.
+\end{verse}
+In addition, advanced users may wish to become more familiar with the state-space
+representation of an ARIMA process:
+\begin{verse}
+\bibentry{BroDav91}.
+\end{verse}
+
+\subsubsection*{Contributors}
+The ARIMA function is part of the stats package by William Venables
+and Brian Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+Justin Grimmer added Zelig functionality.
+ \end{document}
diff --git a/inst/doc/blogit.Rnw b/inst/doc/blogit.Rnw
new file mode 100644
index 0000000..1bf68de
--- /dev/null
+++ b/inst/doc/blogit.Rnw
@@ -0,0 +1,359 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/blogit}
+\include{zinput}
+
+%\VignetteIndexEntry{Bivariate Logistic Regression for Two Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model,logistic regression, dichotomous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+
+\section{{\tt blogit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{blogit}
+
+Use the bivariate logistic regression model if you have two binary
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables. Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the odds ratio $\psi$,
+which describes the dependence of one marginal on the other. Each of
+these systematic components may be modeled as functions of (possibly
+different) sets of explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2 ,
+ mu2 = Y2 ~ X1 + X3),
+ model = "blogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate logit specification, there are three equations which
+correspond to each dependent variable ($Y_1$, $Y_2$), and $\psi$, the
+odds ratio. You should provide a list of formulas for each equation or,
+you may use {\tt cbind()} if the right hand side is the same for both equations
+<<InputValues.list>>=
+formulae <- list(cbind(Y1,Y2) ~ X1 + X2)
+@
+which means that all the explanatory variables in equations 1 and 2
+(corresponding to $Y_1$ and $Y_2$) are included, but only an intercept
+is estimated (all explanatory variables are omitted) for equation 3
+($\psi$).
+
+You may use the function {\tt tag()} to constrain variables across
+equations:
+<<InputValues.list.mu>>=
+formulae <- list(mu1 = y1 ~ x1 + tag(x3, "x3"),
+ mu2 = y2 ~ x2 + tag(x3, "x3"))
+@
+where {\tt tag()} is a special function that constrains variables to
+have the same effect across equations. Thus, the coefficient for {\tt
+x3} in equation {\tt mu1} is constrained to be equal to the
+coefficient for {\tt x3} in equation {\tt mu2}.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bl}
+
+Load the data and estimate the model:
+<<BasicExample.data>>=
+ data(sanction)
+## sanction
+@
+<<BasicExample.zelig>>=
+ z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
+ model = "blogit", data = sanction)
+@
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the odds ratio parameter;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share an odds ratio.
+\newline \newline 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):
+<<BasicExample.setx.low>>=
+ x.low <- setx(z.out1, cost = 1)
+@
+<<BasicExample.setx.high>>=
+x.high <- setx(z.out1, cost = 4)
+@
+Simulate fitted values and first differences:
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+ summary(s.out1)
+@
+\begin{center}
+<<label=BasicExamplePlot,fig=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.logit}
+
+Using sample data \texttt{sanction}, estimate the statistical model,
+with {\tt import} a function of {\tt coop} in the first equation and {\tt export} a
+function of {\tt cost} and {\tt target} in the second equation:
+<<JointExample.zelig>>=
+ z.out2 <- zelig(list(import ~ coop, export ~ cost + target),
+ model = "blogit", data = sanction)
+ summary(z.out2)
+@
+Set the explanatory variables to their means:
+<<JointExample.setx>>=
+ x.out2 <- setx(z.out2)
+@
+Simulate draws from the posterior distribution:
+<<JointExample.sim>>=
+ s.out2 <- sim(z.out2, x = x.out2)
+ summary(s.out2)
+@
+\begin{center}
+<<label=JointExamplePlot,fig=true>>=
+ plot(s.out2)
+@
+\end{center}
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model
+\label{pdep.l}
+
+Using the sample data \texttt{sanction}
+The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated. For example,
+<<JointEstimation.zelig>>=
+ z.out3 <- zelig(list(import ~ tag(coop,"coop") + tag(cost,"cost") +
+ tag(target,"target"),
+ export ~ tag(coop,"coop") + tag(cost,"cost") +
+ tag(target,"target")),
+ model = "blogit", data = sanction)
+ summary(z.out3)
+@
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
+Example~\ref{basic.bl} which returns two parameter estimates for each
+of the explanatory variables. \newline \newline Set values for the
+explanatory variables:
+<<JointEstimation.setx>>=
+x.out3 <- setx(z.out3, cost = 1:4)
+@
+Draw simulated expected values:
+<<JointEstimation.sim>>=
+ s.out3 <- sim(z.out3, x = x.out3)
+ summary(s.out3)
+@
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index). We model the joint
+outcome $(Y_1$, $Y_2)$ using a marginal probability for each dependent
+variable, and the odds ratio, which parameterizes the relationship
+between the two dependent variables. Define $Y_{rs}$ such that it is
+equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise, where $r$ and
+$s$ take a value of either 0 or 1. Then, the model is defined as follows,
+
+\begin{itemize}
+
+\item The \emph{stochastic component} is
+\begin{eqnarray*}
+ Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+ Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+ Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+\end{eqnarray*}
+where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$.
+
+
+\item The \emph{systematic components} model the marginal probabilities,
+ $\pi_j=\Pr(Y_j=1)$, as well as the odds ratio. The odds ratio
+ is defined as $\psi = \pi_{00} \pi_{01}/\pi_{10}\pi_{11}$ and
+ describes the relationship between the two outcomes. Thus, for each
+ observation we have
+\begin{eqnarray*}
+\pi_j & = & \frac{1}{1 + \exp(-x_j \beta_j)} \quad \textrm{ for} \quad
+j=1,2, \\
+\psi &= & \exp(x_3 \beta_3).
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the bivariate logit model
+ are the predicted joint probabilities. Simulations of $\beta_1$,
+ $\beta_2$, and $\beta_3$ (drawn from their sampling distributions)
+ are substituted into the systematic components $(\pi_1, \pi_2,
+ \psi)$ to find simulations of the predicted joint probabilities:
+\begin{eqnarray*}
+\pi_{11} & = & \left\{ \begin{array}{ll}
+ \frac{1}{2}(\psi - 1)^{-1} - {a - \sqrt{a^2 + b}} &
+ \textrm{for} \; \psi \ne 1 \\
+ \pi_1 \pi_2 & \textrm{for} \; \psi = 1
+ \end{array} \right., \\
+\pi_{10} &=& \pi_1 - \pi_{11}, \\
+\pi_{01} &=& \pi_2 - \pi_{11}, \\
+\pi_{00} &=& 1 - \pi_{10} - \pi_{01} - \pi_{11},
+\end{eqnarray*}
+where $a = 1 + (\pi_1 + \pi_2)(\psi - 1)$, $b = -4 \psi(\psi - 1)
+\pi_1 \pi_2$, and the joint probabilities for each observation must sum
+to one. For $n$ simulations, the expected values form an $n \times 4$
+matrix for each observation in {\tt x}.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ multinomial distribution given the expected joint probabilities.
+
+\item The first differences ({\tt qi\$fd}) for each
+ of the predicted joint probabilities are given by $$\textrm{FD}_{rs}
+ = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s \mid x).$$
+
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+ probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+ the counterfactual expected value of $Y_{ij}$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ \widehat{Y_{ij}(t_i=0)} \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+ $Y_{ij}$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "blogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through {\tt summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times 3$ matrix of the linear
+ predictors $x_j \beta_j$.
+ \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays indexed by simulation
+ $\times$ quantity $\times$ {\tt x}-observation (for more than one
+ {\tt x}-observation; otherwise the quantities are matrices).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected joint probabilities (or expected
+ values) for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+ distribution defined by the expected joint probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the
+ expected joint probabilities for the values specified in {\tt x} and
+ {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+ probabilities for given {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate logit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
diff --git a/inst/doc/blogit.pdf b/inst/doc/blogit.pdf
new file mode 100644
index 0000000..a1afb41
Binary files /dev/null and b/inst/doc/blogit.pdf differ
diff --git a/inst/doc/blogit.tex b/inst/doc/blogit.tex
new file mode 100644
index 0000000..6780481
--- /dev/null
+++ b/inst/doc/blogit.tex
@@ -0,0 +1,375 @@
+
+\include{zinput}
+
+%\VignetteIndexEntry{Bivariate Logistic Regression for Two Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model,logistic regression, dichotomous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+
+\section{{\tt blogit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{blogit}
+
+Use the bivariate logistic regression model if you have two binary
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables. Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the odds ratio $\psi$,
+which describes the dependence of one marginal on the other. Each of
+these systematic components may be modeled as functions of (possibly
+different) sets of explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2 ,
+ mu2 = Y2 ~ X1 + X3),
+ model = "blogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate logit specification, there are three equations which
+correspond to each dependent variable ($Y_1$, $Y_2$), and $\psi$, the
+odds ratio. You should provide a list of formulas for each equation or,
+you may use {\tt cbind()} if the right hand side is the same for both equations
+\begin{Schunk}
+\begin{Sinput}
+> formulae <- list(cbind(Y1, Y2) ~ X1 + X2)
+\end{Sinput}
+\end{Schunk}
+which means that all the explanatory variables in equations 1 and 2
+(corresponding to $Y_1$ and $Y_2$) are included, but only an intercept
+is estimated (all explanatory variables are omitted) for equation 3
+($\psi$).
+
+You may use the function {\tt tag()} to constrain variables across
+equations:
+\begin{Schunk}
+\begin{Sinput}
+> formulae <- list(mu1 = y1 ~ x1 + tag(x3, "x3"), mu2 = y2 ~ x2 +
++ tag(x3, "x3"))
+\end{Sinput}
+\end{Schunk}
+where {\tt tag()} is a special function that constrains variables to
+have the same effect across equations. Thus, the coefficient for {\tt
+x3} in equation {\tt mu1} is constrained to be equal to the
+coefficient for {\tt x3} in equation {\tt mu2}.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bl}
+
+Load the data and estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
++ model = "blogit", data = sanction)
+\end{Sinput}
+\end{Schunk}
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the odds ratio parameter;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share an odds ratio.
+\newline \newline 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):
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out1, cost = 1)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out1, cost = 4)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values and first differences:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/blogit-BasicExamplePlot}
+\end{center}
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.logit}
+
+Using sample data \texttt{sanction}, estimate the statistical model,
+with {\tt import} a function of {\tt coop} in the first equation and {\tt export} a
+function of {\tt cost} and {\tt target} in the second equation:
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(list(import ~ coop, export ~ cost + target),
++ model = "blogit", data = sanction)
+> summary(z.out2)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means:
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2)
+\end{Sinput}
+\end{Schunk}
+Simulate draws from the posterior distribution:
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/blogit-JointExamplePlot}
+\end{center}
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model
+\label{pdep.l}
+
+Using the sample data \texttt{sanction}
+The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated. For example,
+\begin{Schunk}
+\begin{Sinput}
+> z.out3 <- zelig(list(import ~ tag(coop, "coop") + tag(cost, "cost") +
++ tag(target, "target"), export ~ tag(coop, "coop") + tag(cost,
++ "cost") + tag(target, "target")), model = "blogit", data = sanction)
+> summary(z.out3)
+\end{Sinput}
+\end{Schunk}
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
+Example~\ref{basic.bl} which returns two parameter estimates for each
+of the explanatory variables. \newline \newline Set values for the
+explanatory variables:
+\begin{Schunk}
+\begin{Sinput}
+> x.out3 <- setx(z.out3, cost = 1:4)
+\end{Sinput}
+\end{Schunk}
+Draw simulated expected values:
+\begin{Schunk}
+\begin{Sinput}
+> s.out3 <- sim(z.out3, x = x.out3)
+> summary(s.out3)
+\end{Sinput}
+\end{Schunk}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index). We model the joint
+outcome $(Y_1$, $Y_2)$ using a marginal probability for each dependent
+variable, and the odds ratio, which parameterizes the relationship
+between the two dependent variables. Define $Y_{rs}$ such that it is
+equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise, where $r$ and
+$s$ take a value of either 0 or 1. Then, the model is defined as follows,
+
+\begin{itemize}
+
+\item The \emph{stochastic component} is
+\begin{eqnarray*}
+ Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+ Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+ Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+\end{eqnarray*}
+where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$.
+
+
+\item The \emph{systematic components} model the marginal probabilities,
+ $\pi_j=\Pr(Y_j=1)$, as well as the odds ratio. The odds ratio
+ is defined as $\psi = \pi_{00} \pi_{01}/\pi_{10}\pi_{11}$ and
+ describes the relationship between the two outcomes. Thus, for each
+ observation we have
+\begin{eqnarray*}
+\pi_j & = & \frac{1}{1 + \exp(-x_j \beta_j)} \quad \textrm{ for} \quad
+j=1,2, \\
+\psi &= & \exp(x_3 \beta_3).
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the bivariate logit model
+ are the predicted joint probabilities. Simulations of $\beta_1$,
+ $\beta_2$, and $\beta_3$ (drawn from their sampling distributions)
+ are substituted into the systematic components $(\pi_1, \pi_2,
+ \psi)$ to find simulations of the predicted joint probabilities:
+\begin{eqnarray*}
+\pi_{11} & = & \left\{ \begin{array}{ll}
+ \frac{1}{2}(\psi - 1)^{-1} - {a - \sqrt{a^2 + b}} &
+ \textrm{for} \; \psi \ne 1 \\
+ \pi_1 \pi_2 & \textrm{for} \; \psi = 1
+ \end{array} \right., \\
+\pi_{10} &=& \pi_1 - \pi_{11}, \\
+\pi_{01} &=& \pi_2 - \pi_{11}, \\
+\pi_{00} &=& 1 - \pi_{10} - \pi_{01} - \pi_{11},
+\end{eqnarray*}
+where $a = 1 + (\pi_1 + \pi_2)(\psi - 1)$, $b = -4 \psi(\psi - 1)
+\pi_1 \pi_2$, and the joint probabilities for each observation must sum
+to one. For $n$ simulations, the expected values form an $n \times 4$
+matrix for each observation in {\tt x}.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ multinomial distribution given the expected joint probabilities.
+
+\item The first differences ({\tt qi\$fd}) for each
+ of the predicted joint probabilities are given by $$\textrm{FD}_{rs}
+ = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s \mid x).$$
+
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+ probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+ the counterfactual expected value of $Y_{ij}$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ \widehat{Y_{ij}(t_i=0)} \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+ $Y_{ij}$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "blogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through {\tt summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times 3$ matrix of the linear
+ predictors $x_j \beta_j$.
+ \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays indexed by simulation
+ $\times$ quantity $\times$ {\tt x}-observation (for more than one
+ {\tt x}-observation; otherwise the quantities are matrices).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected joint probabilities (or expected
+ values) for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+ distribution defined by the expected joint probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the
+ expected joint probabilities for the values specified in {\tt x} and
+ {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+ probabilities for given {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate logit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
diff --git a/inst/doc/bprobit.Rnw b/inst/doc/bprobit.Rnw
new file mode 100644
index 0000000..41088a0
--- /dev/null
+++ b/inst/doc/bprobit.Rnw
@@ -0,0 +1,391 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/bprobit}
+\include{zinput}
+%\VignetteIndexEntry{Bivariate Probit Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model,prpbit, logistic regression, dichotomous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt bprobit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{bprobit}
+
+Use the bivariate probit regression model if you have two binaryrun
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables. Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the correlation parameter
+$\rho$ for the two marginal distributions. Each of these systematic
+components may be modeled as functions of (possibly different) sets of
+explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2,
+ mu2 = Y2 ~ X1 + X3,
+ rho = ~ 1),
+ model = "bprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate probit specification, there are three equations
+which correspond to each dependent variable ($Y_1$, $Y_2$), and the
+correlation parameter $\rho$. Since the correlation parameter does
+not correspond to one of the dependent variables, the model estimates
+$\rho$ as a constant by default. Hence, only two formulas (for
+$\mu_1$ and $\mu_2$) are required. If the explanatory variables for
+$\mu_1$ and $\mu_2$ are the same and effects are estimated separately
+for each parameter, you may use the following short hand:
+<<InputValues.list>>=
+fml <- list(cbind(Y1,Y2) ~ X1 + X2)
+@
+which has the same meaning as:
+<<InputValues.list.rho>>=
+fml <- list(mu1 = Y1 ~ X1 + X2,
+ mu2 = Y2 ~ X1 + X2,
+ rho = ~ 1)
+@
+You may use the function {\tt tag()} to constrain variables across
+equations. The {\tt tag()} function takes a variable and a label for
+the effect parameter. Below, the constrained effect of {\tt
+x3} in both equations is called the {\tt age} parameter:
+<<InputValues.list.mu>>=
+fml <- list(mu1 = y1 ~ x1 + tag(x3, "age"),
+ mu2 = y2 ~ x2 + tag(x3, "age"))
+@
+You may also constrain different variables across different equations
+to have the same effect.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bp}
+
+Load the data and estimate the model:
+<<BasicExample.data>>=
+ data(sanction)
+@
+<<BasicExample.zelig>>=
+ z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
+ model = "bprobit", data = sanction)
+@
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the correlation coefficient;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share a correlation parameter.
+\newline \newline 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):
+<<BasicExample.setx>>=
+ x.low <- setx(z.out1, cost = 1)
+ x.high <- setx(z.out1, cost = 4)
+@
+Simulate fitted values and first differences:
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+ summary(s.out1)
+@
+\begin{center}
+<<label=BasicExamplePlot,fig=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.probit}
+
+Using the sample data \texttt{sanction}, estimate the statistical model,
+with {\tt import} a function of {\tt coop} in the first equation and
+{\tt export} a function of {\tt cost} and {\tt target} in the second equation:
+<<JointEstimation.list>>=
+ fml2 <- list(mu1 = import ~ coop,
+ mu2 = export ~ cost + target)
+@
+<<JointEstimation.zelig>>=
+ z.out2 <- zelig(fml2, model = "bprobit", data = sanction)
+ summary(z.out2)
+@
+Set the explanatory variables to their means:
+<<JointEstimation.setx>>=
+ x.out2 <- setx(z.out2)
+@
+Simulate draws from the posterior distribution:
+<<JointEstimation.sim>>=
+ s.out2 <- sim(z.out2, x = x.out2)
+ summary(s.out2)
+@
+\begin{center}
+<<label=JointEstimationPlot,fig=true>>=
+ plot(s.out2)
+@
+\end{center}
+
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model
+\label{pdep.p}
+
+Using the sample data \texttt{sanction}.
+The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated. For example,
+<<JointEstimationParam.list>>=
+ fml3 <- list(mu1 = import ~ tag(coop,"coop") + tag(cost,"cost") +
+ tag(target,"target"),
+ mu2 = export ~ tag(coop,"coop") + tag(cost,"cost") +
+ tag(target,"target"))
+@
+<<JointEstimationParam.zelig>>=
+ z.out3 <- zelig(fml3, model = "bprobit", data = sanction)
+ summary(z.out3)
+@
+
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
+Example~\ref{basic.bp} which returns two parameter estimates for each
+of the explanatory variables. \newline \newline Set values for the
+explanatory variables:
+<<JointEstimationParam.setx>>=
+ x.out3 <- setx(z.out3, cost = 1:4)
+@
+Draw simulated expected values:
+<<JointEstimationParam.sim>>=
+ s.out3 <- sim(z.out3, x = x.out3)
+ summary(s.out3)
+@
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index $i$). We model the joint
+outcome $(Y_1$, $Y_2)$ using two marginal probabilities for each
+dependent variable, and the correlation parameter, which describes how
+the two dependent variables are related.
+%Define $Y_{rs}$ such that it
+%is equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise where $r$
+%and $s$ take a value of either 0 or 1. Then, the model is defined as
+%follows,
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by two latent (unobserved)
+ continuous variables which follow the bivariate Normal distribution:
+\begin{eqnarray*}
+ \left ( \begin{array}{c}
+ Y_1^* \\
+ Y_2^*
+ \end{array}
+ \right ) &\sim &
+ N_2 \left \{ \left (
+ \begin{array}{c}
+ \mu_1 \\ \mu_2
+ \end{array} \right ), \left( \begin{array}{cc}
+ 1 & \rho \\
+ \rho & 1
+ \end{array} \right) \right\},
+\end{eqnarray*}
+where $\mu_j$ is a mean for $Y_j^*$ and $\rho$ is a scalar correlation
+parameter. The following observation mechanism links the observed
+dependent variables, $Y_j$, with these latent variables
+\begin{eqnarray*}
+Y_j & = & \left \{ \begin{array}{cc}
+ 1 & {\rm if} \; Y_j^* \ge 0, \\
+ 0 & {\rm otherwise.}
+ \end{array}
+ \right.
+\end{eqnarray*}
+
+%Alternatively, the stochastic component for the observed dependent
+%variables can be written as
+%\begin{eqnarray*}
+% Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+% Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+% Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+%\end{eqnarray*}
+%where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+%$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$. Each of these joint
+%probabilities is modeled using the bivariate normal cumulative
+%distribution function.
+
+\item The \emph{systemic components} for each observation are
+ \begin{eqnarray*}
+ \mu_j & = & x_{j} \beta_j \quad {\rm for} \quad j=1,2, \\
+ \rho & = & \frac{\exp(x_3 \beta_3) - 1}{\exp(x_3 \beta_3) + 1}.
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+For $n$ simulations, expected values form an $n \times 4$
+matrix.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the binomial probit model
+ are the predicted joint probabilities. Simulations of $\beta_1$,
+ $\beta_2$, and $\beta_3$ (drawn form their sampling distributions)
+ are substituted into the systematic components, to find simulations
+ of the predicted joint probabilities $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$:
+\begin{eqnarray*}
+\pi_{11} &= \Pr(Y_1^* \geq 0 , Y_2^* \geq 0) &= \int_0^{\infty}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^* \\
+\pi_{10} &= \Pr(Y_1^* \geq 0 , Y_2^* < 0) &= \int_0^{\infty}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{01} &= \Pr(Y_1^* < 0 , Y_2^* \geq 0) &= \int_{-\infty}^{0}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{11} &= \Pr(Y_1^* < 0 , Y_2^* < 0) &= \int_{-\infty}^{0}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\end{eqnarray*}
+where $r$ and $s$ may take a value of either 0 or 1, $\phi_2$ is the
+bivariate Normal density.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ multinomial distribution given the expected joint probabilities.
+
+\item The first difference ({\tt qi\$fd}) in each of the predicted joint
+ probabilities are given by
+ $$\textrm{FD}_{rs} = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s
+ \mid x).$$
+
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+ probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+ the counterfactual expected value of $Y_{ij}$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ \widehat{Y_{ij}(t_i=0)}\right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+ $Y_{ij}$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
+ model = "bprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through
+\texttt{summary(z.out)}. Other elements available through the {\tt
+ \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times 3$ matrix of the linear
+ predictors $x_j \beta_j$.
+ \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays indexed by simulation
+ $\times$ quantity $\times$ {\tt x}-observation (for more than one
+ {\tt x}-observation; otherwise the quantities are matrices). Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values (joint predicted
+ probabilities) for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+ distribution defined by the joint predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+ probabilities for given {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate probit function is part of the VGAM package by Thomas Yee.
+Please cite this model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM package. Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
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+
+\include{zinput}
+%\VignetteIndexEntry{Bivariate Probit Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model,prpbit, logistic regression, dichotomous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt bprobit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{bprobit}
+
+Use the bivariate probit regression model if you have two binaryrun
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables. Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the correlation parameter
+$\rho$ for the two marginal distributions. Each of these systematic
+components may be modeled as functions of (possibly different) sets of
+explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2,
+ mu2 = Y2 ~ X1 + X3,
+ rho = ~ 1),
+ model = "bprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate probit specification, there are three equations
+which correspond to each dependent variable ($Y_1$, $Y_2$), and the
+correlation parameter $\rho$. Since the correlation parameter does
+not correspond to one of the dependent variables, the model estimates
+$\rho$ as a constant by default. Hence, only two formulas (for
+$\mu_1$ and $\mu_2$) are required. If the explanatory variables for
+$\mu_1$ and $\mu_2$ are the same and effects are estimated separately
+for each parameter, you may use the following short hand:
+\begin{Schunk}
+\begin{Sinput}
+> fml <- list(cbind(Y1, Y2) ~ X1 + X2)
+\end{Sinput}
+\end{Schunk}
+which has the same meaning as:
+\begin{Schunk}
+\begin{Sinput}
+> fml <- list(mu1 = Y1 ~ X1 + X2, mu2 = Y2 ~ X1 + X2, rho = ~1)
+\end{Sinput}
+\end{Schunk}
+You may use the function {\tt tag()} to constrain variables across
+equations. The {\tt tag()} function takes a variable and a label for
+the effect parameter. Below, the constrained effect of {\tt
+x3} in both equations is called the {\tt age} parameter:
+\begin{Schunk}
+\begin{Sinput}
+> fml <- list(mu1 = y1 ~ x1 + tag(x3, "age"), mu2 = y2 ~ x2 + tag(x3,
++ "age"))
+\end{Sinput}
+\end{Schunk}
+You may also constrain different variables across different equations
+to have the same effect.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bp}
+
+Load the data and estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
++ model = "bprobit", data = sanction)
+\end{Sinput}
+\end{Schunk}
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the correlation coefficient;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share a correlation parameter.
+\newline \newline 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):
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out1, cost = 1)
+> x.high <- setx(z.out1, cost = 4)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values and first differences:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/bprobit-BasicExamplePlot}
+\end{center}
+
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.probit}
+
+Using the sample data \texttt{sanction}, estimate the statistical model,
+with {\tt import} a function of {\tt coop} in the first equation and
+{\tt export} a function of {\tt cost} and {\tt target} in the second equation:
+\begin{Schunk}
+\begin{Sinput}
+> fml2 <- list(mu1 = import ~ coop, mu2 = export ~ cost + target)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(fml2, model = "bprobit", data = sanction)
+> summary(z.out2)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means:
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2)
+\end{Sinput}
+\end{Schunk}
+Simulate draws from the posterior distribution:
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/bprobit-JointEstimationPlot}
+\end{center}
+
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model
+\label{pdep.p}
+
+Using the sample data \texttt{sanction}.
+The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated. For example,
+\begin{Schunk}
+\begin{Sinput}
+> fml3 <- list(mu1 = import ~ tag(coop, "coop") + tag(cost, "cost") +
++ tag(target, "target"), mu2 = export ~ tag(coop, "coop") +
++ tag(cost, "cost") + tag(target, "target"))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out3 <- zelig(fml3, model = "bprobit", data = sanction)
+> summary(z.out3)
+\end{Sinput}
+\end{Schunk}
+
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
+Example~\ref{basic.bp} which returns two parameter estimates for each
+of the explanatory variables. \newline \newline Set values for the
+explanatory variables:
+\begin{Schunk}
+\begin{Sinput}
+> x.out3 <- setx(z.out3, cost = 1:4)
+\end{Sinput}
+\end{Schunk}
+Draw simulated expected values:
+\begin{Schunk}
+\begin{Sinput}
+> s.out3 <- sim(z.out3, x = x.out3)
+> summary(s.out3)
+\end{Sinput}
+\end{Schunk}
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index $i$). We model the joint
+outcome $(Y_1$, $Y_2)$ using two marginal probabilities for each
+dependent variable, and the correlation parameter, which describes how
+the two dependent variables are related.
+%Define $Y_{rs}$ such that it
+%is equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise where $r$
+%and $s$ take a value of either 0 or 1. Then, the model is defined as
+%follows,
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by two latent (unobserved)
+ continuous variables which follow the bivariate Normal distribution:
+\begin{eqnarray*}
+ \left ( \begin{array}{c}
+ Y_1^* \\
+ Y_2^*
+ \end{array}
+ \right ) &\sim &
+ N_2 \left \{ \left (
+ \begin{array}{c}
+ \mu_1 \\ \mu_2
+ \end{array} \right ), \left( \begin{array}{cc}
+ 1 & \rho \\
+ \rho & 1
+ \end{array} \right) \right\},
+\end{eqnarray*}
+where $\mu_j$ is a mean for $Y_j^*$ and $\rho$ is a scalar correlation
+parameter. The following observation mechanism links the observed
+dependent variables, $Y_j$, with these latent variables
+\begin{eqnarray*}
+Y_j & = & \left \{ \begin{array}{cc}
+ 1 & {\rm if} \; Y_j^* \ge 0, \\
+ 0 & {\rm otherwise.}
+ \end{array}
+ \right.
+\end{eqnarray*}
+
+%Alternatively, the stochastic component for the observed dependent
+%variables can be written as
+%\begin{eqnarray*}
+% Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+% Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+% Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+%\end{eqnarray*}
+%where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+%$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$. Each of these joint
+%probabilities is modeled using the bivariate normal cumulative
+%distribution function.
+
+\item The \emph{systemic components} for each observation are
+ \begin{eqnarray*}
+ \mu_j & = & x_{j} \beta_j \quad {\rm for} \quad j=1,2, \\
+ \rho & = & \frac{\exp(x_3 \beta_3) - 1}{\exp(x_3 \beta_3) + 1}.
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+For $n$ simulations, expected values form an $n \times 4$
+matrix.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the binomial probit model
+ are the predicted joint probabilities. Simulations of $\beta_1$,
+ $\beta_2$, and $\beta_3$ (drawn form their sampling distributions)
+ are substituted into the systematic components, to find simulations
+ of the predicted joint probabilities $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$:
+\begin{eqnarray*}
+\pi_{11} &= \Pr(Y_1^* \geq 0 , Y_2^* \geq 0) &= \int_0^{\infty}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^* \\
+\pi_{10} &= \Pr(Y_1^* \geq 0 , Y_2^* < 0) &= \int_0^{\infty}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{01} &= \Pr(Y_1^* < 0 , Y_2^* \geq 0) &= \int_{-\infty}^{0}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{11} &= \Pr(Y_1^* < 0 , Y_2^* < 0) &= \int_{-\infty}^{0}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\end{eqnarray*}
+where $r$ and $s$ may take a value of either 0 or 1, $\phi_2$ is the
+bivariate Normal density.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ multinomial distribution given the expected joint probabilities.
+
+\item The first difference ({\tt qi\$fd}) in each of the predicted joint
+ probabilities are given by
+ $$\textrm{FD}_{rs} = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s
+ \mid x).$$
+
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+ probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+ the counterfactual expected value of $Y_{ij}$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+ \widehat{Y_{ij}(t_i=0)}\right\} \textrm{ for } j = 1,2,
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+ $Y_{ij}$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
+ model = "bprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through
+\texttt{summary(z.out)}. Other elements available through the {\tt
+ \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times 3$ matrix of the linear
+ predictors $x_j \beta_j$.
+ \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays indexed by simulation
+ $\times$ quantity $\times$ {\tt x}-observation (for more than one
+ {\tt x}-observation; otherwise the quantities are matrices). Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values (joint predicted
+ probabilities) for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+ distribution defined by the joint predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+ probabilities for given {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate probit function is part of the VGAM package by Thomas Yee.
+Please cite this model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM package. Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/coda_diag.tex b/inst/doc/coda_diag.tex
new file mode 100644
index 0000000..c915940
--- /dev/null
+++ b/inst/doc/coda_diag.tex
@@ -0,0 +1,32 @@
+\subsubsection{Convergence}
+
+Users should verify that the Markov Chain converges to its stationary
+distribution. After running the \texttt{zelig()} function but before
+performing \texttt{setx()}, users may conduct the following
+convergence diagnostics tests:
+
+\begin{itemize}
+\item \texttt{geweke.diag(z.out\$coefficients)}: The Geweke diagnostic tests
+the null hypothesis that the Markov chain is in the stationary distribution
+and produces z-statistics for each estimated parameter.
+
+\item \texttt{heidel.diag(z.out\$coefficients)}: The Heidelberger-Welch
+diagnostic first tests the null hypothesis that the Markov Chain is in the
+stationary distribution and produces p-values for each estimated parameter.
+ Calling \texttt{heidel.diag()} also produces
+output that indicates whether the mean of a marginal posterior distribution
+can be estimated with sufficient precision, assuming that the Markov Chain is
+in the stationary distribution.
+
+\item \texttt{raftery.diag(z.out\$coefficients)}: The Raftery diagnostic
+indicates how long the Markov Chain should run before considering draws from
+the marginal posterior distributions sufficiently representative of the
+stationary distribution.
+\end{itemize}
+
+\noindent If there is evidence of non-convergence, adjust the values
+for \texttt{burnin} and \texttt{mcmc} and rerun \texttt{zelig()}.
+
+Advanced users may wish to refer to \texttt{help(geweke.diag)},
+\texttt{help(heidel.diag)}, and \texttt{help(raftery.diag)} for more
+information about these diagnostics.
diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
index 7adc70e..de40dad 100644
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+\@writefile{toc}{\contentsline {subsubsection}{See Also}{350}{section*.451}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{350}{section*.452}}
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deleted file mode 100644
index ce3056f..0000000
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deleted file mode 100644
index 954f7b3..0000000
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index f42b95a..63eacb6 100644
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diff --git a/inst/doc/commandsRd/CVS.tex b/inst/doc/commandsRd/CVS.tex
new file mode 100644
index 0000000..776df6c
--- /dev/null
+++ b/inst/doc/commandsRd/CVS.tex
@@ -0,0 +1,2 @@
+\section{{\tt }: }\label{ss:}
+
diff --git a/inst/doc/commandsRd/MatchIt.url.tex b/inst/doc/commandsRd/MatchIt.url.tex
new file mode 100644
index 0000000..34cd196
--- /dev/null
+++ b/inst/doc/commandsRd/MatchIt.url.tex
@@ -0,0 +1,6 @@
+\section{{\tt MatchIt.url}: Table of links for Zelig}\label{ss:MatchIt.url}
+\keyword{datasets}{MatchIt.url}
+\begin{Description}\relax
+Table of linds for \code{help.zelig} for the companion MatchIt package.
+\end{Description}
+
diff --git a/inst/doc/commandsRd/PErisk.tex b/inst/doc/commandsRd/PErisk.tex
new file mode 100644
index 0000000..e54e5f0
--- /dev/null
+++ b/inst/doc/commandsRd/PErisk.tex
@@ -0,0 +1,67 @@
+\section{{\tt PErisk}: Political Economic Risk Data from 62 Countries in 1987}\label{ss:PErisk}
+\keyword{datasets}{PErisk}
+\begin{Description}\relax
+Political Economic Risk Data from 62 Countries in 1987.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(PErisk)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame with 62 observations on the following 6 variables.
+All data points are from 1987. See Quinn (2004) for more
+details.
+
+country: a factor with levels 'Argentina' 'Australia' 'Austria'
+'Bangladesh' 'Belgium' 'Bolivia' 'Botswana' 'Brazil' 'Burma'
+'Cameroon' 'Canada' 'Chile' 'Colombia' 'Congo-Kinshasa'
+'Costa Rica' 'Cote d'Ivoire' 'Denmark' 'Dominican Republic'
+'Ecuador' 'Finland' 'Gambia, The' 'Ghana' 'Greece' 'Hungary'
+'India' 'Indonesia' 'Iran' 'Ireland' 'Israel' 'Italy' 'Japan'
+'Kenya' 'Korea, South' 'Malawi' 'Malaysia' 'Mexico' 'Morocco'
+'New Zealand' 'Nigeria' 'Norway' 'Papua New Guinea'
+'Paraguay' 'Philippines' 'Poland' 'Portugal' 'Sierra Leone'
+'Singapore' 'South Africa' 'Spain' 'Sri Lanka' 'Sweden'
+'Switzerland' 'Syria' 'Thailand' 'Togo' 'Tunisia' 'Turkey'
+'United Kingdom' 'Uruguay' 'Venezuela' 'Zambia' 'Zimbabwe'
+
+courts: an ordered factor with levels '0' < '1'.'courts' is an
+indicator of whether the country in question is judged to
+have an independent judiciary. From Henisz (2002).
+
+barb2: a numeric vector giving the natural log of the black market
+premium in each country. The black market premium is coded as
+the black market exchange rate (local currency per dollar)
+divided by the official exchange rate minus 1. From
+Marshall, Gurr, and Harff (2002).
+
+prsexp2: an ordered factor with levels '0' < '1' < '2' < '3' < '4'
+< '5', giving the lack of expropriation risk. From Marshall,
+Gurr, and Harff (2002).
+
+prscorr2: an ordered factor with levels '0' < '1' < '2' < '3' < '4'
+< '5', measuring the lack of corruption. From Marshall, Gurr,
+and Harff (2002).
+
+gdpw2: a numeric vector giving the natural log of real GDP per
+worker in 1985 international prices. From Alvarez et al.
+(1999).
+\end{Format}
+\begin{Source}\relax
+Mike Alvarez, Jose Antonio Cheibub, Fernando Limongi, and Adam
+Przeworski. 1999. ``ACLP Political and Economic Database.'' <URL:
+http://www.ssc.upenn.edu/~cheibub/data/>.
+
+Witold J. Henisz. 2002. ``The Political Constraint Index (POLCON)
+Dataset.'' \ <URL:
+http://www-management.wharton.upenn.edu/henisz/POLCON/ContactInfo.
+html>.
+
+Monty G. Marshall, Ted Robert Gurr, and Barbara Harff. 2002.
+``State Failure Task Force Problem Set.'' <URL:
+http://www.cidcm.umd.edu/inscr/stfail/index.htm>.
+\end{Source}
+\begin{References}\relax
+Kevin M. Quinn. 2004. ``Bayesian Factor Analysis for Mixed Ordinal
+and Continuous Response.'' \emph{Political Analyis}. Vol. 12, pp.338--353.
+\end{References}
+
diff --git a/inst/doc/commandsRd/SupremeCourt.tex b/inst/doc/commandsRd/SupremeCourt.tex
new file mode 100644
index 0000000..fa043da
--- /dev/null
+++ b/inst/doc/commandsRd/SupremeCourt.tex
@@ -0,0 +1,26 @@
+\section{{\tt SupremeCourt}: U.S. Supreme Court Vote Matrix}\label{ss:SupremeCourt}
+\keyword{datasets}{SupremeCourt}
+\begin{Description}\relax
+This dataframe contains a matrix votes cast by U.S. Supreme Court
+justices in all cases in the 2000 term.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(SupremeCourt)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+The dataframe has contains data for justices Rehnquist, Stevens,
+O'Connor, Scalia, Kennedy, Souter, Thomas, Ginsburg, and Breyer
+for the 2000 term of the U.S. Supreme Court. It contains data
+from 43 non-unanimous cases. The votes are coded liberal (1) and
+conservative (0) using the protocol of Spaeth (2003). The unit
+of analysis is the case citation (ANALU=0). We are concerned with
+formally decided cases issued with written opinions, after full
+oral argument and cases decided by an equally divided vote
+(DECTYPE=1,5,6,7).
+\end{Format}
+\begin{Source}\relax
+Harold J. Spaeth (2005). ``Original United States Supreme Court
+Database: 1953-2004 Terms.''
+<URL:http://www.as.uky.edu/polisci/ulmerproject/sctdata.htm>.
+\end{Source}
+
diff --git a/inst/doc/commandsRd/Weimar.tex b/inst/doc/commandsRd/Weimar.tex
new file mode 100644
index 0000000..2768278
--- /dev/null
+++ b/inst/doc/commandsRd/Weimar.tex
@@ -0,0 +1,26 @@
+\section{{\tt Weimar}: 1932 Weimar election data}\label{ss:Weimar}
+\keyword{datasets}{Weimar}
+\begin{Description}\relax
+This data set contains election results for 10 kreise (equivalent to precincts) from the 1932 Weimar (German) election.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(Weimar)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 11 variables and 10 observations. The variables are {
+{Nazi}{Number of votes for the Nazi party}
+{Government}{Number of votes for the Government}
+{Communists}{Number of votes for the Communist party}
+{FarRight}{Number of votes for far right parties}
+{Other}{Number of votes for other parties, and non-voters}
+{shareunemployed}{Proportion unemployed}
+{shareblue}{Proportion working class}
+{sharewhite}{Proportion white-collar workers}
+{sharedomestic}{Proportion domestic servants}
+{shareprotestants}{Proportion Protestant}
+}
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+
diff --git a/inst/doc/commandsRd/Zelig.tex b/inst/doc/commandsRd/Zelig.tex
new file mode 100644
index 0000000..70e3e5c
--- /dev/null
+++ b/inst/doc/commandsRd/Zelig.tex
@@ -0,0 +1,62 @@
+\section{{\tt zelig}: Estimating a Statistical Model}\label{ss:zelig}
+\aliasA{Zelig}{zelig}{Zelig}
+\keyword{file}{zelig}
+\begin{Description}\relax
+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).
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+z.out <- zelig(formula, model, data, by, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{formula}] a symbolic representation of the model to be
+estimated, in the form \code{y \textasciitilde{}\\, 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\textasciicircum{}2)}.
+\item[\code{model}] the name of a statistical model, enclosed in \code{""}.
+Type \code{help.zelig("models")} to see a list of currently supported
+models.
+\item[\code{data}] the name of a data frame containing the variables
+referenced in the formula, or a list of multiply imputed data frames
+each having the same variable names and row numbers (created by \code{mi}).
+\item[\code{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 \textasciitilde{} x1 + x2, data = mydata, model = "ls", by = "state")}
+You may also use \code{by} to run models using MatchIt subclass.
+\item[\code{...}] additional arguments passed to \code{zelig},
+depending on the model to be estimated.
+\end{ldescription}
+\end{Arguments}
+\begin{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)}.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual is available at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/Zelig.url.tex b/inst/doc/commandsRd/Zelig.url.tex
new file mode 100644
index 0000000..b4beedf
--- /dev/null
+++ b/inst/doc/commandsRd/Zelig.url.tex
@@ -0,0 +1,6 @@
+\section{{\tt Zelig.url}: Table of links for Zelig}\label{ss:Zelig.url}
+\keyword{datasets}{Zelig.url}
+\begin{Description}\relax
+Table of linds for \code{help.zelig} for the core Zelig package.
+\end{Description}
+
diff --git a/inst/doc/commandsRd/approval.tex b/inst/doc/commandsRd/approval.tex
new file mode 100644
index 0000000..a4caeb3
--- /dev/null
+++ b/inst/doc/commandsRd/approval.tex
@@ -0,0 +1,20 @@
+\section{{\tt approval}: U.S. Presidential Approval Data}\label{ss:approval}
+\keyword{datasets}{approval}
+\begin{Description}\relax
+Monthy public opinion data for 2001-2006.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(approval)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 8 variables ("month", "year", "approve",
+"disapprove", "unsure", "sept.oct.2001", "iraq.war", and "avg.price")
+and 65 observations.
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+\begin{References}\relax
+Stuff here
+\end{References}
+
diff --git a/inst/doc/commandsRd/coalition.tex b/inst/doc/commandsRd/coalition.tex
new file mode 100644
index 0000000..4cfc6d6
--- /dev/null
+++ b/inst/doc/commandsRd/coalition.tex
@@ -0,0 +1,31 @@
+\section{{\tt coalition}: Coalition Dissolution in Parliamentary Democracies}\label{ss:coalition}
+\keyword{datasets}{coalition}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(coalition)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+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).
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commandsRd/current.packages.tex b/inst/doc/commandsRd/current.packages.tex
new file mode 100644
index 0000000..47b8fd3
--- /dev/null
+++ b/inst/doc/commandsRd/current.packages.tex
@@ -0,0 +1,35 @@
+\section{{\tt current.packages}: Find all packages in a dependency chain}\label{ss:current.packages}
+\keyword{file}{current.packages}
+\begin{Description}\relax
+Use \code{current.packages} to find all the packages suggested or
+required by a given package, and the currently installed version number
+for each.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+current.packages(package)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{package}] a character string corresponding to the name of an
+installed package
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A matrix containing the current version number of the packages
+suggested or required by \code{package}.
+\end{Value}
+\begin{Author}\relax
+Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{packageDescription}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+current.packages("Zelig")
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/dims.tex b/inst/doc/commandsRd/dims.tex
new file mode 100644
index 0000000..67acdd9
--- /dev/null
+++ b/inst/doc/commandsRd/dims.tex
@@ -0,0 +1,37 @@
+\section{{\tt dims}: Return Dimensions of Vectors, Arrays, and Data Frames}\label{ss:dims}
+\keyword{file}{dims}
+\begin{Description}\relax
+Retrieve the dimensions of a vector, array, or data frame.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+dims(x)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{x}] An R object. For example, a vector, matrix, array, or data
+frame.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+The function \code{dims} performs exactly the same as \code{dim}, and
+additionally returns the \code{length} of vectors (treating them as
+one-dimensional arrays).
+\end{Value}
+\begin{Author}\relax
+Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{dim}, \code{length}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+a <- 1:12
+dims(a)
+
+a <- matrix(1, nrow = 4, ncol = 9)
+dims(a)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/eidat.tex b/inst/doc/commandsRd/eidat.tex
new file mode 100644
index 0000000..40c02cf
--- /dev/null
+++ b/inst/doc/commandsRd/eidat.tex
@@ -0,0 +1,14 @@
+\section{{\tt eidat}: Simulation Data for Ecological Inference}\label{ss:eidat}
+\keyword{datasets}{eidat}
+\begin{Description}\relax
+This dataframe contains a simulated data set to illustrate the models
+for ecological inference.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(eidat)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 4 variables ("t0", "t1", "x0", "x1") and 10
+observations.
+\end{Format}
+
diff --git a/inst/doc/commandsRd/friendship.tex b/inst/doc/commandsRd/friendship.tex
new file mode 100644
index 0000000..034ceeb
--- /dev/null
+++ b/inst/doc/commandsRd/friendship.tex
@@ -0,0 +1,15 @@
+\section{{\tt friendship}: Simulated Example of Schoolchildren Friendship Network}\label{ss:friendship}
+\keyword{datasets}{friendship}
+\begin{Description}\relax
+This data set contains five sociomatrices of simulated data on friendship ties among schoolchildren.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(friendship)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", and "perpower".
+\end{Format}
+\begin{Source}\relax
+fictitious
+\end{Source}
+
diff --git a/inst/doc/commandsRd/gsource.tex b/inst/doc/commandsRd/gsource.tex
new file mode 100644
index 0000000..863a91c
--- /dev/null
+++ b/inst/doc/commandsRd/gsource.tex
@@ -0,0 +1,49 @@
+\section{{\tt gsource}: Read Data As a Space-Delimited Table}\label{ss:gsource}
+\keyword{file}{gsource}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+gsource(var.names = NULL, variables)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{var.names}] An optional vector of character strings representing
+the column names. By default, \code{var.names = NULL}.
+\item[\code{variables}] A single character string representing the data.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+The output from \code{gsource} is a data frame, which you may save to
+an object in your workspace.
+\end{Value}
+\begin{Author}\relax
+Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{read.table}, \code{scan}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+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 ")
+## End(Not run)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/help.zelig.tex b/inst/doc/commandsRd/help.zelig.tex
new file mode 100644
index 0000000..da777c7
--- /dev/null
+++ b/inst/doc/commandsRd/help.zelig.tex
@@ -0,0 +1,29 @@
+\section{{\tt help.zelig}: HTML Help for Zelig Commands and Models}\label{ss:help.zelig}
+\keyword{documentation}{help.zelig}
+\begin{Description}\relax
+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}.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+help.zelig(...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{...}] 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.
+\end{ldescription}
+\end{Arguments}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau<\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The complete document is available online at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/hoff.tex b/inst/doc/commandsRd/hoff.tex
new file mode 100644
index 0000000..203f2ca
--- /dev/null
+++ b/inst/doc/commandsRd/hoff.tex
@@ -0,0 +1,24 @@
+\section{{\tt hoff}: Social Security Expenditure Data}\label{ss:hoff}
+\keyword{datasets}{hoff}
+\begin{Description}\relax
+This data set contains annual social security expenditure (as percent
+of budget lagged by two years), the
+relative frequency of mentions social justice received in the party's
+platform in each year, and whether the president is Republican or
+Democrat.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(hoff)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 5 variables ("year", "L2SocSec", "Just503D", "Just503R", "RGovDumy") and 36 observations.
+\end{Format}
+\begin{Source}\relax
+ICPSR (replication dataset s1109)
+\end{Source}
+\begin{References}\relax
+Gary King and Michael Laver. ``On Party Platforms, Mandates, and
+Government Spending,'' \emph{American Political Science Review},
+Vol. 87, No. 3 (September, 1993): pp. 744-750.
+\end{References}
+
diff --git a/inst/doc/commandsRd/immigration.tex b/inst/doc/commandsRd/immigration.tex
new file mode 100644
index 0000000..56a5f4f
--- /dev/null
+++ b/inst/doc/commandsRd/immigration.tex
@@ -0,0 +1,28 @@
+\section{{\tt immigration}: Individual Preferences Over Immigration Policy}\label{ss:immigration}
+\aliasA{immi1}{immigration}{immi1}
+\aliasA{immi2}{immigration}{immi2}
+\aliasA{immi3}{immigration}{immi3}
+\aliasA{immi4}{immigration}{immi4}
+\aliasA{immi5}{immigration}{immi5}
+\keyword{datasets}{immigration}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Format}\relax
+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.
+\end{Format}
+\begin{Source}\relax
+National Election Survey
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commandsRd/macro.tex b/inst/doc/commandsRd/macro.tex
new file mode 100644
index 0000000..32e2cbc
--- /dev/null
+++ b/inst/doc/commandsRd/macro.tex
@@ -0,0 +1,28 @@
+\section{{\tt macro}: Macroeconomic Data}\label{ss:macro}
+\keyword{datasets}{macro}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(macro)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 6 variables ("country", "year", "gdp",
+"unem", "capmob", and "trade") and 350 observations.
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commandsRd/match.data.tex b/inst/doc/commandsRd/match.data.tex
new file mode 100644
index 0000000..09d1d89
--- /dev/null
+++ b/inst/doc/commandsRd/match.data.tex
@@ -0,0 +1,43 @@
+\section{{\tt match.data}: Output matched data sets}\label{ss:match.data}
+\keyword{methods}{match.data}
+\begin{Description}\relax
+The code \code{match.data} creates output data sets from the \code{matchit}
+matching algorithm.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+match.data <- match.data(object, group = "all")
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] Stored output from \code{matchit}.
+\item[\code{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.
+\end{ldescription}
+\end{Arguments}
+\begin{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:
+\begin{ldescription}
+\item[\code{pscore}] The propensity score for each unit.
+\item[\code{psclass}] The subclass index for each unit (if applicable).
+\item[\code{psweights}] The weight for each unit (generated from the matching
+procedure).
+\end{ldescription}
+
+See the \code{matchit} documentation for more details on these items.
+\end{Value}
+\begin{Author}\relax
+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}>
+\end{Author}
+\begin{SeeAlso}\relax
+The complete documentation for \code{matchit} is available online at
+\url{http://gking.harvard.edu/matchit}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/mexico.tex b/inst/doc/commandsRd/mexico.tex
new file mode 100644
index 0000000..90d1bfa
--- /dev/null
+++ b/inst/doc/commandsRd/mexico.tex
@@ -0,0 +1,24 @@
+\section{{\tt mexico}: Voting Data from the 1988 Mexican Presidental Election}\label{ss:mexico}
+\keyword{datasets}{mexico}
+\begin{Description}\relax
+This dataset contains voting data for the 1988 Mexican presidential
+election.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(mexico)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 33 variables and 1,359 observations.
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commands/mi.aux b/inst/doc/commandsRd/mi.aux
similarity index 50%
rename from inst/doc/commands/mi.aux
rename to inst/doc/commandsRd/mi.aux
index 34e3439..bb7623b 100644
--- a/inst/doc/commands/mi.aux
+++ b/inst/doc/commandsRd/mi.aux
@@ -1,14 +1,8 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.2}{\tt mi}: Create a list of multiply imputed data frames}{115}{section.11.2}}
-\newlabel{mi.command}{{11.2}{115}{{\tt mi}: Create a list of multiply imputed data frames\relax }{section.11.2}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{115}{section*.95}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{115}{section*.96}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{115}{section*.97}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{115}{section*.98}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{115}{section*.99}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{115}{section*.100}}
-\@setckpt{commands/mi}{
-\setcounter{page}{116}
+\@writefile{toc}{\contentsline {section}{\numberline {11.2}{\tt mi}: Bundle multiply imputed data sets as a list}{114}{section.11.2}}
+\newlabel{ss:mi}{{11.2}{114}{{\tt mi}: Bundle multiply imputed data sets as a list\relax }{section.11.2}{}}
+\@setckpt{commandsRd/mi}{
+\setcounter{page}{115}
\setcounter{equation}{0}
\setcounter{enumi}{4}
\setcounter{enumii}{3}
@@ -25,6 +19,9 @@
\setcounter{subparagraph}{0}
\setcounter{figure}{0}
\setcounter{table}{0}
+\setcounter{LT at tables}{0}
+\setcounter{LT at chunks}{0}
+\setcounter{FancyVerbLine}{0}
\setcounter{NAT at ctr}{0}
\setcounter{parentequation}{0}
\setcounter{Item}{216}
@@ -33,9 +30,9 @@
\setcounter{lchapter}{0}
\setcounter{lsection}{0}
\setcounter{lsubsection}{0}
-\setcounter{lsubsubsection}{0}
+\setcounter{lsubsubsection}{7}
\setcounter{lparagraph}{0}
\setcounter{lsubparagraph}{0}
\setcounter{lsubsubparagraph}{0}
-\setcounter{section at level}{3}
+\setcounter{section at level}{1}
}
diff --git a/inst/doc/commandsRd/mi.tex b/inst/doc/commandsRd/mi.tex
new file mode 100644
index 0000000..a11b51b
--- /dev/null
+++ b/inst/doc/commandsRd/mi.tex
@@ -0,0 +1,39 @@
+\section{{\tt mi}: Bundle multiply imputed data sets as a list}\label{ss:mi}
+\keyword{methods}{mi}
+\begin{Description}\relax
+The code \code{mi} bundles multiply imputed data sets as a
+list for further analysis.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+ mi(...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{...}] multiply imputed data sets, separated by commas. The
+arguments can be tagged by \code{name=data} where \code{name} is the
+element named used for the data set \code{data}.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+The list containing each multiply imputed data set as an
+element. The class name is \code{mi}. The list can be inputted into
+\code{zelig} for statistical analysis with multiply imputed data
+sets. See \code{zelig} for details.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual is available at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+ data(immi1, immi2, immi3, immi4, immi5)
+ mi(immi1, immi2, immi3, immi4, immi5)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/mid.tex b/inst/doc/commandsRd/mid.tex
new file mode 100644
index 0000000..02688ca
--- /dev/null
+++ b/inst/doc/commandsRd/mid.tex
@@ -0,0 +1,28 @@
+\section{{\tt mid}: Militarized Interstate Disputes}\label{ss:mid}
+\keyword{datasets}{mid}
+\begin{Description}\relax
+A small sample from the militarized interstate disputes database,
+available at \url{http://pss.la.psu.edu/MID_DATA.HTM}.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(mid)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+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.
+\end{Format}
+\begin{Source}\relax
+Militarized Interstate Disputes database
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commandsRd/model.end.tex b/inst/doc/commandsRd/model.end.tex
new file mode 100644
index 0000000..e6e4326
--- /dev/null
+++ b/inst/doc/commandsRd/model.end.tex
@@ -0,0 +1,44 @@
+\section{{\tt model.end}: Cleaning up after optimization}\label{ss:model.end}
+\keyword{utilities}{model.end}
+\begin{Description}\relax
+The \code{model.end} function creates a list of regression output from \code{\LinkA{optim}{optim}}
+output. The list includes coefficients (from the \code{\LinkA{optim}{optim}} \code{par} output), a
+variance-covariance matrix (from the \code{\LinkA{optim}{optim}} Hessian output), and any terms, contrasts, or
+xlevels (from the model frame). Use \code{model.end} after calling \code{\LinkA{optim}{optim}}, but before
+assigning a
+class to the regression output.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+model.end(res, mf)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{res}] the output from \code{\LinkA{optim}{optim}} or another fitting-algorithm
+\item[\code{mf}] the model frame output by \code{\LinkA{model.frame}{model.frame}}
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A list of regression output, including:
+\begin{ldescription}
+\item[\code{coefficients}] the optimized parameters
+\item[\code{variance}] the variance-covariance matrix (the negative
+inverse of the Hessian matrix returned from the optimization
+procedure)
+\item[\code{terms}] the terms object. See \code{\LinkA{terms.object}{terms.object}}
+for more information
+\item[\code{...}] additional elements passed from \code{res}
+\end{ldescription}
+
+normal-bracket37bracket-normal
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual at \url{http://gking.harvard.edu/zelig} for examples.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/model.frame.multiple.tex b/inst/doc/commandsRd/model.frame.multiple.tex
new file mode 100644
index 0000000..1c30acd
--- /dev/null
+++ b/inst/doc/commandsRd/model.frame.multiple.tex
@@ -0,0 +1,56 @@
+\section{{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula}\label{ss:model.frame.multiple}
+\keyword{utilities}{model.frame.multiple}
+\begin{Description}\relax
+Use \code{model.frame.multiple} after \code{\LinkA{parse.par}{parse.par}} to create a
+data frame of the unique variables identified in the formula (or list
+of formulas).
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+model.frame.multiple(formula, data, eqn = NULL, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{formula}] a list of formulas of class \code{"multiple"}, returned from \code{\LinkA{parse.par}{parse.par}}
+\item[\code{data}] a data frame containing all the variables used in \code{formula}
+\item[\code{eqn}] an optional character string or vector of character strings specifying
+the equations (specified in \code{describe.mymodel}) for which you would like to
+pull out the relevant variables.
+\item[\code{...}] additional arguments passed to
+\code{\LinkA{model.frame.default}{model.frame.default}}
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+The output is a data frame (with a terms attribute) containing all the
+unique explanatory and response variables identified in the list of
+formulas. By default, missing (\code{NA}) values are listwise deleted.
+
+If \code{as.factor} appears on the left-hand side, the response
+variables will be returned as an indicator (0/1) matrix with columns
+corresponding to the unique levels in the factor variable.
+
+If any formula contains more than one \code{tag} statement, \code{model.frame.multiple}
+will return the original variable in the data frame and use the \code{tag} information in the terms
+attribute only.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{model.matrix.default}{model.matrix.default}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+data(sanction)
+formulae <- list(import ~ coop + cost + target,
+ export ~ coop + cost + target)
+fml <- parse.formula(formulae, model = "bivariate.logit")
+D <- model.frame(fml, data = sanction)
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/model.matrix.multiple.tex b/inst/doc/commandsRd/model.matrix.multiple.tex
new file mode 100644
index 0000000..da76b16
--- /dev/null
+++ b/inst/doc/commandsRd/model.matrix.multiple.tex
@@ -0,0 +1,83 @@
+\section{{\tt model.matrix.multiple}: Design matrix for multivariate models}\label{ss:model.matrix.multiple}
+\keyword{utilities}{model.matrix.multiple}
+\begin{Description}\relax
+Use \code{model.matrix.multiple} after \code{\LinkA{parse.formula}{parse.formula}} to
+create a design matrix for multiple-equation models.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+model.matrix.multiple(object, data, shape = "compact", eqn = NULL, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] the list of formulas output from \code{\LinkA{parse.formula}{parse.formula}}
+\item[\code{data}] a data frame created with \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
+\item[\code{shape}] a character string specifying the shape of the outputed matrix. Available options are
+\Itemize{
+\item["compact"] (default) the output matrix will be an \eqn{n \times v}{n x v},
+where \eqn{v}{} is the number of unique variables in all of the equations
+(including the intercept term)
+\item["array"] the output is an \eqn{n \times K \times J}{n x K x J} array where \eqn{J}{} is the
+total number of equations and \eqn{K}{} is the total number of parameters
+across all the equations. If a variable is not in a certain equation,
+it is observed as a vector of 0s.
+\item["stacked"] the output will be a \eqn{2n \times K}{2n x K} matrix where \eqn{K}{} is the total number of
+parameters across all the equations.
+}
+\item[\code{eqn}] a character string or a vector of character strings identifying the equations from which to
+construct the design matrix. The defaults to \code{NULL}, which only uses the systematic
+parameters (for which \code{DepVar = TRUE} in the appropriate \code{describe.model} function)
+\item[\code{...}] additional arguments passed to \code{model.matrix.default}
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A design matrix or array, depending on the options chosen in \code{shape}, with appropriate terms
+attributes.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+
+# Let's say that the name of the model is "bivariate.probit", and
+# the corresponding describe function is describe.bivariate.probit(),
+# which identifies mu1 and mu2 as systematic components, and an
+# ancillary parameter rho, which may be parameterized, but is estimated
+# as a scalar by default. Let par be the parameter vector (including
+# parameters for rho), formulae a user-specified formula, and mydata
+# the user specified data frame.
+
+# Acceptable combinations of parse.par() and model.matrix() are as follows:
+## Setting up
+## Not run:
+data(sanction)
+formulae <- cbind(import, export) ~ coop + cost + target
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = sanction)
+terms <- attr(D, "terms")
+
+## Intuitive option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
+eta <- X
+
+## Memory-efficient (compact) option (default)
+Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
+eta <- X
+
+## Computationally-efficient (array) option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
+eta <- apply(X, 3, '
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/network.aux b/inst/doc/commandsRd/network.aux
new file mode 100644
index 0000000..d287bdd
--- /dev/null
+++ b/inst/doc/commandsRd/network.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {11.3}{\tt network}: Format matricies into a data frame for social network analysis}{115}{section.11.3}}
+\newlabel{ss:network}{{11.3}{115}{{\tt network}: Format matricies into a data frame for social network analysis\relax }{section.11.3}{}}
+\@setckpt{commandsRd/network}{
+\setcounter{page}{116}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
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+}
diff --git a/inst/doc/commandsRd/network.tex b/inst/doc/commandsRd/network.tex
new file mode 100644
index 0000000..03cce83
--- /dev/null
+++ b/inst/doc/commandsRd/network.tex
@@ -0,0 +1,42 @@
+\section{{\tt network}: Format matricies into a data frame for social network analysis}\label{ss:network}
+\keyword{methods}{network}
+\begin{Description}\relax
+This function accepts individual matricies as its inputs,
+combining the input matricies into a single data frame which can then be
+used in the \code{data} argument for social network analysis (models
+\code{"netlm"} and \code{"netlogit"}) in Zelig.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+network(...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{...}] matricies representing variables, with rows and columns corresponding to
+individuals. These can be given as named arguments and should be
+given in the order the in which the user wishes them to appear in
+the output data frame.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+The \code{network} function creates a data frame which
+contains matricies instead of vectors as its variables. Inputs to the
+function should all be square matricies and can be given as named
+arguments.
+\end{Value}
+\begin{Author}\relax
+Skyler J. Cranmer
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual is available at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}## Not run:
+## Let Var1, Var2, Var3, Var4, and Var5 be matrices
+friendship <- network(Var1, Var2, Var3, Var4, Var5)
+## End(Not run)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/newpainters.tex b/inst/doc/commandsRd/newpainters.tex
new file mode 100644
index 0000000..882a6a4
--- /dev/null
+++ b/inst/doc/commandsRd/newpainters.tex
@@ -0,0 +1,32 @@
+\section{{\tt newpainters}: The Discretized Painter's Data of de Piles}\label{ss:newpainters}
+\keyword{datasets}{newpainters}
+\begin{Description}\relax
+The original painters data contain the subjective assessment,
+on a 0 to 20 integer scale, of 54 classical painters. The
+newpainters data discretizes the subjective assessment by
+quartiles with thresholds 25\%, 50\%, 75\%. The painters were
+assessed on four characteristics: composition, drawing,
+colour and expression. The data is due to the Eighteenth century
+art critic, de Piles.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(newpainters)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 5 variables ("Composition", "Drawing", "Colour",
+"Expression", and "School") and 54 observations.
+\end{Format}
+\begin{Source}\relax
+A. J. Weekes (1986).``A Genstat Primer''. Edward Arnold.
+
+M. Davenport and G. Studdert-Kennedy (1972). ``The statistical
+analysis of aesthetic judgement: an exploration.'' \emph{Applied
+Statistics}, vol. 21, pp. 324--333.
+
+I. T. Jolliffe (1986) ``Principal Component Analysis.'' Springer.
+\end{Source}
+\begin{References}\relax
+Venables, W. N. and Ripley, B. D. (2002) ``Modern Applied
+Statistics with S,'' Fourth edition. Springer.
+\end{References}
+
diff --git a/inst/doc/commandsRd/parse.formula.tex b/inst/doc/commandsRd/parse.formula.tex
new file mode 100644
index 0000000..83e689d
--- /dev/null
+++ b/inst/doc/commandsRd/parse.formula.tex
@@ -0,0 +1,77 @@
+\section{{\tt parse.formula}: Parsing user-input formulas into multiple syntax}\label{ss:parse.formula}
+\keyword{utilities}{parse.formula}
+\begin{Description}\relax
+Parse the input formula (or list of formulas) into the
+standard format described below. Since labels for this format will vary
+by model, \code{parse.formula} will evaluate a function \code{describe.model},
+where \code{model} is given as an input to \code{parse.formula}.
+
+If the \code{describe.model} function has more than one parameter for
+which \code{ExpVar = TRUE} and \code{DepVar = TRUE}, then the
+user-specified equations must have labels to match those parameters,
+else \code{parse.formula} should return an error. In addition, if the
+formula entries are not unambiguous, then \code{parse.formula} returns an error.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+parse.formula(formula, model, data = NULL)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{formula}] either a single formula or a list of \code{formula} objects
+\item[\code{model}] a character string specifying the name of the model
+\item[\code{data}] an optional data frame for models that require a factor response variable
+\end{ldescription}
+\end{Arguments}
+\begin{Details}\relax
+Acceptable user inputs are as follows:
+
+\Tabular{lll}{
+& User Input & Output from \code{parse.formula}\\
+& & \\
+Same covariates, & cbind(y1, y2) ~ x1 + x2 * x3 & list(mu1 = y1 ~ x1 + x2 * x3,\\
+separate effects & & mu2 = y2 ~ x1 + x2 * x3,\\
+& & rho = ~ 1)\\
+& & \\
+With \code{rho} as a & list(cbind(y1, y2) ~ x1 + x2, & list(mu1 = y1 ~ x1 + x2,\\
+systematic equation & rho = ~ x4 + x5) & mu2 = y2 ~ x1 + x2,\\
+& & rho = ~ x4 + x5)\\
+& & \\
+With constraints & list(mu1 = y1 ~ x1 + tag(x2, "x2"), & list(mu1 = y1 ~ x1 + tag(x2, "x2"),\\
+(same variable) & mu2 = y2 ~ x3 + tag(x2, "x2")) & mu2 = y2 ~ x3 + tag(x2, "x2"),\\
+& & rho = ~ 1)\\
+& & \\
+With constraints & list(mu1 = y1 ~ x1 + tag(x2, "z1"), & list(mu1 = y1 ~ x1 + tag(x2, "z1"),\\
+(different variables) & mu2 = y2 ~ x3 + tag(x4, "z1")) & mu2 = y2 ~ x3 + tag(x4, "z1"),\\
+& & rho = ~ 1)\\
+}
+\end{Details}
+\begin{Value}
+The output is a list of formula objects with class
+\code{c("multiple", "list")}. Let's say that the name of the model is
+\code{"bivariate.probit"}, and the corresponding describe function is
+\code{describe.bivariate.probit}, which identifies \code{mu1} and
+\code{mu2} as systematic components, and an ancillary parameter \code{rho}, which
+may be parameterized, but is estimated as a scalar by default.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{model.frame.multiple}{model.frame.multiple}},
+\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+data(sanction)
+formulae <- list(cbind(import, export) ~ coop + cost + target)
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = sanction)
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/parse.par.tex b/inst/doc/commandsRd/parse.par.tex
new file mode 100644
index 0000000..877e3d7
--- /dev/null
+++ b/inst/doc/commandsRd/parse.par.tex
@@ -0,0 +1,82 @@
+\section{{\tt parse.par}: Select and reshape parameter vectors}\label{ss:parse.par}
+\keyword{utilities}{parse.par}
+\begin{Description}\relax
+The \code{parse.par} function reshapes parameter vectors for
+comfortability with the output matrix from \code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}.
+Use \code{parse.par} to identify sets of parameters; for example, within
+optimization functions that require vector input, or within \code{qi}
+functions that take matrix input of all parameters as a lump.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+parse.par(par, terms, shape = "matrix", eqn = NULL)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{par}] the vector (or matrix) of parameters
+\item[\code{terms}] the terms from either \code{\LinkA{model.frame.multiple}{model.frame.multiple}} or
+\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}
+\item[\code{shape}] a character string (either \code{"matrix"} or \code{"vector"})
+that identifies the type of output structure
+\item[\code{eqn}] a character string (or strings) that identify the
+parameters that you would like to subset from the larger \code{par}
+structure
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A matrix or vector of the sub-setted (and reshaped) parameters for the specified
+parameters given in \code{"eqn"}. By default, \code{eqn = NULL}, such that all systematic
+components are selected. (Systematic components have \code{ExpVar = TRUE} in the appropriate
+\code{describe.model} function.)
+
+If an ancillary parameter (for which \code{ExpVar = FALSE} in
+\code{describe.model}) is specified in \code{eqn}, it is
+always returned as a vector (ignoring \code{shape}). (Ancillary
+parameters are all parameters that have intercept only formulas.)
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+# Let's say that the name of the model is "bivariate.probit", and
+# the corresponding describe function is describe.bivariate.probit(),
+# which identifies mu1 and mu2 as systematic components, and an
+# ancillary parameter rho, which may be parameterized, but is estimated
+# as a scalar by default. Let par be the parameter vector (including
+# parameters for rho), formulae a user-specified formula, and mydata
+# the user specified data frame.
+
+# Acceptable combinations of parse.par() and model.matrix() are as follows:
+## Setting up
+## Not run:
+data(sanction)
+formulae <- cbind(import, export) ~ coop + cost + target
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = sanction)
+terms <- attr(D, "terms")
+
+## Intuitive option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
+eta <- X
+
+## Memory-efficient (compact) option (default)
+Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
+eta <- X
+
+## Computationally-efficient (array) option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
+eta <- apply(X, 3, '
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/plot.ci.aux b/inst/doc/commandsRd/plot.ci.aux
new file mode 100644
index 0000000..326d849
--- /dev/null
+++ b/inst/doc/commandsRd/plot.ci.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {11.4}{\tt plot.ci}: Plotting Vertical confidence Intervals}{116}{section.11.4}}
+\newlabel{ss:plot.ci}{{11.4}{116}{{\tt plot.ci}: Plotting Vertical confidence Intervals\relax }{section.11.4}{}}
+\@setckpt{commandsRd/plot.ci}{
+\setcounter{page}{118}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
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+\setcounter{mpfootnote}{0}
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diff --git a/inst/doc/commandsRd/plot.ci.tex b/inst/doc/commandsRd/plot.ci.tex
new file mode 100644
index 0000000..20f73dd
--- /dev/null
+++ b/inst/doc/commandsRd/plot.ci.tex
@@ -0,0 +1,68 @@
+\section{{\tt plot.ci}: Plotting Vertical confidence Intervals}\label{ss:plot.ci}
+\keyword{hplot}{plot.ci}
+\begin{Description}\relax
+The \code{plot.ci} command generates vertical
+confidence intervals for linear or generalized linear univariate
+response models.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+plot.ci(x, CI = 95, qi = "ev", main = "", ylab = NULL, xlab = NULL,
+ xlim = NULL, ylim = NULL, col = c("red", "blue"), ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{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[\code{CI}] the selected confidence interval. Defaults to 95
+percent.
+\item[\code{qi}] the selected quantity of interest. Defaults to
+expected values.
+\item[\code{main}] a title for the plot.
+\item[\code{ylab}] label for the y-axis.
+\item[\code{xlab}] label for the x-axis.
+\item[\code{xlim}] limits on the x-axis.
+\item[\code{ylim}] limits on the y-axis.
+\item[\code{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[\code{...}] Additional parameters passed to \code{plot}.
+\end{ldescription}
+\end{Arguments}
+\begin{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}).
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual is available at
+\url{http://gking.harvard.edu/zelig}, and users may also wish to see
+\code{plot}, \code{lines}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+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"))
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/plot.zelig.aux b/inst/doc/commandsRd/plot.zelig.aux
new file mode 100644
index 0000000..7a64e65
--- /dev/null
+++ b/inst/doc/commandsRd/plot.zelig.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {10.5}{\tt plot.zelig}: Graphing Quantities of Interest}{102}{section.10.5}}
+\newlabel{ss:plot.zelig}{{10.5}{102}{{\tt plot.zelig}: Graphing Quantities of Interest\relax }{section.10.5}{}}
+\@setckpt{commandsRd/plot.zelig}{
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diff --git a/inst/doc/commandsRd/plot.zelig.tex b/inst/doc/commandsRd/plot.zelig.tex
new file mode 100644
index 0000000..59e2843
--- /dev/null
+++ b/inst/doc/commandsRd/plot.zelig.tex
@@ -0,0 +1,48 @@
+\section{{\tt plot.zelig}: Graphing Quantities of Interest}\label{ss:plot.zelig}
+\aliasA{plot}{plot.zelig}{plot}
+\keyword{hplot}{plot.zelig}
+\begin{Description}\relax
+The \code{zelig} method for the generic \code{plot}
+command generates default plots for \code{\LinkA{sim}{sim}} output with
+one-observation values in \code{x} and \code{x1}.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+## S3 method for class 'zelig':
+plot(x, xlab = "", user.par = FALSE, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{x}] stored output from \code{\LinkA{sim}{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{\LinkA{plot.ci}{plot.ci}}.
+\item[\code{xlab}] a character string for the x-axis label for all graphs.
+\item[\code{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[\code{...}] 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.
+\end{ldescription}
+\end{Arguments}
+\begin{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}).
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual at
+\url{http://gking.harvard.edu/zelig} and \code{plot}, \code{lines},
+and \code{par}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/put.start.tex b/inst/doc/commandsRd/put.start.tex
new file mode 100644
index 0000000..0a27044
--- /dev/null
+++ b/inst/doc/commandsRd/put.start.tex
@@ -0,0 +1,33 @@
+\section{{\tt put.start}: Set specific starting values for certain parameters}\label{ss:put.start}
+\keyword{utilities}{put.start}
+\begin{Description}\relax
+After calling \code{\LinkA{set.start}{set.start}} to create default starting values, use \code{put.start}
+to change starting values for specific parameters or parameter sets.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+put.start(start.val, value, terms, eqn)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{start.val}] the vector of starting values created by \code{\LinkA{set.start}{set.start}}
+\item[\code{value}] the scalar or vector of replacement starting values
+\item[\code{terms}] the terms output from \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
+\item[\code{eqn}] character vector of the parameters for which you would like to replace
+the default values with \code{value}
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A vector of starting values (of the same length as \code{start.val})
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{set.start}{set.start}}, and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/repl.aux b/inst/doc/commandsRd/repl.aux
new file mode 100644
index 0000000..84819ae
--- /dev/null
+++ b/inst/doc/commandsRd/repl.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {10.7}{\tt repl}: Replicating Analyses}{104}{section.10.7}}
+\newlabel{ss:repl}{{10.7}{104}{{\tt repl}: Replicating Analyses\relax }{section.10.7}{}}
+\@setckpt{commandsRd/repl}{
+\setcounter{page}{106}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{10}
+\setcounter{section}{7}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{LT at tables}{0}
+\setcounter{LT at chunks}{0}
+\setcounter{FancyVerbLine}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{216}
+\setcounter{Hfootnote}{8}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{7}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{1}
+}
diff --git a/inst/doc/commandsRd/repl.tex b/inst/doc/commandsRd/repl.tex
new file mode 100644
index 0000000..cddfe5d
--- /dev/null
+++ b/inst/doc/commandsRd/repl.tex
@@ -0,0 +1,89 @@
+\section{{\tt repl}: Replicating Analyses}\label{ss:repl}
+\methaliasA{repl.default}{repl}{repl.default}
+\methaliasA{repl.zelig}{repl}{repl.zelig}
+\keyword{file}{repl}
+\begin{Description}\relax
+The generic function \code{repl} command takes
+\code{\LinkA{zelig}{zelig}} or
+\code{\LinkA{sim}{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{\LinkA{zelig}{zelig}} output. In the case of \code{\LinkA{sim}{sim}}
+output, the replicated analyses may differ slightly due to
+stochastic randomness in the simulation procedure.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+repl(object, data, ...)
+## Default S3 method:
+repl(object, data = NULL, ...)
+## S3 method for class 'zelig':
+repl(object, data = NULL, prev = NULL, x = NULL, x1 = NULL,
+ bootfn = NULL, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] Stored output from either \code{\LinkA{zelig}{zelig}} or
+\code{\LinkA{sim}{sim}}.
+\item[\code{data}] You may manually input the data frame name rather
+than allowing \code{repl} to draw the data frame name from the object
+to be replicated.
+\item[\code{prev}] When replicating \code{\LinkA{sim}{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[\code{x}] When replicating \code{\LinkA{sim}{sim}} output, you may
+optionally use an alternative \code{\LinkA{setx}{setx}} value for the \code{x}
+input.
+\item[\code{x1}] When replicating \code{\LinkA{sim}{sim}} output, you may
+optionally use an alternative \code{\LinkA{setx}{setx}} object for the \code{x1}
+input to replicating the \code{\LinkA{sim}{sim}} object.
+\item[\code{bootfn}] When replicating \code{\LinkA{sim}{sim}} output with
+bootstrapped parameters, you should manually specify the
+\code{bootfn} if a non-default option was used.
+\item[\code{...}] Additional arguments passed to either \code{\LinkA{zelig}{zelig}} or
+\code{\LinkA{sim}{sim}}.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+For \code{\LinkA{zelig}{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{\LinkA{sim}{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.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{zelig}{zelig}}, \code{\LinkA{setx}{setx}}, and
+\code{\LinkA{sim}{sim}}. In addition, the full Zelig manual may be
+accessed online at \url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+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)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/rocplot.aux b/inst/doc/commandsRd/rocplot.aux
new file mode 100644
index 0000000..1e5aa03
--- /dev/null
+++ b/inst/doc/commandsRd/rocplot.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {11.5}{\tt rocplot}: Receiver Operator Characteristic Plots}{118}{section.11.5}}
+\newlabel{ss:rocplot}{{11.5}{118}{{\tt rocplot}: Receiver Operator Characteristic Plots\relax }{section.11.5}{}}
+\@setckpt{commandsRd/rocplot}{
+\setcounter{page}{120}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{2}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{11}
+\setcounter{section}{5}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{LT at tables}{0}
+\setcounter{LT at chunks}{0}
+\setcounter{FancyVerbLine}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{216}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{7}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{1}
+}
diff --git a/inst/doc/commandsRd/rocplot.tex b/inst/doc/commandsRd/rocplot.tex
new file mode 100644
index 0000000..f933f24
--- /dev/null
+++ b/inst/doc/commandsRd/rocplot.tex
@@ -0,0 +1,87 @@
+\section{{\tt rocplot}: Receiver Operator Characteristic Plots}\label{ss:rocplot}
+\aliasA{ROC}{rocplot}{ROC}
+\aliasA{roc}{rocplot}{roc}
+\aliasA{ROCplot}{rocplot}{ROCplot}
+\keyword{file}{rocplot}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+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,
+ plot = TRUE, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{y1}] Response variable for the first model.
+\item[\code{y2}] Response variable for the second model.
+\item[\code{fitted1}] Fitted values for the first model. These values
+may represent either the in-sample or out-of-sample fitted values.
+\item[\code{fitted2}] Fitted values for the second model.
+\item[\code{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[\code{lty1, lty2}] The line type for the first model (\code{lty1}) and
+the second model (\code{lty2}), defaulting to solid and dashed,
+respectively.
+\item[\code{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[\code{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[\code{main}] a title for the plot. Defaults to \code{ROC Curve}.
+\item[\code{xlab}] a label for the x-axis. Defaults to \code{Proportion of 1's
+ Correctly Predicted}.
+\item[\code{ylab}] a label for the y-axis. Defaults to \code{Proportion of 0's
+ Correctly Predicted}.
+\item[\code{plot}] defaults to \code{TRUE}, which generates a plot to the
+selected device. If \code{FALSE}, returns a list of
+items (see below).
+\item[\code{...}] Additional parameters passed to plot, including
+\code{xlab}, \code{ylab}, and \code{main}.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+If \code{plot = TRUE}, \code{rocplot} generates an ROC plot for
+two logit or probit models. If \code{plot = FALSE}, \code{rocplot}
+returns a list with the following elements: normal-bracket63bracket-normal
+\begin{ldescription}
+\item[\code{roc1}] a matrix containing a vector of x-coordinates and
+y-coordinates corresponding to the number of ones and zeros correctly
+predicted for the first model.
+\item[\code{roc2}] a matrix containing a vector of x-coordinates and
+y-coordinates corresponding to the number of ones and zeros correctly
+predicted for the second model.
+\item[\code{area1}] the area under the first ROC curve, calculated using
+Reimann sums.
+\item[\code{area2}] the area under the second ROC curve, calculated using
+Reimann sums.
+\end{ldescription}
+
+normal-bracket63bracket-normal
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual (available at
+\url{http://gking.harvard.edu/zelig}), \code{plot}, \code{lines}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+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))
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/sanction.tex b/inst/doc/commandsRd/sanction.tex
new file mode 100644
index 0000000..7aec455
--- /dev/null
+++ b/inst/doc/commandsRd/sanction.tex
@@ -0,0 +1,24 @@
+\section{{\tt sanction}: Multilateral Economic Sanctions}\label{ss:sanction}
+\keyword{datasets}{sanction}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(sanction)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 8 variables ("mil", "coop", "target",
+"import", "export", "cost", "num", and "ncost") and 78 observations.
+For full variable description, see Martin, 1992.
+\end{Format}
+\begin{Source}\relax
+Martin, 1992
+\end{Source}
+\begin{References}\relax
+Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral
+Economic Sanctions}, Princeton: Princeton University Press.
+\end{References}
+
diff --git a/inst/doc/commandsRd/set.start.tex b/inst/doc/commandsRd/set.start.tex
new file mode 100644
index 0000000..a81b9dd
--- /dev/null
+++ b/inst/doc/commandsRd/set.start.tex
@@ -0,0 +1,42 @@
+\section{{\tt set.start}: Set starting values for all parameters}\label{ss:set.start}
+\keyword{utilities}{set.start}
+\begin{Description}\relax
+After using \code{\LinkA{parse.par}{parse.par}} and \code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, use
+\code{set.start} to set starting values for all parameters. By default, starting values are set to 0. If
+you wish to select alternative starting values for certain parameters, use \code{\LinkA{put.start}{put.start}} after
+\code{set.start}.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+set.start(start.val = NULL, terms)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{start.val}] user-specified starting values. If \code{NULL} (default), the default
+starting values for all parameters are set to 0.
+\item[\code{terms}] the terms output from \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+A named vector of starting values for all parameters specified in \code{terms}, defaulting to 0.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
+<\email{falimadhi at iq.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{put.start}{put.start}}, \code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{model.frame.multiple}{model.frame.multiple}}, and the
+full Zelig manual at \url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+fml <- parse.formula(formula, model = "bivariate.probit")
+D <- model.frame(fml, data = data)
+terms <- attr(D, "terms")
+start.val <- set.start(start.val = NULL, terms)
+## End(Not run)\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commands/setx.aux b/inst/doc/commandsRd/setx.aux
similarity index 50%
rename from inst/doc/commands/setx.aux
rename to inst/doc/commandsRd/setx.aux
index a30fcef..56c1ac2 100644
--- a/inst/doc/commands/setx.aux
+++ b/inst/doc/commandsRd/setx.aux
@@ -1,18 +1,7 @@
\relax
\@writefile{toc}{\contentsline {section}{\numberline {10.2}{\tt setx}: Setting Explanatory Variable Values}{93}{section.10.2}}
\newlabel{ss:setx}{{10.2}{93}{{\tt setx}: Setting Explanatory Variable Values\relax }{section.10.2}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{93}{section*.40}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{93}{section*.41}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{93}{section*.42}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{94}{section*.43}}
-\@writefile{toc}{\contentsline {subsubsection}{Example: Unconditional Prediction}{94}{section*.44}}
-\@writefile{toc}{\contentsline {subsubsection}{Example: Conditional Prediction With MatchIt Data}{94}{section*.45}}
-\citation{KinTomWit00}
-\citation{KinTomWit00}
-\@writefile{toc}{\contentsline {subsubsection}{Example: Conditional Prediction With Multiple Analyses}{95}{section*.46}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{95}{section*.47}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{95}{section*.48}}
-\@setckpt{commands/setx}{
+\@setckpt{commandsRd/setx}{
\setcounter{page}{96}
\setcounter{equation}{0}
\setcounter{enumi}{4}
@@ -30,6 +19,9 @@
\setcounter{subparagraph}{0}
\setcounter{figure}{0}
\setcounter{table}{0}
+\setcounter{LT at tables}{0}
+\setcounter{LT at chunks}{0}
+\setcounter{FancyVerbLine}{0}
\setcounter{NAT at ctr}{0}
\setcounter{parentequation}{0}
\setcounter{Item}{216}
@@ -38,9 +30,9 @@
\setcounter{lchapter}{0}
\setcounter{lsection}{0}
\setcounter{lsubsection}{0}
-\setcounter{lsubsubsection}{0}
+\setcounter{lsubsubsection}{7}
\setcounter{lparagraph}{0}
\setcounter{lsubparagraph}{0}
\setcounter{lsubsubparagraph}{0}
-\setcounter{section at level}{3}
+\setcounter{section at level}{1}
}
diff --git a/inst/doc/commandsRd/setx.tex b/inst/doc/commandsRd/setx.tex
new file mode 100644
index 0000000..c729e8b
--- /dev/null
+++ b/inst/doc/commandsRd/setx.tex
@@ -0,0 +1,113 @@
+\section{{\tt setx}: Setting Explanatory Variable Values}\label{ss:setx}
+\keyword{file}{setx}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+x.out <- setx(object, fn = list(numeric = mean, ordered = median,
+ others = mode),
+ data = NULL, cond = FALSE, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] the saved output from \code{\LinkA{zelig}{zelig}}.
+\item[\code{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{...}
+below.
+In the special case \code{fn = NULL}, \code{setx} will return all
+of the observations without applying any function to the data.
+\item[\code{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[\code{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{...}. If \code{cond = TRUE} and \code{data = NULL},
+\code{setx} will prompt you for a data frame.
+\item[\code{...}] 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.
+\end{ldescription}
+\end{Arguments}
+\begin{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{\LinkA{zelig}{zelig}},
+\code{setx} returns the selected values calculated over the entire
+data frame. If you wish to calculate values over just one subset of
+the data frame, the 5th subset for example, you may use:
+\code{x.out <- setx(z.out[[5]])}
+
+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.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual may be accessed online at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+# 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:
+## Not run:
+quants <- function(x)
+ quantile(x, 0.25)
+x.out <- setx(z.out, fn = list(numeric = quants))
+## End(Not run)
+
+# Conditional prediction:
+## Not run:
+library(MatchIt)
+data(lalonde)
+match.out <- matchit(treat ~ age + educ + black + hispan + married +
+ nodegree + re74 + re75, data = lalonde)
+z.out <- zelig(re78 ~ distance, data = match.data(match.out, "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)
+## End(Not run)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/sim.aux b/inst/doc/commandsRd/sim.aux
new file mode 100644
index 0000000..1db6466
--- /dev/null
+++ b/inst/doc/commandsRd/sim.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {10.4}{\tt sim}: Simulating Quantities of Interest}{99}{section.10.4}}
+\newlabel{ss:sim}{{10.4}{99}{{\tt sim}: Simulating Quantities of Interest\relax }{section.10.4}{}}
+\@setckpt{commandsRd/sim}{
+\setcounter{page}{102}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
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+}
diff --git a/inst/doc/commandsRd/sim.tex b/inst/doc/commandsRd/sim.tex
new file mode 100644
index 0000000..8fe7036
--- /dev/null
+++ b/inst/doc/commandsRd/sim.tex
@@ -0,0 +1,132 @@
+\section{{\tt sim}: Simulating Quantities of Interest}\label{ss:sim}
+\keyword{file}{sim}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
+ bootstrap = FALSE, bootfn = NULL, ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] the output object from \code{\LinkA{zelig}{zelig}}.
+\item[\code{x}] values of explanatory variables used for simulation,
+generated by \code{\LinkA{setx}{setx}}.
+\item[\code{x1}] optional values of explanatory variables (generated by a
+second call of \code{\LinkA{setx}{setx}}), used to simulate first
+differences and risk ratios. (Not available for conditional
+prediction.)
+\item[\code{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[\code{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[\code{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{\LinkA{sim}{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[\code{...}] additional optional arguments passed to
+\code{boot}.
+\end{ldescription}
+\end{Arguments}
+\begin{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: normal-bracket109bracket-normal
+\begin{ldescription}
+\item[\code{x}] the \code{\LinkA{setx}{setx}} values for the explanatory variables,
+used to calculate the quantities of interest (expected values,
+predicted values, etc.).
+\item[\code{x1}] the optional \code{\LinkA{setx}{setx}} object used to simulate
+first differences, and other model-specific quantities of
+interest, such as risk-ratios.
+\item[\code{call}] the options selected for \code{\LinkA{sim}{sim}}, used to
+replicate quantities of interest.
+\item[\code{zelig.call}] the original command and options for
+\code{\LinkA{zelig}{zelig}}, used to replicate analyses.
+\item[\code{num}] the number of simulations requested.
+\item[\code{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[\code{qi\$ev}] simulations of the expected values given the
+model and \code{x}.
+\item[\code{qi\$pr}] simulations of the predicted values given by the
+fitted values.
+\item[\code{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[\code{qi\$rr}] simulations of the risk ratios for binary and
+multinomial models. See specific models for details.
+\item[\code{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[\code{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}{}.
+\end{ldescription}
+
+normal-bracket109bracket-normal
+
+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.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig at \url{http://gking.harvard.edu/zelig}, and \code{boot}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/sna.ex.tex b/inst/doc/commandsRd/sna.ex.tex
new file mode 100644
index 0000000..2ccc739
--- /dev/null
+++ b/inst/doc/commandsRd/sna.ex.tex
@@ -0,0 +1,15 @@
+\section{{\tt sna.ex}: Simulated Example of Social Network Data}\label{ss:sna.ex}
+\keyword{datasets}{sna.ex}
+\begin{Description}\relax
+This data set contains five sociomatrices of simulated data social network data.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(sna.ex)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+Each variable in the dataset is a 25 by 25 matrix of simulated social network data. The matrices are labeled "Var1", "Var2", "Var3", "Var4", and "Var5".
+\end{Format}
+\begin{Source}\relax
+fictitious
+\end{Source}
+
diff --git a/inst/doc/acknowledgments.aux b/inst/doc/commandsRd/summary.aux
similarity index 51%
rename from inst/doc/acknowledgments.aux
rename to inst/doc/commandsRd/summary.aux
index 5214eec..7db5746 100644
--- a/inst/doc/acknowledgments.aux
+++ b/inst/doc/commandsRd/summary.aux
@@ -1,33 +1,36 @@
\relax
-\@setckpt{acknowledgments}{
-\setcounter{page}{7}
+\@setckpt{commandsRd/summary}{
+\setcounter{page}{102}
\setcounter{equation}{0}
-\setcounter{enumi}{0}
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+\setcounter{section at level}{1}
}
diff --git a/inst/doc/commandsRd/summary.zelig.tex b/inst/doc/commandsRd/summary.zelig.tex
new file mode 100644
index 0000000..949f5cb
--- /dev/null
+++ b/inst/doc/commandsRd/summary.zelig.tex
@@ -0,0 +1,51 @@
+\section{{\tt summary.zelig}: Summary of Simulated Quantities of Interest}\label{ss:summary.zelig}
+\aliasA{summary}{summary.zelig}{summary}
+\keyword{file}{summary.zelig}
+\begin{Description}\relax
+Summarizes the object of class \code{\LinkA{zelig}{zelig}} (output
+from \code{\LinkA{sim}{sim}}) which contains simulated quantities of
+interst.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+## S3 method for class 'zelig':
+summary(object, subset = NULL, CI = 95, stats = c("mean", "sd"), ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] output object from \code{\LinkA{sim}{sim}} (of class
+\code{"zelig"}).
+\item[\code{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[\code{stats}] summary statistics to be calculated.
+\item[\code{CI}] a confidence interval to be calculated.
+\item[\code{...}] further arguments passed to or from other methods.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+\begin{ldescription}
+\item[\code{sim}] number of simulations, i.e., posterior draws.
+\item[\code{x}] values of explanatory variables used for simulation.
+\item[\code{x1}] values of explanatory variables used for simulation of first
+differences etc.
+\item[\code{qi.stats}] summary of quantities of interst. Use
+\code{\LinkA{names}{names}} to view the model-specific items available in
+\code{qi.stats}.
+\end{ldescription}
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{\LinkA{zelig}{zelig}}, \code{\LinkA{setx}{setx}}, \code{\LinkA{sim}{sim}},
+and \code{\LinkA{names}{names}}, and the full Zelig manual at
+\url{http://gking.harvard.edu/zelig}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/swiss.tex b/inst/doc/commandsRd/swiss.tex
new file mode 100644
index 0000000..0cbf5f7
--- /dev/null
+++ b/inst/doc/commandsRd/swiss.tex
@@ -0,0 +1,40 @@
+\section{{\tt swiss}: Swiss Fertility and Socioeconomic Indicators (1888) Data}\label{ss:swiss}
+\keyword{datasets}{swiss}
+\begin{Description}\relax
+Standardized fertility measure and socio-economic indicators for
+each of 47 French-speaking provinces of Switzerland at about 1888.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(swiss)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame with 47 observations on 6 variables, each of which
+is in percent, i.e., in [0,100].
+
+[,1] Fertility Ig, "common standardized fertility measure"
+[,2] Agriculture
+[,3] Examination
+nation
+[,4] Education
+[,5] Catholic
+[,6] Infant.Mortality live births who live less than 1 year.
+
+All variables but 'Fert' give proportions of the population.
+\end{Format}
+\begin{Source}\relax
+Project "16P5", pages 549-551 in
+
+Mosteller, F. and Tukey, J. W. (1977) ``Data Analysis and
+Regression: A Second Course in Statistics''. Addison-Wesley,
+Reading Mass.
+
+indicating their source as "Data used by permission of Franice van
+de Walle. Office of Population Research, Princeton University,
+1976. Unpublished data assembled under NICHD contract number No
+1-HD-O-2077."
+\end{Source}
+\begin{References}\relax
+Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) ``The New S
+Language''. Wadsworth \& Brooks/Cole.
+\end{References}
+
diff --git a/inst/doc/commandsRd/ternaryplot.aux b/inst/doc/commandsRd/ternaryplot.aux
new file mode 100644
index 0000000..e9e929b
--- /dev/null
+++ b/inst/doc/commandsRd/ternaryplot.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {11.6}{\tt ternaryplot}: Ternary diagram}{120}{section.11.6}}
+\newlabel{ss:ternaryplot}{{11.6}{120}{{\tt ternaryplot}: Ternary diagram\relax }{section.11.6}{}}
+\@setckpt{commandsRd/ternaryplot}{
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diff --git a/inst/doc/commandsRd/ternaryplot.tex b/inst/doc/commandsRd/ternaryplot.tex
new file mode 100644
index 0000000..c525fe0
--- /dev/null
+++ b/inst/doc/commandsRd/ternaryplot.tex
@@ -0,0 +1,77 @@
+\section{{\tt ternaryplot}: Ternary diagram}\label{ss:ternaryplot}
+\keyword{hplot}{ternaryplot}
+\begin{Description}\relax
+Visualizes compositional, 3-dimensional data in an equilateral triangle
+(from the vcd library, Version 0.1-3.3, Date 2004-04-21), using plot graphics.
+Differs from implementation in vcd (0.9-7), which uses grid graphics.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+ternaryplot(x, scale = 1, dimnames = NULL, dimnames.position = c("corner","edge","none"),
+ dimnames.color = "black", id = NULL, id.color = "black", coordinates = FALSE,
+ grid = TRUE, grid.color = "gray", labels = c("inside", "outside", "none"),
+ labels.color = "darkgray", border = "black", bg = "white", pch = 19, cex = 1,
+ prop.size = FALSE, col = "red", main = "ternary plot", ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{x}] a matrix with three columns.
+\item[\code{scale}] row sums scale to be used.
+\item[\code{dimnames}] dimension labels (defaults to the column names of
+\code{x}).
+\item[\code{dimnames.position, dimnames.color}] position and color of dimension labels.
+\item[\code{id}] optional labels to be plotted below the plot
+symbols. \code{coordinates} and \code{id} are mutual exclusive.
+\item[\code{id.color}] color of these labels.
+\item[\code{coordinates}] if \code{TRUE}, the coordinates of the points are
+plotted below them. \code{coordinates} and \code{id} are mutual exclusive.
+\item[\code{grid}] if \code{TRUE}, a grid is plotted. May optionally
+be a string indicating the line type (default: \code{"dotted"}).
+\item[\code{grid.color}] grid color.
+\item[\code{labels, labels.color}] position and color of the grid labels.
+\item[\code{border}] color of the triangle border.
+\item[\code{bg}] triangle background.
+\item[\code{pch}] plotting character. Defaults to filled dots.
+\item[\code{cex}] a numerical value giving the amount by which plotting text
+and symbols should be scaled relative to the default. Ignored for
+the symbol size if \code{prop.size} is not \code{FALSE}.
+\item[\code{prop.size}] if \code{TRUE}, the symbol size is plotted
+proportional to the row sum of the three variables, i.e. represents
+the weight of the observation.
+\item[\code{col}] plotting color.
+\item[\code{main}] main title.
+\item[\code{...}] additional graphics parameters (see \code{par})
+\end{ldescription}
+\end{Arguments}
+\begin{Details}\relax
+A points' coordinates are found by computing the gravity center
+of mass points using the data entries as weights. Thus, the coordinates
+of a point P(a,b,c), \eqn{a + b + c = 1}{}, are: P(b + c/2, c * sqrt(3)/2).
+\end{Details}
+\begin{Author}\relax
+David Meyer\\
+\email{david.meyer at ci.tuwien.ac.at}
+\end{Author}
+\begin{References}\relax
+M. Friendly (2000),
+\emph{Visualizing Categorical Data}. SAS Institute, Cary, NC.
+\end{References}
+\begin{SeeAlso}\relax
+\code{\LinkA{ternarypoints}{ternarypoints}}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+data(mexico)
+if (require(VGAM)) {
+z.out <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
+ model = "mlogit", data = mexico)
+x.out <- setx(z.out)
+s.out <- sim(z.out, x = x.out)
+
+ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
+ main = "1988 Mexican Presidential Election")
+}
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/ternarypoints.aux b/inst/doc/commandsRd/ternarypoints.aux
new file mode 100644
index 0000000..a3e8393
--- /dev/null
+++ b/inst/doc/commandsRd/ternarypoints.aux
@@ -0,0 +1,38 @@
+\relax
+\@writefile{toc}{\contentsline {section}{\numberline {11.7}{\tt ternarypoints}: Adding Points to Ternary Diagrams}{122}{section.11.7}}
+\newlabel{ss:ternarypoints}{{11.7}{122}{{\tt ternarypoints}: Adding Points to Ternary Diagrams\relax }{section.11.7}{}}
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diff --git a/inst/doc/commandsRd/ternarypoints.tex b/inst/doc/commandsRd/ternarypoints.tex
new file mode 100644
index 0000000..0ab5675
--- /dev/null
+++ b/inst/doc/commandsRd/ternarypoints.tex
@@ -0,0 +1,39 @@
+\section{{\tt ternarypoints}: Adding Points to Ternary Diagrams}\label{ss:ternarypoints}
+\keyword{aplot}{ternarypoints}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+ternarypoints(object, pch = 19, col = "blue", ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{object}] The input object must be a matrix with three
+columns.
+\item[\code{pch}] The selected type of point. By default, \code{pch =
+ 19}, solid disks.
+\item[\code{col}] The color of the points. By default, \code{col =
+ "blue"}.
+\item[\code{...}] Additional parameters passed to \code{points}.
+\end{ldescription}
+\end{Arguments}
+\begin{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.
+\end{Value}
+\begin{Author}\relax
+Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
+<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+The full Zelig manual at
+\url{http://gking.harvard.edu/zelig}, \code{points}, and
+\code{\LinkA{ternaryplot}{ternaryplot}}.
+\end{SeeAlso}
+
diff --git a/inst/doc/commandsRd/tobin.tex b/inst/doc/commandsRd/tobin.tex
new file mode 100644
index 0000000..ade51e9
--- /dev/null
+++ b/inst/doc/commandsRd/tobin.tex
@@ -0,0 +1,23 @@
+\section{{\tt tobin}: Tobin's Tobit Data}\label{ss:tobin}
+\keyword{datasets}{tobin}
+\begin{Description}\relax
+Economists fit a parametric censored data model called the
+`tobit'. These data are from Tobin's original paper.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(tobin)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame with 20 observations on the following 3 variables.
+
+durable: Durable goods purchase
+
+age: Age in years
+
+quant: Liquidity ratio (x 1000)
+\end{Format}
+\begin{Source}\relax
+J. Tobin, Estimation of relationships for limited dependent
+variables, Econometrica, v26, 24-36, 1958.
+\end{Source}
+
diff --git a/inst/doc/commandsRd/turnout.tex b/inst/doc/commandsRd/turnout.tex
new file mode 100644
index 0000000..b1ea522
--- /dev/null
+++ b/inst/doc/commandsRd/turnout.tex
@@ -0,0 +1,25 @@
+\section{{\tt turnout}: Turnout Data Set from the National Election Survey}\label{ss:turnout}
+\keyword{datasets}{turnout}
+\begin{Description}\relax
+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.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(turnout)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 5 variables ("race", "age", "educate",
+"income", and "vote") and 2,000 observations.
+\end{Format}
+\begin{Source}\relax
+National Election Survey
+\end{Source}
+\begin{References}\relax
+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.
+\end{References}
+
diff --git a/inst/doc/commandsRd/user.prompt.tex b/inst/doc/commandsRd/user.prompt.tex
new file mode 100644
index 0000000..74c9a56
--- /dev/null
+++ b/inst/doc/commandsRd/user.prompt.tex
@@ -0,0 +1,25 @@
+\section{{\tt user.prompt}: Pause in demo files}\label{ss:user.prompt}
+\keyword{file}{user.prompt}
+\begin{Description}\relax
+Use \code{user.prompt} while writing demo files to force users to hit
+return before continuing.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+user.prompt()
+\end{verbatim}
+\end{Usage}
+\begin{Author}\relax
+Olivia Lau <\email{olau at fas.harvard.edu}>
+\end{Author}
+\begin{SeeAlso}\relax
+\code{readline}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}
+## Not run:
+user.prompt()
+## End(Not run)
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/commandsRd/zeligVDC.tex b/inst/doc/commandsRd/zeligVDC.tex
new file mode 100644
index 0000000..f21c05b
--- /dev/null
+++ b/inst/doc/commandsRd/zeligVDC.tex
@@ -0,0 +1,62 @@
+\section{{\tt zeligDescribeModelXML}: Zelig interface functions}\label{ss:zeligDescribeModelXML}
+\aliasA{zeligGetSpecial}{zeligDescribeModelXML}{zeligGetSpecial}
+\aliasA{zeligInstalledModels}{zeligDescribeModelXML}{zeligInstalledModels}
+\aliasA{zeligListModels}{zeligDescribeModelXML}{zeligListModels}
+\aliasA{zeligModelDependency}{zeligDescribeModelXML}{zeligModelDependency}
+\keyword{IO}{zeligDescribeModelXML}
+\keyword{print}{zeligDescribeModelXML}
+\begin{Description}\relax
+Zelig interface functions. Used by VDC DSB to communicate with Zelig.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+ zeligDescribeModelXML(modelName,force=FALSE,schemaVersion="1.1")
+ zeligInstalledModels(inZeligOnly=TRUE,schemaVersion="1.1")
+ zeligListModels(inZeligOnly=TRUE)
+ zeligModelDependency(modelName,repos)
+ zeligGetSpecial(modelName)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{modelName}] Name of model as returned by zeligInstalledModels or zeligListModels.
+\item[\code{inZeligOnly}] Flag, include only models in official Zelig distribution
+\item[\code{repos}] URL of default repository to use
+\item[\code{schemaVersion}] version of Zelig schema
+\item[\code{force}] generate a description even if no custom description supplied
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+Use zeligInstalledModels and zeligListModels to determine what models are available in zelig
+for a particular schema level. Use zmodel2string(zeligDescribeModel()) to generate an XML
+instance describing a model. Use zeligModelDependencies to generate a list of package
+dependencies for models. Use zeligGetSpecial to get the name special function, if any,
+to apply to the outcome variables. All functions return NULL if results are
+not available for that model.
+\end{Value}
+\begin{Author}\relax
+Micah Altman
+\email{thedata-users\@lists.sourceforge.net}
+\url{http://thedata.org}
+\end{Author}
+\begin{SeeAlso}\relax
+\LinkA{zelig}{zelig}
+\end{SeeAlso}
+\begin{Examples}
+\begin{ExampleCode}## Not run:
+ # show all available models
+ zeligListModels(inZeligOnly=FALSE)
+ # show installed models
+ zeligInstalledModels()
+ # show dependency for normal.bayes
+ zeligModelDependency("normal.bayes","http://cran.r-project.org/")
+ # description of logit
+ cat(zeligDescribeModelXML("ologit"))
+ # special function for factor analysis
+ zeligGetSpecial("factor.mix")
+## End(Not run)
+
+
+\end{ExampleCode}
+\end{Examples}
+
diff --git a/inst/doc/contributors.tex b/inst/doc/contributors.tex
new file mode 100644
index 0000000..c2830e4
--- /dev/null
+++ b/inst/doc/contributors.tex
@@ -0,0 +1,15 @@
+function is part of the MCMCpack library by Andrew D.
+Martin and Kevin M. Quinn. If you use this model, please cite:
+\begin{verse}
+\bibentry{MarQui05}.
+\end{verse}
+The convergence diagnostics are part of the CODA library
+by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
+should be cited as:
+\begin{verse}
+\bibentry{PluBesCowVin05}.
+\end{verse}
+\noindent Sample data are adapted from
+\begin{verse}
+\bibentry{MarQui05}.
+\end{verse}
diff --git a/inst/doc/contributors2.tex b/inst/doc/contributors2.tex
new file mode 100644
index 0000000..0958836
--- /dev/null
+++ b/inst/doc/contributors2.tex
@@ -0,0 +1,11 @@
+function is part of the MCMCpack library by Andrew D.
+Martin and Kevin M. Quinn. If you use this model, please cite:
+\begin{verse}
+\bibentry{MarQui05}
+\end{verse}
+The convergence diagnostics are part of the CODA library
+by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
+should be cited as:
+\begin{verse}
+\bibentry{PluBesCowVin05}
+\end{verse}
diff --git a/inst/doc/ei.RxC.Rnw b/inst/doc/ei.RxC.Rnw
new file mode 100644
index 0000000..2b5f23c
--- /dev/null
+++ b/inst/doc/ei.RxC.Rnw
@@ -0,0 +1,234 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/eiRxC}
+\include{zinput}
+%\VignetteIndexEntry{Hierarchical Multinomial-Dirichlet Ecological Inference Model}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, many contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
+ Inference Model for $R \times C$ Tables}
+\label{eiRxC}
+
+Given $n$ contingency tables, each with observed marginals (column and
+row totals), ecological inference ({\sc ei}) estimates the internal
+cell values in each table. The hierarchical Multinomial-Dirichlet
+model estimates cell counts in $R \times C$ tables. The model is
+implemented using a nonlinear least squares approximation and, with
+bootstrapping for standard errors, had good frequentist properties.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1,
+ covar = NULL,
+ model = "eiRxC", data = mydata)
+> x.out <- setx(z.out, fn = NULL)
+> s.out <- sim(z.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
+ numeric vectors (either counts, or proportions that sum to one for
+ each row) containing the column margins of the units to be analyzed.
+
+\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
+ (either counts, or proportions that sum to one for each row)
+ containing the row margins of the units to be analyzed.
+
+\item {\tt covar}: (optional) a covariate that varies across tables,
+specified as \verb|covar = ~ Z1|, for example. (The model only
+accepts one covariate.)
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples: No covariate \\
+ Attaching the example dataset:
+<<ExampleNoCov.data>>=
+ data(Weimar)
+@
+Estimating the model:
+<<ExampleNoCov.zelig>>=
+ z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
+ shareunemployed + shareblue + sharewhite + shareself +
+ sharedomestic, model = "ei.RxC", data = Weimar)
+ summary(z.out)
+@
+
+Setting values for in-sample simulations given marginal values:
+<<ExampleNoCov.setx>>=
+ x.out <- setx(z.out)
+@
+
+Estimate fractions of different social groups that support political parties:
+<<ExampleNoCov.sim>>=
+ s.out <- sim(z.out)
+@
+
+Summarizing fractions of different social groups that support political parties:
+<<ExampleNoCov.summary>>=
+ summary(s.out)
+@
+
+ \item Example of covariates being present in the model \\
+
+ Using the example dataset Weimar and estimating the model
+<<ExampleCov.zelig>>=
+ z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
+ shareunemployed + shareblue + sharewhite + shareself +
+ sharedomestic,
+ covar = ~ shareprotestants,
+ model = "ei.RxC", data = Weimar)
+ summary(z.out)
+@
+
+Set the covariate to its default (mean/median) value
+
+<<ExampleCov.setx>>=
+ x.out <- setx(z.out)
+@
+
+Estimate fractions of different social groups that support political parties:
+<<ExampleCov.sim>>=
+ s.out <- sim(z.out)
+@
+
+Summarizing fractions of different social groups that support political parties:
+<<ExampleCov.summary>>=
+ summary(s.out)
+@
+\end{enumerate}
+
+\clearpage
+
+\subsubsection{Model}
+Consider the following $5 \times 5$ contingency table for the voting
+patterns in Weimar Germany. For each geographical unit $i$ ($i = 1,
+\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
+$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
+would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
+which are the fractions of people in social class $r$ who vote for
+party $c$, for all $r$ and $c$.
+\begin{table}[!h]
+ \begin{center}
+ \begin{tabular}{l|ccccc|c}
+ & Nazi & Government & Communists & Far Right & Other \\
+ \hline
+ Unemployed & $\beta_{11}^{i}$ & $\beta_{12}^{i}$ & $\beta_{13}^{i}$ & $\beta_{14}^{i}$ & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$ \\
+ Blue & $\beta_{21}^{i}$ & $\beta_{22}^{i}$ & $\beta_{23}^{i}$ & $\beta_{24}^{i}$ & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$ \\
+ White & $\beta_{31}^{i}$ & $\beta_{32}^{i}$ & $\beta_{33}^{i}$ & $\beta_{34}^{i}$ & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$ \\
+ Self & $\beta_{41}^{i}$ & $\beta_{42}^{i}$ & $\beta_{43}^{i}$ & $\beta_{44}^{i}$ & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$ \\
+ Domestic & $\beta_{51}^{i}$ & $\beta_{52}^{i}$ & $\beta_{53}^{i}$ & $\beta_{54}^{i}$ & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$ \\
+ \hline
+ & $T_{1i}$ & $T_{2i}$ & $T_{3i}$ & $T_{4i}$ & $1-\sum_{c=1}^4 \beta_{ci}$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent
+The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
+observed as counts or fractions.
+
+Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
+of voting age persons who turn out to vote for different parties.
+There are three levels of hierarchy in the
+Multinomial-Dirichlet {\sc ei} model. At the first stage, we model
+the data as:
+\begin{itemize}
+
+\item The \emph{stochastic component} is described $T_i^{'}$ which
+follows a multinomial distribution:
+\begin{eqnarray*}
+T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
+\end{eqnarray*}
+
+\item The \emph{systematic components} are
+\begin{eqnarray*}
+\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} & c=1,\dots,C
+\end{eqnarray*}
+
+\end{itemize}
+
+At the second stage, we use an optional covariate to model
+$\Theta_{ci}$'s and $\beta_{qrc}^{i}$:
+\begin{itemize}
+\item The \emph{stochastic component} is described by
+$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for
+ $i=1,\dots,,p$ and $r=1,\dots,R$, which follows a Dirichlet distribution:
+
+ \begin{eqnarray*}
+ \beta_r^{i} &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
+ \end{eqnarray*}
+
+\item The \emph{systematic components} are
+\begin{eqnarray*}
+ \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)}
+\end{eqnarray*}
+for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
+
+In the third stage, we assume that the regression parameters (the
+$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
+and put a flat prior on these regression parameters. The parameters
+$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
+mean $\frac{1}{\lambda}$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2,
+ model = "eiRxC", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)}. For example,
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object
+ \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
+ also $\delta_{ij}$, if covariates are present). The parameters are
+ returned as a single vector of length $R\times (C-1)$. If there is a
+ covariate, $\delta$ is concatenated to it.
+
+\item From the \texttt{sim()} output object, you may extract the
+ parameters $\beta_{ij}$ corresponding to the estimated fractions of
+ different social groups that support different political parties, by
+ using \texttt{s.out\$qi\$ev}. For each precinct, that will be a
+ matrix with dimensions: simulations $\times R \times C$.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Please cite the model as
+\begin{verse}
+\bibentry{RosJiaKin01}.
+\end{verse}
+
+Jason Wittenberg, Ferdinand Alimadhi, and Olivia Lau implemented the
+$R \times C$ {\sc ei} model for Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/ei.RxC.pdf b/inst/doc/ei.RxC.pdf
new file mode 100644
index 0000000..45a1821
Binary files /dev/null and b/inst/doc/ei.RxC.pdf differ
diff --git a/inst/doc/ei.RxC.tex b/inst/doc/ei.RxC.tex
new file mode 100644
index 0000000..94c428d
--- /dev/null
+++ b/inst/doc/ei.RxC.tex
@@ -0,0 +1,238 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Hierarchical Multinomial-Dirichlet Ecological Inference Model}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, many contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
+ Inference Model for $R \times C$ Tables}
+\label{eiRxC}
+
+Given $n$ contingency tables, each with observed marginals (column and
+row totals), ecological inference ({\sc ei}) estimates the internal
+cell values in each table. The hierarchical Multinomial-Dirichlet
+model estimates cell counts in $R \times C$ tables. The model is
+implemented using a nonlinear least squares approximation and, with
+bootstrapping for standard errors, had good frequentist properties.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1,
+ covar = NULL,
+ model = "eiRxC", data = mydata)
+> x.out <- setx(z.out, fn = NULL)
+> s.out <- sim(z.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
+ numeric vectors (either counts, or proportions that sum to one for
+ each row) containing the column margins of the units to be analyzed.
+
+\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
+ (either counts, or proportions that sum to one for each row)
+ containing the row margins of the units to be analyzed.
+
+\item {\tt covar}: (optional) a covariate that varies across tables,
+specified as \verb|covar = ~ Z1|, for example. (The model only
+accepts one covariate.)
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples: No covariate \\
+ Attaching the example dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(Weimar)
+\end{Sinput}
+\end{Schunk}
+Estimating the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight,
++ Other) ~ shareunemployed + shareblue + sharewhite + shareself +
++ sharedomestic, model = "ei.RxC", data = Weimar)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Setting values for in-sample simulations given marginal values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Estimate fractions of different social groups that support political parties:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Summarizing fractions of different social groups that support political parties:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+
+ \item Example of covariates being present in the model \\
+
+ Using the example dataset Weimar and estimating the model
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight,
++ Other) ~ shareunemployed + shareblue + sharewhite + shareself +
++ sharedomestic, covar = ~shareprotestants, model = "ei.RxC",
++ data = Weimar)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Set the covariate to its default (mean/median) value
+
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Estimate fractions of different social groups that support political parties:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Summarizing fractions of different social groups that support political parties:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\clearpage
+
+\subsubsection{Model}
+Consider the following $5 \times 5$ contingency table for the voting
+patterns in Weimar Germany. For each geographical unit $i$ ($i = 1,
+\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
+$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
+would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
+which are the fractions of people in social class $r$ who vote for
+party $c$, for all $r$ and $c$.
+\begin{table}[!h]
+ \begin{center}
+ \begin{tabular}{l|ccccc|c}
+ & Nazi & Government & Communists & Far Right & Other \\
+ \hline
+ Unemployed & $\beta_{11}^{i}$ & $\beta_{12}^{i}$ & $\beta_{13}^{i}$ & $\beta_{14}^{i}$ & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$ \\
+ Blue & $\beta_{21}^{i}$ & $\beta_{22}^{i}$ & $\beta_{23}^{i}$ & $\beta_{24}^{i}$ & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$ \\
+ White & $\beta_{31}^{i}$ & $\beta_{32}^{i}$ & $\beta_{33}^{i}$ & $\beta_{34}^{i}$ & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$ \\
+ Self & $\beta_{41}^{i}$ & $\beta_{42}^{i}$ & $\beta_{43}^{i}$ & $\beta_{44}^{i}$ & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$ \\
+ Domestic & $\beta_{51}^{i}$ & $\beta_{52}^{i}$ & $\beta_{53}^{i}$ & $\beta_{54}^{i}$ & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$ \\
+ \hline
+ & $T_{1i}$ & $T_{2i}$ & $T_{3i}$ & $T_{4i}$ & $1-\sum_{c=1}^4 \beta_{ci}$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent
+The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
+observed as counts or fractions.
+
+Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
+of voting age persons who turn out to vote for different parties.
+There are three levels of hierarchy in the
+Multinomial-Dirichlet {\sc ei} model. At the first stage, we model
+the data as:
+\begin{itemize}
+
+\item The \emph{stochastic component} is described $T_i^{'}$ which
+follows a multinomial distribution:
+\begin{eqnarray*}
+T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
+\end{eqnarray*}
+
+\item The \emph{systematic components} are
+\begin{eqnarray*}
+\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} & c=1,\dots,C
+\end{eqnarray*}
+
+\end{itemize}
+
+At the second stage, we use an optional covariate to model
+$\Theta_{ci}$'s and $\beta_{qrc}^{i}$:
+\begin{itemize}
+\item The \emph{stochastic component} is described by
+$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for
+ $i=1,\dots,,p$ and $r=1,\dots,R$, which follows a Dirichlet distribution:
+
+ \begin{eqnarray*}
+ \beta_r^{i} &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
+ \end{eqnarray*}
+
+\item The \emph{systematic components} are
+\begin{eqnarray*}
+ \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)}
+\end{eqnarray*}
+for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
+
+In the third stage, we assume that the regression parameters (the
+$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
+and put a flat prior on these regression parameters. The parameters
+$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
+mean $\frac{1}{\lambda}$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2,
+ model = "eiRxC", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)}. For example,
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object
+ \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
+ also $\delta_{ij}$, if covariates are present). The parameters are
+ returned as a single vector of length $R\times (C-1)$. If there is a
+ covariate, $\delta$ is concatenated to it.
+
+\item From the \texttt{sim()} output object, you may extract the
+ parameters $\beta_{ij}$ corresponding to the estimated fractions of
+ different social groups that support different political parties, by
+ using \texttt{s.out\$qi\$ev}. For each precinct, that will be a
+ matrix with dimensions: simulations $\times R \times C$.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Please cite the model as
+\begin{verse}
+\bibentry{RosJiaKin01}.
+\end{verse}
+
+Jason Wittenberg, Ferdinand Alimadhi, and Olivia Lau implemented the
+$R \times C$ {\sc ei} model for Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/ei.dynamic.Rnw b/inst/doc/ei.dynamic.Rnw
new file mode 100644
index 0000000..b8ad6d3
--- /dev/null
+++ b/inst/doc/ei.dynamic.Rnw
@@ -0,0 +1,309 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/eiDynamic}
+\include{zinput}
+%\VignetteIndexEntry{Quinn's Dynamic Ecological Inference}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+%%% the following inputs do not work
+%\SweaveInput{ei.hier}
+%\SweaveInput{eiRxC}
+\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
+Model}
+
+\label{ei.dynamic}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
+$2 \times 2$ tables with temporal dependence across tables (units).
+The model is implemented using a Markov Chain Monte Carlo algorithm
+(via a combination of slice and Gibbs sampling).
+For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
+dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "MCMCei.dynamic", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts in each contingency table (unit). If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}.
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 5,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed of 12345.
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to
+specify priors and other parameters:
+
+\begin{itemize}
+\item \texttt{W}: a $p \times p$ numeric matrix describing the
+structure of the temporal dependence among elements of $\theta_0$ and
+$\theta_1$. The default value is 0, which constructs a weight matrix
+corresponding to random walk priors for $\theta_0$ and $\theta_1$
+(assuming that the tables are equally spaced throughout time, and that
+the elements of \texttt{t0},
+\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered).
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.825.
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.0105.
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.825.
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.0105.
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+<<Examples.data>>=
+ data(eidat)
+
+@
+Estimating the model using \texttt{ei.dynamic}:
+<<Examples.zelig>>=
+ z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic",
+ data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
+ verbose = TRUE)
+ summary(z.out)
+@
+Setting values for in-sample simulations given the marginal values
+of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
+<<Examples.setx>>=
+ x.out <- setx(z.out, fn = NULL, cond = TRUE)
+@
+In-sample simulations from the posterior distribution:
+<<Examples.sim>>=
+s.out <- sim(z.out, x = x.out)
+@
+Summarizing in-sample simulations at aggregate level
+weighted by the count in each unit:
+<<Examples.summary>>=
+ summary(s.out)
+@
+Summarizing in-sample simulations at unit level for the first 5 units:
+<<Examples.summary.subset>>=
+ summary(s.out, subset = 1:5)
+@
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example. For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+\begin{table}[h!]
+ \begin{center}
+ \begin{tabular}{l|cc|c}
+ & No Vote & Vote & \\
+ \hline
+ Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
+ White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
+ \hline
+ & $t_i^0$ & $t_i^1$ & $N_i$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
+observed as either counts or fractions. If fractions, the counts can
+be obtained by multiplying by the total counts per table $N_i =
+n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
+nearest integer. Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
+
+The hierarchical Bayesian model for ecological inference in
+$2 \times 2$ is illustrated as following:
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right) ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right) \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
+and $\beta_i^w$ as well as their aggregate summaries are the focus
+of inference.
+
+\item The \emph{systematic component} of the model is
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0} \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
+\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
+\theta_{i}^{1} \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
+\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right)
+\end{eqnarray*}
+where $P$ is a $p \times p$ matrix whose off diagonal elements
+$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
+elements of the weight matrix $W$), and diagonal elements
+$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
+$\sigma_{1}^2$ have hyperprior distributions as given below.
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right) ,\\
+\sigma_{1}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right) ,
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
+ are the scale parameters. \\
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "ei.dynamic", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
+posterior distribution of the quantities of interest by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out},
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution. The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$.
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the ecological table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal
+cell given the observed marginals.
+\item \texttt{qi\$pr}: the simulated expected values of each internal
+cell given the observed marginals.
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The dynamic {\sc ei} model was developed in
+\begin{verse}
+\bibentry{Qui04}.
+\end{verse}
+The \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.dynamic} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/ei.dynamic.pdf b/inst/doc/ei.dynamic.pdf
new file mode 100644
index 0000000..3f18d9a
Binary files /dev/null and b/inst/doc/ei.dynamic.pdf differ
diff --git a/inst/doc/ei.dynamic.tex b/inst/doc/ei.dynamic.tex
new file mode 100644
index 0000000..ddb4402
--- /dev/null
+++ b/inst/doc/ei.dynamic.tex
@@ -0,0 +1,306 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Quinn's Dynamic Ecological Inference}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+%%% the following inputs do not work
+%\SweaveInput{ei.hier}
+%\SweaveInput{eiRxC}
+\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
+Model}
+
+\label{ei.dynamic}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
+$2 \times 2$ tables with temporal dependence across tables (units).
+The model is implemented using a Markov Chain Monte Carlo algorithm
+(via a combination of slice and Gibbs sampling).
+For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
+dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "MCMCei.dynamic", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts in each contingency table (unit). If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}.
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 5,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed of 12345.
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to
+specify priors and other parameters:
+
+\begin{itemize}
+\item \texttt{W}: a $p \times p$ numeric matrix describing the
+structure of the temporal dependence among elements of $\theta_0$ and
+$\theta_1$. The default value is 0, which constructs a weight matrix
+corresponding to random walk priors for $\theta_0$ and $\theta_1$
+(assuming that the tables are equally spaced throughout time, and that
+the elements of \texttt{t0},
+\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered).
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.825.
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.0105.
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.825.
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.0105.
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(eidat)
+\end{Sinput}
+\end{Schunk}
+Estimating the model using \texttt{ei.dynamic}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic",
++ data = eidat, mcmc = 40000, thin = 10, burnin = 10000, verbose = TRUE)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for in-sample simulations given the marginal values
+of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+\end{Sinput}
+\end{Schunk}
+In-sample simulations from the posterior distribution:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+Summarizing in-sample simulations at aggregate level
+weighted by the count in each unit:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+Summarizing in-sample simulations at unit level for the first 5 units:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out, subset = 1:5)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example. For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+\begin{table}[h!]
+ \begin{center}
+ \begin{tabular}{l|cc|c}
+ & No Vote & Vote & \\
+ \hline
+ Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
+ White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
+ \hline
+ & $t_i^0$ & $t_i^1$ & $N_i$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
+observed as either counts or fractions. If fractions, the counts can
+be obtained by multiplying by the total counts per table $N_i =
+n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
+nearest integer. Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
+
+The hierarchical Bayesian model for ecological inference in
+$2 \times 2$ is illustrated as following:
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right) ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right) \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
+and $\beta_i^w$ as well as their aggregate summaries are the focus
+of inference.
+
+\item The \emph{systematic component} of the model is
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0} \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
+\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
+\theta_{i}^{1} \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
+\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right)
+\end{eqnarray*}
+where $P$ is a $p \times p$ matrix whose off diagonal elements
+$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
+elements of the weight matrix $W$), and diagonal elements
+$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
+$\sigma_{1}^2$ have hyperprior distributions as given below.
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right) ,\\
+\sigma_{1}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right) ,
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
+ are the scale parameters. \\
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "ei.dynamic", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
+posterior distribution of the quantities of interest by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out},
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution. The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$.
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the ecological table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal
+cell given the observed marginals.
+\item \texttt{qi\$pr}: the simulated expected values of each internal
+cell given the observed marginals.
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The dynamic {\sc ei} model was developed in
+\begin{verse}
+\bibentry{Qui04}.
+\end{verse}
+The \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.dynamic} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/ei.hier.Rnw b/inst/doc/ei.hier.Rnw
new file mode 100644
index 0000000..75538ae
--- /dev/null
+++ b/inst/doc/ei.hier.Rnw
@@ -0,0 +1,311 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/eiHier}
+\include{zinput}
+%\VignetteIndexEntry{Hierarchical Ecological Inference Model}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, two contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+%\SweaveInput{ei.dynamic}
+\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
+$2 \times 2$ Tables}
+
+\label{ei.hier}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+The hierarchical {\sc ei} model estimates a Bayesian model for $2
+\times 2$ tables. The model is implemented using a Markov Chain Monte
+Carlo algorithm (via a combination of slice and Gibbs sampling).
+For a Bayesian implementation of {\sc ei} that accounts for temporal
+dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
+For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "MCMCei.hier", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts per contingency table (unit). If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}.
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.hier} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 5,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
+corresponds to a random seed of 12345.
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to specify
+ prior parameters used in the model:
+
+\begin{itemize}
+\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
+
+\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
+
+\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
+
+\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.825).
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.825).
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+<<Examples.data>>=
+ data(eidat)
+ eidat
+@
+Estimating the model using \texttt{ei.hier}:
+<<Examples.zelig>>=
+z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier",
+ data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
+ verbose = TRUE)
+ summary(z.out)
+@
+
+Setting values for in-sample simulations given marginal values
+of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
+<<Examples.setx>>=
+ x.out <- setx(z.out, fn = NULL, cond = TRUE)
+@
+
+In-sample simulations from the posterior distribution:
+<<Examples.sim>>=
+ s.out <- sim(z.out, x = x.out)
+@
+Summarizing in-sample simulations at aggregate level
+weighted by the count in each unit:
+<<Examples.summary>>=
+summary(s.out)
+@
+Summarizing in-sample simulations at unit level for the first 5 units:
+<<Examples.summary.subset>>=
+summary(s.out, subset = 1:5)
+@
+
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example. For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+
+\begin{table}[!h]
+ \begin{center}
+ \begin{tabular}{l|cc|c}
+ & No Vote & Vote & \\
+ \hline
+ Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
+ White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
+ \hline
+ & $t_i^0$ & $t_i^1$ & $N_i$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
+$t_i^1$ are observed as either counts or fractions. If fractions, the
+counts can be obtained by multiplying by the total counts per table
+$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
+nearest integer. Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
+
+The hierarchical Bayesian model for ecological inference in $2 \times
+2$ is illustrated as following:
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right) ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right) \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
+and $\beta_i^w$ as well as their aggregate level summaries are the focus
+of inference.
+
+\item The \emph{systematic component} is
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0} \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left( \mu
+_{0},\sigma_{0}^{2}\right),\\
+\theta_{i}^{1} \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left( \mu
+_{1},\sigma_{1}^{2}\right)
+\end{eqnarray*}
+where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$
+and $\sigma_{1}^{2}$ are the variances of the two corresponding
+(independent) normal distributions.
+
+\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
+\begin{eqnarray*}
+\mu_{0} &\sim& \textrm{Normal} \left( m_{0}, M_{0} \right) ,\\
+\mu_{1} &\sim& \textrm{Normal} \left( m_{1}, M_{1} \right) ,
+\end{eqnarray*}
+where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
+distributions while $M_{0}$ and $M_{1}$ are the variances.
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
+\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
+the scale parameters.
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "ei.hier", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)},
+see the draws from the posterior distribution of
+the quantities of interest by using \texttt{z.out\$coefficients},
+and a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out},
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution. The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$.
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the contingency table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal cell
+given the observed marginals.
+\item \texttt{qi\$pr}: the simulated expected values of each internal cell
+given the observed marginals.
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The hierarchical {\sc ei} \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.hier} to work with Zelig.
+i
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/ei.hier.pdf b/inst/doc/ei.hier.pdf
new file mode 100644
index 0000000..10bdb8c
Binary files /dev/null and b/inst/doc/ei.hier.pdf differ
diff --git a/inst/doc/ei.hier.tex b/inst/doc/ei.hier.tex
new file mode 100644
index 0000000..4e9d54a
--- /dev/null
+++ b/inst/doc/ei.hier.tex
@@ -0,0 +1,309 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Hierarchical Ecological Inference Model}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,ecological, two contingency tables, Gibbs sampling}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+%\SweaveInput{ei.dynamic}
+\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
+$2 \times 2$ Tables}
+
+\label{ei.hier}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+The hierarchical {\sc ei} model estimates a Bayesian model for $2
+\times 2$ tables. The model is implemented using a Markov Chain Monte
+Carlo algorithm (via a combination of slice and Gibbs sampling).
+For a Bayesian implementation of {\sc ei} that accounts for temporal
+dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
+For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "MCMCei.hier", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts per contingency table (unit). If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}.
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.hier} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 5,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
+corresponds to a random seed of 12345.
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to specify
+ prior parameters used in the model:
+
+\begin{itemize}
+\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
+
+\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
+
+\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
+
+\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.825).
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.825).
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(eidat)
+> eidat
+\end{Sinput}
+\end{Schunk}
+Estimating the model using \texttt{ei.hier}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier", data = eidat,
++ mcmc = 40000, thin = 10, burnin = 10000, verbose = TRUE)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Setting values for in-sample simulations given marginal values
+of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+\end{Sinput}
+\end{Schunk}
+
+In-sample simulations from the posterior distribution:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+Summarizing in-sample simulations at aggregate level
+weighted by the count in each unit:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+Summarizing in-sample simulations at unit level for the first 5 units:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out, subset = 1:5)
+\end{Sinput}
+\end{Schunk}
+
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example. For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+
+\begin{table}[!h]
+ \begin{center}
+ \begin{tabular}{l|cc|c}
+ & No Vote & Vote & \\
+ \hline
+ Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
+ White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
+ \hline
+ & $t_i^0$ & $t_i^1$ & $N_i$
+ \end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
+$t_i^1$ are observed as either counts or fractions. If fractions, the
+counts can be obtained by multiplying by the total counts per table
+$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
+nearest integer. Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
+
+The hierarchical Bayesian model for ecological inference in $2 \times
+2$ is illustrated as following:
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right) ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right) \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
+and $\beta_i^w$ as well as their aggregate level summaries are the focus
+of inference.
+
+\item The \emph{systematic component} is
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0} \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left( \mu
+_{0},\sigma_{0}^{2}\right),\\
+\theta_{i}^{1} \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left( \mu
+_{1},\sigma_{1}^{2}\right)
+\end{eqnarray*}
+where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$
+and $\sigma_{1}^{2}$ are the variances of the two corresponding
+(independent) normal distributions.
+
+\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
+\begin{eqnarray*}
+\mu_{0} &\sim& \textrm{Normal} \left( m_{0}, M_{0} \right) ,\\
+\mu_{1} &\sim& \textrm{Normal} \left( m_{1}, M_{1} \right) ,
+\end{eqnarray*}
+where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
+distributions while $M_{0}$ and $M_{1}$ are the variances.
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
+\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
+the scale parameters.
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
+ model = "ei.hier", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)},
+see the draws from the posterior distribution of
+the quantities of interest by using \texttt{z.out\$coefficients},
+and a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out},
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution. The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$.
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the contingency table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal cell
+given the observed marginals.
+\item \texttt{qi\$pr}: the simulated expected values of each internal cell
+given the observed marginals.
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The hierarchical {\sc ei} \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.hier} to work with Zelig.
+i
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/exp.Rnw b/inst/doc/exp.Rnw
new file mode 100644
index 0000000..12f6fa6
--- /dev/null
+++ b/inst/doc/exp.Rnw
@@ -0,0 +1,302 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/exp}
+\include{zinput}
+%\VignetteIndexEntry{Exponential Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model,exponential,regression, time}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt exp}: Exponential Regression for Duration Dependent Variables}\label{exp}
+
+Use the exponential duration regression model if you have a dependent
+variable representing a duration (time until an event). The model
+assumes a constant hazard rate for all events. The dependent variable
+may be censored (for observations have not yet been completed when
+data were collected).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Exponential models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+ e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study and is know to live at least as long as $y_i$). If
+$c_i$ is omitted, all Y are assumed to be completed; that is, time
+defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for exponential regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+<<Example.data>>=
+ data(coalition)
+@
+
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
+ data = coalition)
+@
+View the regression output:
+<<Example.summary>>=
+ 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:
+<<Example.setx>>=
+ x.low <- setx(z.out, numst2 = 0)
+ x.high <- setx(z.out, numst2 = 1)
+@
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+@
+Summarize quantities of interest and produce some plots:
+<<Example.summary>>=
+ summary(s.out)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+ plot(s.out)
+@
+\end{center}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c \\
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
+ the exponential distribution whose density function is given by
+ \begin{equation*}
+ f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
+ \end{equation*}
+ for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
+ $\lambda_i$.
+
+ In addition, survival models like the exponential have three
+ additional properties. The hazard function $h(t)$ measures the
+ probability of not surviving past time $t$ given survival up to
+ $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
+ the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
+ fraction still surviving at time $t$. The cumulative hazard
+ function $H(t)$ describes the probability of dying before time $t$.
+ In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case
+ of the exponential model,
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\lambda_i} \\
+S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
+H(t) &=& \frac{t}{\lambda_i}
+\end{eqnarray*}
+For the exponential model, the hazard function $h(t)$ is constant over
+time. The Weibull model and lognormal models allow the hazard
+function to vary as a function of elapsed time (see \Sref{weibull} and
+\Sref{lognorm} respectively).
+
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+ \begin{equation*}
+ \lambda_i = \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the exponential model are
+ simulations of the expected duration given $x_i$ and draws of
+ $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ exponential distribution with rate equal to the expected value.
+
+\item The first difference (or difference in expected values, {\tt
+ qi\$ev.diff}), is
+\begin{equation}
+\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x),
+\end{equation}
+where $x$ and $x_1$ are different vectors of values for the
+explanatory variables.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
+ \right\}, \end{equation*} where $t_i$ is a binary explanatory
+ variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+ groups. When $Y_i(t_i=1)$ is censored rather than observed, we
+ replace it with a simulation from the model given available
+ knowledge of the censoring process. Variation in the simulations
+ is due to two factors: uncertainty in the imputation process for
+ censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
+ counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the same
+ except that the treatment indicator is switched to $t_i=0$.
+
+ \item In conditional prediction models, the average predicted
+ treatment effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
+ binary explanatory variable defining the treatment ($t_i=1$) and
+ control ($t_i=0$) groups. When $Y_i(t_i=1)$ is censored rather than
+ observed, we replace it with a simulation from the model given
+ available knowledge of the censoring process. Variation in the
+ simulations is due to two factors: uncertainty in the imputation
+ process for censored $y_i^*$ and uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+ for observations in the treatment group, under the assumption that
+ everything stays the same except that the treatment indicator is
+ switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
+ C) \~\, X, model = "exp", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+ z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}. Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and scale
+ parameter. While the scale parameter varies for the Weibull
+ distribution, it is fixed to 1 for the exponential distribution
+ (which is modeled as a special case of the Weibull).
+ \item {\tt var}: the variance-covariance matrix for the estimates
+ of $\beta$.
+ \item {\tt loglik}: a vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: the vector of
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics. For
+ example, {\tt summary(z.out)\$table}
+ \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt s.out}:
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by the expected values.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/exp.pdf b/inst/doc/exp.pdf
new file mode 100644
index 0000000..28fd25b
Binary files /dev/null and b/inst/doc/exp.pdf differ
diff --git a/inst/doc/exp.tex b/inst/doc/exp.tex
new file mode 100644
index 0000000..7c3bb10
--- /dev/null
+++ b/inst/doc/exp.tex
@@ -0,0 +1,304 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Exponential Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model,exponential,regression, time}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt exp}: Exponential Regression for Duration Dependent Variables}\label{exp}
+
+Use the exponential duration regression model if you have a dependent
+variable representing a duration (time until an event). The model
+assumes a constant hazard rate for all events. The dependent variable
+may be censored (for observations have not yet been completed when
+data were collected).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Exponential models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+ e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study and is know to live at least as long as $y_i$). If
+$c_i$ is omitted, all Y are assumed to be completed; that is, time
+defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for exponential regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(coalition)
+\end{Sinput}
+\end{Schunk}
+
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
++ data = coalition)
+\end{Sinput}
+\end{Schunk}
+View the regression output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{Sinput}
+\end{Schunk}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{Sinput}
+\end{Schunk}
+Summarize quantities of interest and produce some plots:
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/exp-ExamplePlot}
+\end{center}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c \\
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
+ the exponential distribution whose density function is given by
+ \begin{equation*}
+ f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
+ \end{equation*}
+ for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
+ $\lambda_i$.
+
+ In addition, survival models like the exponential have three
+ additional properties. The hazard function $h(t)$ measures the
+ probability of not surviving past time $t$ given survival up to
+ $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
+ the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
+ fraction still surviving at time $t$. The cumulative hazard
+ function $H(t)$ describes the probability of dying before time $t$.
+ In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case
+ of the exponential model,
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\lambda_i} \\
+S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
+H(t) &=& \frac{t}{\lambda_i}
+\end{eqnarray*}
+For the exponential model, the hazard function $h(t)$ is constant over
+time. The Weibull model and lognormal models allow the hazard
+function to vary as a function of elapsed time (see \Sref{weibull} and
+\Sref{lognorm} respectively).
+
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+ \begin{equation*}
+ \lambda_i = \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the exponential model are
+ simulations of the expected duration given $x_i$ and draws of
+ $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+ exponential distribution with rate equal to the expected value.
+
+\item The first difference (or difference in expected values, {\tt
+ qi\$ev.diff}), is
+\begin{equation}
+\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x),
+\end{equation}
+where $x$ and $x_1$ are different vectors of values for the
+explanatory variables.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
+ \right\}, \end{equation*} where $t_i$ is a binary explanatory
+ variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+ groups. When $Y_i(t_i=1)$ is censored rather than observed, we
+ replace it with a simulation from the model given available
+ knowledge of the censoring process. Variation in the simulations
+ is due to two factors: uncertainty in the imputation process for
+ censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
+ counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the same
+ except that the treatment indicator is switched to $t_i=0$.
+
+ \item In conditional prediction models, the average predicted
+ treatment effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
+ binary explanatory variable defining the treatment ($t_i=1$) and
+ control ($t_i=0$) groups. When $Y_i(t_i=1)$ is censored rather than
+ observed, we replace it with a simulation from the model given
+ available knowledge of the censoring process. Variation in the
+ simulations is due to two factors: uncertainty in the imputation
+ process for censored $y_i^*$ and uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+ for observations in the treatment group, under the assumption that
+ everything stays the same except that the treatment indicator is
+ switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
+ C) \~\, X, model = "exp", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+ z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}. Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and scale
+ parameter. While the scale parameter varies for the Weibull
+ distribution, it is fixed to 1 for the exponential distribution
+ (which is modeled as a special case of the Weibull).
+ \item {\tt var}: the variance-covariance matrix for the estimates
+ of $\beta$.
+ \item {\tt loglik}: a vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: the vector of
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics. For
+ example, {\tt summary(z.out)\$table}
+ \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt s.out}:
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by the expected values.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/factor.bayes.Rnw b/inst/doc/factor.bayes.Rnw
new file mode 100644
index 0000000..67faae2
--- /dev/null
+++ b/inst/doc/factor.bayes.Rnw
@@ -0,0 +1,293 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/factorBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,latent factors,continuous,explanatory variables, normal, Gibbs}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{factor.bayes}: Bayesian Factor Analysis}
+\label{factor.bayes}
+
+Given some unobserved explanatory variables and observed dependent
+variables, the Normal theory factor analysis model estimates the
+latent factors. The model is implemented using a Markov Chain Monte
+Carlo algorithm (Gibbs sampling with data augmentation). For factor
+analysis with ordinal dependent variables, see ordered factor analysis
+(\Sref{factor.ord}), and for a mix of types of dependent variables,
+see the mixed factor analysis model (\Sref{factor.mix}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2,
+ model = "factor.bayes", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} takes the following functions for {\tt factor.bayes}:
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in
+factor analysis (manifest variables), assumed to be normally
+distributed. The model requires a minimum of three manifest variables.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for
+model specification:
+\begin{itemize}
+\item \texttt{lambda.constraints}: list containing the equality or
+inequality constraints on the factor loadings. Choose from one of the
+following forms:
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+
+\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
+are rescaled to zero mean, but retain observed variance). If
+\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
+unit variance.
+
+\end{itemize}
+ In addition, {\tt zelig()} accepts the following additional inputs
+for {\tt bayes.factor}:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$, either a scalar (all unconstrained loadings are set
+to that value), or a matrix with compatible dimensions. The default
+is \texttt{NA}, where the start value are set to be 0 for
+unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
+factor loadings (depending on the nature of the constraints).
+
+\item \texttt{Psi.start}: starting values for the uniquenesses, either
+a scalar (the starting values for all diagonal elements of $\Psi$ are
+set to be this value), or a vector with length equal to the number of
+manifest variables. In the latter case, the starting values of the
+diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
+default value is \texttt{NA} where the starting values of the all the
+uniquenesses are set to be 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+stores the posterior draws of the factor scores. (Storing factor
+scores may take large amount of memory for a large number of draws
+or observations.)
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to
+specify prior parameters:
+
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+
+\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for
+the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for
+the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(swiss)
+ names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort")
+@
+Factor analysis:
+<<BasicExample.zelig>>=
+ z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
+ model = "factor.bayes", data = swiss, factors = 2,
+ verbose = TRUE, a0 = 1, b0 = 0.15,
+ burnin = 5000, mcmc = 50000)
+@
+%it is not in the demo and it fails
+Checking for convergence before summarizing the estimates:
+<<BasicExample.coeff>>=
+ algor <- try(geweke.diag(z.out$coefficients), silent=T)
+if(class(algor)=="try-error")
+print(algor)
+@
+
+Since the algorithm did not converge, we now add some constraints on
+$\Lambda$.
+
+\item {Putting Constraints on $\Lambda$} \\
+Put constraints on Lambda to optimize the algorithm:
+<<Constraints.zelig>>=
+z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
+ model = "factor.bayes", data = swiss, factors = 2,
+ lambda.constraints = list(Exam=list(1,"+"),
+ Exam=list(2,"-"), Educ=c(2,0),
+ InfMort=c(1,0)),
+ verbose = TRUE, a0 = 1, b0 = 0.15,
+ burnin = 5000, mcmc = 50000)
+
+ geweke.diag(z.out$coefficients)
+ heidel.diag(z.out$coefficients)
+ raftery.diag(z.out$coefficients)
+ summary(z.out)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+Suppose for observation $i$ we observe $K$ variables and hypothesize
+that there are $d$ underlying factors such that:
+\begin{eqnarray*}
+Y_i = \Lambda \phi_i+\epsilon_i
+\end{eqnarray*}
+where $Y_{i}$ is the vector of $K$ manifest variables for observation
+$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
+is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
+need to be estimated.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+\epsilon_{i} \sim \textrm{Normal}(0, \Psi).
+\end{eqnarray*}
+where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
+of $\Psi$ are referred to as uniquenesses.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_i = E(Y_i) = \Lambda\phi_i
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
+\begin{eqnarray*}
+\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for }
+i = 1, \ldots, k.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is
+\begin{eqnarray*}
+\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
+\end{eqnarray*}
+where $I_d$ is a $ d\times d $ identity matrix.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings and the uniquenesses. If
+\texttt{store.scores = TRUE}, the estimated factors scores are also
+contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian factor analysis
+\input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.bayes} to work with Zelig.
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+ \end{document}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/factor.bayes.pdf b/inst/doc/factor.bayes.pdf
new file mode 100644
index 0000000..199d174
Binary files /dev/null and b/inst/doc/factor.bayes.pdf differ
diff --git a/inst/doc/factor.bayes.tex b/inst/doc/factor.bayes.tex
new file mode 100644
index 0000000..0de5d80
--- /dev/null
+++ b/inst/doc/factor.bayes.tex
@@ -0,0 +1,283 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,latent factors,continuous,explanatory variables, normal, Gibbs}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{factor.bayes}: Bayesian Factor Analysis}
+\label{factor.bayes}
+
+Given some unobserved explanatory variables and observed dependent
+variables, the Normal theory factor analysis model estimates the
+latent factors. The model is implemented using a Markov Chain Monte
+Carlo algorithm (Gibbs sampling with data augmentation). For factor
+analysis with ordinal dependent variables, see ordered factor analysis
+(\Sref{factor.ord}), and for a mix of types of dependent variables,
+see the mixed factor analysis model (\Sref{factor.mix}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2,
+ model = "factor.bayes", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} takes the following functions for {\tt factor.bayes}:
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in
+factor analysis (manifest variables), assumed to be normally
+distributed. The model requires a minimum of three manifest variables.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for
+model specification:
+\begin{itemize}
+\item \texttt{lambda.constraints}: list containing the equality or
+inequality constraints on the factor loadings. Choose from one of the
+following forms:
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+
+\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
+are rescaled to zero mean, but retain observed variance). If
+\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
+unit variance.
+
+\end{itemize}
+ In addition, {\tt zelig()} accepts the following additional inputs
+for {\tt bayes.factor}:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$, either a scalar (all unconstrained loadings are set
+to that value), or a matrix with compatible dimensions. The default
+is \texttt{NA}, where the start value are set to be 0 for
+unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
+factor loadings (depending on the nature of the constraints).
+
+\item \texttt{Psi.start}: starting values for the uniquenesses, either
+a scalar (the starting values for all diagonal elements of $\Psi$ are
+set to be this value), or a vector with length equal to the number of
+manifest variables. In the latter case, the starting values of the
+diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
+default value is \texttt{NA} where the starting values of the all the
+uniquenesses are set to be 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+stores the posterior draws of the factor scores. (Storing factor
+scores may take large amount of memory for a large number of draws
+or observations.)
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to
+specify prior parameters:
+
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+
+\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for
+the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for
+the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(swiss)
+> names(swiss) <- c("Fert", "Agr", "Exam", "Educ", "Cath", "InfMort")
+\end{Sinput}
+\end{Schunk}
+Factor analysis:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
++ model = "factor.bayes", data = swiss, factors = 2, verbose = TRUE,
++ a0 = 1, b0 = 0.15, burnin = 5000, mcmc = 50000)
+\end{Sinput}
+\end{Schunk}
+%it is not in the demo and it fails
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> algor <- try(geweke.diag(z.out$coefficients), silent = T)
+> if (class(algor) == "try-error") print(algor)
+\end{Sinput}
+\end{Schunk}
+
+Since the algorithm did not converge, we now add some constraints on
+$\Lambda$.
+
+\item {Putting Constraints on $\Lambda$} \\
+Put constraints on Lambda to optimize the algorithm:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
++ model = "factor.bayes", data = swiss, factors = 2, lambda.constraints = list(Exam = list(1,
++ "+"), Exam = list(2, "-"), Educ = c(2, 0), InfMort = c(1,
++ 0)), verbose = TRUE, a0 = 1, b0 = 0.15, burnin = 5000,
++ mcmc = 50000)
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+Suppose for observation $i$ we observe $K$ variables and hypothesize
+that there are $d$ underlying factors such that:
+\begin{eqnarray*}
+Y_i = \Lambda \phi_i+\epsilon_i
+\end{eqnarray*}
+where $Y_{i}$ is the vector of $K$ manifest variables for observation
+$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
+is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
+need to be estimated.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+\epsilon_{i} \sim \textrm{Normal}(0, \Psi).
+\end{eqnarray*}
+where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
+of $\Psi$ are referred to as uniquenesses.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_i = E(Y_i) = \Lambda\phi_i
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
+\begin{eqnarray*}
+\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for }
+i = 1, \ldots, k.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is
+\begin{eqnarray*}
+\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
+\end{eqnarray*}
+where $I_d$ is a $ d\times d $ identity matrix.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings and the uniquenesses. If
+\texttt{store.scores = TRUE}, the estimated factors scores are also
+contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian factor analysis
+\input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.bayes} to work with Zelig.
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+ \end{document}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/factor.mix.Rnw b/inst/doc/factor.mix.Rnw
new file mode 100644
index 0000000..db753bb
--- /dev/null
+++ b/inst/doc/factor.mix.Rnw
@@ -0,0 +1,299 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/factorMix}
+\include{zinput}
+%\VignetteIndexEntry{Mixed Data Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,mixed, factors latent, continuous, ordinal,Gibbs}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\
+\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
+
+Mixed data factor analysis takes both continuous and ordinal dependent
+variables and estimates a model for a given number of latent factors.
+The model is estimated using a Markov Chain Monte Carlo algorithm
+(Gibbs sampler with data augmentation). Alternative models include
+Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
+variables (\Sref{factor.ord}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
+ model = "factor.mix", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.mix}:
+\begin{itemize}
+
+\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
+interest, which can be a mix of ordinal and continuous variables. You
+must have more dependent variables than factors.
+
+\item \texttt{factors}: The number of the factors to be fitted.
+
+\end{itemize}
+
+
+\subsubsection{Additional Inputs}
+
+The model accepts the following additional arguments to monitor
+convergence:
+\begin{itemize}
+\item \texttt{lambda.constraints}: A list that contains the equality or
+inequality constraints on the factor loadings.
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+Unlike Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
+negative item difficulty parameters and should not be constrained in
+general.
+
+\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
+continuous manifest variables to have mean 0.
+
+\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
+continuous manifest variables to have unit variance.
+\end{itemize}
+
+
+\noindent \texttt{factor.mix} accepts the following additional arguments
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded. The default value is 1,000.
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin.
+ The default value is 20,000.
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter, which can be either a
+scalar or a vector of length $K$. The value of the tuning parameter
+must be positive. The default value is \texttt{1.2}.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen. The default
+is \texttt{FALSE}.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (starting
+values of the unconstrained loadings will be set to that value), or a
+matrix with compatible dimensions. The default is \texttt{NA}, where
+the start values for the first column of $\Lambda$ are set based on
+the observed pattern, while for the rest of the columns of $\Lambda$,
+the start values are set to be 0 for unconstrained factor loadings,
+and 1 or $-$1 for constrained factor loadings (depending on the nature
+of the constraints).
+
+\item \texttt{psi.start}: starting values for the diagonals of the error variance
+(uniquenesses) matrix. Since the starting values for the ordinal
+variables are constrained to 1 (to identify the model), you may only
+specify the starting values for the continuous variables. For the
+continuous variables, you may specify {\tt psi.start} as a scalar or a
+vector with length equal to the number of continuous variables. If a
+scalar, that starting value is recycled for all continuous variables.
+If a vector, the starting values should correspond to each of the
+continuous variables. The default value is \texttt{NA}, which means
+the starting values of all the continuous variable uniqueness are set
+to 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+the posterior draws of the factor scores are stored. (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+Use the following additional arguments to specify prior parameters used in the model:
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, then that value will be the prior mean
+for all the factor loadings. The default value is 0.
+
+\item \texttt{L0}: precision parameter of Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.
+If a scalar value, then the precision matrix will be
+a diagonal matrix with the diagonal elements set to that value.
+The default value is 0 which leads to an improper prior.
+
+\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\end{itemize}
+Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<Examples.data>>=
+ data(PErisk)
+@
+Factor analysis for mixed data using \texttt{factor.mix}:
+<<Examples.zelig>>=
+ z.out<-zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~ NULL,
+ data = PErisk, model = "factor.mix", factors = 1,
+ burnin = 5000, mcmc = 100000, thin = 50, verbose = TRUE,
+ L0 = 0.25, tune=1.2)
+@
+
+Checking for convergence before summarizing the estimates:
+<<Examples.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<Examples.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<Examples.summary>>=
+ summary(z.out)
+@
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a $K$-vector of observed variables for observation $i$,
+The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
+ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for
+$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be
+governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$.
+There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let
+$Y_{ik}^*=Y_{ik}$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^* &\sim& \textrm{Normal}_K (\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+For ordinal $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
+\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
+variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
+It follows that the probability of observing $Y_{ik}$ belonging to category
+$j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad
+\textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the
+Normal distribution with mean $\mu_{ik}$ and variance 1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i &=& \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
+$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both
+$\Lambda$ and $\phi$ are estimated..
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1.
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If
+\texttt{store.scores = TRUE}, the estimated factors scores are also contained in
+\texttt{coefficients}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Factor analysis for mixed dependent variables \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.mix} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/factor.mix.pdf b/inst/doc/factor.mix.pdf
new file mode 100644
index 0000000..4c78bf6
Binary files /dev/null and b/inst/doc/factor.mix.pdf differ
diff --git a/inst/doc/factor.mix.tex b/inst/doc/factor.mix.tex
new file mode 100644
index 0000000..8c5c9dd
--- /dev/null
+++ b/inst/doc/factor.mix.tex
@@ -0,0 +1,295 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Mixed Data Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,mixed, factors latent, continuous, ordinal,Gibbs}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\
+\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
+
+Mixed data factor analysis takes both continuous and ordinal dependent
+variables and estimates a model for a given number of latent factors.
+The model is estimated using a Markov Chain Monte Carlo algorithm
+(Gibbs sampler with data augmentation). Alternative models include
+Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
+variables (\Sref{factor.ord}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
+ model = "factor.mix", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.mix}:
+\begin{itemize}
+
+\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
+interest, which can be a mix of ordinal and continuous variables. You
+must have more dependent variables than factors.
+
+\item \texttt{factors}: The number of the factors to be fitted.
+
+\end{itemize}
+
+
+\subsubsection{Additional Inputs}
+
+The model accepts the following additional arguments to monitor
+convergence:
+\begin{itemize}
+\item \texttt{lambda.constraints}: A list that contains the equality or
+inequality constraints on the factor loadings.
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+Unlike Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
+negative item difficulty parameters and should not be constrained in
+general.
+
+\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
+continuous manifest variables to have mean 0.
+
+\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
+continuous manifest variables to have unit variance.
+\end{itemize}
+
+
+\noindent \texttt{factor.mix} accepts the following additional arguments
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded. The default value is 1,000.
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin.
+ The default value is 20,000.
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter, which can be either a
+scalar or a vector of length $K$. The value of the tuning parameter
+must be positive. The default value is \texttt{1.2}.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen. The default
+is \texttt{FALSE}.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (starting
+values of the unconstrained loadings will be set to that value), or a
+matrix with compatible dimensions. The default is \texttt{NA}, where
+the start values for the first column of $\Lambda$ are set based on
+the observed pattern, while for the rest of the columns of $\Lambda$,
+the start values are set to be 0 for unconstrained factor loadings,
+and 1 or $-$1 for constrained factor loadings (depending on the nature
+of the constraints).
+
+\item \texttt{psi.start}: starting values for the diagonals of the error variance
+(uniquenesses) matrix. Since the starting values for the ordinal
+variables are constrained to 1 (to identify the model), you may only
+specify the starting values for the continuous variables. For the
+continuous variables, you may specify {\tt psi.start} as a scalar or a
+vector with length equal to the number of continuous variables. If a
+scalar, that starting value is recycled for all continuous variables.
+If a vector, the starting values should correspond to each of the
+continuous variables. The default value is \texttt{NA}, which means
+the starting values of all the continuous variable uniqueness are set
+to 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+the posterior draws of the factor scores are stored. (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+Use the following additional arguments to specify prior parameters used in the model:
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, then that value will be the prior mean
+for all the factor loadings. The default value is 0.
+
+\item \texttt{L0}: precision parameter of Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.
+If a scalar value, then the precision matrix will be
+a diagonal matrix with the diagonal elements set to that value.
+The default value is 0 which leads to an improper prior.
+
+\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\end{itemize}
+Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(PErisk)
+\end{Sinput}
+\end{Schunk}
+Factor analysis for mixed data using \texttt{factor.mix}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~
++ NULL, data = PErisk, model = "factor.mix", factors = 1, burnin = 5000,
++ mcmc = 1e+05, thin = 50, verbose = TRUE, L0 = 0.25, tune = 1.2)
+\end{Sinput}
+\end{Schunk}
+
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a $K$-vector of observed variables for observation $i$,
+The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
+ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for
+$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be
+governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$.
+There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let
+$Y_{ik}^*=Y_{ik}$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^* &\sim& \textrm{Normal}_K (\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+For ordinal $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
+\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
+variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
+It follows that the probability of observing $Y_{ik}$ belonging to category
+$j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad
+\textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the
+Normal distribution with mean $\mu_{ik}$ and variance 1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i &=& \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
+$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both
+$\Lambda$ and $\phi$ are estimated..
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1.
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If
+\texttt{store.scores = TRUE}, the estimated factors scores are also contained in
+\texttt{coefficients}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Factor analysis for mixed dependent variables \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.mix} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/factor.ord.Rnw b/inst/doc/factor.ord.Rnw
new file mode 100644
index 0000000..90ccf90
--- /dev/null
+++ b/inst/doc/factor.ord.Rnw
@@ -0,0 +1,276 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/factorOrd}
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Data Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,factors latent, ordinal,Gibbs}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}
+\label{factor.ord}
+
+Given some unobserved explanatory variables and observed ordinal
+dependent variables, this model estimates latent factors using a Gibbs
+sampler with data augmentation. For factor analysis for continuous
+data, see \Sref{factor.bayes}. For factor analysis for mixed data
+(including both continuous and ordinal variables), see
+\Sref{factor.mix}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
+ model = "factor.ord", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.ord}: :
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
+factor analysis (manifest variables), assumed to be ordinal
+variables. The number of manifest variables must be greater than the
+number of the factors.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults
+to 1).
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following arguments for model
+specification:
+\begin{itemize}
+\item \texttt{lambda.constraints}: list that contains the equality or
+inequality constraints on the factor loadings. A typical entry in the list
+has one of the following forms:
+\begin{itemize}
+\item {\tt varname = list()}: by default, no constraints are imposed.
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+%Unlike \texttt{factanal} the $\Lambda$ matrix
+%has \texttt{factors+1} columns.
+The first column of $\Lambda$ should not be constrained in general.
+
+\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
+manifest variables that have no variation before fitting the model.
+
+\end{itemize}
+
+The model accepts the following arguments to monitor the convergence
+of the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
+either a scalar or a vector of length $K$. The value of the tuning
+parameter must be positive. The default value is 1.2.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The
+default is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (all
+unconstrained loadings are set to that value), or a matrix with
+compatible dimensions. The default is {\tt NA}, such that the start
+values for the first column are set based on the observed pattern,
+while the remaining columns have start values set to 0 for
+unconstrained factor loadings, and -1 or 1 for constrained loadings
+(depending on the nature of the constraints).
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+stores the posterior draws of the factor scores. (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<Examples.data>>=
+ data(newpainters)
+@
+
+Factor analysis for ordinal data using \texttt{factor.ord}:
+<<Examples.zelig>>=
+ z.out <- zelig(cbind(Composition,Drawing,Colour,Expression)~NULL,
+ data=newpainters, model="factor.ord",
+ factors=1, L0=0.5,
+ burin=5000,mcmc=30000, thin=5, tune=1.2,verbose=TRUE)
+
+@
+Checking for convergence before summarizing the estimates:
+<<Examples.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<Examples.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<Examples.raftery>>=
+ raftery.diag(z.out$coefficients)
+@
+<<Examples.summary>>=
+ summary(z.out)
+@
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of $K$ observed ordinal variables for
+observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
+integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
+to be governed by another $k$-vector of unobserved continuous variable
+$Y_i^*$. There are $d$ underlying factors.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of the latent
+variable $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^* \sim \textrm{Normal}_K(\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
+for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+Instead of $Y_{ik}^*$, we observe ordinal variable $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
+\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
+the $k$th variable with the following constraints, $\gamma_{lk} <
+\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
+k}=\infty$ for any $k=1, \ldots, K$. It follows that the probability
+of observing $Y_{ik}$ belonging to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
+function of the Normal distribution with mean $\mu_{ik}$ and variance
+1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i = \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
+variable, $\phi_i$ is a $d$-vector of factor scores for observation
+$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
+
+\item The independent conjugate \emph{prior} for each element of $\Lambda$,
+$\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If
+\texttt{store.scores=TRUE}, the estimated factors scores are also contained in
+\texttt{coefficients}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The \texttt{factor.ord} \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.ord} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/factor.ord.pdf b/inst/doc/factor.ord.pdf
new file mode 100644
index 0000000..d071f59
Binary files /dev/null and b/inst/doc/factor.ord.pdf differ
diff --git a/inst/doc/factor.ord.tex b/inst/doc/factor.ord.tex
new file mode 100644
index 0000000..78b8111
--- /dev/null
+++ b/inst/doc/factor.ord.tex
@@ -0,0 +1,274 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Data Factor Analysis}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,factors latent, ordinal,Gibbs}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}
+\label{factor.ord}
+
+Given some unobserved explanatory variables and observed ordinal
+dependent variables, this model estimates latent factors using a Gibbs
+sampler with data augmentation. For factor analysis for continuous
+data, see \Sref{factor.bayes}. For factor analysis for mixed data
+(including both continuous and ordinal variables), see
+\Sref{factor.mix}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
+ model = "factor.ord", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.ord}: :
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
+factor analysis (manifest variables), assumed to be ordinal
+variables. The number of manifest variables must be greater than the
+number of the factors.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults
+to 1).
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following arguments for model
+specification:
+\begin{itemize}
+\item \texttt{lambda.constraints}: list that contains the equality or
+inequality constraints on the factor loadings. A typical entry in the list
+has one of the following forms:
+\begin{itemize}
+\item {\tt varname = list()}: by default, no constraints are imposed.
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize}
+%Unlike \texttt{factanal} the $\Lambda$ matrix
+%has \texttt{factors+1} columns.
+The first column of $\Lambda$ should not be constrained in general.
+
+\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
+manifest variables that have no variation before fitting the model.
+
+\end{itemize}
+
+The model accepts the following arguments to monitor the convergence
+of the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
+either a scalar or a vector of length $K$. The value of the tuning
+parameter must be positive. The default value is 1.2.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The
+default is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (all
+unconstrained loadings are set to that value), or a matrix with
+compatible dimensions. The default is {\tt NA}, such that the start
+values for the first column are set based on the observed pattern,
+while the remaining columns have start values set to 0 for
+unconstrained factor loadings, and -1 or 1 for constrained loadings
+(depending on the nature of the constraints).
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
+stores the posterior draws of the factor scores. (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$. If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(newpainters)
+\end{Sinput}
+\end{Schunk}
+
+Factor analysis for ordinal data using \texttt{factor.ord}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Composition, Drawing, Colour, Expression) ~
++ NULL, data = newpainters, model = "factor.ord", factors = 1,
++ L0 = 0.5, burin = 5000, mcmc = 30000, thin = 5, tune = 1.2,
++ verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of $K$ observed ordinal variables for
+observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
+integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
+to be governed by another $k$-vector of unobserved continuous variable
+$Y_i^*$. There are $d$ underlying factors.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of the latent
+variable $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^* \sim \textrm{Normal}_K(\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
+for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+Instead of $Y_{ik}^*$, we observe ordinal variable $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
+\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
+the $k$th variable with the following constraints, $\gamma_{lk} <
+\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
+k}=\infty$ for any $k=1, \ldots, K$. It follows that the probability
+of observing $Y_{ik}$ belonging to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
+function of the Normal distribution with mean $\mu_{ik}$ and variance
+1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i = \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
+variable, $\phi_i$ is a $d$-vector of factor scores for observation
+$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
+
+\item The independent conjugate \emph{prior} for each element of $\Lambda$,
+$\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If
+\texttt{store.scores=TRUE}, the estimated factors scores are also contained in
+\texttt{coefficients}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The \texttt{factor.ord} \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.ord} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/figs/increase.eps b/inst/doc/figs/increase.eps
deleted file mode 100644
index 899c0e0..0000000
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
-4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk4)UlVDP]F4V)rRk
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diff --git a/inst/doc/forMake.sh b/inst/doc/forMake.sh
new file mode 100755
index 0000000..ac0dfb8
--- /dev/null
+++ b/inst/doc/forMake.sh
@@ -0,0 +1,93 @@
+#!/bin/sh
+
+
+##
+## This file is called by Makefile. It does the followings:
+## 1.run pdf latex to each tex file produced by R CMD build
+## 2.remove header and footer or each tex file so that it's ready for inclusion by zelig.tex
+## 3.convert all Rd files to tex file so that they are ready for inclusion by zelig.tex
+##
+
+## get name of the models and save them in a file
+echo "library(Zelig); a<-zeligListModels(); write(a,file='tmp.txt');" | R --vanilla --slave
+
+## at this point, Sweave should have been run and produced tex files
+for f in `cat tmp.txt`
+ do
+if [ "$f" != "beta" ]
+then
+ #if [ "$RBUILD" != "TRUE" ]
+ # then
+ echo "Sweave(\"$f.Rnw\")" | R --slave
+ #fi
+
+ ## create pdfs for all the tex file produced by sweave
+ pdflatex $f.tex
+
+ ## remove header and footer from the tex file so that they are ready
+ ## for inclusion in big zelig manual
+ perl -i -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' $f.tex
+ perl -i -p -e 's#\\end{document}# #i' $f.tex
+fi
+done
+
+## run R CMD Rdconv to rd files
+## create commandsRd folder if does not exists
+
+if [ ! -d commandsRd ]
+then
+ mkdir commandsRd
+fi
+Rdfiles=`ls ../../man`
+for rd in ${Rdfiles}
+ do
+ newname=`basename ${rd} .Rd`
+ R CMD Rdconv -t=latex ../../man/${rd} -o commandsRd/${newname}.tex
+
+ ### perl -i -pe 's#HeaderA{#section{{\\tt #i' commandsRd/${newname}.tex
+ perl -i -pe 's#HeaderA{(.*)}{(.*)}{(.*)}#section{{\\tt \1}: \2}\\label{ss:\3}#i' commandsRd/${newname}.tex
+done
+
+
+## create the big Zelig manual
+pdflatex zelig
+bibtex zelig
+pdflatex zelig
+pdflatex zelig
+pdflatex zelig
+
+
+#for f in `cat tmp.txt`
+#do
+#rm -f $f.tex
+#done
+
+## remove the files with the name of the models
+rm tmp.txt
+
+
+
+## do some cleanup
+rm -f *.aux *.toc *.log *.out *.blg *.bbl
+
+
+
+
+
+
+
+
+#### here is how u delete the \end{document}
+#### perl -i.old -p -e 's#\\end{document}# #i' try
+
+#### delete first 10 lines
+#### perl -i.old -ne 'print unless 1 .. 10' foo.txt
+
+#### \SweaveOpts{result=hide}
+#### perl -i.old -p -e 's#\\include{zinput}#\\SweaveOpts{results=hide}\n\\include{zinput}#i' *.Rnw
+
+### change dependencies line
+### perl -i.old -p -e 's#See Package dependencies#Zelig#i' *.Rnw
+
+#### thats how u remove the header from tex files
+#### perl -i.old -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' try
diff --git a/inst/doc/gamma.Rnw b/inst/doc/gamma.Rnw
new file mode 100644
index 0000000..c94ac50
--- /dev/null
+++ b/inst/doc/gamma.Rnw
@@ -0,0 +1,264 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gamma}
+\include{zinput}
+%\VignetteIndexEntry{Gamma Regression for Continuous, Positive Dependent Variables}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,regression,gamma distribution}
+%\VignettePackage{Zelig, stats}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}\label{gamma}
+
+Use the gamma regression model if you have a positive-valued dependent
+variable such as the number of years a parliamentary cabinet endures,
+or the seconds you can stay airborne while jumping. The gamma
+distribution assumes that all waiting times are complete by the end
+of the study (censoring is not allowed).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "gamma", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for gamma regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+<<Example.data>>=
+ data(coalition)
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
+@
+View the regression output:
+<<Example.summary>>=
+ 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:
+<<Example.setx>>=
+ x.low <- setx(z.out, numst2 = 0)
+ x.high <- setx(z.out, numst2 = 1)
+@
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+@
+<<Example.summary>>=
+summary(s.out)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+ plot(s.out)
+@
+\end{center}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The Gamma distribution with scale parameter $\alpha$ has a
+\emph{stochastic component}:
+\begin{eqnarray*}
+Y &\sim& \textrm{Gamma}(y_i \mid \lambda_i, \alpha) \\
+f(y) &=& \frac{1}{\alpha^{\lambda_i} \, \Gamma \lambda_i} \, y_i^{\lambda_i
+ - 1} \exp -\left\{ \frac{y_i}{\alpha} \right\}
+\end{eqnarray*}
+for $\alpha, \lambda_i, y_i > 0$. \\
+
+\item The \emph{systematic component} is given by
+\begin{equation*}
+ \lambda_i = \frac{1}{x_i \beta}
+\end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+ of the stochastic component given draws of $\alpha$ and
+ $\beta$ from their posteriors: $$E(Y) = \alpha_i \lambda.$$
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma
+ distribution for each given set of parameters $(\alpha, \lambda_i)$.
+\item If {\tt x1} is specified, {\tt sim()} also returns the
+ differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
+ E(Y \mid x)$$.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "gamma", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values.
+ \item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by $(\alpha_i, \lambda)$.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ values for the specified values in {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The gamma model is part of the stats package by William N. Venables and
+Brian D. Ripley. Users should cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/gamma.pdf b/inst/doc/gamma.pdf
new file mode 100644
index 0000000..6f03b9c
Binary files /dev/null and b/inst/doc/gamma.pdf differ
diff --git a/inst/doc/gamma.tex b/inst/doc/gamma.tex
new file mode 100644
index 0000000..31a70fb
--- /dev/null
+++ b/inst/doc/gamma.tex
@@ -0,0 +1,266 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Gamma Regression for Continuous, Positive Dependent Variables}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,regression,gamma distribution}
+%\VignettePackage{Zelig, stats}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}\label{gamma}
+
+Use the gamma regression model if you have a positive-valued dependent
+variable such as the number of years a parliamentary cabinet endures,
+or the seconds you can stay airborne while jumping. The gamma
+distribution assumes that all waiting times are complete by the end
+of the study (censoring is not allowed).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "gamma", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for gamma regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(coalition)
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
+\end{Sinput}
+\end{Schunk}
+View the regression output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{Sinput}
+\end{Schunk}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/gamma-ExamplePlot}
+\end{center}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The Gamma distribution with scale parameter $\alpha$ has a
+\emph{stochastic component}:
+\begin{eqnarray*}
+Y &\sim& \textrm{Gamma}(y_i \mid \lambda_i, \alpha) \\
+f(y) &=& \frac{1}{\alpha^{\lambda_i} \, \Gamma \lambda_i} \, y_i^{\lambda_i
+ - 1} \exp -\left\{ \frac{y_i}{\alpha} \right\}
+\end{eqnarray*}
+for $\alpha, \lambda_i, y_i > 0$. \\
+
+\item The \emph{systematic component} is given by
+\begin{equation*}
+ \lambda_i = \frac{1}{x_i \beta}
+\end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+ of the stochastic component given draws of $\alpha$ and
+ $\beta$ from their posteriors: $$E(Y) = \alpha_i \lambda.$$
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma
+ distribution for each given set of parameters $(\alpha, \lambda_i)$.
+\item If {\tt x1} is specified, {\tt sim()} also returns the
+ differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
+ E(Y \mid x)$$.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "gamma", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values.
+ \item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by $(\alpha_i, \lambda)$.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ values for the specified values in {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The gamma model is part of the stats package by William N. Venables and
+Brian D. Ripley. Users should cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
index 2080880..d05b287 100644
--- a/inst/doc/index.shtml
+++ b/inst/doc/index.shtml
@@ -12,7 +12,7 @@
<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
<br /><br />
<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:2.8-3</b>
+<b>Version:2.8-4</b>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
diff --git a/inst/doc/install.R b/inst/doc/install.R
deleted file mode 100644
index 8abf04b..0000000
--- a/inst/doc/install.R
+++ /dev/null
@@ -1,13 +0,0 @@
-if (! "MASS" %in% installed.packages()[,"Package"])
- install.packages("MASS", CRAN = "http://cran.cnr.berkeley.edu/")
-
-install.packages("Zelig", repos = "http://cran.us.r-project.org/")
-##install.packages("Zelig", repos = "http://gking.harvard.edu")
-install.packages("zoo", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("sandwich", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("MCMCpack", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("coda", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("lattice", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("mvtnorm", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("VGAM", repos = "http://cran.cnr.berkeley.edu/")
-install.packages("sna", repos = "http://cran.cnr.berkeley.edu/")
diff --git a/inst/doc/irt1d.Rnw b/inst/doc/irt1d.Rnw
new file mode 100644
index 0000000..6561f14
--- /dev/null
+++ b/inst/doc/irt1d.Rnw
@@ -0,0 +1,279 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/irt1d}
+\include{zinput}
+%\VignetteIndexEntry{ One Dimensional Item Response Mode}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
+%\VignettePackage{Zelig, stats}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{irt1d}: One Dimensional Item Response Model}
+
+\label{irt1d}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points). The model is estimated using the Markov
+Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
+Use this model if you believe that the ideal points lie in one
+dimension, and see the $k$-dimensional item response model
+(\Sref{irtkd}) for $k$ hypothesized latent variables.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irt1d} accepts the following argument:
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
+so on.
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irt1d} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item \texttt{theta.constraints}: a list specifying possible equality
+or inequality constraints on the ability parameters $\theta$. A
+typical entry takes one of the following forms:
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = c}: constrains the ability parameter for the subject named
+\texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = "+"}: constrains the ability parameter for the subject named
+\texttt{varname} to be positive.
+
+\item \texttt{varname = "-"}: constrains the ability parameter for the
+subject named {\tt varname} to be negative.
+
+\end{itemize}
+\end{itemize}
+
+The model also accepts the following arguments to monitor the sampling
+scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{theta.start}: starting values for the subject abilities
+(ideal points), either a scalar or a vector with length equal to the
+number of subjects. If a scalar, that value will be the starting value
+for all subjects. The default is \texttt{NA}, which sets the starting
+values based on an eigenvalue-eigenvector decomposition of the
+agreement score matrix formed from the model response matrix
+(\texttt{cbind(Y1, Y2, ...)}).
+
+\item \texttt{alpha.start}: starting values for the difficulty
+parameters $\alpha$, either a scalar or a vector with length equal to
+the number of the items. If a scalar, the value will be the starting
+value for all $\alpha$. The default is \texttt{NA}, which sets the
+starting values based on a series of probit regressions that condition
+on \texttt{theta.start}.
+
+\item \texttt{beta.start}: starting values for the $\beta$ discrimination
+parameters, either a scalar or a vector with length equal to the
+number of the items. If a scalar, the value will be the starting value
+for all $\beta$. The default is \texttt{NA}, which sets the starting
+values based on a series of probit regressions conditioning on
+\texttt{theta.start}.
+
+\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
+posterior draws of the item parameters. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.
+
+\end{itemize}
+
+\noindent \texttt{irt1d} accepts the following additional arguments to
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{t0}: prior mean of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{T0}: prior precision of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
+a vector of length 2. If it takes a scalar value, then the prior means for
+both $\alpha$ and $\beta$ will be set to that value. The default is 0.
+
+\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be
+a scalar or a $2 \times 2$ matrix. If it takes a scalar value,
+then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
+is assumed to be same for all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<Example.data>>=
+ data(SupremeCourt)
+names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
+ "Kennedy","Souter","Thomas","Ginsburg","Breyer")
+
+@
+Fitting a one-dimensional item response theory model using \texttt{irt1d}:
+<<Example.zelig>>=
+ z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
+ Souter, Thomas, Ginsburg, Breyer) ~ NULL,
+ data = SupremeCourt, model = "irt1d",
+ B0.alpha = 0.2, B0.beta = 0.2, burnin = 500, mcmc = 10000,
+ thin = 20, verbose = TRUE)
+@
+
+Checking for convergence before summarizing the estimates:
+<<Example.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<Example.heidel>>=
+heidel.diag(z.out$coefficients)
+@
+<<Example.summary>>=
+ summary(z.out)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
+$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
+an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
+abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
+$\beta_j$ as follows:
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij}) \\
+& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation is independently
+and identically distributed with
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, $\theta_i$ is the
+subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
+are the item parameters. Both subject abilities and item parameters
+are estimated from the model, such that the model is identified by
+placing constraints on the subject ability parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal} \left( t_{0},T_{0}^{-1}\right)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left( ab_{0},AB_{0}^{-1}\right)
+\end{eqnarray*}
+where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
+precision matrix.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
+$\beta$ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The unidimensional item-response \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irt1d} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/irt1d.pdf b/inst/doc/irt1d.pdf
new file mode 100644
index 0000000..c8bbf75
Binary files /dev/null and b/inst/doc/irt1d.pdf differ
diff --git a/inst/doc/irt1d.tex b/inst/doc/irt1d.tex
new file mode 100644
index 0000000..e3b9f8d
--- /dev/null
+++ b/inst/doc/irt1d.tex
@@ -0,0 +1,274 @@
+
+\include{zinput}
+%\VignetteIndexEntry{ One Dimensional Item Response Mode}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
+%\VignettePackage{Zelig, stats}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{irt1d}: One Dimensional Item Response Model}
+
+\label{irt1d}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points). The model is estimated using the Markov
+Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
+Use this model if you believe that the ideal points lie in one
+dimension, and see the $k$-dimensional item response model
+(\Sref{irtkd}) for $k$ hypothesized latent variables.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irt1d} accepts the following argument:
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
+so on.
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irt1d} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item \texttt{theta.constraints}: a list specifying possible equality
+or inequality constraints on the ability parameters $\theta$. A
+typical entry takes one of the following forms:
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = c}: constrains the ability parameter for the subject named
+\texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = "+"}: constrains the ability parameter for the subject named
+\texttt{varname} to be positive.
+
+\item \texttt{varname = "-"}: constrains the ability parameter for the
+subject named {\tt varname} to be negative.
+
+\end{itemize}
+\end{itemize}
+
+The model also accepts the following arguments to monitor the sampling
+scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{theta.start}: starting values for the subject abilities
+(ideal points), either a scalar or a vector with length equal to the
+number of subjects. If a scalar, that value will be the starting value
+for all subjects. The default is \texttt{NA}, which sets the starting
+values based on an eigenvalue-eigenvector decomposition of the
+agreement score matrix formed from the model response matrix
+(\texttt{cbind(Y1, Y2, ...)}).
+
+\item \texttt{alpha.start}: starting values for the difficulty
+parameters $\alpha$, either a scalar or a vector with length equal to
+the number of the items. If a scalar, the value will be the starting
+value for all $\alpha$. The default is \texttt{NA}, which sets the
+starting values based on a series of probit regressions that condition
+on \texttt{theta.start}.
+
+\item \texttt{beta.start}: starting values for the $\beta$ discrimination
+parameters, either a scalar or a vector with length equal to the
+number of the items. If a scalar, the value will be the starting value
+for all $\beta$. The default is \texttt{NA}, which sets the starting
+values based on a series of probit regressions conditioning on
+\texttt{theta.start}.
+
+\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
+posterior draws of the item parameters. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.
+
+\end{itemize}
+
+\noindent \texttt{irt1d} accepts the following additional arguments to
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{t0}: prior mean of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{T0}: prior precision of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
+a vector of length 2. If it takes a scalar value, then the prior means for
+both $\alpha$ and $\beta$ will be set to that value. The default is 0.
+
+\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be
+a scalar or a $2 \times 2$ matrix. If it takes a scalar value,
+then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
+is assumed to be same for all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(SupremeCourt)
+> names(SupremeCourt) <- c("Rehnquist", "Stevens", "OConnor", "Scalia",
++ "Kennedy", "Souter", "Thomas", "Ginsburg", "Breyer")
+\end{Sinput}
+\end{Schunk}
+Fitting a one-dimensional item response theory model using \texttt{irt1d}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
++ Souter, Thomas, Ginsburg, Breyer) ~ NULL, data = SupremeCourt,
++ model = "irt1d", B0.alpha = 0.2, B0.beta = 0.2, burnin = 500,
++ mcmc = 10000, thin = 20, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
+$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
+an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
+abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
+$\beta_j$ as follows:
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij}) \\
+& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation is independently
+and identically distributed with
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, $\theta_i$ is the
+subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
+are the item parameters. Both subject abilities and item parameters
+are estimated from the model, such that the model is identified by
+placing constraints on the subject ability parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal} \left( t_{0},T_{0}^{-1}\right)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left( ab_{0},AB_{0}^{-1}\right)
+\end{eqnarray*}
+where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
+precision matrix.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
+$\beta$ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The unidimensional item-response \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irt1d} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/irtkd.Rnw b/inst/doc/irtkd.Rnw
new file mode 100644
index 0000000..f18c707
--- /dev/null
+++ b/inst/doc/irtkd.Rnw
@@ -0,0 +1,281 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/irtkd}
+\include{zinput}
+%\VignetteIndexEntry{K-Dimensional Item Response Model}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
+%\VignettePackage{Zelig, stats}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
+
+\label{irtkd}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points). The model is estimated using the Markov
+Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
+data augmentation. Use this model if you believe that the ideal
+points lie in $k$ dimensions. See the unidimensional item response
+model (\Sref{irt1d}) for a single hypothesized latent variable.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1,
+ model = "irtkd", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irtkd} accepts the following arguments:
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on.
+
+\item \texttt{dimensions}: The number of dimensions in the latent space. The
+default is 1.
+
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irtkd} accepts the following additional arguments for model specification:
+\begin{itemize}
+\item \texttt{item.constraints}: a list of lists specifying possible
+simple equality or inequality constraints on the item parameters.
+A typical entry has one of the following forms:
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be equal
+to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be negative.
+\end{itemize}
+In a $k$ dimensional model, the first item parameter for item $i$ is
+the difficulty parameter $\alpha_i$, the second item parameter is the
+discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
+item parameter is the discrimination parameter on dimension 2,
+$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
+discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
+difficulty parameter($\alpha$) should not be constrained in general.
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen. The default
+is \texttt{FALSE}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{alphabeta.start}: starting values for the item parameters
+$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
+matrix. If it is a scalar, then that value will be the starting value
+for all the elements of \texttt{alphabeta.start}. The default is
+\texttt{NA} which sets the starting values for the unconstrained
+elements based on a series of proportional odds logistic
+regressions. The starting values for the inequality constrained
+elements are set to be either 1.0 or -1.0 depending on the nature of
+the constraints.
+
+\item \texttt{store.item}: defaults to {\tt FALSE}. If {\tt TRUE} stores the
+posterior draws of the item parameters. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
+posterior draws of the subject abilities. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments to
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
+a vector of compatible length. If a scalar value, then the prior means
+for both $\alpha$ and $\beta$ will be set to that value. The default
+is 0.
+
+\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
+scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
+precision will be a blocked diagonal matrix with elements
+\texttt{diag(B0,items)}. The prior precision is assumed to be same for
+all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(SupremeCourt)
+names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
+ "Kennedy","Souter","Thomas","Ginsburg","Breyer")
+@
+Fitting a one-dimensional item response theory model using \texttt{irtkd}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy, Souter,
+ Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
+ data = SupremeCourt, model = "irtkd", B0 = 0.25,
+ burnin = 5000, mcmc = 50000,
+ thin = 10, verbose = TRUE)
+@
+
+Checking for convergence before summarizing the estimates:
+<<BasicExample.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<BasicExample.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+ raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
+$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be
+determined by unobserved utility $Z_{ij}$, which is a function
+of subject abilities (ideal points) $\theta_i$ and item parameters
+$\alpha_j$ and $\beta_j$,
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+In the $k$-dimensional item response theory model, each subject's ability is
+represented by a $k$-vector, $\theta_i$. Each item has a difficulty
+parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
+$\beta_j$. In one-dimensional item response theory model, $k = 1$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij})\\
+& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation has
+a standard normal distribution,
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, while $\theta_i$
+contains the $k$-dimensional subject abilities(ideal points), and
+$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
+abilities and item parameters need to estimated from the model. The
+model is identified by placing constraints on the item parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal}_k(0, I_k)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
+\end{eqnarray*}
+where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1)
+\times (k+1)$ prior precision matrix which is assumed to be diagonal.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
+ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The $k$ dimensional item response \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irtkd} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/irtkd.pdf b/inst/doc/irtkd.pdf
new file mode 100644
index 0000000..ce5fa57
Binary files /dev/null and b/inst/doc/irtkd.pdf differ
diff --git a/inst/doc/irtkd.tex b/inst/doc/irtkd.tex
new file mode 100644
index 0000000..709501b
--- /dev/null
+++ b/inst/doc/irtkd.tex
@@ -0,0 +1,279 @@
+
+\include{zinput}
+%\VignetteIndexEntry{K-Dimensional Item Response Model}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
+%\VignettePackage{Zelig, stats}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
+
+\label{irtkd}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points). The model is estimated using the Markov
+Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
+data augmentation. Use this model if you believe that the ideal
+points lie in $k$ dimensions. See the unidimensional item response
+model (\Sref{irt1d}) for a single hypothesized latent variable.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1,
+ model = "irtkd", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irtkd} accepts the following arguments:
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on.
+
+\item \texttt{dimensions}: The number of dimensions in the latent space. The
+default is 1.
+
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irtkd} accepts the following additional arguments for model specification:
+\begin{itemize}
+\item \texttt{item.constraints}: a list of lists specifying possible
+simple equality or inequality constraints on the item parameters.
+A typical entry has one of the following forms:
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be equal
+to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be negative.
+\end{itemize}
+In a $k$ dimensional model, the first item parameter for item $i$ is
+the difficulty parameter $\alpha_i$, the second item parameter is the
+discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
+item parameter is the discrimination parameter on dimension 2,
+$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
+discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
+difficulty parameter($\alpha$) should not be constrained in general.
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen. The default
+is \texttt{FALSE}.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{alphabeta.start}: starting values for the item parameters
+$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
+matrix. If it is a scalar, then that value will be the starting value
+for all the elements of \texttt{alphabeta.start}. The default is
+\texttt{NA} which sets the starting values for the unconstrained
+elements based on a series of proportional odds logistic
+regressions. The starting values for the inequality constrained
+elements are set to be either 1.0 or -1.0 depending on the nature of
+the constraints.
+
+\item \texttt{store.item}: defaults to {\tt FALSE}. If {\tt TRUE} stores the
+posterior draws of the item parameters. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
+posterior draws of the subject abilities. (For a large number of draws or
+a large number observations, this may take a lot of memory.)
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments to
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
+a vector of compatible length. If a scalar value, then the prior means
+for both $\alpha$ and $\beta$ will be set to that value. The default
+is 0.
+
+\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
+scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
+precision will be a blocked diagonal matrix with elements
+\texttt{diag(B0,items)}. The prior precision is assumed to be same for
+all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(SupremeCourt)
+> names(SupremeCourt) <- c("Rehnquist", "Stevens", "OConnor", "Scalia",
++ "Kennedy", "Souter", "Thomas", "Ginsburg", "Breyer")
+\end{Sinput}
+\end{Schunk}
+Fitting a one-dimensional item response theory model using \texttt{irtkd}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
++ Souter, Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
++ data = SupremeCourt, model = "irtkd", B0 = 0.25, burnin = 5000,
++ mcmc = 50000, thin = 10, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
+$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be
+determined by unobserved utility $Z_{ij}$, which is a function
+of subject abilities (ideal points) $\theta_i$ and item parameters
+$\alpha_j$ and $\beta_j$,
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+In the $k$-dimensional item response theory model, each subject's ability is
+represented by a $k$-vector, $\theta_i$. Each item has a difficulty
+parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
+$\beta_j$. In one-dimensional item response theory model, $k = 1$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij})\\
+& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation has
+a standard normal distribution,
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, while $\theta_i$
+contains the $k$-dimensional subject abilities(ideal points), and
+$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
+abilities and item parameters need to estimated from the model. The
+model is identified by placing constraints on the item parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal}_k(0, I_k)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
+\end{eqnarray*}
+where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1)
+\times (k+1)$ prior precision matrix which is assumed to be diagonal.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
+ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The $k$ dimensional item response \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irtkd} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/logit.Rnw b/inst/doc/logit.Rnw
new file mode 100644
index 0000000..5a0c2d5
--- /dev/null
+++ b/inst/doc/logit.Rnw
@@ -0,0 +1,310 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/logit}
+\include{zinput}
+%\VignetteIndexEntry{Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,logistic,dichotomous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt logit}: Logistic Regression for Dichotomous Dependent
+Variables}\label{logit}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables. For a Bayesian
+implementation, see \Sref{logit.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for logistic regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame) The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example}
+
+Attaching the sample turnout dataset:
+<<Example.data>>=
+ data(turnout)
+@
+Estimating parameter values for the logistic regression:
+<<Example.zelig>>=
+ z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+@
+Setting values for the explanatory variables to their default values:
+<<Example.setx>>=
+ x.out1 <- setx(z.out1)
+@
+Simulating quantities of interest from the posterior distribution.
+<<Example.sim>>=
+ s.out1 <- sim(z.out1, x = x.out1)
+@
+<<Example.summary>>=
+ summary(s.out1)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item {Simulating First Differences}
+
+Estimating the risk difference (and risk ratio) between low education
+(25th percentile) and high education (75th percentile) while all the
+other variables held at their default values.
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
+ x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+@
+
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.summary>>=
+ summary(s.out2)
+@
+\begin{center}
+<<label=FirstDifferencesPlot,fig=true>>=
+ plot(s.out2)
+@
+\end{center}
+
+
+\item {Presenting Results: An ROC Plot} \label{ROC}
+
+ One can use an ROC plot to evaluate the fit of alternative model
+ specifications. (Use {\tt demo(roc)} to view this example, or see
+ King and Zeng (2002)\nocite{KinZen02}.)
+<<ROC.zelig>>=
+ z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
+ data = turnout)
+ z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+@
+\begin{center}
+<<label=ROCPlot,fig=true, echo=true>>=
+
+rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+\begin{itemize}
+
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_i &\sim& \textrm{Bernoulli}(y_i \mid \pi_i) \\
+ &=& \pi_i^{y_i} (1-\pi_i)^{1-y_i}
+\end{eqnarray*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+\end{equation*}
+where $x_i$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the logit model are
+ simulations of the predicted probability of a success: $$E(Y) =
+ \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ given draws of $\beta$ from
+ its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
+ distribution with mean equal to the simulated expected value $\pi_i$.
+
+\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)}\right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
+ model = "logit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values for the
+ systemic component, $\pi_i$.
+ \item {\tt linear.predictors}: the vector of $x_{i}\beta$
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt data}: the name of the input data frame.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values for the
+ specified values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the expected
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The logit function is part of the base package by William N. Venables
+and Brian D. Ripley. Please cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/logit.bayes.Rnw b/inst/doc/logit.bayes.Rnw
new file mode 100644
index 0000000..e9a41ee
--- /dev/null
+++ b/inst/doc/logit.bayes.Rnw
@@ -0,0 +1,275 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/logitBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,bayes,dichotomous, Metropolis}
+%\VignettePackage{Zelig, MCMCpack}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
+
+\label{logit.bayes}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables using a random walk
+Metropolis algorithm. For a maximum likelihood implementation, see
+\Sref{logit}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5) before using the posterior density for
+inference. The default value is 1.1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The
+default is \texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the maximum
+likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(turnout)
+@
+Estimating the logistic regression using \texttt{logit.bayes}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(vote ~ race + educate, model = "logit.bayes",
+ data = turnout, verbose = TRUE)
+@
+Convergence diagnostics before summarizing the estimates:
+<<BasicExample.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<BasicExample.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+ raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary.zout>>=
+summary(z.out)
+@
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<BasicExample.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting when education is set to be low (25th percentile) versus
+high (75th percentile) while all the other variables held at their
+default values.
+<<FirstDifferences.setx.high>>=
+ x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+@
+<<FirstDifferences.setx.low>>=
+x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+@
+<<FirstDifferences.sim>>=
+s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.summary>>=
+summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
+the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \rm{Bernoulli}(\pi_{i})\\
+& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the logit model are
+simulations of the predicted probability of a success:
+\begin{eqnarray*}
+E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the logit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "logit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian logistic regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{logit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/logit.bayes.pdf b/inst/doc/logit.bayes.pdf
new file mode 100644
index 0000000..e980cc9
Binary files /dev/null and b/inst/doc/logit.bayes.pdf differ
diff --git a/inst/doc/logit.bayes.tex b/inst/doc/logit.bayes.tex
new file mode 100644
index 0000000..b932eca
--- /dev/null
+++ b/inst/doc/logit.bayes.tex
@@ -0,0 +1,288 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model,bayes,dichotomous, Metropolis}
+%\VignettePackage{Zelig, MCMCpack}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
+
+\label{logit.bayes}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables using a random walk
+Metropolis algorithm. For a maximum likelihood implementation, see
+\Sref{logit}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5) before using the posterior density for
+inference. The default value is 1.1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The
+default is \texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the maximum
+likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(turnout)
+\end{Sinput}
+\end{Schunk}
+Estimating the logistic regression using \texttt{logit.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(vote ~ race + educate, model = "logit.bayes",
++ data = turnout, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Convergence diagnostics before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting when education is set to be low (25th percentile) versus
+high (75th percentile) while all the other variables held at their
+default values.
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
+the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \rm{Bernoulli}(\pi_{i})\\
+& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the logit model are
+simulations of the predicted probability of a success:
+\begin{eqnarray*}
+E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the logit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "logit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian logistic regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{logit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/logit.pdf b/inst/doc/logit.pdf
new file mode 100644
index 0000000..5948ea9
Binary files /dev/null and b/inst/doc/logit.pdf differ
diff --git a/inst/doc/logit.tex b/inst/doc/logit.tex
new file mode 100644
index 0000000..338103f
--- /dev/null
+++ b/inst/doc/logit.tex
@@ -0,0 +1,323 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,logistic,dichotomous, regression}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt logit}: Logistic Regression for Dichotomous Dependent
+Variables}\label{logit}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables. For a Bayesian
+implementation, see \Sref{logit.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for logistic regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame) The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example}
+
+Attaching the sample turnout dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(turnout)
+\end{Sinput}
+\end{Schunk}
+Estimating parameter values for the logistic regression:
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their default values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out1 <- setx(z.out1)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/logit-ExamplePlot}
+\end{center}
+
+\item {Simulating First Differences}
+
+Estimating the risk difference (and risk ratio) between low education
+(25th percentile) and high education (75th percentile) while all the
+other variables held at their default values.
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
+> x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+\end{Sinput}
+\end{Schunk}
+
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/logit-FirstDifferencesPlot}
+\end{center}
+
+
+\item {Presenting Results: An ROC Plot} \label{ROC}
+
+ One can use an ROC plot to evaluate the fit of alternative model
+ specifications. (Use {\tt demo(roc)} to view this example, or see
+ King and Zeng (2002)\nocite{KinZen02}.)
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
++ data = turnout)
+> z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/logit-ROCPlot}
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+\begin{itemize}
+
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_i &\sim& \textrm{Bernoulli}(y_i \mid \pi_i) \\
+ &=& \pi_i^{y_i} (1-\pi_i)^{1-y_i}
+\end{eqnarray*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+\end{equation*}
+where $x_i$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the logit model are
+ simulations of the predicted probability of a success: $$E(Y) =
+ \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ given draws of $\beta$ from
+ its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
+ distribution with mean equal to the simulated expected value $\pi_i$.
+
+\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)}\right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
+ model = "logit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values for the
+ systemic component, $\pi_i$.
+ \item {\tt linear.predictors}: the vector of $x_{i}\beta$
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt data}: the name of the input data frame.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values for the
+ specified values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the expected
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The logit function is part of the base package by William N. Venables
+and Brian D. Ripley. Please cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/lognorm.Rnw b/inst/doc/lognorm.Rnw
new file mode 100644
index 0000000..132a363
--- /dev/null
+++ b/inst/doc/lognorm.Rnw
@@ -0,0 +1,291 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/lognormal}
+\include{zinput}
+%\VignetteIndexEntry{Log-Normal Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model,log, normal,bounded, regression, time durarion}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt lognorm}: Log-Normal Regression for Duration
+Dependent Variables}\label{lognorm}
+
+The log-normal model describes an event's duration, the dependent
+variable, as a function of a set of explanatory variables. The
+log-normal model may take time censored dependent variables, and
+allows the hazard rate to increase and decrease.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Log-normal models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example) of each subject, and the associated
+$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
+censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
+if the duration is censored ({\it e.g.}, the subject is still alive at
+the end of the study). If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for lognormal regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+<<Example.data>>=
+ data(coalition)
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
+ data = coalition)
+@
+View the regression output:
+<<Example.summary>>=
+ 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:
+<<Example.setx>>=
+ x.low <- setx(z.out, numst2 = 0)
+ x.high <- setx(z.out, numst2 = 1)
+@
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
+ qi\$fd}):
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+@
+<<Example.summary>>=
+ summary(s.out)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+ plot(s.out)
+@
+\end{center}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$ with the density
+function $f(y)$ and the corresponding distribution function
+$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
+observations at a fixed time $y_c$ such that the fully observed
+dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c \\
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable, $Y^*$. For the lognormal model,
+there are two equivalent representations:
+ \begin{eqnarray*}
+ Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
+} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
+\end{eqnarray*}
+where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
+of the Normal distribution. (Note that the output from {\tt zelig()}
+parameterizes {\tt scale}$ = \sigma$.)
+
+ In addition, survival models like the lognormal have three additional
+properties. The hazard function $h(t)$ measures the probability of not surviving
+ past time $t$ given survival up to $t$. In general, the hazard
+ function is equal to $f(t)/S(t)$ where the survival function $S(t) =
+ 1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
+ time $t$. The cumulative hazard function $H(t)$ describes the
+ probability of dying before time $t$. In general, $H(t)=
+\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model,
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
+\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
+S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
+H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function for the Normal
+distribution.
+
+\item The \emph{systematic component} is described as:
+\begin{equation*}
+\mu_i = x_i \beta .
+\end{equation*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the lognormal model are
+ simulations of the expected duration:
+\begin{equation*}
+E(Y) = \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
+\end{equation*}
+given draws of $\beta$ and $\sigma$ from their sampling distributions.
+
+\item The predicted value is a draw from the log-normal distribution
+ given simulations of the parameters $(\lambda_i, \sigma)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
+ defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
+ simulation from the model given available knowledge of the censoring
+ process. Variation in the simulations is due to two factors:
+ uncertainty in the imputation process for censored $y_i^*$ and
+ uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+ expected value of $Y_i$ for observations in the treatment group,
+ under the assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+\begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
+\widehat{Y_i(t_i=0)} \},
+\end{equation*}
+where $t_i$ is a binary explanatory
+ variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+ groups. When $Y_i(t_i=1)$ is censored rather than observed, we
+ replace it with a simulation from the model given available
+ knowledge of the censoring process. Variation in the simulations
+ are due to two factors: uncertainty in the imputation process for
+ censored $y_i^*$ and uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+ for observations in the treatment group, under the assumption that
+ everything stays the same except that the treatment indicator is
+ switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
+ C) \~\, X, model = "lognorm", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+ z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}. Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.
+ \item {\tt var}: Variance-covariance matrix.
+ \item {\tt loglik}: Vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: the vector of
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\lambda_i, \sigma)$.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library, and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/lognorm.pdf b/inst/doc/lognorm.pdf
new file mode 100644
index 0000000..8eef0cf
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diff --git a/inst/doc/lognorm.tex b/inst/doc/lognorm.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/lognorm.tex
@@ -0,0 +1,293 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Log-Normal Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model,log, normal,bounded, regression, time durarion}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt lognorm}: Log-Normal Regression for Duration
+Dependent Variables}\label{lognorm}
+
+The log-normal model describes an event's duration, the dependent
+variable, as a function of a set of explanatory variables. The
+log-normal model may take time censored dependent variables, and
+allows the hazard rate to increase and decrease.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Log-normal models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example) of each subject, and the associated
+$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
+censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
+if the duration is censored ({\it e.g.}, the subject is still alive at
+the end of the study). If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for lognormal regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(coalition)
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
++ data = coalition)
+\end{Sinput}
+\end{Schunk}
+View the regression output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{Sinput}
+\end{Schunk}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
+ qi\$fd}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/lognormal-ExamplePlot}
+\end{center}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$ with the density
+function $f(y)$ and the corresponding distribution function
+$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
+observations at a fixed time $y_c$ such that the fully observed
+dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c \\
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable, $Y^*$. For the lognormal model,
+there are two equivalent representations:
+ \begin{eqnarray*}
+ Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
+} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
+\end{eqnarray*}
+where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
+of the Normal distribution. (Note that the output from {\tt zelig()}
+parameterizes {\tt scale}$ = \sigma$.)
+
+ In addition, survival models like the lognormal have three additional
+properties. The hazard function $h(t)$ measures the probability of not surviving
+ past time $t$ given survival up to $t$. In general, the hazard
+ function is equal to $f(t)/S(t)$ where the survival function $S(t) =
+ 1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
+ time $t$. The cumulative hazard function $H(t)$ describes the
+ probability of dying before time $t$. In general, $H(t)=
+\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model,
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
+\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
+S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
+H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function for the Normal
+distribution.
+
+\item The \emph{systematic component} is described as:
+\begin{equation*}
+\mu_i = x_i \beta .
+\end{equation*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the lognormal model are
+ simulations of the expected duration:
+\begin{equation*}
+E(Y) = \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
+\end{equation*}
+given draws of $\beta$ and $\sigma$ from their sampling distributions.
+
+\item The predicted value is a draw from the log-normal distribution
+ given simulations of the parameters $(\lambda_i, \sigma)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
+ defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
+ simulation from the model given available knowledge of the censoring
+ process. Variation in the simulations is due to two factors:
+ uncertainty in the imputation process for censored $y_i^*$ and
+ uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+ expected value of $Y_i$ for observations in the treatment group,
+ under the assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+\begin{equation*}
+ \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
+\widehat{Y_i(t_i=0)} \},
+\end{equation*}
+where $t_i$ is a binary explanatory
+ variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+ groups. When $Y_i(t_i=1)$ is censored rather than observed, we
+ replace it with a simulation from the model given available
+ knowledge of the censoring process. Variation in the simulations
+ are due to two factors: uncertainty in the imputation process for
+ censored $y_i^*$ and uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+ for observations in the treatment group, under the assumption that
+ everything stays the same except that the treatment indicator is
+ switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
+ C) \~\, X, model = "lognorm", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+ z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}. Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.
+ \item {\tt var}: Variance-covariance matrix.
+ \item {\tt loglik}: Vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: the vector of
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\lambda_i, \sigma)$.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library, and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/ls.Rnw b/inst/doc/ls.Rnw
new file mode 100644
index 0000000..b49ee63
--- /dev/null
+++ b/inst/doc/ls.Rnw
@@ -0,0 +1,274 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/ls}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt ls}: Least Squares Regression for Continuous
+Dependent Variables}
+\label{ls}
+
+Use least squares regression analysis to estimate the best linear
+predictor for the specified dependent variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "ls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for least squares regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors based on
+sandwich estimators (see \cite{Zeileis04}, \cite{Huber81}, and
+\cite{White80}). The default type of robust standard error is
+heteroskedastic consistent (HC), \emph{not} heteroskedastic and
+autocorrelation consistent (HAC).
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: choose from
+\begin{itemize}
+\item {\tt "vcovHC"}: (the default if {\tt robust = TRUE}), HC standard errors.
+\item {\tt "vcovHAC"}: HAC standard errors without weights.
+\item {\tt "kernHAC"}: HAC standard errors using the weights given in
+\cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the weights given in
+\cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: only applies to the HAC methods above. Defaults to
+{\tt NULL} (the observations are chronologically ordered as in the
+original data). Optionally, you may specify a time index (either as
+{\tt order.by = z}, where {\tt z} exists outside the data frame; or
+as {\tt order.by = \~{}z}, where {\tt z} is a variable in the data
+frame). The observations are chronologically ordered by the size of
+{\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data:
+<<Examples.data>>=
+ data(macro)
+@
+Estimate model:
+<<Examples.zelig>>=
+ z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
+@
+Summarize regression coefficients:
+<<Examples.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for the trade variable:
+<<Examples.setx>>=
+ 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:
+<<Examples.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Examples.summary.sim>>=
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplesPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item Using Dummy Variables
+
+Estimate a model with fixed effects for each country (see
+\Sref{factors} for help with dummy variables). Note that you do not
+need to create dummy variables, as the program will automatically
+parse the unique values in the selected variable into discrete levels.
+<<Dummy.zelig>>=
+ z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
+ model = "ls", data = macro)
+@
+Set values for the explanatory variables, using the default mean/mode
+values, with country set to the United States and Japan, respectively:
+<<Dummy.setx>>=
+ x.US <- setx(z.out2, country = "United States")
+ x.Japan <- setx(z.out2, country = "Japan")
+@
+Simulate quantities of interest:
+<<Dummy.sim>>=
+ s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+@
+\begin{center}
+<<label=DummyPlot,fig=true,echo=true>>=
+ plot(s.out2)
+@
+\end{center}
+
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item The \emph{stochastic component} is described by a density
+ with mean $\mu_i$ and the common variance $\sigma^2$
+ \begin{equation*}
+ Y_i \; \sim \; f(y_i \mid \mu_i, \sigma^2).
+ \end{equation*}
+\item The \emph{systematic component} models the conditional mean as
+ \begin{equation*}
+ \mu_i = x_i \beta
+ \end{equation*}
+ where $x_i$ is the vector of covariates, and $\beta$ is the vector
+ of coefficients.
+
+ The least squares estimator is the best linear predictor of a
+ dependent variable given $x_i$, and minimizes the sum of squared
+ residuals, $\sum_{i=1}^n (Y_i-x_i \beta)^2$.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component,
+\begin{equation*}
+E(Y) = x_i \beta,\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "ls", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+ \item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \begin{equation*}
+ \hat{\beta} \; = \; \left(\sum_{i=1}^n x_i' x_i\right)^{-1} \sum x_i y_i
+ \end{equation*}
+ \item {\tt sigma}: the square root of the estimate variance of the
+ random error $e$:
+ \begin{equation*}
+ \hat{\sigma} \; = \; \frac{\sum (Y_i-x_i\hat{\beta})^2}{n-k}
+ \end{equation*}
+ \item {\tt r.squared}: the fraction of the variance explained by
+ the model.
+ \begin{equation*}
+ R^2 \; = \; 1 - \frac{\sum (Y_i-x_i\hat{\beta})^2}{\sum (y_i -
+ \bar{y})^2}
+ \end{equation*}
+ \item {\tt adj.r.squared}: the above $R^2$ statistic, penalizing
+ for an increased number of explanatory variables.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first differences (or
+ differences in expected values) for the specified values of {\tt
+ x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The least squares regression is part of the stats package by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+\texttt{help(lm)} and \texttt{help(lm.fit)}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/ls.pdf b/inst/doc/ls.pdf
new file mode 100644
index 0000000..5506d73
Binary files /dev/null and b/inst/doc/ls.pdf differ
diff --git a/inst/doc/ls.tex b/inst/doc/ls.tex
new file mode 100644
index 0000000..f0b3bea
--- /dev/null
+++ b/inst/doc/ls.tex
@@ -0,0 +1,285 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt ls}: Least Squares Regression for Continuous
+Dependent Variables}
+\label{ls}
+
+Use least squares regression analysis to estimate the best linear
+predictor for the specified dependent variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "ls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for least squares regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors based on
+sandwich estimators (see \cite{Zeileis04}, \cite{Huber81}, and
+\cite{White80}). The default type of robust standard error is
+heteroskedastic consistent (HC), \emph{not} heteroskedastic and
+autocorrelation consistent (HAC).
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: choose from
+\begin{itemize}
+\item {\tt "vcovHC"}: (the default if {\tt robust = TRUE}), HC standard errors.
+\item {\tt "vcovHAC"}: HAC standard errors without weights.
+\item {\tt "kernHAC"}: HAC standard errors using the weights given in
+\cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the weights given in
+\cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: only applies to the HAC methods above. Defaults to
+{\tt NULL} (the observations are chronologically ordered as in the
+original data). Optionally, you may specify a time index (either as
+{\tt order.by = z}, where {\tt z} exists outside the data frame; or
+as {\tt order.by = \~{}z}, where {\tt z} is a variable in the data
+frame). The observations are chronologically ordered by the size of
+{\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(macro)
+\end{Sinput}
+\end{Schunk}
+Estimate model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
+\end{Sinput}
+\end{Schunk}
+Summarize regression coefficients:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out1)
+\end{Sinput}
+\end{Schunk}
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for the trade variable:
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
+> x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
+\end{Sinput}
+\end{Schunk}
+Generate first differences for the effect of high versus low trade on
+GDP:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/ls-ExamplesPlot}
+\end{center}
+
+\item Using Dummy Variables
+
+Estimate a model with fixed effects for each country (see
+\Sref{factors} for help with dummy variables). Note that you do not
+need to create dummy variables, as the program will automatically
+parse the unique values in the selected variable into discrete levels.
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
++ model = "ls", data = macro)
+\end{Sinput}
+\end{Schunk}
+Set values for the explanatory variables, using the default mean/mode
+values, with country set to the United States and Japan, respectively:
+\begin{Schunk}
+\begin{Sinput}
+> x.US <- setx(z.out2, country = "United States")
+> x.Japan <- setx(z.out2, country = "Japan")
+\end{Sinput}
+\end{Schunk}
+Simulate quantities of interest:
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/ls-DummyPlot}
+\end{center}
+
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item The \emph{stochastic component} is described by a density
+ with mean $\mu_i$ and the common variance $\sigma^2$
+ \begin{equation*}
+ Y_i \; \sim \; f(y_i \mid \mu_i, \sigma^2).
+ \end{equation*}
+\item The \emph{systematic component} models the conditional mean as
+ \begin{equation*}
+ \mu_i = x_i \beta
+ \end{equation*}
+ where $x_i$ is the vector of covariates, and $\beta$ is the vector
+ of coefficients.
+
+ The least squares estimator is the best linear predictor of a
+ dependent variable given $x_i$, and minimizes the sum of squared
+ residuals, $\sum_{i=1}^n (Y_i-x_i \beta)^2$.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component,
+\begin{equation*}
+E(Y) = x_i \beta,\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "ls", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+ \item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \begin{equation*}
+ \hat{\beta} \; = \; \left(\sum_{i=1}^n x_i' x_i\right)^{-1} \sum x_i y_i
+ \end{equation*}
+ \item {\tt sigma}: the square root of the estimate variance of the
+ random error $e$:
+ \begin{equation*}
+ \hat{\sigma} \; = \; \frac{\sum (Y_i-x_i\hat{\beta})^2}{n-k}
+ \end{equation*}
+ \item {\tt r.squared}: the fraction of the variance explained by
+ the model.
+ \begin{equation*}
+ R^2 \; = \; 1 - \frac{\sum (Y_i-x_i\hat{\beta})^2}{\sum (y_i -
+ \bar{y})^2}
+ \end{equation*}
+ \item {\tt adj.r.squared}: the above $R^2$ statistic, penalizing
+ for an increased number of explanatory variables.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first differences (or
+ differences in expected values) for the specified values of {\tt
+ x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The least squares regression is part of the stats package by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+\texttt{help(lm)} and \texttt{help(lm.fit)}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/mlogit.Rnw b/inst/doc/mlogit.Rnw
new file mode 100644
index 0000000..0e35fad
--- /dev/null
+++ b/inst/doc/mlogit.Rnw
@@ -0,0 +1,285 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/mlogit}
+\include{zinput}
+%\VignetteIndexEntry{Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{{\tt mlogit}: Multinomial Logistic Regression for
+Dependent Variables with Unordered Categorical Values}\label{mlogit}
+Use the multinomial logit distribution to model unordered categorical
+variables. The dependent variable may be in the format of either
+character strings or integer values. See
+%\Sref{mlogit.bayes}
+for a Bayesian version of this model.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+If the user wishes to use the same formula across all levels, then
+\verb|formula <- as.factor(Y) ~ X1 + X2| may be used.
+If the user wants to use different formula for each level then the following
+syntax should be used:
+\begin{verbatim}
+formulae <- list(list(id(Y, "apples")~ X1,
+ id(Y, "bananas")~ X1 + X2)
+\end{verbatim}
+where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.
+By default, oranges is the last level and omitted. (You cannot
+specify a different base level at this time.)
+For $J$ equations, there must be $J + 1$ levels.
+
+\subsubsection{Examples} \label{ternary}
+
+\begin{enumerate}
+
+\item {The same formula for each level}
+
+Load the sample data:
+<<Examples.data>>=
+ data(mexico)
+@
+Estimate the empirical model:
+<<Examples.zelig>>=
+ z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
+ model = "mlogit", data = mexico)
+@
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:
+<<Examples.setx>>=
+ x.weak <- setx(z.out1, pristr = 1)
+ x.strong <- setx(z.out1, pristr = 3)
+@
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+<<Examples.sim>>=
+ s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
+
+@
+<<Examples.summary>>=
+summary(s.out1)
+@
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:
+<<Examples.weak>>=
+ ev.weak <- s.out1$qi$ev + s.out1$qi$fd
+
+@
+Plot the differences in the predicted probabilities.
+<<Examples.library>>=
+ library(vcd)
+@
+%%%%does not work but the demo gives same error message
+%%%funlegend=ternarypoints,legendargs=parpoint
+\begin{center}
+<<label=TernaryPlot,fig=true, echo=true>>=
+ternaryplot(x=s.out1$qi$ev, pch = ".", col = "blue",main = "1988 Mexican Presidential Election")
+
+###ternarypoints(ev.weak, pch = ".", col = "red")
+@
+\end{center}
+\item {Different formula for each level}
+
+Estimate the empirical model:
+<<Levels.zelig>>=
+ z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok,
+ id(vote88, "2") ~ othsocok),
+ model = "mlogit", data = mexico)
+@
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:
+<<Levels.setx>>=
+ x.weak <- setx(z.out2, pristr = 1)
+ x.strong <- setx(z.out2, pristr = 3)
+@
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+<<Levels.sim>>=
+ s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
+@
+<<Levels.summary>>=
+ summary(s.out1)
+@
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:
+<<Levels.weak>>=
+ ev.weak <- s.out1$qi$ev + s.out1$qi$fd
+@
+Using the vcd package, plot the differences in the predicted probabilities.
+\begin{center}
+<<label=LevelsTernaryPlot,fig=true,echo=true>>=
+ternaryplot(s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
+###ternarypoints(ev.weak, pch = ".", col = "red")
+@
+\end{center}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the unordered categorical dependent variable that takes
+one of the values from 1 to $J$, where $J$ is the total number of
+categories.
+
+\begin{itemize}
+\item The stochastic component is given by
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}),
+ \end{equation*}
+ where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$.
+
+\item The systemic component is given by:
+ \begin{equation*}
+ \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+ \exp(x_{i}\beta_{k})},
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables for observation
+ $i$, and $\beta_j$ is the vector of coefficients for category $j$.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the predicted probability
+ for each category:
+\begin{equation*}
+ E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+ \exp(x_{i}\beta_{k})}.
+\end{equation*}
+
+\item The predicted value ({\tt qi\$pr}) is a draw from the
+ multinomial distribution defined by the predicted probabilities.
+
+\item The first difference in predicted
+ probabilities ({\tt qi\$fd}), for each category is given by:
+\begin{equation*}
+\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
+\quad j=1,\dots,J.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "mlogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+ predictors $x_i \beta_j$.
+ \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models,
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Further Information}
+
+The multinomial logit model is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/mlogit.bayes.Rnw b/inst/doc/mlogit.bayes.Rnw
new file mode 100644
index 0000000..27aaf9c
--- /dev/null
+++ b/inst/doc/mlogit.bayes.Rnw
@@ -0,0 +1,307 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/mlogitBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
+Regression}
+
+\label{mlogit.bayes}
+
+Use Bayesian multinomial logistic regression to model unordered
+categorical variables. The dependent variable may be in the format of
+either character strings or integer values. The model is estimated
+via a random walk Metropolis algorithm or a slice sampler. See
+\Sref{mlogit} for the maximum-likelihood estimation of this model.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
+\begin{itemize}
+\item \texttt{baseline}: either a character string or numeric value
+(equal to one of the observed values in the dependent variable)
+specifying a baseline category. The default value is \texttt{NA}
+which sets the baseline to the first alphabetical or numerical unique
+value of the dependent variable.
+\end{itemize}
+
+The model accepts the following additional arguments to monitor the
+Markov chains:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
+to use Metropolis Algorithm or slice sampler. The default value is
+\texttt{"MH"}.
+
+\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
+either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
+vector). The tuning parameter should be set such that the acceptance
+rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: defaults to \texttt{FALSE}.
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
+vector). The default is \texttt{NA} where the maximum likelihood
+estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following arguments to specify the priors for the model:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a scalar or
+vector. If a scalar, that value will be the prior mean for all the
+coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix with the dimensions equal to the number of
+coefficients or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0 which
+leads to an improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(mexico)
+@
+Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes",
+ data = mexico)
+@
+Checking for convergence before summarizing the estimates:
+<<BasicExample.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@ \end{verbatim}
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution
+given \texttt{x.out}.
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<BasicExample.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+voting different candidates when \texttt{pristr} (the strength of the
+PRI) is set to be weak (equal to 1) versus strong (equal to 3)
+while all the other variables held at their default values.
+<<FirstDifferences.setx>>=
+ x.weak <- setx(z.out, pristr = 1)
+ x.strong <- setx(z.out, pristr = 3)
+@
+<<FirstDifferences.sim>>=
+s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
+@
+<<FirstDifferences.summary>>=
+summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the (unordered) categorical dependent variable for observation
+$i$ which takes an integer values $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
+\textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta_j$ is the vector of coefficient for
+category $j$. Category $J$ is assumed to be the baseline category.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta_j \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
+\textrm{ for } j = 1, \ldots, J-1,
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
+ regression model are the predicted probability of belonging to each
+ category:
+\begin{eqnarray*}
+\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
+\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+and
+\begin{eqnarray*}
+\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
+\end{eqnarray*}
+given the posterior draws of $\beta_j$ for all categories from the
+MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the draws of
+$Y_i$ from a multinomial distribution whose parameters are the expected
+values(\texttt{qi\$ev}) computed based on the posterior draws
+of $\beta$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the
+multinomial logistic model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ for each category except the baseline
+category.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) of
+each of the $J$ categories given the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values drawn from the
+multinomial distribution defined by the expected values(\texttt{qi\$ev})
+given the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the
+expected values of each of the $J$ categories simulated
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian multinomial logistic regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{mlogit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/mlogit.bayes.pdf b/inst/doc/mlogit.bayes.pdf
new file mode 100644
index 0000000..b9e8398
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diff --git a/inst/doc/mlogit.bayes.tex b/inst/doc/mlogit.bayes.tex
new file mode 100644
index 0000000..af34de7
--- /dev/null
+++ b/inst/doc/mlogit.bayes.tex
@@ -0,0 +1,316 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
+Regression}
+
+\label{mlogit.bayes}
+
+Use Bayesian multinomial logistic regression to model unordered
+categorical variables. The dependent variable may be in the format of
+either character strings or integer values. The model is estimated
+via a random walk Metropolis algorithm or a slice sampler. See
+\Sref{mlogit} for the maximum-likelihood estimation of this model.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
+\begin{itemize}
+\item \texttt{baseline}: either a character string or numeric value
+(equal to one of the observed values in the dependent variable)
+specifying a baseline category. The default value is \texttt{NA}
+which sets the baseline to the first alphabetical or numerical unique
+value of the dependent variable.
+\end{itemize}
+
+The model accepts the following additional arguments to monitor the
+Markov chains:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
+to use Metropolis Algorithm or slice sampler. The default value is
+\texttt{"MH"}.
+
+\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
+either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
+vector). The tuning parameter should be set such that the acceptance
+rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: defaults to \texttt{FALSE}.
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
+vector). The default is \texttt{NA} where the maximum likelihood
+estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following arguments to specify the priors for the model:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a scalar or
+vector. If a scalar, that value will be the prior mean for all the
+coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix with the dimensions equal to the number of
+coefficients or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0 which
+leads to an improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(mexico)
+\end{Sinput}
+\end{Schunk}
+Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes",
++ data = mexico)
+\end{Sinput}
+\end{Schunk}
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution
+given \texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+voting different candidates when \texttt{pristr} (the strength of the
+PRI) is set to be weak (equal to 1) versus strong (equal to 3)
+while all the other variables held at their default values.
+\begin{Schunk}
+\begin{Sinput}
+> x.weak <- setx(z.out, pristr = 1)
+> x.strong <- setx(z.out, pristr = 3)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the (unordered) categorical dependent variable for observation
+$i$ which takes an integer values $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
+\textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta_j$ is the vector of coefficient for
+category $j$. Category $J$ is assumed to be the baseline category.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta_j \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
+\textrm{ for } j = 1, \ldots, J-1,
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
+ regression model are the predicted probability of belonging to each
+ category:
+\begin{eqnarray*}
+\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
+\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+and
+\begin{eqnarray*}
+\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
+\end{eqnarray*}
+given the posterior draws of $\beta_j$ for all categories from the
+MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the draws of
+$Y_i$ from a multinomial distribution whose parameters are the expected
+values(\texttt{qi\$ev}) computed based on the posterior draws
+of $\beta$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the
+multinomial logistic model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ for each category except the baseline
+category.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) of
+each of the $J$ categories given the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values drawn from the
+multinomial distribution defined by the expected values(\texttt{qi\$ev})
+given the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the
+expected values of each of the $J$ categories simulated
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian multinomial logistic regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{mlogit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/mlogit.pdf b/inst/doc/mlogit.pdf
new file mode 100644
index 0000000..7ae233f
Binary files /dev/null and b/inst/doc/mlogit.pdf differ
diff --git a/inst/doc/mlogit.tex b/inst/doc/mlogit.tex
new file mode 100644
index 0000000..82e1ba8
--- /dev/null
+++ b/inst/doc/mlogit.tex
@@ -0,0 +1,296 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
+%\VignetteDepends{Zelig, VGAM}
+%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{{\tt mlogit}: Multinomial Logistic Regression for
+Dependent Variables with Unordered Categorical Values}\label{mlogit}
+Use the multinomial logit distribution to model unordered categorical
+variables. The dependent variable may be in the format of either
+character strings or integer values. See
+%\Sref{mlogit.bayes}
+for a Bayesian version of this model.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+If the user wishes to use the same formula across all levels, then
+\verb|formula <- as.factor(Y) ~ X1 + X2| may be used.
+If the user wants to use different formula for each level then the following
+syntax should be used:
+\begin{verbatim}
+formulae <- list(list(id(Y, "apples")~ X1,
+ id(Y, "bananas")~ X1 + X2)
+\end{verbatim}
+where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.
+By default, oranges is the last level and omitted. (You cannot
+specify a different base level at this time.)
+For $J$ equations, there must be $J + 1$ levels.
+
+\subsubsection{Examples} \label{ternary}
+
+\begin{enumerate}
+
+\item {The same formula for each level}
+
+Load the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(mexico)
+\end{Sinput}
+\end{Schunk}
+Estimate the empirical model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
++ model = "mlogit", data = mexico)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:
+\begin{Schunk}
+\begin{Sinput}
+> x.weak <- setx(z.out1, pristr = 1)
+> x.strong <- setx(z.out1, pristr = 3)
+\end{Sinput}
+\end{Schunk}
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:
+\begin{Schunk}
+\begin{Sinput}
+> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
+\end{Sinput}
+\end{Schunk}
+Plot the differences in the predicted probabilities.
+\begin{Schunk}
+\begin{Sinput}
+> library(vcd)
+\end{Sinput}
+\end{Schunk}
+%%%%does not work but the demo gives same error message
+%%%funlegend=ternarypoints,legendargs=parpoint
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> ternaryplot(x = s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/mlogit-TernaryPlot}
+\end{center}
+\item {Different formula for each level}
+
+Estimate the empirical model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok, id(vote88,
++ "2") ~ othsocok), model = "mlogit", data = mexico)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:
+\begin{Schunk}
+\begin{Sinput}
+> x.weak <- setx(z.out2, pristr = 1)
+> x.strong <- setx(z.out2, pristr = 3)
+\end{Sinput}
+\end{Schunk}
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:
+\begin{Schunk}
+\begin{Sinput}
+> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
+\end{Sinput}
+\end{Schunk}
+Using the vcd package, plot the differences in the predicted probabilities.
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> ternaryplot(s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/mlogit-LevelsTernaryPlot}
+\end{center}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the unordered categorical dependent variable that takes
+one of the values from 1 to $J$, where $J$ is the total number of
+categories.
+
+\begin{itemize}
+\item The stochastic component is given by
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}),
+ \end{equation*}
+ where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$.
+
+\item The systemic component is given by:
+ \begin{equation*}
+ \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+ \exp(x_{i}\beta_{k})},
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables for observation
+ $i$, and $\beta_j$ is the vector of coefficients for category $j$.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the predicted probability
+ for each category:
+\begin{equation*}
+ E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+ \exp(x_{i}\beta_{k})}.
+\end{equation*}
+
+\item The predicted value ({\tt qi\$pr}) is a draw from the
+ multinomial distribution defined by the predicted probabilities.
+
+\item The first difference in predicted
+ probabilities ({\tt qi\$fd}), for each category is given by:
+\begin{equation*}
+\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
+\quad j=1,\dots,J.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "mlogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+ predictors $x_i \beta_j$.
+ \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models,
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Further Information}
+
+The multinomial logit model is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+ \end{document}
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/mloglm.Rnw b/inst/doc/mloglm.Rnw
new file mode 100644
index 0000000..d52deb1
--- /dev/null
+++ b/inst/doc/mloglm.Rnw
@@ -0,0 +1,129 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/mloglm}
+\include{zinput}
+%\VignetteIndexEntry{Multinomial Log-Linear Regression for Contingency Table Models}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, log-linear,multinomial, regression, contingency table}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@ \include{zinput}
+
+\section{{\tt mloglm}: Multinomial Log-Linear Regression
+for Contingency Table Models}\label{mloglm}
+
+Log-linear models are for modeling contingency tables, the
+cross-tabulation of discrete individual-level variables. Contingency
+table models take as the ``unit of analysis'' for the purpose of the
+statistical procedure, the cell of a contingency table. The
+``dependent variable'' is then the count within each cell, and the
+explanatory variables indicate what categories the cells fall into.
+These models are highly efficient computationally since there are so
+few ``observations,'' but they are asymptotically equivalent to
+logistic regression models run on the unpacked individual level data.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> estimate <- zelig(Y ~ X1 + X2, model = "mloglm", data = mydata)
+> Xval <- setx(estimate)
+> results <- sim(estimate, x = Xval)
+\end{verbatim}
+
+\subsubsection{Examples}
+
+\subsubsection{Model}
+
+\subsubsection{Quantities of Interest}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{estimate <- zelig(y \~\,
+ x, model = "mloglm", data)}, then you may examine the available
+information in \texttt{estimate} by using \texttt{names(estimate)},
+see the {\tt coefficients} by using {\tt estimate\$coefficients}, and
+a default summary of information through \texttt{summary(estimate)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt estimate}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt deviance}: the residual deviance.
+ \item {\tt fitted.values}: the $n \times m$ matrix of in-sample
+ fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt edf}: the effective degrees of freedom.
+ \item {\tt AIC}: Akaike's An Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt Hessian}: the Hessian matrix.
+ \end{itemize}
+
+\item From {\tt summary(estimate)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt results}:
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected (or fitted values) for
+ the specified values of {\tt x}.
+ \item {\tt qi\$rd}: the difference in the expected values (or first
+ difference) for the values specified in {\tt x} and
+ {\tt x1}.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The multinomial logit model is part of the nnet library by Brian D.
+Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{Ripley1996}.
+\end{verse}
+
+Advanced users may wish to refer to the R-help for
+\texttt{help(multinom)} and
+\begin{verse}
+\bibentry{VenRip2002}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/mloglm.pdf b/inst/doc/mloglm.pdf
new file mode 100644
index 0000000..a352245
Binary files /dev/null and b/inst/doc/mloglm.pdf differ
diff --git a/inst/doc/mloglm.tex b/inst/doc/mloglm.tex
new file mode 100644
index 0000000..9e364a1
--- /dev/null
+++ b/inst/doc/mloglm.tex
@@ -0,0 +1,116 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Multinomial Log-Linear Regression for Contingency Table Models}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, log-linear,multinomial, regression, contingency table}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt mloglm}: Multinomial Log-Linear Regression
+for Contingency Table Models}\label{mloglm}
+
+Log-linear models are for modeling contingency tables, the
+cross-tabulation of discrete individual-level variables. Contingency
+table models take as the ``unit of analysis'' for the purpose of the
+statistical procedure, the cell of a contingency table. The
+``dependent variable'' is then the count within each cell, and the
+explanatory variables indicate what categories the cells fall into.
+These models are highly efficient computationally since there are so
+few ``observations,'' but they are asymptotically equivalent to
+logistic regression models run on the unpacked individual level data.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> estimate <- zelig(Y ~ X1 + X2, model = "mloglm", data = mydata)
+> Xval <- setx(estimate)
+> results <- sim(estimate, x = Xval)
+\end{verbatim}
+
+\subsubsection{Examples}
+
+\subsubsection{Model}
+
+\subsubsection{Quantities of Interest}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{estimate <- zelig(y \~\,
+ x, model = "mloglm", data)}, then you may examine the available
+information in \texttt{estimate} by using \texttt{names(estimate)},
+see the {\tt coefficients} by using {\tt estimate\$coefficients}, and
+a default summary of information through \texttt{summary(estimate)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt estimate}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt deviance}: the residual deviance.
+ \item {\tt fitted.values}: the $n \times m$ matrix of in-sample
+ fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt edf}: the effective degrees of freedom.
+ \item {\tt AIC}: Akaike's An Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt Hessian}: the Hessian matrix.
+ \end{itemize}
+
+\item From {\tt summary(estimate)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt results}:
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected (or fitted values) for
+ the specified values of {\tt x}.
+ \item {\tt qi\$rd}: the difference in the expected values (or first
+ difference) for the values specified in {\tt x} and
+ {\tt x1}.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The multinomial logit model is part of the nnet library by Brian D.
+Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{Ripley1996}.
+\end{verse}
+
+Advanced users may wish to refer to the R-help for
+\texttt{help(multinom)} and
+\begin{verse}
+\bibentry{VenRip2002}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/arima.aux b/inst/doc/models/arima.aux
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diff --git a/inst/doc/negbin.Rnw b/inst/doc/negbin.Rnw
new file mode 100644
index 0000000..5fcb8f1
--- /dev/null
+++ b/inst/doc/negbin.Rnw
@@ -0,0 +1,265 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/negbin}
+\include{zinput}
+%\VignetteIndexEntry{Negative Binomial Regression for Event Count Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, binomial,negative, regression, count}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+
+\section{{\tt negbin}: Negative Binomial Regression for Event
+Count Dependent Variables}\label{negbin}
+
+Use the negative binomial regression if you have a count of events for
+each observation of your dependent variable. The negative binomial
+model is frequently used to estimate over-dispersed event count
+models.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "negbin", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for negative binomial regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:
+<<Example.data>>=
+ data(sanction)
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
+@
+<<Example.summary>>=
+summary(z.out)
+@
+Set values for the explanatory variables to their default mean values:
+<<Example.setx>>=
+ x.out <- setx(z.out)
+@
+Simulate fitted values:
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.out)
+@
+<<Example.summary.sim>>=
+summary(s.out)
+@
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer value.
+
+\begin{itemize}
+\item The negative binomial distribution is derived by letting the
+ mean of the Poisson distribution vary according to a fixed
+ parameter $\zeta$ given by the Gamma distribution. The
+ \emph{stochastic component} is given by
+ \begin{eqnarray*}
+ Y_i \mid \zeta_i & \sim & \textrm{Poisson}(\zeta_i \mu_i),\\
+ \zeta_i & \sim & \frac{1}{\theta}\textrm{Gamma}(\theta).
+ \end{eqnarray*}
+ The marginal distribution of $Y_i$ is then the negative binomial
+ with mean $\mu_i$ and variance $\mu_i + \mu_i^2/\theta$:
+ \begin{eqnarray*}
+ Y_i & \sim & \textrm{NegBin}(\mu_i, \theta), \\
+ & = & \frac{\Gamma (\theta + y_i)}{y! \, \Gamma(\theta)}
+ \frac{\mu_i^{y_i} \, \theta^{\theta}}{(\mu_i + \theta)^{\theta + y_i}},
+ \end{eqnarray*}
+ where $\theta$ is the systematic parameter of the Gamma
+ distribution modeling $\zeta_i$.
+
+ \item The \emph{systematic component} is given by
+ \begin{equation*}
+ \mu_i = \exp(x_i \beta)
+ \end{equation*}
+ where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+ the vector of coefficients.
+ \end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+ of the stochastic component. Thus, $$E(Y) = \mu_i = \exp(x_i
+ \beta),$$ given simulations of $\beta$.
+
+\item The predicted value ({\tt qi\$pr}) drawn from the distribution
+ defined by the set of parameters $(\mu_i, \theta)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "negbin", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt theta}: the maximum likelihood estimate for the
+ stochastic parameter $\theta$.
+ \item {\tt SE.theta}: the standard error for {\tt theta}.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the fitted values for the systemic
+ component $\lambda$.
+ \item {\tt linear.predictors}: a vector of $x_{i} \beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values given the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\mu_i, \theta)$.
+ \item {\tt qi\$fd}: the simulated first differences in the
+ simulated expected values given the specified values of {\tt x}
+ and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The negative binomial model is part of the MASS library by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to {\tt help(glm.nb)} in
+the MASS library and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/negbin.pdf b/inst/doc/negbin.pdf
new file mode 100644
index 0000000..d43be6e
Binary files /dev/null and b/inst/doc/negbin.pdf differ
diff --git a/inst/doc/negbin.tex b/inst/doc/negbin.tex
new file mode 100644
index 0000000..4af95d8
--- /dev/null
+++ b/inst/doc/negbin.tex
@@ -0,0 +1,264 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Negative Binomial Regression for Event Count Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, binomial,negative, regression, count}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+
+\section{{\tt negbin}: Negative Binomial Regression for Event
+Count Dependent Variables}\label{negbin}
+
+Use the negative binomial regression if you have a count of events for
+each observation of your dependent variable. The negative binomial
+model is frequently used to estimate over-dispersed event count
+models.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "negbin", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for negative binomial regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set values for the explanatory variables to their default mean values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer value.
+
+\begin{itemize}
+\item The negative binomial distribution is derived by letting the
+ mean of the Poisson distribution vary according to a fixed
+ parameter $\zeta$ given by the Gamma distribution. The
+ \emph{stochastic component} is given by
+ \begin{eqnarray*}
+ Y_i \mid \zeta_i & \sim & \textrm{Poisson}(\zeta_i \mu_i),\\
+ \zeta_i & \sim & \frac{1}{\theta}\textrm{Gamma}(\theta).
+ \end{eqnarray*}
+ The marginal distribution of $Y_i$ is then the negative binomial
+ with mean $\mu_i$ and variance $\mu_i + \mu_i^2/\theta$:
+ \begin{eqnarray*}
+ Y_i & \sim & \textrm{NegBin}(\mu_i, \theta), \\
+ & = & \frac{\Gamma (\theta + y_i)}{y! \, \Gamma(\theta)}
+ \frac{\mu_i^{y_i} \, \theta^{\theta}}{(\mu_i + \theta)^{\theta + y_i}},
+ \end{eqnarray*}
+ where $\theta$ is the systematic parameter of the Gamma
+ distribution modeling $\zeta_i$.
+
+ \item The \emph{systematic component} is given by
+ \begin{equation*}
+ \mu_i = \exp(x_i \beta)
+ \end{equation*}
+ where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+ the vector of coefficients.
+ \end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+ of the stochastic component. Thus, $$E(Y) = \mu_i = \exp(x_i
+ \beta),$$ given simulations of $\beta$.
+
+\item The predicted value ({\tt qi\$pr}) drawn from the distribution
+ defined by the set of parameters $(\mu_i, \theta)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "negbin", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt theta}: the maximum likelihood estimate for the
+ stochastic parameter $\theta$.
+ \item {\tt SE.theta}: the standard error for {\tt theta}.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the fitted values for the systemic
+ component $\lambda$.
+ \item {\tt linear.predictors}: a vector of $x_{i} \beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values given the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\mu_i, \theta)$.
+ \item {\tt qi\$fd}: the simulated first differences in the
+ simulated expected values given the specified values of {\tt x}
+ and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The negative binomial model is part of the MASS library by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to {\tt help(glm.nb)} in
+the MASS library and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/netlogit.Rnw b/inst/doc/netlogit.Rnw
new file mode 100644
index 0000000..5373bfe
--- /dev/null
+++ b/inst/doc/netlogit.Rnw
@@ -0,0 +1,199 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netlogit}
+\include{zinput}
+%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, network,logistic, regression, dichotomous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt netlogit}: Network Logistic Regression for
+Dichotomous Proximity Matrix Dependent Variables}\label{netlogit}
+
+Use network logistic squares regression analysis for a dependent
+variable that is a binary-valued proximity matrix (a.k.a.\
+sociomatrices, adjacency matrices, or matrix representations of
+directed graphs).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data and format it for social network analysis:
+<<BasicExample.data>>=
+data(friendship)
+@
+Estimate model:
+<<BasicExample.zelig>>=
+ z.out <- zelig(friends ~ advice + prestige + perpower,
+ model = "netlogit", data = friendship)
+ summary(z.out)
+@
+Setting values for the explanatory variables to their default values:
+<<BasicExample.setx>>=
+x.out <- setx(z.out)
+@
+Simulating quantities of interest from the sampling distribution.
+<<BasicExample.sim>>=
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+@
+\begin{center}
+<<label=BasicExamplePlot, fig=true, echo=true>>=
+ plot(s.out)
+@
+\end{center}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low personal
+power (25th percentile) and high education (75th percentile) while all
+the other variables are held at their default values.
+
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
+ x.low <- setx(z.out, educate = quantile(friendship$perpower, prob = 0.25))
+@
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+ summary(s.out2)
+@
+\begin{center}
+<<label=FirstDifferencesPlot,fig=true,echo=true>>=
+ plot(s.out2)
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+The {\tt netlogit} model performs a logistic regression of the
+sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
+ties, on a set of sociomatrices $\mathbf{X}$. This network regression
+model is a directly analogue to standard logistic regression
+element-wise on the appropriately vectorized matrices. Sociomatrices
+are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
+represent the sociomatrix. The vectorization which produces the $Y$
+vector from the $\mathbf{Y}$ matrix is preformed by simple
+row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
+$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
+element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
+element of $Y$ and so on. Once the input matrices are vectorized,
+standard logistic regression is performed.
+
+Let $Y_{i}$ be the binary dependent variable, produced by vectorizing
+a binary sociomatrix, for observation $i$ which takes the value of
+either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
+& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
+\end{eqnarray*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
+\end{equation*}
+where $x_{i}$ is the vector of $k$ covariates for observation $i$ and
+$\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network logistic regression are the
+same as those for the standard logistic regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the netlogit model are
+simulations of the predicted probability of a success:
+\begin{equation*}
+E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
+distribution with mean equal to the simulated expected value
+$\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, you run {\tt z.out <- zelig(y ~ x, model
+="netlogit", data)}, then you may examine the available information in
+{\tt z.out} by using {\tt names(z.out)}, see the coefficients by using
+{\tt z.out\$coefficients}, and a default summary of information
+through {\tt summary(z.out)}. Other elements available through the
+{\tt \$} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the
+explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of
+the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the
+maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the
+maximized log-likelihood plus the number of coefficients times log
+$n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+\end{itemize}
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+covariances.
+\item {\tt ctable}: a $2 \times 2$ table of predicted vs.\ actual
+values. Each cell tabulates whether the predicted value $\widehat{Y}
+\in \{0,1\}$ corresponds to the observed $Y \in \{0,1\}$.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the
+specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected
+probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network logistic regression is part of the sna package by Carter
+T. Butts. Please cite the model as \\
+\begin{verse}
+\bibentry{ButCar01}.
+\end{verse}
+In addition, advanced users may wish to refer to {\tt help(netlogit)}.
+Sample data are fictional. Skyler J.\ Cranmer added Zelig functionality.
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/netlogit.pdf b/inst/doc/netlogit.pdf
new file mode 100644
index 0000000..4a860dc
Binary files /dev/null and b/inst/doc/netlogit.pdf differ
diff --git a/inst/doc/netlogit.tex b/inst/doc/netlogit.tex
new file mode 100644
index 0000000..ef428e2
--- /dev/null
+++ b/inst/doc/netlogit.tex
@@ -0,0 +1,206 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, network,logistic, regression, dichotomous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt netlogit}: Network Logistic Regression for
+Dichotomous Proximity Matrix Dependent Variables}\label{netlogit}
+
+Use network logistic squares regression analysis for a dependent
+variable that is a binary-valued proximity matrix (a.k.a.\
+sociomatrices, adjacency matrices, or matrix representations of
+directed graphs).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data and format it for social network analysis:
+\begin{Schunk}
+\begin{Sinput}
+> data(friendship)
+\end{Sinput}
+\end{Schunk}
+Estimate model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(friends ~ advice + prestige + perpower, model = "netlogit",
++ data = friendship)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their default values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the sampling distribution.
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/netlogit-BasicExamplePlot}
+\end{center}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low personal
+power (25th percentile) and high education (75th percentile) while all
+the other variables are held at their default values.
+
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, perpower = quantile(friendship$perpower,
++ prob = 0.75))
+> x.low <- setx(z.out, educate = quantile(friendship$perpower,
++ prob = 0.25))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/netlogit-FirstDifferencesPlot}
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+The {\tt netlogit} model performs a logistic regression of the
+sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
+ties, on a set of sociomatrices $\mathbf{X}$. This network regression
+model is a directly analogue to standard logistic regression
+element-wise on the appropriately vectorized matrices. Sociomatrices
+are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
+represent the sociomatrix. The vectorization which produces the $Y$
+vector from the $\mathbf{Y}$ matrix is preformed by simple
+row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
+$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
+element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
+element of $Y$ and so on. Once the input matrices are vectorized,
+standard logistic regression is performed.
+
+Let $Y_{i}$ be the binary dependent variable, produced by vectorizing
+a binary sociomatrix, for observation $i$ which takes the value of
+either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
+& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
+\end{eqnarray*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
+\end{equation*}
+where $x_{i}$ is the vector of $k$ covariates for observation $i$ and
+$\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network logistic regression are the
+same as those for the standard logistic regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the netlogit model are
+simulations of the predicted probability of a success:
+\begin{equation*}
+E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
+distribution with mean equal to the simulated expected value
+$\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, you run {\tt z.out <- zelig(y ~ x, model
+="netlogit", data)}, then you may examine the available information in
+{\tt z.out} by using {\tt names(z.out)}, see the coefficients by using
+{\tt z.out\$coefficients}, and a default summary of information
+through {\tt summary(z.out)}. Other elements available through the
+{\tt \$} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the
+explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of
+the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the
+maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the
+maximized log-likelihood plus the number of coefficients times log
+$n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+\end{itemize}
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+covariances.
+\item {\tt ctable}: a $2 \times 2$ table of predicted vs.\ actual
+values. Each cell tabulates whether the predicted value $\widehat{Y}
+\in \{0,1\}$ corresponds to the observed $Y \in \{0,1\}$.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the
+specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected
+probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network logistic regression is part of the sna package by Carter
+T. Butts. Please cite the model as \\
+\begin{verse}
+\bibentry{ButCar01}.
+\end{verse}
+In addition, advanced users may wish to refer to {\tt help(netlogit)}.
+Sample data are fictional. Skyler J.\ Cranmer added Zelig functionality.
+ \end{document}
diff --git a/inst/doc/netls.Rnw b/inst/doc/netls.Rnw
new file mode 100644
index 0000000..563cab5
--- /dev/null
+++ b/inst/doc/netls.Rnw
@@ -0,0 +1,190 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netls, eval=true}
+\include{zinput}
+%\VignetteIndexEntry{Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, network,least squares, regression,continuous, proximity matrix}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F>>=
+pkg <- search()
+
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt netls}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}\label{netls}
+
+Use network least squares regression analysis to estimate the
+best linear predictor when the dependent variable is a
+continuously-valued proximity matrix (a.k.a.\ sociomatrices, adjacency
+matrices, or matrix representations of directed graphs).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "netls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example with First Differences
+
+Load sample data and format it for social networkx analysis:
+%library sna required
+<<Examples.data>>=
+ data(sna.ex)
+@
+<<Examples.library, echo=false>>=
+if(is.na(match("sna",.packages()))){
+message("Loading package sna...")
+library(sna)
+}
+@
+Estimate model:
+<<Examples.zelig>>=
+ z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data = sna.ex)
+
+@
+
+Summarize regression results:
+<<Examples.summary>>=
+ summary(z.out)
+@
+
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) for the second
+explanatory variable (Var3).
+<<Examples.setx>>=
+ x.high <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.8))
+ x.low <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.2))
+@
+Generate first differences for the effect of high versus low values of
+Var3 on the outcome variable.
+<<Examples.sim>>=
+ try(s.out <- sim(z.out, x = x.high, x1 = x.low))
+ try(summary(s.out))
+@
+\begin{center}
+<<label=ExamplesPlot, fig=true, echo=true>>=
+ plot(s.out)
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+The {\tt netls} model performs a least squares regression of the
+sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
+ties, on a set of sociomatrices $\mathbf{X}$. This network regression
+model is a directly analogue to standard least squares regression
+element-wise on the appropriately vectorized matrices. Sociomatrices
+are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
+represent the sociomatrix. The vectorization which produces the $Y$
+vector from the $\mathbf{Y}$ matrix is preformed by simple
+row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
+$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
+element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
+element of $Y$ and so on. Once the input matrices are vectorized,
+standard least squares regression is performed. As such:
+\begin{itemize}
+\item The \emph{stochastic component} is described by a density with
+mean $\mu_{i}$ and the common variance $\sigma^{2}$
+\begin{equation*}
+Y_{i} \sim f(y_{i} | \mu_{i}, \sigma^{2}).
+\end{equation*}
+
+\item The \emph{systematic component} models the conditional mean as
+\begin{equation*}
+\mu_{i} = x_{i}\beta
+\end{equation*}
+where $x_{i}$ is the vector of covariates, and $\beta$ is the vector of coefficients.
+\end{itemize}
+The least squares estimator is the best linear predictor of a
+dependent variable given $x_{i}$, and minimizes the sum of squared
+errors $\sum_{i = 1}^{n} (Y_{i} - x_{i}\beta)^{2}$.
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network least squares regression
+are the same as those for the standard least squares regression.
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations from
+the stochastic component,
+\begin{equation*}
+E(Y) = x_{i}\beta,
+\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+FD = E(Y | x_{1}) - E(Y | x)
+\end{equation*}
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, you run {\tt z.out <- zelig(y ~ x,
+model="netls", data)}, then you may examine the available information
+in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
+using {\tt z.out\$coefficients}, and a default summary of information
+through {\tt summary(z.out)}. Other elements available through the
+{\tt \$} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+\end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\begin{equation*}
+\hat{\beta} = \left( \sum_{i = 1}^{n} x'_{i}x_{i} \right)^{-1} \sum x_{i}y_{i}
+\end{equation*}
+\item {\tt sigma}: the square root of the estimate variance of the
+random error $\varepsilon$:
+\begin{equation*}
+\hat{\sigma} = \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{n - k}
+\end{equation*}
+\item {\tt r.squared}: the fraction of the variance explained by the model.
+\begin{equation*}
+R^{2} = 1 - \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{\sum (y_{i}
+- \bar{y})^{2}}
+\end{equation*}
+\item {\tt adj.r.squared}: the above $R^{2}$ statistic, penalizing for
+an increased number of explanatory variables.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences (or differences in
+expected values) for the specified values of {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network least squares regression is part of the sna package by
+Carter T. Butts. Please cite the model as
+\begin{verse}
+\bibentry{ButCar01}.
+\end{verse}
+In addition, advanced users may wish to refer to {\tt
+help(netlm)}. Sample data are fictional. Skyler J. Cranmer added Zelig
+functionality.
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/netls.pdf b/inst/doc/netls.pdf
new file mode 100644
index 0000000..b04a91d
Binary files /dev/null and b/inst/doc/netls.pdf differ
diff --git a/inst/doc/netls.tex b/inst/doc/netls.tex
new file mode 100644
index 0000000..3e734b8
--- /dev/null
+++ b/inst/doc/netls.tex
@@ -0,0 +1,182 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, network,least squares, regression,continuous, proximity matrix}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt netls}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}\label{netls}
+
+Use network least squares regression analysis to estimate the
+best linear predictor when the dependent variable is a
+continuously-valued proximity matrix (a.k.a.\ sociomatrices, adjacency
+matrices, or matrix representations of directed graphs).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "netls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example with First Differences
+
+Load sample data and format it for social networkx analysis:
+%library sna required
+\begin{Schunk}
+\begin{Sinput}
+> data(sna.ex)
+\end{Sinput}
+\end{Schunk}
+Estimate model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data = sna.ex)
+\end{Sinput}
+\end{Schunk}
+
+Summarize regression results:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) for the second
+explanatory variable (Var3).
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.8))
+> x.low <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.2))
+\end{Sinput}
+\end{Schunk}
+Generate first differences for the effect of high versus low values of
+Var3 on the outcome variable.
+\begin{Schunk}
+\begin{Sinput}
+> try(s.out <- sim(z.out, x = x.high, x1 = x.low))
+> try(summary(s.out))
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/netls-ExamplesPlot}
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+The {\tt netls} model performs a least squares regression of the
+sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
+ties, on a set of sociomatrices $\mathbf{X}$. This network regression
+model is a directly analogue to standard least squares regression
+element-wise on the appropriately vectorized matrices. Sociomatrices
+are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
+represent the sociomatrix. The vectorization which produces the $Y$
+vector from the $\mathbf{Y}$ matrix is preformed by simple
+row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
+$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
+element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
+element of $Y$ and so on. Once the input matrices are vectorized,
+standard least squares regression is performed. As such:
+\begin{itemize}
+\item The \emph{stochastic component} is described by a density with
+mean $\mu_{i}$ and the common variance $\sigma^{2}$
+\begin{equation*}
+Y_{i} \sim f(y_{i} | \mu_{i}, \sigma^{2}).
+\end{equation*}
+
+\item The \emph{systematic component} models the conditional mean as
+\begin{equation*}
+\mu_{i} = x_{i}\beta
+\end{equation*}
+where $x_{i}$ is the vector of covariates, and $\beta$ is the vector of coefficients.
+\end{itemize}
+The least squares estimator is the best linear predictor of a
+dependent variable given $x_{i}$, and minimizes the sum of squared
+errors $\sum_{i = 1}^{n} (Y_{i} - x_{i}\beta)^{2}$.
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network least squares regression
+are the same as those for the standard least squares regression.
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations from
+the stochastic component,
+\begin{equation*}
+E(Y) = x_{i}\beta,
+\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+FD = E(Y | x_{1}) - E(Y | x)
+\end{equation*}
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, you run {\tt z.out <- zelig(y ~ x,
+model="netls", data)}, then you may examine the available information
+in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
+using {\tt z.out\$coefficients}, and a default summary of information
+through {\tt summary(z.out)}. Other elements available through the
+{\tt \$} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+\end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\begin{equation*}
+\hat{\beta} = \left( \sum_{i = 1}^{n} x'_{i}x_{i} \right)^{-1} \sum x_{i}y_{i}
+\end{equation*}
+\item {\tt sigma}: the square root of the estimate variance of the
+random error $\varepsilon$:
+\begin{equation*}
+\hat{\sigma} = \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{n - k}
+\end{equation*}
+\item {\tt r.squared}: the fraction of the variance explained by the model.
+\begin{equation*}
+R^{2} = 1 - \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{\sum (y_{i}
+- \bar{y})^{2}}
+\end{equation*}
+\item {\tt adj.r.squared}: the above $R^{2}$ statistic, penalizing for
+an increased number of explanatory variables.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences (or differences in
+expected values) for the specified values of {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network least squares regression is part of the sna package by
+Carter T. Butts. Please cite the model as
+\begin{verse}
+\bibentry{ButCar01}.
+\end{verse}
+In addition, advanced users may wish to refer to {\tt
+help(netlm)}. Sample data are fictional. Skyler J. Cranmer added Zelig
+functionality.
+ \end{document}
diff --git a/inst/doc/normal.Rnw b/inst/doc/normal.Rnw
new file mode 100644
index 0000000..29d7596
--- /dev/null
+++ b/inst/doc/normal.Rnw
@@ -0,0 +1,308 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/normal}
+\include{zinput}
+%\VignetteIndexEntry{Normal Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, normal,regression,continuous, least squares}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
+\label{normal}
+
+The Normal regression model is a close variant of the more standard
+least squares regression model (see \Sref{ls}). Both models specify a
+continuous dependent variable as a linear function of a set of
+explanatory variables. The Normal model reports maximum likelihood
+(rather than least squares) estimates. The two models differ only in
+their estimate for the stochastic parameter $\sigma$.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for normal regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data:
+<<Examples.data>>=
+ data(macro)
+@
+Estimate model:
+<<Examples.zelig>>=
+ z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal",
+ data = macro)
+@
+Summarize of regression coefficients:
+<<Examples.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for trade:
+<<Examples.setx>>=
+ 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:
+<<Examples.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Examples.summary.sim>>=
+ summary(s.out1)
+@
+%plot does not work
+A visual summary of quantities of interest:
+\begin{center}
+<<label=ExamplesPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item Using Dummy Variables
+%the code in this section does not work well but there is no demo for this part either
+
+Estimate a model with a dummy variable for each year and country (see
+\ref{factors} for help with dummy variables). 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.
+<<Dummy.zelig>>=
+ 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
+variables, with country set to the United States and Japan,
+respectively:
+<<Dummy.setx>>=
+### x.US <- try(setx(z.out2, country = "United States"),silent=T)
+### x.Japan <- try(setx(z.out2, country = "Japan"),silent=T)
+@
+Simulate quantities of interest:
+<<Dummy.sim>>=
+### s.out2 <- try(sim(z.out2, x = x.US, x1 = x.Japan), silent=T)
+@
+<<Dummy.summary>>=
+###try(summary(s.out2))
+@
+%plot does not work
+\begin{center}
+<<label=DummyPlot,fig=true,echo=false>>=
+# plot(s.out2)
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the continuous dependent variable for observation $i$.
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal
+ model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2).
+ \end{equation*}
+
+\item The \emph{systematic component} is
+ \begin{equation*}
+ \mu_i \;= \; x_i \beta,
+ \end{equation*}
+ where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
+ given a draw of $\beta$ from its posterior.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
+ defined by the set of parameters $(\mu_i, \sigma)$.
+
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+\textrm{FD}\; = \;E(Y \mid x_1) - E(Y \mid x)
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "normal", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: fitted values. For the normal model,
+ these are identical to the {\tt linear predictors}.
+ \item {\tt linear.predictors}: fitted values. For the normal
+ model, these are identical to {\tt fitted.values}.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\mu_i, \sigma)$.
+ \item {\tt qi\$fd}: the simulated first difference in the simulated
+ expected values for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Normal model is part of the stats package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)}, \texttt{help(family)}, and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/normal.bayes.Rnw b/inst/doc/normal.bayes.Rnw
new file mode 100644
index 0000000..0325c34
--- /dev/null
+++ b/inst/doc/normal.bayes.Rnw
@@ -0,0 +1,266 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/normalBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Normal Linear Regression}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, normal,linear, regression,bayes, continuous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
+\label{normal.bayes}
+
+Use Bayesian regression to specify a continuous dependent variable as
+a linear function of specified explanatory variables. The model is
+implemented using a Gibbs sampler. See \Sref{normal} for the
+maximum-likelihood implementation or \Sref{ls} for the ordinary least
+squares variation.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA}, which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, which uses the
+least squares estimates as the starting values.
+
+\end{itemize}
+
+Use the following arguments to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCregress)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(macro)
+@
+
+Estimating linear regression using \texttt{normal.bayes}:
+<<BasicExample.zelig>>=
+z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
+ data = macro, verbose = TRUE)
+@
+Checking for convergence before summarizing the estimates:
+<<BasicExample.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<BasicExample.heidel>>=
+heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@
+
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}:
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<BasicExample.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) trade on GDP:
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
+ x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
+@
+Estimating the first difference for the effect of
+high versus low trade on unemployment rate:
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.summary.sim>>=
+ summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*}
+where $\epsilon_{i}=Y_i-\mu_i$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
+\begin{eqnarray*}
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2},
+\frac{d_0}{2} \right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
+scale parameters for $\sigma^{2}$. Note that $\beta$ and $\sigma^2$
+are assumed to be \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the linear regression model are
+calculated as following:
+\begin{eqnarray*}
+E(Y) = x_{i} \beta,
+\end{eqnarray*}
+given posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) for the linear regression model
+is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "normal.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The Bayesian regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{normal.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/normal.bayes.pdf b/inst/doc/normal.bayes.pdf
new file mode 100644
index 0000000..c44ac57
Binary files /dev/null and b/inst/doc/normal.bayes.pdf differ
diff --git a/inst/doc/normal.bayes.tex b/inst/doc/normal.bayes.tex
new file mode 100644
index 0000000..2fcba46
--- /dev/null
+++ b/inst/doc/normal.bayes.tex
@@ -0,0 +1,277 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Normal Linear Regression}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, normal,linear, regression,bayes, continuous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
+\label{normal.bayes}
+
+Use Bayesian regression to specify a continuous dependent variable as
+a linear function of specified explanatory variables. The model is
+implemented using a Gibbs sampler. See \Sref{normal} for the
+maximum-likelihood implementation or \Sref{ls} for the ordinary least
+squares variation.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA}, which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, which uses the
+least squares estimates as the starting values.
+
+\end{itemize}
+
+Use the following arguments to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCregress)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(macro)
+\end{Sinput}
+\end{Schunk}
+
+Estimating linear regression using \texttt{normal.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
++ data = macro, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) trade on GDP:
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
+> x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
+\end{Sinput}
+\end{Schunk}
+Estimating the first difference for the effect of
+high versus low trade on unemployment rate:
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*}
+where $\epsilon_{i}=Y_i-\mu_i$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
+\begin{eqnarray*}
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2},
+\frac{d_0}{2} \right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
+scale parameters for $\sigma^{2}$. Note that $\beta$ and $\sigma^2$
+are assumed to be \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the linear regression model are
+calculated as following:
+\begin{eqnarray*}
+E(Y) = x_{i} \beta,
+\end{eqnarray*}
+given posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) for the linear regression model
+is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "normal.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The Bayesian regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{normal.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/normal.pdf b/inst/doc/normal.pdf
new file mode 100644
index 0000000..587d9f9
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diff --git a/inst/doc/normal.tex b/inst/doc/normal.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/normal.tex
@@ -0,0 +1,299 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Normal Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, normal,regression,continuous, least squares}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
+\label{normal}
+
+The Normal regression model is a close variant of the more standard
+least squares regression model (see \Sref{ls}). Both models specify a
+continuous dependent variable as a linear function of a set of
+explanatory variables. The Normal model reports maximum likelihood
+(rather than least squares) estimates. The two models differ only in
+their estimate for the stochastic parameter $\sigma$.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for normal regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(macro)
+\end{Sinput}
+\end{Schunk}
+Estimate model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal",
++ data = macro)
+\end{Sinput}
+\end{Schunk}
+Summarize of regression coefficients:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out1)
+\end{Sinput}
+\end{Schunk}
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for trade:
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
+> x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
+\end{Sinput}
+\end{Schunk}
+Generate first differences for the effect of high versus low trade on
+GDP:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+%plot does not work
+A visual summary of quantities of interest:
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/normal-ExamplesPlot}
+\end{center}
+
+\item Using Dummy Variables
+%the code in this section does not work well but there is no demo for this part either
+
+Estimate a model with a dummy variable for each year and country (see
+\ref{factors} for help with dummy variables). 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.
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year) +
++ as.factor(country), model = "normal", data = macro)
+\end{Sinput}
+\end{Schunk}
+Set values for the explanatory variables, using the default mean/mode
+variables, with country set to the United States and Japan,
+respectively:
+Simulate quantities of interest:
+%plot does not work
+\begin{center}
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the continuous dependent variable for observation $i$.
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal
+ model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2).
+ \end{equation*}
+
+\item The \emph{systematic component} is
+ \begin{equation*}
+ \mu_i \;= \; x_i \beta,
+ \end{equation*}
+ where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
+ given a draw of $\beta$ from its posterior.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
+ defined by the set of parameters $(\mu_i, \sigma)$.
+
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+\textrm{FD}\; = \;E(Y \mid x_1) - E(Y \mid x)
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "normal", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: fitted values. For the normal model,
+ these are identical to the {\tt linear predictors}.
+ \item {\tt linear.predictors}: fitted values. For the normal
+ model, these are identical to {\tt fitted.values}.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by $(\mu_i, \sigma)$.
+ \item {\tt qi\$fd}: the simulated first difference in the simulated
+ expected values for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Normal model is part of the stats package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)}, \texttt{help(family)}, and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/ologit.Rnw b/inst/doc/ologit.Rnw
new file mode 100644
index 0000000..29c76f4
--- /dev/null
+++ b/inst/doc/ologit.Rnw
@@ -0,0 +1,267 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/ologit}
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
+Categorical Dependent Variables}\label{ologit}
+
+Use the ordinal logit regression model if your dependent variable is
+ordered and categorical, either in the form of integer values or character strings.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order automatically order the values. If {\tt Y} takes
+on values composed of character strings, such as ``strongly agree'',
+``agree'', and ``disagree'', {\tt as.factor()} will order the values
+in the order in which they appear in {\tt Y}. You will need to
+replace your dependent variable with a factored variable prior to
+estimating the model through {\tt zelig()}. See Section \ref{factors}
+for more information on creating ordered factors and Example
+\ref{ord.fact} below.
+
+\subsubsection{Example}
+
+\begin{enumerate}
+
+\item {Creating An Ordered Dependent Variable} \label{ord.fact}
+
+Load the sample data:
+<<Example.data>>=
+ data(sanction)
+@
+Create an ordered dependent variable:
+<<Example.factor>>=
+ sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+@
+Set the explanatory variables to their observed values:
+<<Example.setx>>=
+ x.out <- setx(z.out, fn = NULL)
+@
+Simulate fitted values given {\tt x.out} and view the results:
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.out)
+@
+<<Example.summary>>=
+ summary(s.out)
+@
+
+\item {First Differences}
+
+Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
+<<FirstDifferences.zelig>>=
+ z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
+ data = sanction)
+@
+<<FirstDifferences.summary>>=
+summary(z.out)
+@
+Set the explanatory variables to their means, with {\tt 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:
+<<FirstDifferences.setx>>=
+ x.low <- setx(z.out, mil = 0)
+ x.high <- setx(z.out, mil = 1)
+@
+Generate simulated fitted values and first differences, and view the results:
+<<FirstDifferences.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+ summary(s.out)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be the ordered categorical dependent variable for
+observation $i$ that takes one of the integer values from $1$ to $J$
+where $J$ is the total number of categories.
+
+\begin{itemize}
+\item The \emph{stochastic component} begins with an unobserved continuous
+ variable, $Y^*_i$, which follows the standard logistic distribution
+ with a parameter $\mu_i$,
+ \begin{equation*}
+ Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
+ \end{equation*}
+ to which we add an observation mechanism
+ \begin{equation*}
+ Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+ \quad {\rm for} \quad j=1,\dots,J.
+ \end{equation*}
+ where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
+ $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
+ $\tau_J=\infty$.
+
+\item The \emph{systematic component} has the following form, given
+ the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
+ \begin{equation*}
+ \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
+ x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
+ \end{equation*}
+ which implies:
+ \begin{equation*}
+ \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
+ x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)}.
+ \end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal logit model
+ are simulations of the predicted probabilities for each category:
+\begin{equation*}
+E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
+{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)},
+\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the logit
+ distribution described by $\mu_i$, and observed as one of $J$
+ discrete outcomes.
+
+\item The difference in each of the predicted probabilities ({\tt
+ qi\$fd}) is given by
+ \begin{equation*}
+ \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+ j=1,\dots,J.
+ \end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "ologit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt zeta}: a vector containing the estimated class
+ boundaries $\tau_j$.
+ \item {\tt deviance}: the residual deviance.
+ \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
+ fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt edf}: the effective degrees of freedom.
+ \item {\tt Hessian}: the Hessian matrix.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, and $t$-statistics.
+ \end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal logit model is part of the MASS library by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+{\tt help(polr)} in the MASS libary and \cite{McCNel89}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/ologit.pdf b/inst/doc/ologit.pdf
new file mode 100644
index 0000000..14518c1
Binary files /dev/null and b/inst/doc/ologit.pdf differ
diff --git a/inst/doc/ologit.tex b/inst/doc/ologit.tex
new file mode 100644
index 0000000..24a1401
--- /dev/null
+++ b/inst/doc/ologit.tex
@@ -0,0 +1,273 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
+Categorical Dependent Variables}\label{ologit}
+
+Use the ordinal logit regression model if your dependent variable is
+ordered and categorical, either in the form of integer values or character strings.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order automatically order the values. If {\tt Y} takes
+on values composed of character strings, such as ``strongly agree'',
+``agree'', and ``disagree'', {\tt as.factor()} will order the values
+in the order in which they appear in {\tt Y}. You will need to
+replace your dependent variable with a factored variable prior to
+estimating the model through {\tt zelig()}. See Section \ref{factors}
+for more information on creating ordered factors and Example
+\ref{ord.fact} below.
+
+\subsubsection{Example}
+
+\begin{enumerate}
+
+\item {Creating An Ordered Dependent Variable} \label{ord.fact}
+
+Load the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Create an ordered dependent variable:
+\begin{Schunk}
+\begin{Sinput}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
++ "little effect", "modest loss", "major loss"))
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their observed values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out, fn = NULL)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values given {\tt x.out} and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+
+\item {First Differences}
+
+Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
++ data = sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means, with {\tt 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:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, mil = 0)
+> x.high <- setx(z.out, mil = 1)
+\end{Sinput}
+\end{Schunk}
+Generate simulated fitted values and first differences, and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be the ordered categorical dependent variable for
+observation $i$ that takes one of the integer values from $1$ to $J$
+where $J$ is the total number of categories.
+
+\begin{itemize}
+\item The \emph{stochastic component} begins with an unobserved continuous
+ variable, $Y^*_i$, which follows the standard logistic distribution
+ with a parameter $\mu_i$,
+ \begin{equation*}
+ Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
+ \end{equation*}
+ to which we add an observation mechanism
+ \begin{equation*}
+ Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+ \quad {\rm for} \quad j=1,\dots,J.
+ \end{equation*}
+ where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
+ $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
+ $\tau_J=\infty$.
+
+\item The \emph{systematic component} has the following form, given
+ the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
+ \begin{equation*}
+ \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
+ x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
+ \end{equation*}
+ which implies:
+ \begin{equation*}
+ \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
+ x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)}.
+ \end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal logit model
+ are simulations of the predicted probabilities for each category:
+\begin{equation*}
+E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
+{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)},
+\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the logit
+ distribution described by $\mu_i$, and observed as one of $J$
+ discrete outcomes.
+
+\item The difference in each of the predicted probabilities ({\tt
+ qi\$fd}) is given by
+ \begin{equation*}
+ \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+ j=1,\dots,J.
+ \end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "ologit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt zeta}: a vector containing the estimated class
+ boundaries $\tau_j$.
+ \item {\tt deviance}: the residual deviance.
+ \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
+ fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt edf}: the effective degrees of freedom.
+ \item {\tt Hessian}: the Hessian matrix.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, and $t$-statistics.
+ \end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal logit model is part of the MASS library by William N.
+Venables and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+{\tt help(polr)} in the MASS libary and \cite{McCNel89}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
diff --git a/inst/doc/oprobit.Rnw b/inst/doc/oprobit.Rnw
new file mode 100644
index 0000000..ff14046
--- /dev/null
+++ b/inst/doc/oprobit.Rnw
@@ -0,0 +1,285 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/oprobit}
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Probit Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, probit,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
+Categorical Dependent Variables}\label{oprobit}
+
+Use the ordinal probit regression model if your dependent variables
+are ordered and categorical. They may take on either integer values
+or character strings. For a Bayesian implementation of this model,
+see \Sref{oprobit.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order it automatically. If {\tt Y} takes on values
+composed of character strings, such as ``strongly agree'', ``agree'',
+and ``disagree'', {\tt as.factor()} will order the values in the order
+in which they appear in {\tt Y}. You will need to replace your
+dependent variable with a factored variable prior to estimating the
+model through {\tt zelig()}. See \Sref{factors} for more information
+on creating ordered factors and Example \ref{ord.fact.p} below.
+
+\subsubsection{Example}
+\begin{enumerate}
+\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
+
+Load the sample data:
+<<Example.data>>=
+ data(sanction)
+@
+Create an ordered dependent variable:
+<<Example.factor>>=
+ sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
+ summary(z.out)
+@
+Set the explanatory variables to their observed values:
+<<Example.setx>>=
+ x.out <- setx(z.out, fn = NULL)
+@
+Simulate fitted values given {\tt x.out} and view the results:
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+@
+%plot does not work but is nnot included in the demo
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=false>>=
+# plot(s.out)
+@
+\end{center}
+
+\item {First Differences}
+
+Using the sample data \texttt{sanction}, let us estimate the empirical model and return the coefficients:
+<<FirstDifferences.zelig>>=
+ z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
+ data = sanction)
+@
+<<FirstDifferences.summary>>=
+summary(z.out)
+@
+Set the explanatory variables to their means, with {\tt 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:
+<<FirstDifferences.setx>>=
+ x.low <- setx(z.out, mil = 0)
+ x.high <- setx(z.out, mil = 1)
+@
+Generate simulated fitted values and first differences, and view the results:
+<<FirstDifferences.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+@
+<<FirstDifferences.summary.sim>>=
+summary(s.out)
+@
+\begin{center}
+<<label=FirstDifferencesPlot,fig=true,echo=true>>=
+ plot(s.out)
+@
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+ Let $Y_i$ be the ordered categorical dependent variable for
+ observation $i$ that takes one of the integer values from $1$ to $J$
+ where $J$ is the total number of categories.
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved continuous
+ variable, $Y^*_i$, which follows the normal distribution with mean
+ $\mu_i$ and unit variance
+ \begin{equation*}
+ Y_i^* \; \sim \; N(\mu_i, 1).
+ \end{equation*}
+ The observation mechanism is
+ \begin{equation*}
+ Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+ \quad {\rm for} \quad j=1,\dots,J.
+ \end{equation*}
+ where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
+ following constraints; $\tau_l < \tau_m$ for all $l<m$ and
+ $\tau_0=-\infty$ and $\tau_J=\infty$.
+
+ Given this observation mechanism, the probability for each category,
+ is given by
+ \begin{equation*}
+ \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
+ \mu_i) \quad {\rm for} \quad j=1,\dots,J
+ \end{equation*}
+ where $\Phi(\mu_i)$ is the cumulative distribution function for the
+ Normal distribution with mean $\mu_i$ and unit variance.
+
+\item The \emph{systematic component} is given by
+ \begin{equation*}
+ \mu_i \; = \; x_i \beta
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal probit model
+ are simulations of the predicted probabilities for each category:
+\begin{equation*}
+ E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
+ - \Phi(\tau_{j-1} \mid \mu_i) \quad {\rm for} \quad j=1,\dots,J,
+\end{equation*}
+given draws of $\beta$ from its posterior.
+
+\item The predicted value ({\tt qi\$pr}) is the observed value of
+ $Y_i$ given the underlying standard normal distribution described by
+ $\mu_i$.
+
+\item The difference in each of the predicted probabilities ({\tt
+ qi\$fd}) is given by
+ \begin{equation*}
+ \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+ j=1,\dots,J.
+ \end{equation*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "oprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+ predictors $x_i \beta_j$.
+ \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
+\end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal probit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
diff --git a/inst/doc/oprobit.bayes.Rnw b/inst/doc/oprobit.bayes.Rnw
new file mode 100644
index 0000000..d4cdcb7
--- /dev/null
+++ b/inst/doc/oprobit.bayes.Rnw
@@ -0,0 +1,303 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/oprobitBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Ordered Probit Regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, bayes,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
+
+\label{oprobit.bayes}
+
+Use the ordinal probit regression model if your dependent variables are ordered and
+categorical. They may take either integer values or character strings. The model
+is estimated using a Gibbs sampler with data augmentation. For a
+maximum-likelihood implementation of this models, see \Sref{oprobit}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments to monitor the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
+The default value is \texttt{NA} which corresponds to 0.05 divided by
+the number of categories in the response variable.
+
+\item \texttt{verbose}: defaults to {\tt FALSE} If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, which uses the
+maximum likelihood estimates as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0 which leads to an improper prior.
+\end{itemize}
+
+\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)}
+for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(sanction)
+@
+Estimating ordered probit regression using \texttt{oprobit.bayes}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes",
+ data = sanction, verbose=TRUE)
+@
+
+Creating an ordered dependent variable:
+<<BasicExample.factor>>=
+sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+@
+
+Checking for convergence before summarizing the estimates:
+<<BasicExample.heidel>>=
+heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}.
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+ summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+incurring different level of cost when there is no military action versus
+military action while all the other variables held at their
+default values.
+
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, mil=0)
+ x.low <- setx(z.out, mil=1)
+@
+<<FirstDifferences.sim>>=
+s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+ summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_{i}$ be the ordered categorical dependent variable for
+observation $i$ which takes an integer value $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved
+continuous variable, $Y_i^*$,
+\begin{eqnarray*}
+Y_{i}^* \sim \textrm{Normal}(\mu_i, 1).
+\end{eqnarray*}
+Instead of $Y_i^*$, we observe categorical variable $Y_i$,
+\begin{eqnarray*}
+Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
+for } j=1,\ldots, J.
+\end{eqnarray*}
+where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
+the following constraints, $\tau_l < \tau_m$ for $l < m$, and
+$\tau_0=-\infty, \tau_J=\infty$.
+
+The probability of observing $Y_i$ equal to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i)
+\textrm{ for } j=1,\ldots, J
+\end{eqnarray*}
+where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
+of the Normal distribution with mean $\mu_i$ and variance 1.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\mu_{i}= x_i \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
+the predicted probability of belonging to each category:
+\begin{eqnarray*}
+\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ and threshold parameters $\tau$
+from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the observed values of
+$Y_i$ given the observation scheme and the posterior draws of $\beta$
+and cut points $\tau$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the
+ordered probit model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of observations in the treatment group that belong to category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of observations in the treatment group that belong to category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ and threshold parameters $\tau$.
+Note, element $\tau_1$ is normalized to 0 and is not returned in the
+\texttt{coefficients} object.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) of
+each of the $J$ categories for the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values (observed values)
+ for the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the
+expected values of each of the $J$ categories simulated
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian ordinal probit regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{oprobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/oprobit.bayes.pdf b/inst/doc/oprobit.bayes.pdf
new file mode 100644
index 0000000..9465dee
Binary files /dev/null and b/inst/doc/oprobit.bayes.pdf differ
diff --git a/inst/doc/oprobit.bayes.tex b/inst/doc/oprobit.bayes.tex
new file mode 100644
index 0000000..3b62857
--- /dev/null
+++ b/inst/doc/oprobit.bayes.tex
@@ -0,0 +1,309 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Ordered Probit Regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, bayes,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
+
+\label{oprobit.bayes}
+
+Use the ordinal probit regression model if your dependent variables are ordered and
+categorical. They may take either integer values or character strings. The model
+is estimated using a Gibbs sampler with data augmentation. For a
+maximum-likelihood implementation of this models, see \Sref{oprobit}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments to monitor the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
+The default value is \texttt{NA} which corresponds to 0.05 divided by
+the number of categories in the response variable.
+
+\item \texttt{verbose}: defaults to {\tt FALSE} If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, which uses the
+maximum likelihood estimates as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0 which leads to an improper prior.
+\end{itemize}
+
+\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)}
+for more information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Estimating ordered probit regression using \texttt{oprobit.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes", data = sanction,
++ verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+
+Creating an ordered dependent variable:
+\begin{Schunk}
+\begin{Sinput}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
++ "little effect", "modest loss", "major loss"))
+\end{Sinput}
+\end{Schunk}
+
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+incurring different level of cost when there is no military action versus
+military action while all the other variables held at their
+default values.
+
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, mil = 0)
+> x.low <- setx(z.out, mil = 1)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_{i}$ be the ordered categorical dependent variable for
+observation $i$ which takes an integer value $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved
+continuous variable, $Y_i^*$,
+\begin{eqnarray*}
+Y_{i}^* \sim \textrm{Normal}(\mu_i, 1).
+\end{eqnarray*}
+Instead of $Y_i^*$, we observe categorical variable $Y_i$,
+\begin{eqnarray*}
+Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
+for } j=1,\ldots, J.
+\end{eqnarray*}
+where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
+the following constraints, $\tau_l < \tau_m$ for $l < m$, and
+$\tau_0=-\infty, \tau_J=\infty$.
+
+The probability of observing $Y_i$ equal to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i)
+\textrm{ for } j=1,\ldots, J
+\end{eqnarray*}
+where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
+of the Normal distribution with mean $\mu_i$ and variance 1.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\mu_{i}= x_i \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
+the predicted probability of belonging to each category:
+\begin{eqnarray*}
+\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ and threshold parameters $\tau$
+from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the observed values of
+$Y_i$ given the observation scheme and the posterior draws of $\beta$
+and cut points $\tau$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the
+ordered probit model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of observations in the treatment group that belong to category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of observations in the treatment group that belong to category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ and threshold parameters $\tau$.
+Note, element $\tau_1$ is normalized to 0 and is not returned in the
+\texttt{coefficients} object.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) of
+each of the $J$ categories for the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values (observed values)
+ for the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the
+expected values of each of the $J$ categories simulated
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian ordinal probit regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{oprobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/oprobit.pdf b/inst/doc/oprobit.pdf
new file mode 100644
index 0000000..b8c0dc5
Binary files /dev/null and b/inst/doc/oprobit.pdf differ
diff --git a/inst/doc/oprobit.tex b/inst/doc/oprobit.tex
new file mode 100644
index 0000000..6aad487
--- /dev/null
+++ b/inst/doc/oprobit.tex
@@ -0,0 +1,291 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Probit Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, probit,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
+Categorical Dependent Variables}\label{oprobit}
+
+Use the ordinal probit regression model if your dependent variables
+are ordered and categorical. They may take on either integer values
+or character strings. For a Bayesian implementation of this model,
+see \Sref{oprobit.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order it automatically. If {\tt Y} takes on values
+composed of character strings, such as ``strongly agree'', ``agree'',
+and ``disagree'', {\tt as.factor()} will order the values in the order
+in which they appear in {\tt Y}. You will need to replace your
+dependent variable with a factored variable prior to estimating the
+model through {\tt zelig()}. See \Sref{factors} for more information
+on creating ordered factors and Example \ref{ord.fact.p} below.
+
+\subsubsection{Example}
+\begin{enumerate}
+\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
+
+Load the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Create an ordered dependent variable:
+\begin{Schunk}
+\begin{Sinput}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
++ "little effect", "modest loss", "major loss"))
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their observed values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out, fn = NULL)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values given {\tt x.out} and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+%plot does not work but is nnot included in the demo
+\begin{center}
+\end{center}
+
+\item {First Differences}
+
+Using the sample data \texttt{sanction}, let us estimate the empirical model and return the coefficients:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
++ data = sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means, with {\tt 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:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, mil = 0)
+> x.high <- setx(z.out, mil = 1)
+\end{Sinput}
+\end{Schunk}
+Generate simulated fitted values and first differences, and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/oprobit-FirstDifferencesPlot}
+\end{center}
+\end{enumerate}
+
+\subsubsection{Model}
+ Let $Y_i$ be the ordered categorical dependent variable for
+ observation $i$ that takes one of the integer values from $1$ to $J$
+ where $J$ is the total number of categories.
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved continuous
+ variable, $Y^*_i$, which follows the normal distribution with mean
+ $\mu_i$ and unit variance
+ \begin{equation*}
+ Y_i^* \; \sim \; N(\mu_i, 1).
+ \end{equation*}
+ The observation mechanism is
+ \begin{equation*}
+ Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+ \quad {\rm for} \quad j=1,\dots,J.
+ \end{equation*}
+ where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
+ following constraints; $\tau_l < \tau_m$ for all $l<m$ and
+ $\tau_0=-\infty$ and $\tau_J=\infty$.
+
+ Given this observation mechanism, the probability for each category,
+ is given by
+ \begin{equation*}
+ \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
+ \mu_i) \quad {\rm for} \quad j=1,\dots,J
+ \end{equation*}
+ where $\Phi(\mu_i)$ is the cumulative distribution function for the
+ Normal distribution with mean $\mu_i$ and unit variance.
+
+\item The \emph{systematic component} is given by
+ \begin{equation*}
+ \mu_i \; = \; x_i \beta
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal probit model
+ are simulations of the predicted probabilities for each category:
+\begin{equation*}
+ E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
+ - \Phi(\tau_{j-1} \mid \mu_i) \quad {\rm for} \quad j=1,\dots,J,
+\end{equation*}
+given draws of $\beta$ from its posterior.
+
+\item The predicted value ({\tt qi\$pr}) is the observed value of
+ $Y_i$ given the underlying standard normal distribution described by
+ $\mu_i$.
+
+\item The difference in each of the predicted probabilities ({\tt
+ qi\$fd}) is given by
+ \begin{equation*}
+ \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+ j=1,\dots,J.
+ \end{equation*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "oprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the named vector of coefficients.
+ \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+ fitted values.
+ \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+ predictors $x_i \beta_j$.
+ \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.total}: the total degrees of freedom.
+ \item {\tt rss}: the residual sum of squares.
+ \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+ \item {\tt coef3}: a table of the coefficients with their associated
+ standard errors and $t$-statistics.
+ \item {\tt cov.unscaled}: the variance-covariance matrix.
+ \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
+\end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal probit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library. Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End:
+ \end{document}
+
+
+
+
+
+
+
diff --git a/inst/doc/poisson.Rnw b/inst/doc/poisson.Rnw
new file mode 100644
index 0000000..d2b16ab
--- /dev/null
+++ b/inst/doc/poisson.Rnw
@@ -0,0 +1,251 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/poisson}
+\include{zinput}
+%\VignetteIndexEntry{Poisson Regression for Event Count Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, poisson,regression, count}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt poisson}: Poisson Regression for Event Count
+Dependent Variables}\label{poisson}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time (see the negative binomial model, \Sref{negbin},
+for over-dispersed event counts.) For a Bayesian implementation of
+this model, see \Sref{poisson.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for poisson regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:
+<<Example.data>>=
+ data(sanction)
+@
+Estimate Poisson model:
+<<Example.zelig>>=
+ z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
+@
+<<Example.summary>>=
+summary(z.out)
+@
+Set values for the explanatory variables to their default mean values:
+<<Example.setx>>=
+ x.out <- setx(z.out)
+@
+Simulate fitted values:
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.out)
+@
+<<Example.summary.sim>>=
+summary(s.out)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+ plot(s.out)
+@
+\end{center}
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer.
+
+\begin{itemize}
+\item The Poisson distribution has \emph{stochastic component}
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Poisson}(\lambda_i),
+ \end{equation*}
+ where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is
+ \begin{equation*}
+ \lambda_i \; = \; \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component, $$E(Y) = \lambda_i = \exp(x_i
+ \beta),$$ given draws of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a random draw from the
+ poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference in the expected values ({\tt qi\$fd}) is given by:
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "poisson", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the fitted values for the systemic
+ component $\lambda$.
+ \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values given the
+ specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distributions defined by $\lambda_i$.
+ \item {\tt qi\$fd}: the simulated first differences in the expected
+ values given the specified values of {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Poisson model is part of the stats package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to {\tt help(glm)} and {\tt
+ help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/poisson.bayes.Rnw b/inst/doc/poisson.bayes.Rnw
new file mode 100644
index 0000000..f09757d
--- /dev/null
+++ b/inst/doc/poisson.bayes.Rnw
@@ -0,0 +1,264 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/poissonBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Poisson Regression}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, poisson,bayes,count,Metropolis}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
+
+\label{poisson.bayes}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time. The model is fit using a random walk
+Metropolis algorithm. For a maximum-likelihood estimation of this
+model see \Sref{poisson}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following argument to monitor the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: default to {\tt FALSE}.
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will be the prior mean for
+all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(sanction)
+@
+Estimating the Poisson regression using \texttt{poisson.bayes}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
+ data = sanction, verbose = TRUE)
+@
+Checking convergence diagnostics before summarizing the estimates:
+<<BasicExample.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<BasicExample.heidel>>=
+heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<BasicExample.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Estimating the first difference in the number of countries imposing sanctions
+when the number of targets is set to be its maximum versus its minimum :
+<<FirstDifferences.setx>>=
+ x.max <- setx(z.out, target = max(sanction$target))
+ x.min <- setx(z.out, target = min(sanction$target))
+@
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.max, x1 = x.min)
+ summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the number of independent events that occur during
+a fixed time period.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \textrm{Poisson}(\lambda_i)
+\end{eqnarray*}
+where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\lambda_{i}= \exp(x_{i} \beta)
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the Poisson model are
+calculated as following:
+\begin{eqnarray*}
+E(Y\mid X) = \lambda_i = \exp(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
+distribution with parameter $\lambda_i$.
+
+\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
+as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "poisson.bayes", data)
+\end{verbatim}
+
+\noindent you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian Poisson regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{poisson.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/poisson.bayes.pdf b/inst/doc/poisson.bayes.pdf
new file mode 100644
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diff --git a/inst/doc/poisson.bayes.tex b/inst/doc/poisson.bayes.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/poisson.bayes.tex
@@ -0,0 +1,273 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Poisson Regression}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, poisson,bayes,count,Metropolis}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
+
+\label{poisson.bayes}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time. The model is fit using a random walk
+Metropolis algorithm. For a maximum-likelihood estimation of this
+model see \Sref{poisson}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following argument to monitor the Markov chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: default to {\tt FALSE}.
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will be the prior mean for
+all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Estimating the Poisson regression using \texttt{poisson.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
++ data = sanction, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Checking convergence diagnostics before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Estimating the first difference in the number of countries imposing sanctions
+when the number of targets is set to be its maximum versus its minimum :
+\begin{Schunk}
+\begin{Sinput}
+> x.max <- setx(z.out, target = max(sanction$target))
+> x.min <- setx(z.out, target = min(sanction$target))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.max, x1 = x.min)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the number of independent events that occur during
+a fixed time period.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \textrm{Poisson}(\lambda_i)
+\end{eqnarray*}
+where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\lambda_{i}= \exp(x_{i} \beta)
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the Poisson model are
+calculated as following:
+\begin{eqnarray*}
+E(Y\mid X) = \lambda_i = \exp(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
+distribution with parameter $\lambda_i$.
+
+\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
+as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "poisson.bayes", data)
+\end{verbatim}
+
+\noindent you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian Poisson regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{poisson.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/poisson.pdf b/inst/doc/poisson.pdf
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diff --git a/inst/doc/poisson.tex b/inst/doc/poisson.tex
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@@ -0,0 +1,253 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Poisson Regression for Event Count Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, poisson,regression, count}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt poisson}: Poisson Regression for Event Count
+Dependent Variables}\label{poisson}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time (see the negative binomial model, \Sref{negbin},
+for over-dispersed event counts.) For a Bayesian implementation of
+this model, see \Sref{poisson.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for poisson regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Estimate Poisson model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set values for the explanatory variables to their default mean values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/poisson-ExamplePlot}
+\end{center}
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer.
+
+\begin{itemize}
+\item The Poisson distribution has \emph{stochastic component}
+ \begin{equation*}
+ Y_i \; \sim \; \textrm{Poisson}(\lambda_i),
+ \end{equation*}
+ where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is
+ \begin{equation*}
+ \lambda_i \; = \; \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component, $$E(Y) = \lambda_i = \exp(x_i
+ \beta),$$ given draws of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a random draw from the
+ poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference in the expected values ({\tt qi\$fd}) is given by:
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "poisson", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the fitted values for the systemic
+ component $\lambda$.
+ \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values given the
+ specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distributions defined by $\lambda_i$.
+ \item {\tt qi\$fd}: the simulated first differences in the expected
+ values given the specified values of {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Poisson model is part of the stats package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to {\tt help(glm)} and {\tt
+ help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/probit.Rnw b/inst/doc/probit.Rnw
new file mode 100644
index 0000000..9e6d090
--- /dev/null
+++ b/inst/doc/probit.Rnw
@@ -0,0 +1,248 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/probit}
+\include{zinput}
+%\VignetteIndexEntry{Probit Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, probit,regression,dichotomous, binary}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt probit}: Probit Regression for Dichotomous Dependent Variables}\label{probit}
+
+Use probit regression to model binary dependent variables
+specified as a function of a set of explanatory variables. For a
+Bayesian implementation of this model, see \Sref{probit.bayes}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for probit regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+Attach the sample turnout dataset:
+<<Examples.data>>=
+ data(turnout)
+@
+Estimate parameter values for the probit regression:
+<<Examples.zelig>>=
+ z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout)
+@
+<<Examples.summary>>=
+ summary(z.out)
+@
+Set values for the explanatory variables to their default values.
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
+Simulate quantities of interest from the posterior distribution.
+<<Examples.sim>>=
+s.out <- sim(z.out, x = x.out)
+@
+<<Examples.summary.sim>>=
+summary(s.out)
+@
+
+\subsubsection{Model}
+Let $Y_i$ be the observed binary dependent variable for observation
+$i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{equation*}
+Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
+\end{equation*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is
+\begin{equation*}
+ \pi_i \; = \; \Phi (x_i \beta)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the
+Normal distribution with mean 0 and unit variance.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is a simulation of predicted
+ probability of success $$E(Y) = \pi_i = \Phi(x_i
+ \beta),$$ given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
+ distribution with mean $\pi_i$.
+
+\item The first difference ({\tt qi\$fd}) in expected values is
+ defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) / \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "probit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the in-sample fitted values.
+ \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt data}: the name of the input data frame.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values, or predicted
+ probabilities, for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distributions defined by the predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first differences in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The probit model is part of the base package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(glm)}
+and \texttt{help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/probit.bayes.Rnw b/inst/doc/probit.bayes.Rnw
new file mode 100644
index 0000000..37144b7
--- /dev/null
+++ b/inst/doc/probit.bayes.Rnw
@@ -0,0 +1,289 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/probitBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Probit Regression for Dichotomous Dependent Variable}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, probit,bayes, regression,dichotomous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{probit.bayes}: Bayesian Probit Regression}
+
+\label{probit.bayes}
+
+Use the probit regression model for model binary dependent variables
+specified as a function of a set of explanatory variables. The model
+is estimated using a Gibbs sampler. For other models suitable for
+binary response variables, see Bayesian logistic
+regression(\Sref{logit.bayes}), maximum likelihood logit regression
+(\Sref{logit}), and maximum likelihood probit regression
+(\Sref{probit}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Using the following arguments to monitor the Markov chains:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+\end{itemize}
+
+Use the following arguments to specify optional output for the model:
+\begin{itemize}
+\item \texttt{bayes.resid}: defaults to {\tt FALSE}. If {\tt TRUE},
+the latent Bayesian residuals for all observations are returned.
+Alternatively, users can specify a vector of observations for which the
+latent residuals should be returned.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<BasicExample.data>>=
+ data(turnout)
+@
+Estimating the probit regression using \texttt{probit.bayes}:
+<<BasicExample.zelig>>=
+ z.out <- zelig(vote ~ race + educate, model = "probit.bayes",
+ data = turnout, verbose = TRUE)
+@
+Checking for convergence before summarizing the estimates:
+<<BasicExample.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<BasicExample.heidel>>=
+ heidel.diag(z.out$coefficients)
+@
+<<BasicExample.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<BasicExample.summary>>=
+summary(z.out)
+@ \end{verbatim}
+Setting values for the explanatory variables to their sample averages:
+<<BasicExample.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}
+<<BasicExample.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<BasicExample.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting when education is set to be low (25th percentile) versus
+high (75th percentile) while all the other variables are held at their
+default values:
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+ x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+@
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.summary>>=
+summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \textrm{Bernoulli}(\pi_{i})\\
+& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
+of $k$ explanatory variables for observation $i$, and $\beta$ is the
+vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0}, B_{0}^{-1} \right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the probit model are
+the predicted probability of a success:
+\begin{eqnarray*}
+E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the probit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "probit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
+or a set of observation numbers is given, this object contains the
+posterior draws of the latent Bayesian residuals of all the observations
+or the observations specified by the user.
+
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Bayesian probit regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{probit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/probit.bayes.pdf b/inst/doc/probit.bayes.pdf
new file mode 100644
index 0000000..c06dbdc
Binary files /dev/null and b/inst/doc/probit.bayes.pdf differ
diff --git a/inst/doc/probit.bayes.tex b/inst/doc/probit.bayes.tex
new file mode 100644
index 0000000..ec02b93
--- /dev/null
+++ b/inst/doc/probit.bayes.tex
@@ -0,0 +1,300 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Probit Regression for Dichotomous Dependent Variable}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, probit,bayes, regression,dichotomous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{probit.bayes}: Bayesian Probit Regression}
+
+\label{probit.bayes}
+
+Use the probit regression model for model binary dependent variables
+specified as a function of a set of explanatory variables. The model
+is estimated using a Gibbs sampler. For other models suitable for
+binary response variables, see Bayesian logistic
+regression(\Sref{logit.bayes}), maximum likelihood logit regression
+(\Sref{logit}), and maximum likelihood probit regression
+(\Sref{probit}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Using the following arguments to monitor the Markov chains:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+\end{itemize}
+
+Use the following arguments to specify optional output for the model:
+\begin{itemize}
+\item \texttt{bayes.resid}: defaults to {\tt FALSE}. If {\tt TRUE},
+the latent Bayesian residuals for all observations are returned.
+Alternatively, users can specify a vector of observations for which the
+latent residuals should be returned.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(turnout)
+\end{Sinput}
+\end{Schunk}
+Estimating the probit regression using \texttt{probit.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(vote ~ race + educate, model = "probit.bayes",
++ data = turnout, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting when education is set to be low (25th percentile) versus
+high (75th percentile) while all the other variables are held at their
+default values:
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim & \textrm{Bernoulli}(\pi_{i})\\
+& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
+of $k$ explanatory variables for observation $i$, and $\beta$ is the
+vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left( b_{0}, B_{0}^{-1} \right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the probit model are
+the predicted probability of a success:
+\begin{eqnarray*}
+E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the probit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "probit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+
+\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
+or a set of observation numbers is given, this object contains the
+posterior draws of the latent Bayesian residuals of all the observations
+or the observations specified by the user.
+
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Bayesian probit regression \input{contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{probit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/probit.pdf b/inst/doc/probit.pdf
new file mode 100644
index 0000000..e6287bf
Binary files /dev/null and b/inst/doc/probit.pdf differ
diff --git a/inst/doc/probit.tex b/inst/doc/probit.tex
new file mode 100644
index 0000000..5ae7d19
--- /dev/null
+++ b/inst/doc/probit.tex
@@ -0,0 +1,247 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Probit Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model, probit,regression,dichotomous, binary}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt probit}: Probit Regression for Dichotomous Dependent Variables}\label{probit}
+
+Use probit regression to model binary dependent variables
+specified as a function of a set of explanatory variables. For a
+Bayesian implementation of this model, see \Sref{probit.bayes}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for probit regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}). The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame). The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+Attach the sample turnout dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(turnout)
+\end{Sinput}
+\end{Schunk}
+Estimate parameter values for the probit regression:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set values for the explanatory variables to their default values.
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulate quantities of interest from the posterior distribution.
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+
+\subsubsection{Model}
+Let $Y_i$ be the observed binary dependent variable for observation
+$i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{equation*}
+Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
+\end{equation*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is
+\begin{equation*}
+ \pi_i \; = \; \Phi (x_i \beta)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the
+Normal distribution with mean 0 and unit variance.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is a simulation of predicted
+ probability of success $$E(Y) = \pi_i = \Phi(x_i
+ \beta),$$ given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
+ distribution with mean $\pi_i$.
+
+\item The first difference ({\tt qi\$fd}) in expected values is
+ defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) / \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "probit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: a vector of the in-sample fitted values.
+ \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt data}: the name of the input data frame.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values, or predicted
+ probabilities, for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distributions defined by the predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first differences in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The probit model is part of the base package by William N. Venables
+and Brian D. Ripley. Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(glm)}
+and \texttt{help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis. Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index 582c27c..10f49c6 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -8,22 +8,23 @@ the {\tt setx()} command. (Occasionally, you may need to use, for example, {\tt
help page instead of the default Zelig help page.)
\input{commands/zelig}
-\include{commands/setx}
-\include{commands/sim}
-\include{commands/summary}
-\include{commands/plot.zelig}
+\include{commandsRd/setx}
+\include{commandsRd/sim}
+\include{commandsRd/sim}
+\include{commandsRd/summary}
+\include{commandsRd/plot.zelig}
\include{commands/print}
-\include{commands/replicate}
+\include{commandsRd/repl}
\chapter{Supplementary Commands}
\input{commands/matchit}
-\include{commands/mi}
-\include{commands/network}
-\include{commands/plot.ci}
-\include{commands/rocplot}
-\include{commands/ternaryplot}
-\include{commands/ternarypoints}
+\include{commandsRd/mi}
+\include{commandsRd/network}
+\include{commandsRd/plot.ci}
+\include{commandsRd/rocplot}
+\include{commandsRd/ternaryplot}
+\include{commandsRd/ternarypoints}
\chapter{Models Zelig Can Run}\label{s:model.details}
@@ -83,43 +84,43 @@ of simulations increases.
%\include{models/3sls}
%\include{models/sur}
-\include{models/arima}
+\include{arima}
%\include{models/beta}
-\include{models/blogit}
-\include{models/bprobit}
-\include{models/ei.dynamic}
-\include{models/ei.hier}
-\include{models/eiRxC}
-\include{models/exp}
-\include{models/factor.bayes}
-\include{models/factor.mix}
-\include{models/factor.ord}
-\include{models/gamma}
-\include{models/irt1d}
-\include{models/irtkd}
-\include{models/logit}
-\include{models/logit.bayes}
-\include{models/lognormal}
-\include{models/ls}
-\include{models/mlogit}
-\include{models/mlogit.bayes}
-% \include{models/mloglm}
-\include{models/negbin}
-\include{models/netls}
-\include{models/netlogit}
-\include{models/normal}
-\include{models/normal.bayes}
-\include{models/ologit}
-\include{models/oprobit}
-\include{models/oprobit.bayes}
-\include{models/poisson}
-\include{models/poisson.bayes}
-\include{models/probit}
-\include{models/probit.bayes}
-\include{models/relogit}
-\include{models/tobit}
-\include{models/tobit.bayes}
-\include{models/weibull}
+\include{blogit}
+\include{bprobit}
+\include{ei.dynamic}
+\include{ei.hier}
+\include{ei.RxC}
+\include{exp}
+\include{factor.bayes}
+\include{factor.mix}
+\include{factor.ord}
+\include{gamma}
+\include{irt1d}
+\include{irtkd}
+\include{logit}
+\include{logit.bayes}
+\include{lognorm}
+\include{ls}
+\include{mlogit}
+\include{mlogit.bayes}
+\include{mloglm}
+\include{negbin}
+\include{netls}
+\include{netlogit}
+\include{normal}
+\include{normal.bayes}
+\include{ologit}
+\include{oprobit}
+\include{oprobit.bayes}
+\include{poisson}
+\include{poisson.bayes}
+\include{probit}
+\include{probit.bayes}
+\include{relogit}
+\include{tobit}
+\include{tobit.bayes}
+\include{weibull}
\chapter{Commands for Programmers and Contributors}
diff --git a/inst/doc/relogit.Rnw b/inst/doc/relogit.Rnw
new file mode 100644
index 0000000..2019214
--- /dev/null
+++ b/inst/doc/relogit.Rnw
@@ -0,0 +1,433 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/relogit}
+\include{zinput}
+%\VignetteIndexEntry{Rare Events Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,logistic,regression,dichotomous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt relogit}: Rare Events Logistic Regression for
+Dichotomous Dependent Variables}
+\label{relogit}
+
+The {\tt relogit} procedure estimates the same model as standard
+logistic regression (appropriate when you have a dichotomous dependent
+variable and a set of explanatory variables; see \Sref{logit}), but
+the estimates are corrected for the bias that occurs when the
+sample is small or the observed events are rare (i.e., if the
+dependent variable has many more 1s than 0s or the reverse). The {\tt
+ relogit} procedure also optionally uses prior correction for
+case-control sampling designs.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
+ case.correct = c("prior", "weighting"),
+ bias.correct = TRUE, robust = FALSE,
+ data = mydata, ...)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+The {\tt relogit} procedure supports four optional arguments in
+addition to the standard arguments for {\tt zelig()}. You may
+additionally use:
+\begin{itemize}
+\item {\tt tau}: a vector containing either one or two values for
+ $\tau$, the true population fraction of ones. Use, for example,
+ {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
+ tau} is 0.05 and the upper bound is 0.1. If left unspecified, only
+finite-sample bias correction is performed, not case-control correction.
+\item {\tt case.correct}: if {\tt tau} is specified, choose a method
+to correct for case-control sampling design: {\tt "prior"} (default)
+or {\tt "weighting"}.
+\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
+ {\tt FALSE} indicating whether the intercept should be corrected for
+ finite sample (rare events) bias.
+\item {\tt robust}: defaults to {\tt FALSE} (except when {\tt
+case.control = "weighting"}; the default in this case becomes {\tt
+robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
+robust standard errors via the {\tt sandwich} package (see
+\cite{Zeileis04}). The default type of robust standard error is
+heteroskedastic and autocorrelation consistent (HAC), and assumes that
+observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame) The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
+the {\tt relogit} procedure performs a standard logistic regression
+without any correction.
+
+\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
+
+Due to memory and space considerations, the data used here are a
+sample drawn from the full data set used in King and Zeng,
+2001,\nocite{KinZen01b} The proportion of militarized interstate
+conflicts to the absence of disputes is $\tau = 1,042 / 303,772
+\approx 0.00343$. To estimate the model,
+<<Example1.data>>=
+ data(mid)
+@
+<<Example1.zelig>>=
+ z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+ data = mid, model = "relogit", tau = 1042/303772)
+@
+Summarize the model output:
+<<Example1.summary>>=
+ summary(z.out1)
+@
+Set the explanatory variables to their means:
+<<Example1.setx>>=
+ x.out1 <- setx(z.out1)
+@
+Simulate quantities of interest:
+<<Example1.sim>>=
+ s.out1 <- sim(z.out1, x = x.out1)
+ summary(s.out1)
+@
+\begin{center}
+<<label=Example1Plot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
+Errors, and Bias Correction}
+
+Suppose that we wish to perform case control correction using
+weighting (rather than the default prior correction). To
+estimate the model:
+<<Example2.zelig>>=
+
+ z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+ data = mid, model = "relogit", tau = 1042/303772,
+ case.control = "weighting", robust = TRUE)
+@
+Summarize the model output:
+<<Example2.summary>>=
+ summary(z.out2)
+@
+Set the explanatory variables to their means:
+<<Example2.setx>>=
+ x.out2 <- setx(z.out2)
+@
+Simulate quantities of interest:
+<<Example2.sim>>=
+ s.out2 <- sim(z.out2, x = x.out2)
+ summary(s.out2)
+@
+
+\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
+
+Suppose that we did not know that $\tau \approx 0.00343$, but only
+that it was somewhere between $(0.002, 0.005)$. To estimate a model
+with a range of feasible estimates for $\tau$ (using the default prior
+correction method for case control correction):
+<<Example3.zelig>>=
+ z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem
+ + years, data = mid, model = "relogit",
+ tau = c(0.002, 0.005))
+@
+Summarize the model output:
+<<Example3.summary>>=
+ summary(z.out2)
+@
+Set the explanatory variables to their means:
+<<Example3.setx>>=
+ x.out2 <- setx(z.out2)
+@
+Simulate quantities of interest:
+<<Example3.sim>>=
+ s.out <- sim(z.out2, x = x.out2)
+@
+<<Example3.summary.sim>>=
+summary(s.out2)
+@
+\begin{center}
+<<label=Example3Plot,fig=true,echo=true>>=
+ plot(s.out2)
+@
+\end{center}
+The cost of giving a range of values for $\tau$ is that point
+estimates are not available for quantities of interest. Instead,
+quantities are presented as confidence intervals with significance
+less than or equal to a specified level (e.g., at least 95\% of the
+simulations are contained in the nominal 95\% confidence interval).
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item Like the standard logistic regression, the \emph{stochastic
+ component} for the rare events logistic regression is:
+\begin{equation*}
+ Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
+\end{equation*}
+where $Y_i$ is the binary dependent variable, and takes a value of
+either 0 or 1.
+
+\item The \emph{systematic component} is:
+ \begin{equation*}
+ \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+ \end{equation*}
+
+
+\item If the sample is generated via a case-control (or choice-based)
+ design, such as when drawing all events (or ``cases'') and a sample
+ from the non-events (or ``controls'') and going backwards to collect
+ the explanatory variables, you must correct for selecting on the
+ dependent variable. While the slope coefficients are approximately
+ unbiased, the constant term may be significantly biased. Zelig has
+two methods for case control correction:
+\begin{enumerate}
+\item The ``prior correction'' method
+adjusts the intercept term. Let $\tau$ be the true population
+fraction of events, $\bar{y}$ the fraction of events in the sample,
+and $\hat{\beta_0}$ the uncorrected intercept term. The corrected
+intercept $\beta_0$ is:
+\begin{equation*}
+\beta = \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
+ \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
+\end{equation*}
+
+\item The ``weighting'' method performs a weighted logistic regression to
+correct for a case-control sampling design. Let the 1 subscript
+denote observations for which the dependent variable is observed as a
+1, and the 0 subscript denote observations for which the dependent
+variable is observed as a 0. Then the vector of weights $w_i$
+\begin{eqnarray*}
+w_1 &=& \frac{\tau}{\bar{y}} \\
+w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
+w_i &=& w_1 Y_i + w_0 (1 - Y_i)
+\end{eqnarray*}
+\end{enumerate}
+ If $\tau$ is unknown, you may alternatively specify an upper and
+ lower bound for the possible range of $\tau$. In this case, the
+ {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
+ a confidence interval (rather than a point estimate) for each
+ quantity of interest. The nominal coverage of the confidence
+ interval is at least as great as the actual coverage.
+
+\item By default, estimates of the the coefficients $\beta$ are
+ bias-corrected to account for finite sample or rare events bias. In
+ addition, quantities of interest, such as predicted probabilities,
+ are also corrected of rare-events bias. If $\widehat{\beta}$ are
+the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
+bias term, the corrected coefficients $\tilde{\beta}$ are
+\begin{equation*}
+\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
+\end{equation*}
+The bias term is
+\begin{equation*}
+\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
+\end{equation*}
+where
+\begin{eqnarray*}
+\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
+Q &=& X(X'WX)^{-1} X' \\
+W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
+\end{eqnarray*}
+where $w_i$ and $w_1$ are given in the ``weighting'' section above.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item For either one or no $\tau$:
+ \begin{itemize}
+ \item The expected values ({\tt qi\$ev}) for the rare events logit
+ are simulations of the predicted probability $$E(Y) = \pi_i =
+ \frac{1}{1 + \exp(-x_i \beta)},$$
+ given draws of $\beta$ from its posterior.
+ \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
+ distribution with mean equal to the simulated $\pi_i$.
+ \item The first difference ({\tt qi\$fd}) is defined as
+ \begin{equation*}
+ \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
+ \end{equation*}
+ \item The risk ratio ({\tt qi\$rr}) is defined as
+ \begin{equation*}
+ \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
+ \end{equation*}
+ \end{itemize}
+ \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
+ the quantities of interest are $n \times 2$ matrices, which report
+ the lower and upper bounds, respectively, for a confidence interval
+ with nominal coverage at least as great as the actual coverage. At
+ worst, these bounds are conservative estimates for the likely range
+ for each quantity of interest. Please refer to \hlink{King and
+ Zeng
+ (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
+ for the specific method of calculating bounded quantities of
+ interest.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "relogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt bias.correct}: {\tt TRUE} if bias correction was
+selected, else {\tt FALSE}.
+ \item {\tt prior.correct}: {\tt TRUE} if prior correction was
+selected, else {\tt FALSE}.
+ \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
+{\tt FALSE}.
+ \item {\tt tau}: the value of {\tt tau} for which case control
+correction was implemented.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values for the
+ systemic component, $\pi_i$.
+ \item {\tt linear.predictors}: the vector of $x_{i} \beta$
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+ Note that for a range of $\tau$, each of the above items may be
+ extracted from the {\tt "lower.estimate"} and {\tt
+ "upper.estimate"} objects in your {\tt zelig} output. Use {\tt
+ lower <- z.out\$lower.estimate}, and then {\tt
+ lower\$coefficients} to extract the coefficients for the
+ empirical estimate generated for the smaller of the two $\tau$.
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values, or predicted
+ probabilities, for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
+ distributions given the predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Differences with Stata Version}
+The Stata version of ReLogit and the R implementation differ slightly
+in their coefficient estimates due to differences in the matrix
+inversion routines implemented in R and Stata. Zelig uses
+orthogonal-triangular decomposition (through {\tt lm.influence()}) to
+compute the bias term, which is more numerically stable than
+standard matrix calculations.
+
+\subsubsection{Contributors}
+
+Please cite the rare events logit model as:
+\begin{verse}
+\bibentry{KinZen01b}.
+\end{verse}
+\begin{verse}
+\bibentry{KinZen01}.
+\end{verse}
+\begin{verse}
+\bibentry{KinZen02b}.
+\end{verse}
+
+Sample data are a selection of observations from
+\begin{verse}
+\bibentry{KinZen01b}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
new file mode 100644
index 0000000..68e2ecd
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diff --git a/inst/doc/relogit.tex b/inst/doc/relogit.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/relogit.tex
@@ -0,0 +1,453 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Rare Events Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,logistic,regression,dichotomous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt relogit}: Rare Events Logistic Regression for
+Dichotomous Dependent Variables}
+\label{relogit}
+
+The {\tt relogit} procedure estimates the same model as standard
+logistic regression (appropriate when you have a dichotomous dependent
+variable and a set of explanatory variables; see \Sref{logit}), but
+the estimates are corrected for the bias that occurs when the
+sample is small or the observed events are rare (i.e., if the
+dependent variable has many more 1s than 0s or the reverse). The {\tt
+ relogit} procedure also optionally uses prior correction for
+case-control sampling designs.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
+ case.correct = c("prior", "weighting"),
+ bias.correct = TRUE, robust = FALSE,
+ data = mydata, ...)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+The {\tt relogit} procedure supports four optional arguments in
+addition to the standard arguments for {\tt zelig()}. You may
+additionally use:
+\begin{itemize}
+\item {\tt tau}: a vector containing either one or two values for
+ $\tau$, the true population fraction of ones. Use, for example,
+ {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
+ tau} is 0.05 and the upper bound is 0.1. If left unspecified, only
+finite-sample bias correction is performed, not case-control correction.
+\item {\tt case.correct}: if {\tt tau} is specified, choose a method
+to correct for case-control sampling design: {\tt "prior"} (default)
+or {\tt "weighting"}.
+\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
+ {\tt FALSE} indicating whether the intercept should be corrected for
+ finite sample (rare events) bias.
+\item {\tt robust}: defaults to {\tt FALSE} (except when {\tt
+case.control = "weighting"}; the default in this case becomes {\tt
+robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
+robust standard errors via the {\tt sandwich} package (see
+\cite{Zeileis04}). The default type of robust standard error is
+heteroskedastic and autocorrelation consistent (HAC), and assumes that
+observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:
+\begin{itemize}
+\item {\tt method}: Choose from
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors.
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}.
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.
+\end{itemize}
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data). Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame) The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}: additional options passed to the functions
+specified in {\tt method}. See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.
+\end{itemize}
+\end{itemize}
+Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
+the {\tt relogit} procedure performs a standard logistic regression
+without any correction.
+
+\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
+
+Due to memory and space considerations, the data used here are a
+sample drawn from the full data set used in King and Zeng,
+2001,\nocite{KinZen01b} The proportion of militarized interstate
+conflicts to the absence of disputes is $\tau = 1,042 / 303,772
+\approx 0.00343$. To estimate the model,
+\begin{Schunk}
+\begin{Sinput}
+> data(mid)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(conflict ~ major + contig + power + maxdem +
++ mindem + years, data = mid, model = "relogit", tau = 1042/303772)
+\end{Sinput}
+\end{Schunk}
+Summarize the model output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out1)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means:
+\begin{Schunk}
+\begin{Sinput}
+> x.out1 <- setx(z.out1)
+\end{Sinput}
+\end{Schunk}
+Simulate quantities of interest:
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.out1)
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/relogit-Example1Plot}
+\end{center}
+
+\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
+Errors, and Bias Correction}
+
+Suppose that we wish to perform case control correction using
+weighting (rather than the default prior correction). To
+estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(conflict ~ major + contig + power + maxdem +
++ mindem + years, data = mid, model = "relogit", tau = 1042/303772,
++ case.control = "weighting", robust = TRUE)
+\end{Sinput}
+\end{Schunk}
+Summarize the model output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out2)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means:
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2)
+\end{Sinput}
+\end{Schunk}
+Simulate quantities of interest:
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+
+\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
+
+Suppose that we did not know that $\tau \approx 0.00343$, but only
+that it was somewhere between $(0.002, 0.005)$. To estimate a model
+with a range of feasible estimates for $\tau$ (using the default prior
+correction method for case control correction):
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(conflict ~ major + contig + power + maxdem +
++ mindem + years, data = mid, model = "relogit", tau = c(0.002,
++ 0.005))
+\end{Sinput}
+\end{Schunk}
+Summarize the model output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out2)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means:
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2)
+\end{Sinput}
+\end{Schunk}
+Simulate quantities of interest:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out2, x = x.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/relogit-Example3Plot}
+\end{center}
+The cost of giving a range of values for $\tau$ is that point
+estimates are not available for quantities of interest. Instead,
+quantities are presented as confidence intervals with significance
+less than or equal to a specified level (e.g., at least 95\% of the
+simulations are contained in the nominal 95\% confidence interval).
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item Like the standard logistic regression, the \emph{stochastic
+ component} for the rare events logistic regression is:
+\begin{equation*}
+ Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
+\end{equation*}
+where $Y_i$ is the binary dependent variable, and takes a value of
+either 0 or 1.
+
+\item The \emph{systematic component} is:
+ \begin{equation*}
+ \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+ \end{equation*}
+
+
+\item If the sample is generated via a case-control (or choice-based)
+ design, such as when drawing all events (or ``cases'') and a sample
+ from the non-events (or ``controls'') and going backwards to collect
+ the explanatory variables, you must correct for selecting on the
+ dependent variable. While the slope coefficients are approximately
+ unbiased, the constant term may be significantly biased. Zelig has
+two methods for case control correction:
+\begin{enumerate}
+\item The ``prior correction'' method
+adjusts the intercept term. Let $\tau$ be the true population
+fraction of events, $\bar{y}$ the fraction of events in the sample,
+and $\hat{\beta_0}$ the uncorrected intercept term. The corrected
+intercept $\beta_0$ is:
+\begin{equation*}
+\beta = \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
+ \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
+\end{equation*}
+
+\item The ``weighting'' method performs a weighted logistic regression to
+correct for a case-control sampling design. Let the 1 subscript
+denote observations for which the dependent variable is observed as a
+1, and the 0 subscript denote observations for which the dependent
+variable is observed as a 0. Then the vector of weights $w_i$
+\begin{eqnarray*}
+w_1 &=& \frac{\tau}{\bar{y}} \\
+w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
+w_i &=& w_1 Y_i + w_0 (1 - Y_i)
+\end{eqnarray*}
+\end{enumerate}
+ If $\tau$ is unknown, you may alternatively specify an upper and
+ lower bound for the possible range of $\tau$. In this case, the
+ {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
+ a confidence interval (rather than a point estimate) for each
+ quantity of interest. The nominal coverage of the confidence
+ interval is at least as great as the actual coverage.
+
+\item By default, estimates of the the coefficients $\beta$ are
+ bias-corrected to account for finite sample or rare events bias. In
+ addition, quantities of interest, such as predicted probabilities,
+ are also corrected of rare-events bias. If $\widehat{\beta}$ are
+the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
+bias term, the corrected coefficients $\tilde{\beta}$ are
+\begin{equation*}
+\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
+\end{equation*}
+The bias term is
+\begin{equation*}
+\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
+\end{equation*}
+where
+\begin{eqnarray*}
+\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
+Q &=& X(X'WX)^{-1} X' \\
+W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
+\end{eqnarray*}
+where $w_i$ and $w_1$ are given in the ``weighting'' section above.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item For either one or no $\tau$:
+ \begin{itemize}
+ \item The expected values ({\tt qi\$ev}) for the rare events logit
+ are simulations of the predicted probability $$E(Y) = \pi_i =
+ \frac{1}{1 + \exp(-x_i \beta)},$$
+ given draws of $\beta$ from its posterior.
+ \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
+ distribution with mean equal to the simulated $\pi_i$.
+ \item The first difference ({\tt qi\$fd}) is defined as
+ \begin{equation*}
+ \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
+ \end{equation*}
+ \item The risk ratio ({\tt qi\$rr}) is defined as
+ \begin{equation*}
+ \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
+ \end{equation*}
+ \end{itemize}
+ \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
+ the quantities of interest are $n \times 2$ matrices, which report
+ the lower and upper bounds, respectively, for a confidence interval
+ with nominal coverage at least as great as the actual coverage. At
+ worst, these bounds are conservative estimates for the likely range
+ for each quantity of interest. Please refer to \hlink{King and
+ Zeng
+ (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
+ for the specific method of calculating bounded quantities of
+ interest.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
+ the simulations are due to uncertainty in simulating
+ $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+ $Y_i$ for observations in the treatment group, under the
+ assumption that everything stays the same except that the
+ treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "relogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt bias.correct}: {\tt TRUE} if bias correction was
+selected, else {\tt FALSE}.
+ \item {\tt prior.correct}: {\tt TRUE} if prior correction was
+selected, else {\tt FALSE}.
+ \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
+{\tt FALSE}.
+ \item {\tt tau}: the value of {\tt tau} for which case control
+correction was implemented.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: the vector of fitted values for the
+ systemic component, $\pi_i$.
+ \item {\tt linear.predictors}: the vector of $x_{i} \beta$
+ \item {\tt aic}: Akaike's Information Criterion (minus twice the
+ maximized log-likelihood plus twice the number of coefficients).
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+ Note that for a range of $\tau$, each of the above items may be
+ extracted from the {\tt "lower.estimate"} and {\tt
+ "upper.estimate"} objects in your {\tt zelig} output. Use {\tt
+ lower <- z.out\$lower.estimate}, and then {\tt
+ lower\$coefficients} to extract the coefficients for the
+ empirical estimate generated for the smaller of the two $\tau$.
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+ \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+ covariances.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values, or predicted
+ probabilities, for the specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
+ distributions given the predicted probabilities.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+ probabilities simulated from {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Differences with Stata Version}
+The Stata version of ReLogit and the R implementation differ slightly
+in their coefficient estimates due to differences in the matrix
+inversion routines implemented in R and Stata. Zelig uses
+orthogonal-triangular decomposition (through {\tt lm.influence()}) to
+compute the bias term, which is more numerically stable than
+standard matrix calculations.
+
+\subsubsection{Contributors}
+
+Please cite the rare events logit model as:
+\begin{verse}
+\bibentry{KinZen01b}.
+\end{verse}
+\begin{verse}
+\bibentry{KinZen01}.
+\end{verse}
+\begin{verse}
+\bibentry{KinZen02b}.
+\end{verse}
+
+Sample data are a selection of observations from
+\begin{verse}
+\bibentry{KinZen01b}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/runSweave.sh b/inst/doc/runSweave.sh
new file mode 100755
index 0000000..4c9263b
--- /dev/null
+++ b/inst/doc/runSweave.sh
@@ -0,0 +1,11 @@
+#!/bin/sh
+
+#
+# run Sweave() in all of Rnw files
+#
+#
+
+for f in `ls *.Rnw`
+do
+ echo "Sweave(\"$f\")" | R --vanilla --slave
+done
diff --git a/inst/doc/tobit.Rnw b/inst/doc/tobit.Rnw
new file mode 100644
index 0000000..6115c44
--- /dev/null
+++ b/inst/doc/tobit.Rnw
@@ -0,0 +1,232 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/tobit}
+\include{zinput}
+%\VignetteIndexEntry{Linear regression for Left-Censored Dependet Variable}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model, linear,regression,left-censored,continuous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{tobit}: Linear Regression for a
+Left-Censored Dependent Variable} \label{tobit}
+
+Tobit regression estimates a linear regression model for a
+left-censored dependent variable, where the dependent variable is
+censored from below. While the classical tobit model has values
+censored at 0, you may select another censoring point. For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
+ model = "tobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: (defaults to 0) The point at which the dependent
+variable is censored from below. If any values in the dependent
+variable are observed to be less than the censoring point, it is
+assumed that that particular observation is censored from below at the
+observed value. (See \Sref{tobit.bayes} for a Bayesian
+implementation that supports both left and right censoring.)
+ \item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "tobit", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(survreg)} for more
+information.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<Examples.data>>=
+ data(tobin)
+@
+Estimating linear regression using \texttt{tobit}:
+<<Examples.zelig>>=
+ z.out <- zelig(durable ~ age + quant, model = "tobit", data = tobin)
+@
+Setting values for the explanatory variables to their sample averages:
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+<<Examples.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<Examples.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+ x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+@
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.sim.summary>>=
+summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item Let $Y_i^*$ be a latent dependent variable which is distributed with
+\emph{stochastic} component
+\begin{eqnarray*}
+Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
+\end{eqnarray*}
+where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
+parameter. $Y_i^*$ is not directly observed, however. Rather we
+observed $Y_i$ which is defined as:
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^* &\textrm{if} & c <Y_i^* \\
+c &\textrm{if} & c \ge Y_i^*
+\end{array}\right.
+\end{equation*}
+where $c$ is the lower bound below which $Y_i^*$ is censored.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i} &=& x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression
+model are the same as the expected value of $Y*$:
+\begin{equation*}
+E(Y^* | X) = \mu_{i} = x_{i} \beta
+\end{equation*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+ values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+ values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The tobit function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/tobit.bayes.Rnw b/inst/doc/tobit.bayes.Rnw
new file mode 100644
index 0000000..c5c9c75
--- /dev/null
+++ b/inst/doc/tobit.bayes.Rnw
@@ -0,0 +1,295 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/tobitBayes}
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Linear Regression for a Censored Dependent Variable}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, linear,bayes,regression,censored,continuous}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
+Censored Dependent Variable} \label{tobit.bayes}
+
+Bayesian tobit regression estimates a linear regression model with a
+censored dependent variable using a Gibbs sampler. The dependent
+variable may be censored from below and/or from above. For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
+ model = "tobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: point at which the dependent variable is censored
+from below. If the dependent variable is only censored from above, set
+\texttt{below = -Inf}. The default value is 0.
+\item \texttt{above}: point at which the dependent variable is censored
+from above. If the dependent variable is only censored from below, set
+\texttt{above = Inf}. The default value is \texttt{Inf}.
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, such that the
+least squares estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will be the prior mean for
+all coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+<<Examples.data>>=
+ data(tobin)
+@
+Estimating linear regression using \texttt{tobit.bayes}:
+<<Examples.zelig>>=
+ z.out <- zelig(durable ~ age + quant, model = "tobit.bayes",
+ data = tobin, verbose=TRUE)
+@
+Checking for convergence before summarizing the estimates:
+<<Examples.geweke>>=
+ geweke.diag(z.out$coefficients)
+@
+<<Examples.heidel>>=
+heidel.diag(z.out$coefficients)
+@
+<<Examples.raftery>>=
+raftery.diag(z.out$coefficients)
+@
+<<Examples.summary>>=
+summary(z.out)
+@
+Setting values for the explanatory variables to their sample averages:
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+<<Examples.sim>>=
+ s.out1 <- sim(z.out, x = x.out)
+@
+<<Examples.summary.sim>>=
+summary(s.out1)
+@
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+<<FirstDifferences.setx>>=
+ x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+ x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+@
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+@
+<<FirstDifferences.summary>>=
+summary(s.out2)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
+we observe $Y_i$ which is defined as following:
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^* &\textrm{if} & c_1<Y_i^*<c_2 \\
+c_1 &\textrm{if} & c_1 \ge Y_i^* \\
+c_2 &\textrm{if} & c_2 \le Y_i^*
+\end{array}\right.
+\end{equation*}
+where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
+$c_2$ is the upper bound above which $Y_i^*$ is censored.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*}
+where $\epsilon_{i}=Y^*_i-\mu_i$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
+given by
+\begin{eqnarray*}
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
+\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
+and scale parameters for $\sigma^{2}$. Note that $\beta$ and
+$\sigma^2$ are assumed \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
+calculated as following. Let
+\begin{eqnarray*}
+\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\
+\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right)
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the (cumulative) Normal density function and
+$\phi(\cdot)$ is the Normal probability density function of the
+standard normal distribution. Then the expected values are
+\begin{eqnarray*}
+E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
+ &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
+\end{eqnarray*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian tobit regression \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{tobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/tobit.bayes.pdf b/inst/doc/tobit.bayes.pdf
new file mode 100644
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diff --git a/inst/doc/tobit.bayes.tex b/inst/doc/tobit.bayes.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/tobit.bayes.tex
@@ -0,0 +1,306 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Bayesian Linear Regression for a Censored Dependent Variable}
+%\VignetteDepends{Zelig, MCMCpack}
+%\VignetteKeyWords{model, linear,bayes,regression,censored,continuous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
+Censored Dependent Variable} \label{tobit.bayes}
+
+Bayesian tobit regression estimates a linear regression model with a
+censored dependent variable using a Gibbs sampler. The dependent
+variable may be censored from below and/or from above. For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
+ model = "tobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: point at which the dependent variable is censored
+from below. If the dependent variable is only censored from above, set
+\texttt{below = -Inf}. The default value is 0.
+\item \texttt{above}: point at which the dependent variable is censored
+from above. If the dependent variable is only censored from below, set
+\texttt{above = Inf}. The default value is \texttt{Inf}.
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is
+\texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov
+chain, either a scalar or vector with length equal to the number
+of estimated coefficients. The default is \texttt{NA}, such that the
+least squares estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will be the prior mean for
+all coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more
+information.
+
+\input{coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(tobin)
+\end{Sinput}
+\end{Schunk}
+Estimating linear regression using \texttt{tobit.bayes}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(durable ~ age + quant, model = "tobit.bayes",
++ data = tobin, verbose = TRUE)
+\end{Sinput}
+\end{Schunk}
+Checking for convergence before summarizing the estimates:
+\begin{Schunk}
+\begin{Sinput}
+> geweke.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> heidel.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> raftery.diag(z.out$coefficients)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+\end{Sinput}
+\end{Schunk}
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
+we observe $Y_i$ which is defined as following:
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^* &\textrm{if} & c_1<Y_i^*<c_2 \\
+c_1 &\textrm{if} & c_1 \ge Y_i^* \\
+c_2 &\textrm{if} & c_2 \le Y_i^*
+\end{array}\right.
+\end{equation*}
+where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
+$c_2$ is the upper bound above which $Y_i^*$ is censored.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*}
+where $\epsilon_{i}=Y^*_i-\mu_i$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
+given by
+\begin{eqnarray*}
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
+\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
+and scale parameters for $\sigma^{2}$. Note that $\beta$ and
+$\sigma^2$ are assumed \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
+calculated as following. Let
+\begin{eqnarray*}
+\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\
+\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right)
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the (cumulative) Normal density function and
+$\phi(\cdot)$ is the Normal probability density function of the
+standard normal distribution. Then the expected values are
+\begin{eqnarray*}
+E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
+ &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
+\end{eqnarray*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian tobit regression \input{contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{tobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+
+
+ \end{document}
diff --git a/inst/doc/tobit.pdf b/inst/doc/tobit.pdf
new file mode 100644
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diff --git a/inst/doc/tobit.tex b/inst/doc/tobit.tex
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+
+\include{zinput}
+%\VignetteIndexEntry{Linear regression for Left-Censored Dependet Variable}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model, linear,regression,left-censored,continuous}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{\texttt{tobit}: Linear Regression for a
+Left-Censored Dependent Variable} \label{tobit}
+
+Tobit regression estimates a linear regression model for a
+left-censored dependent variable, where the dependent variable is
+censored from below. While the classical tobit model has values
+censored at 0, you may select another censoring point. For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
+ model = "tobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: (defaults to 0) The point at which the dependent
+variable is censored from below. If any values in the dependent
+variable are observed to be less than the censoring point, it is
+assumed that that particular observation is censored from below at the
+observed value. (See \Sref{tobit.bayes} for a Bayesian
+implementation that supports both left and right censoring.)
+ \item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "tobit", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(survreg)} for more
+information.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{Schunk}
+\begin{Sinput}
+> data(tobin)
+\end{Sinput}
+\end{Schunk}
+Estimating linear regression using \texttt{tobit}:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(durable ~ age + quant, model = "tobit", data = tobin)
+\end{Sinput}
+\end{Schunk}
+Setting values for the explanatory variables to their sample averages:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out)
+\end{Sinput}
+\end{Schunk}
+Simulating quantities of interest from the posterior distribution given
+\texttt{x.out}.
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+\begin{Schunk}
+\begin{Sinput}
+> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+\end{Sinput}
+\end{Schunk}
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item Let $Y_i^*$ be a latent dependent variable which is distributed with
+\emph{stochastic} component
+\begin{eqnarray*}
+Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
+\end{eqnarray*}
+where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
+parameter. $Y_i^*$ is not directly observed, however. Rather we
+observed $Y_i$ which is defined as:
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^* &\textrm{if} & c <Y_i^* \\
+c &\textrm{if} & c \ge Y_i^*
+\end{array}\right.
+\end{equation*}
+where $c$ is the lower bound below which $Y_i^*$ is censored.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i} &=& x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression
+model are the same as the expected value of $Y*$:
+\begin{equation*}
+E(Y^* | X) = \mu_{i} = x_{i} \beta
+\end{equation*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws
+of the variance $\sigma^2$.
+
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+ values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+ values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The tobit function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+ \end{document}
diff --git a/inst/doc/weibull.Rnw b/inst/doc/weibull.Rnw
new file mode 100644
index 0000000..4dd6b68
--- /dev/null
+++ b/inst/doc/weibull.Rnw
@@ -0,0 +1,296 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/weibull}
+\include{zinput}
+%\VignetteIndexEntry{Weibull Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model, weibull,regression,bounded, duration}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt weibull}: Weibull Regression for Duration
+Dependent Variables}\label{weibull}
+
+Choose the Weibull regression model if the values in your dependent
+variable are duration observations. The Weibull model relaxes the
+exponential model's (see \Sref{exp}) assumption of constant hazard,
+and allows the hazard rate to increase or decrease monotonically with
+respect to elapsed time.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "weibull", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Weibull models require that the dependent variable be in the form {\tt
+ Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+ e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study). If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for weibull regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+<<Example.data>>=
+ data(coalition)
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "weibull",
+ data = coalition)
+@
+View the regression output:
+<<Example.summary>>=
+ 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:
+<<Example.setx>>=
+ x.low <- setx(z.out, numst2 = 0)
+ x.high <- setx(z.out, numst2 = 1)
+@
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+@
+<<Example.summary.sim>>=
+ summary(s.out)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+plot(s.out)
+@
+\end{center}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
+ the Weibull distribution whose density function is given by
+ \begin{equation*}
+ f(y_i^*\mid \lambda_i, \alpha) = \frac{\alpha}{\lambda_i^\alpha}
+ y_i^{* \alpha-1} \exp \left\{ -\left( \frac{y_i^*}{\lambda_i}
+\right)^{\alpha} \right\}
+ \end{equation*}
+ for $y_i^* \ge 0$, the scale parameter $\lambda_i > 0$, and the shape
+ parameter $\alpha > 0$. The mean of this distribution is $\lambda_i
+ \Gamma(1 + 1 / \alpha)$. When $\alpha = 1$, the distribution reduces to
+ the exponential distribution (see Section~\ref{exp}). (Note that
+the output from {\tt zelig()} parameterizes {\tt scale}$ = 1 / \alpha$.)
+
+In addition, survival models like the Weibull have three additional
+properties. The hazard function $h(t)$ measures the probability of
+not surviving past time $t$ given survival up to $t$. In general,
+the hazard function is equal to $f(t)/S(t)$ where the survival
+function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the fraction still
+surviving at time $t$. The cumulative hazard function $H(t)$
+describes the probability of dying before time $t$. In general,
+$H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the Weibull
+model,
+\begin{eqnarray*}
+h(t) &=& \frac{\alpha}{\lambda_i^{\alpha}} t^{\alpha - 1} \\
+S(t) &=& \exp \left\{ -\left( \frac{t}{\lambda_i} \right)^{\alpha} \right\} \\
+H(t) &=& \left( \frac{t}{\lambda_i} \right)^{\alpha}
+\end{eqnarray*}
+For the Weibull model, the hazard function $h(t)$ can increase or
+decrease monotonically over time.
+
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+ \begin{equation*}
+ \lambda_i = \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the Weibull model are
+ simulations of the expected duration:
+\begin{equation*}
+E(Y) = \lambda_i \, \Gamma (1 + \alpha^{-1}),
+\end{equation*}
+given draws of $\beta$ and $\alpha$ from their sampling
+distributions.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from a distribution
+ defined by $(\lambda_i, \alpha)$.
+
+\item The first difference ({\tt qi\$fd}) in expected value is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+ a simulation from the model given available knowledge of the
+ censoring process. Variation in the simulations are due to
+ uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+ expected value of $Y_i$ for observations in the treatment group,
+ under the assumption that everything stays the same except that
+ the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+ a simulation from the model given available knowledge of the
+ censoring process. Variation in the simulations are due to
+ uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the
+ counterfactual predicted value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "weibull", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and ``scale''
+ parameter $1 / \alpha$.
+ \item {\tt var}: the variance-covariance matrix.
+ \item {\tt loglik}: a vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: a vector of the
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by $(\lambda_i, \alpha)$.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Weibull model is part of the survival library by Terry Therneau,
+ported to R by Thomas Lumley. Advanced users may wish to refer to
+\texttt{help(survfit)} in the survival library, and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
new file mode 100644
index 0000000..f84c227
Binary files /dev/null and b/inst/doc/weibull.pdf differ
diff --git a/inst/doc/weibull.tex b/inst/doc/weibull.tex
new file mode 100644
index 0000000..2ff799c
--- /dev/null
+++ b/inst/doc/weibull.tex
@@ -0,0 +1,298 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Weibull Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig, survival}
+%\VignetteKeyWords{model, weibull,regression,bounded, duration}
+%\VignettePackage{Zelig}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
+\begin{document}
+
+
+\section{{\tt weibull}: Weibull Regression for Duration
+Dependent Variables}\label{weibull}
+
+Choose the Weibull regression model if the values in your dependent
+variable are duration observations. The Weibull model relaxes the
+exponential model's (see \Sref{exp}) assumption of constant hazard,
+and allows the hazard rate to increase or decrease monotonically with
+respect to elapsed time.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "weibull", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Weibull models require that the dependent variable be in the form {\tt
+ Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+ e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study). If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values}
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for weibull regression:
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations. Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata. Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
+ model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters. If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(coalition)
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "weibull",
++ data = coalition)
+\end{Sinput}
+\end{Schunk}
+View the regression output:
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{Sinput}
+\end{Schunk}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+\begin{center}
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/weibull-ExamplePlot}
+\end{center}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+ of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
+ the Weibull distribution whose density function is given by
+ \begin{equation*}
+ f(y_i^*\mid \lambda_i, \alpha) = \frac{\alpha}{\lambda_i^\alpha}
+ y_i^{* \alpha-1} \exp \left\{ -\left( \frac{y_i^*}{\lambda_i}
+\right)^{\alpha} \right\}
+ \end{equation*}
+ for $y_i^* \ge 0$, the scale parameter $\lambda_i > 0$, and the shape
+ parameter $\alpha > 0$. The mean of this distribution is $\lambda_i
+ \Gamma(1 + 1 / \alpha)$. When $\alpha = 1$, the distribution reduces to
+ the exponential distribution (see Section~\ref{exp}). (Note that
+the output from {\tt zelig()} parameterizes {\tt scale}$ = 1 / \alpha$.)
+
+In addition, survival models like the Weibull have three additional
+properties. The hazard function $h(t)$ measures the probability of
+not surviving past time $t$ given survival up to $t$. In general,
+the hazard function is equal to $f(t)/S(t)$ where the survival
+function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the fraction still
+surviving at time $t$. The cumulative hazard function $H(t)$
+describes the probability of dying before time $t$. In general,
+$H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the Weibull
+model,
+\begin{eqnarray*}
+h(t) &=& \frac{\alpha}{\lambda_i^{\alpha}} t^{\alpha - 1} \\
+S(t) &=& \exp \left\{ -\left( \frac{t}{\lambda_i} \right)^{\alpha} \right\} \\
+H(t) &=& \left( \frac{t}{\lambda_i} \right)^{\alpha}
+\end{eqnarray*}
+For the Weibull model, the hazard function $h(t)$ can increase or
+decrease monotonically over time.
+
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+ \begin{equation*}
+ \lambda_i = \exp(x_i \beta),
+ \end{equation*}
+ where $x_i$ is the vector of explanatory variables, and $\beta$ is
+ the vector of coefficients.
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the Weibull model are
+ simulations of the expected duration:
+\begin{equation*}
+E(Y) = \lambda_i \, \Gamma (1 + \alpha^{-1}),
+\end{equation*}
+given draws of $\beta$ and $\alpha$ from their sampling
+distributions.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from a distribution
+ defined by $(\lambda_i, \alpha)$.
+
+\item The first difference ({\tt qi\$fd}) in expected value is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+ a simulation from the model given available knowledge of the
+ censoring process. Variation in the simulations are due to
+ uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+ expected value of $Y_i$ for observations in the treatment group,
+ under the assumption that everything stays the same except that
+ the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the
+ treatment ($t_i=1$) and control ($t_i=0$) groups. When
+ $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+ a simulation from the model given available knowledge of the
+ censoring process. Variation in the simulations are due to
+ uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the
+ counterfactual predicted value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "weibull", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt icoef}: parameter estimates for the intercept and ``scale''
+ parameter $1 / \alpha$.
+ \item {\tt var}: the variance-covariance matrix.
+ \item {\tt loglik}: a vector containing the log-likelihood for the
+ model and intercept only (respectively).
+ \item {\tt linear.predictors}: a vector of the
+ $x_{i}\beta$.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt df.null}: the residual degrees of freedom for the null
+ model.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+\item Most of this may be conveniently summarized using {\tt
+ summary(z.out)}. From {\tt summary(z.out)}, you may
+ additionally extract:
+ \begin{itemize}
+ \item {\tt table}: the parameter estimates with their
+ associated standard errors, $p$-values, and $t$-statistics.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from a
+ distribution defined by $(\lambda_i, \alpha)$.
+ \item {\tt qi\$fd}: the simulated first differences between the
+ simulated expected values for {\tt x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Weibull model is part of the survival library by Terry Therneau,
+ported to R by Thomas Lumley. Advanced users may wish to refer to
+\texttt{help(survfit)} in the survival library, and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+ \end{document}
+
+
+
+
+
+
+
+
+
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-\bibcite{AdoKinHer03}{{1}{2003}{{Adolph et~al.}}{{Adolph, Gary~King, and Shotts}}}
-\bibcite{Andrews91}{{2}{1991}{{Andrews}}{{}}}
-\bibcite{BreFriOls84}{{3}{1984}{{Breiman et~al.}}{{Breiman, Friedman, Olshen, and Stone}}}
-\bibcite{BroDav91}{{4}{1991}{{Brockwell and Davis}}{{}}}
-\bibcite{ButCar01}{{5}{2001}{{Butts and Carley}}{{}}}
-\bibcite{DiaSek05}{{6}{2005}{{Diamond and Sekhon}}{{}}}
-\bibcite{Enders04}{{7}{2004}{{Enders}}{{}}}
-\bibcite{Friendly00}{{8}{2000}{{Friendly}}{{}}}
-\bibcite{GelKin94a}{{9}{1994}{{Gelman and King}}{{}}}
-\bibcite{Hansen04}{{10}{2004}{{Hansen}}{{}}}
-\bibcite{HasTib90}{{11}{1990}{{Hastie and Tibshirani}}{{}}}
-\bibcite{HoImaKin05b}{{12}{2005}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
-\bibcite{HoImaKin07}{{13}{2007}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
-\bibcite{Huber81}{{14}{1981}{{Huber}}{{}}}
-\bibcite{Imai05}{{15}{2005}{{Imai}}{{}}}
-\bibcite{KatKin99}{{16}{1999}{{Katz and King}}{{}}}
-\bibcite{King89}{{17}{1989}{{King}}{{}}}
-\bibcite{King95}{{18}{1995}{{King}}{{}}}
-\bibcite{King97}{{19}{1997}{{King}}{{}}}
-\bibcite{KinAltBur90}{{20}{1990}{{King et~al.}}{{King, Alt, Burns, and Laver}}}
-\bibcite{KinHonJos01}{{21}{2001}{{King et~al.}}{{King, Honaker, Joseph, and Scheve}}}
-\bibcite{KinMurSal04}{{22}{2004}{{King et~al.}}{{King, Murray, Salomon, and Tandon}}}
-\bibcite{KinTomWit00}{{23}{2000}{{King et~al.}}{{King, Tomz, and Wittenberg}}}
-\bibcite{KinZen01b}{{24}{2001{a}}{{King and Zeng}}{{}}}
-\bibcite{KinZen01}{{25}{2001{b}}{{King and Zeng}}{{}}}
-\bibcite{KinZen02b}{{26}{2002{a}}{{King and Zeng}}{{}}}
-\bibcite{KinZen02}{{27}{2002{b}}{{King and Zeng}}{{}}}
-\bibcite{KinZen06a}{{28}{2006{a}}{{King and Zeng}}{{}}}
-\bibcite{KinZen06b}{{29}{2006{b}}{{King and Zeng}}{{}}}
-\bibcite{LumHea99}{{30}{1999}{{Lumley and Heagerty}}{{}}}
-\bibcite{MarQui05}{{31}{2005}{{Martin and Quinn}}{{}}}
-\bibcite{Martin92}{{32}{1992}{{Martin}}{{}}}
-\bibcite{McCNel89}{{33}{1989}{{McCullagh and Nelder}}{{}}}
-\bibcite{PluBesCowVin05}{{34}{2005}{{Plummer et~al.}}{{Plummer, Best, Cowles, and Vines}}}
-\bibcite{Qui04}{{35}{2004}{{Quinn}}{{}}}
-\bibcite{Ripley96}{{36}{1996}{{Ripley}}{{}}}
-\bibcite{RosJiaKin01}{{37}{2001}{{Rosen et~al.}}{{Rosen, Jiang, King, and Tanner}}}
-\bibcite{SchSla01}{{38}{2001}{{Scheve and Slaughter}}{{}}}
-\bibcite{StoKinZen06}{{39}{2006}{{Stoll et~al.}}{{Stoll, King, and Zeng}}}
-\bibcite{VenRip02}{{40}{2002}{{Venables and Ripley}}{{}}}
-\bibcite{White80}{{41}{1980}{{White}}{{}}}
-\bibcite{YeeHas03}{{42}{2003}{{Yee and Hastie}}{{}}}
-\bibcite{Zeileis04}{{43}{2004}{{Zeileis}}{{}}}
diff --git a/inst/doc/zelig.bbl b/inst/doc/zelig.bbl
deleted file mode 100644
index 08d46d8..0000000
--- a/inst/doc/zelig.bbl
+++ /dev/null
@@ -1,218 +0,0 @@
-\begin{thebibliography}{43}
-\newcommand{\enquote}[1]{``#1''}
-\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi
-
-\bibitem[{Adolph et~al.(2003)Adolph, Gary~King, and Shotts}]{AdoKinHer03}
-Adolph, C., Gary~King, w. M. C.~H., and Shotts, K.~W. (2003), \enquote{A
- Consensus on Second Stage Analyses in Ecological Inference Models,}
- \textit{Political Analysis}, 11, 86--94,
- http://gking.harvard.edu/files/abs/akhs-abs.shtml.
-
-\bibitem[{Andrews(1991)}]{Andrews91}
-Andrews, D.~W. (1991), \enquote{Heteroskedasticity and Autocorrelation
- Consistent Covariance Matrix Estimation,} \textit{Econometrica}, 59,
- 817--858.
-
-\bibitem[{Breiman et~al.(1984)Breiman, Friedman, Olshen, and
- Stone}]{BreFriOls84}
-Breiman, L., Friedman, J.~H., Olshen, R.~A., and Stone, C.~J. (1984),
- \textit{Classification and Regression Trees}, New York, New York: Chapman \&
- Hall.
-
-\bibitem[{Brockwell and Davis(1991)}]{BroDav91}
-Brockwell, P.~J. and Davis, R.~A. (1991), \textit{Time Series: Theory and
- Methods}, Springer-Verlag, 2nd ed.
-
-\bibitem[{Butts and Carley(2001)}]{ButCar01}
-Butts, C. and Carley, K. (2001), \enquote{Multivariate Methods for
- Interstructural Analysis,} Tech. rep., CASOS working paper, Carnegie Mellon
- University.
-
-\bibitem[{Diamond and Sekhon(2005)}]{DiaSek05}
-Diamond, A. and Sekhon, J. (2005), \enquote{Genetic Matching for Estimating
- Causal Effects: A New Method of Achieving Balance in Observational Studies,}
- http://jsekhon.fas.harvard.edu/.
-
-\bibitem[{Enders(2004)}]{Enders04}
-Enders, W. (2004), \textit{Applied Econometric Time Series}, Wiley, 2nd ed.
-
-\bibitem[{Friendly(2000)}]{Friendly00}
-Friendly, M. (2000), \textit{Visualizing Categorical Data}, SAS Institute.
-
-\bibitem[{Gelman and King(1994)}]{GelKin94a}
-Gelman, A. and King, G. (1994), \enquote{A Unified Method of Evaluating
- Electoral Systems and Redistricting Plans,} \textit{American Journal of
- Political Science}, 38, 514--554,
- http://gking.harvard.edu/files/abs/writeit-abs.shtml.
-
-\bibitem[{Hansen(2004)}]{Hansen04}
-Hansen, B.~B. (2004), \enquote{Full Matching in an Observational Study of
- Coaching for the {SAT},} \textit{Journal of the American Statistical
- Association}, 99, 609--618.
-
-\bibitem[{Hastie and Tibshirani(1990)}]{HasTib90}
-Hastie, T.~J. and Tibshirani, R. (1990), \textit{Generalized Additive Models},
- London: Chapman Hall.
-
-\bibitem[{Ho et~al.(2005)Ho, Imai, King, and Stuart}]{HoImaKin05b}
-Ho, D., Imai, K., King, G., and Stuart, E. (2005), \enquote{Matchit: Matching
- as Nonparametric Preprocessing for Parametric Causal Inference,}
- Http://gking.harvard.edu/matchit/.
-
-\bibitem[{Ho et~al.(2007)Ho, Imai, King, and Stuart}]{HoImaKin07}
---- (2007), \enquote{Matching as Nonparametric Preprocessing for Reducing Model
- Dependence in Parametric Causal Inference,} \textit{Political Analysis},
- http://gking.harvard.edu/files/abs/matchp-abs.shtml.
-
-\bibitem[{Huber(1981)}]{Huber81}
-Huber, P.~J. (1981), \textit{Robust Statistics}, Wiley.
-
-\bibitem[{Imai(2005)}]{Imai05}
-Imai, K. (2005), \enquote{Do Get-Out-The-Vote Calls Reduce Turnout? The
- Importance of Statistical Methods for Field Experiments,} \textit{American
- Political Science Review}, 99, 283--300.
-
-\bibitem[{Katz and King(1999)}]{KatKin99}
-Katz, J. and King, G. (1999), \enquote{A Statistical Model for Multiparty
- Electoral Data,} \textit{American Political Science Review}, 93, 15--32,
- http://gking.harvard.edu/files/abs/multiparty-abs.shtml.
-
-\bibitem[{King(1989)}]{King89}
-King, G. (1989), \textit{Unifying Political Methodology: The Likelihood Theory
- of Statistical Inference}, Michigan University Press.
-
-\bibitem[{King(1995)}]{King95}
---- (1995), \enquote{Replication, Replication,} \textit{PS: Political Science
- and Politics}, 28, 443--499,
- http://gking.harvard.edu/files/abs/replication-abs.shtml.
-
-\bibitem[{King(1997)}]{King97}
---- (1997), \textit{A Solution to the Ecological Inference Problem:
- Reconstructing Individual Behavior from Aggregate Data}, Princeton: Princeton
- University Press, http://gking.harvard.edu/eicamera/kinroot.html.
-
-\bibitem[{King et~al.(1990)King, Alt, Burns, and Laver}]{KinAltBur90}
-King, G., Alt, J., Burns, N., and Laver, M. (1990), \enquote{A Unified Model of
- Cabinet Dissolution in Parliamentary Democracies,} \textit{American Journal
- of Political Science}, 34, 846--871,
- http://gking.harvard.edu/files/abs/coal-abs.shtml.
-
-\bibitem[{King et~al.(2001)King, Honaker, Joseph, and Scheve}]{KinHonJos01}
-King, G., Honaker, J., Joseph, A., and Scheve, K. (2001), \enquote{Analyzing
- Incomplete Political Science Data: An Alternative Algorithm for Multiple
- Imputation,} \textit{American Political Science Review}, 95, 49--69,
- http://gking.harvard.edu/files/abs/evil-abs.shtml.
-
-\bibitem[{King et~al.(2004)King, Murray, Salomon, and Tandon}]{KinMurSal04}
-King, G., Murray, C.~J., Salomon, J.~A., and Tandon, A. (2004),
- \enquote{Enhancing the Validity and Cross-cultural Comparability of
- Measurement in Survey Research,} \textit{American Political Science Review},
- 98, 191--205, http://gking.harvard.edu/files/abs/vign-abs.shtml.
-
-\bibitem[{King et~al.(2000)King, Tomz, and Wittenberg}]{KinTomWit00}
-King, G., Tomz, M., and Wittenberg, J. (2000), \enquote{Making the Most of
- Statistical Analyses: Improving Interpretation and Presentation,}
- \textit{American Journal of Political Science}, 44, 341--355,
- http://gking.harvard.edu/files/abs/making-abs.shtml.
-
-\bibitem[{King and Zeng(2001{\natexlab{a}})}]{KinZen01b}
-King, G. and Zeng, L. (2001{\natexlab{a}}), \enquote{Explaining Rare Events in
- International Relations,} \textit{International Organization}, 55, 693--715,
- http://gking.harvard.edu/files/abs/baby0s-abs.shtml.
-
-\bibitem[{King and Zeng(2001{\natexlab{b}})}]{KinZen01}
---- (2001{\natexlab{b}}), \enquote{Logistic Regression in Rare Events Data,}
- \textit{Political Analysis}, 9, 137--163,
- http://gking.harvard.edu/files/abs/0s-abs.shtml.
-
-\bibitem[{King and Zeng(2002{\natexlab{a}})}]{KinZen02b}
---- (2002{\natexlab{a}}), \enquote{Estimating Risk and Rate Levels, Ratios, and
- Differences in Case-Control Studies,} \textit{Statistics in Medicine}, 21,
- 1409--1427, http://gking.harvard.edu/files/abs/1s-abs.shtml.
-
-\bibitem[{King and Zeng(2002{\natexlab{b}})}]{KinZen02}
---- (2002{\natexlab{b}}), \enquote{Improving Forecasts of State Failure,}
- \textit{World Politics}, 53, 623--658,
- http://gking.harvard.edu/files/abs/civil-abs.shtml.
-
-\bibitem[{King and Zeng(2006{\natexlab{a}})}]{KinZen06a}
---- (2006{\natexlab{a}}), \enquote{The Dangers of Extreme Counterfactuals,}
- \textit{Political Analysis}, 14, 131--159,
- http://gking.harvard.edu/files/abs/counterft-abs.shtml.
-
-\bibitem[{King and Zeng(2006{\natexlab{b}})}]{KinZen06b}
---- (2006{\natexlab{b}}), \enquote{Replication Data Set for `When Can History
- be Our Guide? The Pitfalls of Counterfactual Inference',}
- Http://id.thedata.org/hdl\%3A1902.1\%2FDXRXCFAWPK hdl:1902.1/DXRXCFAWPK
- UNF:3:DaYlT6QSX9r0D50ye+tXpA== Murray Research Archive [distributor].
-
-\bibitem[{Lumley and Heagerty(1999)}]{LumHea99}
-Lumley, T. and Heagerty, P. (1999), \enquote{Weighted Empirical Adaptive
- Variance Estimators for Correlated Data Regression,} \textit{jrssb}, 61,
- 459--477.
-
-\bibitem[{Martin and Quinn(2005)}]{MarQui05}
-Martin, A.~D. and Quinn, K.~M. (2005), \textit{MCMCpack: Markov chain Monte
- Carlo (MCMC) Package}.
-
-\bibitem[{Martin(1992)}]{Martin92}
-Martin, L. (1992), \textit{Coercive Cooperation: Explaining Multilateral
- Economic Sanctions}, Princeton University Press, please inquire with Lisa
- Martin before publishing results from these data, as this dataset includes
- errors that have since been corrected.
-
-\bibitem[{McCullagh and Nelder(1989)}]{McCNel89}
-McCullagh, P. and Nelder, J.~A. (1989), \textit{Generalized Linear Models},
- no.~37 in Monograph on Statistics and Applied Probability, Chapman \& Hall,
- 2nd ed.
-
-\bibitem[{Plummer et~al.(2005)Plummer, Best, Cowles, and
- Vines}]{PluBesCowVin05}
-Plummer, M., Best, N., Cowles, K., and Vines, K. (2005), \textit{coda: Output
- analysis and diagnostics for MCMC}.
-
-\bibitem[{Quinn(2004)}]{Qui04}
-Quinn, K. (2004), \enquote{Ecological Inference in the Presence of Temporal
- Dependence,} in \textit{Ecological Inference: New Methodological Strategies},
- eds. King, G., Rosen, O., and Tanner, M.~A., New York: Cambridge University
- Press.
-
-\bibitem[{Ripley(1996)}]{Ripley96}
-Ripley, B. (1996), \textit{Pattern Recognition and Neural Networks}, Cambridge
- Univeristy Press.
-
-\bibitem[{Rosen et~al.(2001)Rosen, Jiang, King, and Tanner}]{RosJiaKin01}
-Rosen, O., Jiang, W., King, G., and Tanner, M.~A. (2001), \enquote{Bayesian and
- Frequentist Inference for Ecological Inference: The $R \times C$ Case,}
- \textit{Statistica Neerlandica}, 55, 134--156,
- http://gking.harvard.edu/files/abs/rosen-abs.shtml.
-
-\bibitem[{Scheve and Slaughter(2001)}]{SchSla01}
-Scheve, K. and Slaughter, M. (2001), \enquote{Labor Market Competition and
- Individual Preferences over Immigration Policy,} \textit{Review of Economics
- and Statistics}, 83, 133--145, sample data include only the first five of ten
- multiply imputed data sets.
-
-\bibitem[{Stoll et~al.(2006)Stoll, King, and Zeng}]{StoKinZen06}
-Stoll, H., King, G., and Zeng, L. (2006), \enquote{WhatIf: Software for
- Evaluating Counterfactuals,} \textit{Journal of Statistical Software}, 15,
- http://gking.harvard.edu/whatif/.
-
-\bibitem[{Venables and Ripley(2002)}]{VenRip02}
-Venables, W.~N. and Ripley, B.~D. (2002), \textit{Modern Applied Statistics
- with S}, Springer-Verlag, 4th ed.
-
-\bibitem[{White(1980)}]{White80}
-White, H. (1980), \enquote{A Heteroskedasticity-Consistent Covariance Matrix
- Estimator and a Direct Test for Heteroskedasticity,} \textit{Econometrica},
- 48, 817--838.
-
-\bibitem[{Yee and Hastie(2003)}]{YeeHas03}
-Yee, T.~W. and Hastie, T.~J. (2003), \enquote{Reduced-rank vector generalized
- linear models,} \textit{Statistical Modelling}, 3, 15--41.
-
-\bibitem[{Zeileis(2004)}]{Zeileis04}
-Zeileis, A. (2004), \enquote{Econometric Computing with HC and HAC Covariance
- Matrix Estimators,} \textit{Journal of Statistical Software}, 11, 1--17.
-
-\end{thebibliography}
diff --git a/inst/doc/zelig.blg b/inst/doc/zelig.blg
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-[][]/cmr10 at 10.95pt/David Flo-rence Pro-fes-sor of Gov-ern-ment, Har-vard Uni-ve
-r-sity (In-sti-tute for Quan-ti-ta-
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-[]/cmr12/Please di-rect in-quiries and prob-lems about Zelig to our list-serv a
-t []zelig at lists.gking.harvard.edu[].
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-[]/cmr12/If your dataset is in a /cmbx12/tab- or space-delimited .txt file/cmr1
-2/, use /cmtt12/read.table("mydata.txt")
- []
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-age.
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- []/cmtt12/> spss.data <- read.spss("mydata.sav", to.data.frame = TRUE) # For S
-PSS.[]
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- []/cmtt12/> library(Zelig) # Loads the Zelig libr
-ary.
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-[]/cmtt12/> logic <- c(TRUE, FALSE, TRUE, TRUE, TRUE) # Creates `logic' (5 T/F
-values).[]
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- and 20.[]
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-ons.[]
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-[]/cmtt12/> var <- data$var1 # Copies `var1' from `data', creatin
-g `var'.[]
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-g `var'.[]
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-uals[]
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- `y'.[]
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- `y'.[]
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-es).[]
- []
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- []/cmtt12/> v2 <- b > 3 # Creates the matrix `v2' (T/F valu
-es).[]
- []
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- []/cmtt12/> v1 & v2 # Checks if the (i,j) value in `v1'
- and[]
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- [] /cmtt12/[,1] [,2] [,3] [,4] # `v2' are both TRUE. Because col
-umns[]
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-s 1-3[]
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-[]/cmtt12/> var3 <- var1 < var2 # Creates a vector of n T/F obser
-vations.[]
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-[]/cmtt12/> var1[var1 == "don't know"] <- NA # Replaces all "don't know"'s wi
-th NA's.[]
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-[28]) (./Zcommands.tex [29]
-Chapter 4.
-<xymatrix 3x5 334> [30
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-]
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- []/cmtt12/> z.out <- zelig(vote ~ race + educate, model = "logit", data = turn
-out)[]
- []
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- []/cmtt12/> summary(s.out) # Numerical sum
-mary.[]
- []
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- []/cmtt12/> plot(s.out) # Graphical sum
-mary.[]
- []
-
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- []/cmtt12/> z.out <- zelig(vote ~ race + educate, model = "logit", data = turn
-out)[]
- []
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-[32] [33]
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-[]/cmtt12/x.out <- setx(z.out, fn = list(numeric = mean, ordered = median, othe
-rs =
- []
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- []/cmtt12/> z.out <- zelig(vote ~ age + race, model = "logit", data = turnout)
-[]
- []
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-[]/cmtt12/s.out <- sim(z.out, x = x.out, x1 = NULL, num = c(1000, 100), bootstr
-ap =
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-[]/cmr12/Zelig sim-u-lates pa-ram-e-ters from clas-si-cal /cmti12/max-i-mum lik
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- []
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- []
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-islike")[]
- []
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-a[]
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-s.[]
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-w[]
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-e[]
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-n 3.[]
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-= "ls")[]
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- []
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-te")[]
- []
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-price[]
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-oalition)[]
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- Souter,[]
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-macro)[]
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- []/cmtt12/> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogi
-t.bayes",[]
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-b = 0.75))[]
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-ion)[]
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-.75))
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-25))[]
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- + years,[]
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- + years,[]
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- http://gking.harvard.edu/files/abs/baby0s-
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diff --git a/inst/doc/zelig.out b/inst/doc/zelig.out
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index faaaf3a..0000000
--- a/inst/doc/zelig.out
+++ /dev/null
@@ -1,133 +0,0 @@
-\BOOKMARK [0][-]{chapter.1}{Introduction}{}
-\BOOKMARK [1][-]{section.1.1}{What Zelig and R Do}{chapter.1}
-\BOOKMARK [1][-]{section.1.2}{Getting Help}{chapter.1}
-\BOOKMARK [-1][-]{part.1}{I User's Guide}{}
-\BOOKMARK [0][-]{chapter.2}{Installation}{part.1}
-\BOOKMARK [1][-]{section.2.1}{Windows}{chapter.2}
-\BOOKMARK [1][-]{section.2.2}{MacOS X}{chapter.2}
-\BOOKMARK [1][-]{section.2.3}{UNIX and Linux}{chapter.2}
-\BOOKMARK [1][-]{section.2.4}{Version Compatability}{chapter.2}
-\BOOKMARK [0][-]{chapter.3}{Data Analysis Commands}{part.1}
-\BOOKMARK [1][-]{section.3.1}{Command Syntax}{chapter.3}
-\BOOKMARK [2][-]{subsection.3.1.1}{Getting Started}{section.3.1}
-\BOOKMARK [2][-]{subsection.3.1.2}{Details}{section.3.1}
-\BOOKMARK [1][-]{section.3.2}{Data Sets}{chapter.3}
-\BOOKMARK [2][-]{subsection.3.2.1}{Data Structures}{section.3.2}
-\BOOKMARK [2][-]{subsection.3.2.2}{Loading Data}{section.3.2}
-\BOOKMARK [2][-]{subsection.3.2.3}{Saving Data}{section.3.2}
-\BOOKMARK [1][-]{section.3.3}{Variables}{chapter.3}
-\BOOKMARK [2][-]{subsection.3.3.1}{Classes of Variables}{section.3.3}
-\BOOKMARK [2][-]{subsection.3.3.2}{Recoding Variables}{section.3.3}
-\BOOKMARK [0][-]{chapter.4}{Statistical Commands}{part.1}
-\BOOKMARK [1][-]{section.4.1}{Zelig Commands}{chapter.4}
-\BOOKMARK [2][-]{subsection.4.1.1}{Quick Overview}{section.4.1}
-\BOOKMARK [2][-]{subsection.4.1.2}{Examples}{section.4.1}
-\BOOKMARK [2][-]{subsection.4.1.3}{Details}{section.4.1}
-\BOOKMARK [1][-]{section.4.2}{Supported Models}{chapter.4}
-\BOOKMARK [1][-]{section.4.3}{Replication Procedures}{chapter.4}
-\BOOKMARK [2][-]{subsection.4.3.1}{Saving Replication Materials}{section.4.3}
-\BOOKMARK [2][-]{subsection.4.3.2}{Replicating Analyses}{section.4.3}
-\BOOKMARK [0][-]{chapter.5}{Graphing Commands}{part.1}
-\BOOKMARK [1][-]{section.5.1}{Drawing Plots}{chapter.5}
-\BOOKMARK [1][-]{section.5.2}{Adding Points, Lines, and Legends to Existing Plots}{chapter.5}
-\BOOKMARK [1][-]{section.5.3}{Saving Graphs to Files}{chapter.5}
-\BOOKMARK [1][-]{section.5.4}{Examples}{chapter.5}
-\BOOKMARK [2][-]{subsection.5.4.1}{Descriptive Plots: Box-plots}{section.5.4}
-\BOOKMARK [2][-]{subsection.5.4.2}{Density Plots: A Histogram}{section.5.4}
-\BOOKMARK [2][-]{subsection.5.4.3}{Advanced Examples}{section.5.4}
-\BOOKMARK [-1][-]{part.2}{II Advanced Zelig Uses}{}
-\BOOKMARK [0][-]{chapter.6}{R Objects}{part.2}
-\BOOKMARK [1][-]{section.6.1}{Scalar Values}{chapter.6}
-\BOOKMARK [1][-]{section.6.2}{Data Structures}{chapter.6}
-\BOOKMARK [2][-]{subsection.6.2.1}{Arrays}{section.6.2}
-\BOOKMARK [2][-]{subsection.6.2.2}{Lists}{section.6.2}
-\BOOKMARK [2][-]{subsection.6.2.3}{Data Frames}{section.6.2}
-\BOOKMARK [2][-]{subsection.6.2.4}{Identifying Objects and Data Structures}{section.6.2}
-\BOOKMARK [0][-]{chapter.7}{Programming Statements}{part.2}
-\BOOKMARK [1][-]{section.7.1}{Functions}{chapter.7}
-\BOOKMARK [1][-]{section.7.2}{If-Statements}{chapter.7}
-\BOOKMARK [1][-]{section.7.3}{For-Loops}{chapter.7}
-\BOOKMARK [0][-]{chapter.8}{Writing New Models}{part.2}
-\BOOKMARK [1][-]{section.8.1}{Managing Statistical Model Inputs}{chapter.8}
-\BOOKMARK [2][-]{subsection.8.1.1}{Describe the Statistical Model}{section.8.1}
-\BOOKMARK [2][-]{subsection.8.1.2}{Single Response Variable Models: Normal Regression Model}{section.8.1}
-\BOOKMARK [2][-]{subsection.8.1.3}{Multivariate models: Bivariate Normal example}{section.8.1}
-\BOOKMARK [1][-]{section.8.2}{Easy Ways to Manage Matrices}{chapter.8}
-\BOOKMARK [2][-]{subsection.8.2.1}{The Intuitive Layout}{section.8.2}
-\BOOKMARK [2][-]{subsection.8.2.2}{The Computationally-Efficient Layout}{section.8.2}
-\BOOKMARK [2][-]{subsection.8.2.3}{The Memory-Efficient Layout}{section.8.2}
-\BOOKMARK [2][-]{subsection.8.2.4}{Interchanging the Three Methods}{section.8.2}
-\BOOKMARK [0][-]{chapter.9}{Adding Models and Methods to Zelig}{part.2}
-\BOOKMARK [1][-]{section.9.1}{Making the Model Compatible with Zelig}{chapter.9}
-\BOOKMARK [1][-]{section.9.2}{Getting Ready for the GUI}{chapter.9}
-\BOOKMARK [1][-]{section.9.3}{Formatting Reference Manual Pages}{chapter.9}
-\BOOKMARK [-1][-]{part.3}{III Reference Manual}{}
-\BOOKMARK [0][-]{chapter.10}{Main Commands}{part.3}
-\BOOKMARK [1][-]{section.10.1}{zelig: Estimating a Statistical Model}{chapter.10}
-\BOOKMARK [1][-]{section.10.2}{setx: Setting Explanatory Variable Values}{chapter.10}
-\BOOKMARK [1][-]{section.10.3}{sim: Simulating Quantities of Interest}{chapter.10}
-\BOOKMARK [1][-]{section.10.4}{summary: Summarizing Zelig Output}{chapter.10}
-\BOOKMARK [1][-]{section.10.5}{plot: Graphing Quantities of Interest}{chapter.10}
-\BOOKMARK [1][-]{section.10.6}{print: Printing Quantities of Interest}{chapter.10}
-\BOOKMARK [1][-]{section.10.7}{repl: Replicating Analyses}{chapter.10}
-\BOOKMARK [0][-]{chapter.11}{Supplementary Commands}{part.3}
-\BOOKMARK [1][-]{section.11.1}{matchit: Create matched data}{chapter.11}
-\BOOKMARK [1][-]{section.11.2}{mi: Create a list of multiply imputed data frames}{chapter.11}
-\BOOKMARK [2][-]{subsection.11.2.1}{ network: Format Individual Matricies into a Data Frame for Social Network Analysis }{section.11.2}
-\BOOKMARK [1][-]{section.11.3}{plot.ci: Plotting Vertical confidence Intervals}{chapter.11}
-\BOOKMARK [1][-]{section.11.4}{rocplot: Receiver Operator Characteristic Plots}{chapter.11}
-\BOOKMARK [1][-]{section.11.5}{ternaryplot: Ternary Diagram for 3D Data}{chapter.11}
-\BOOKMARK [1][-]{section.11.6}{ternarypoints: Adding Points to Ternary Diagrams}{chapter.11}
-\BOOKMARK [0][-]{chapter.12}{Models Zelig Can Run}{part.3}
-\BOOKMARK [1][-]{section.12.1}{ARIMA : ARIMA Models for Time Series Data}{chapter.12}
-\BOOKMARK [1][-]{section.12.2}{blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.3}{bprobit: Bivariate Probit Regression for Two Dichotomous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.4}{ei.dynamic: Quinn's Dynamic Ecological Inference Model}{chapter.12}
-\BOOKMARK [1][-]{section.12.5}{ei.hier: Hierarchical Ecological Inference Model for 2 2 Tables}{chapter.12}
-\BOOKMARK [1][-]{section.12.6}{ei.RxC: Hierarchical Multinomial-Dirichlet Ecological Inference Model for R C Tables}{chapter.12}
-\BOOKMARK [1][-]{section.12.7}{exp: Exponential Regression for Duration Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.8}{factor.bayes: Bayesian Factor Analysis}{chapter.12}
-\BOOKMARK [1][-]{section.12.9}{factor.mix: Mixed Data Factor Analysis}{chapter.12}
-\BOOKMARK [1][-]{section.12.10}{factor.ord: Ordinal Data Factor Analysis}{chapter.12}
-\BOOKMARK [1][-]{section.12.11}{gamma: Gamma Regression for Continuous, Positive Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.12}{irt1d: One Dimensional Item Response Model}{chapter.12}
-\BOOKMARK [1][-]{section.12.13}{irtkd: k-Dimensional Item Response Theory Model}{chapter.12}
-\BOOKMARK [1][-]{section.12.14}{logit: Logistic Regression for Dichotomous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.15}{logit.bayes: Bayesian Logistic Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.16}{lognorm: Log-Normal Regression for Duration Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.17}{ls: Least Squares Regression for Continuous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.18}{mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}{chapter.12}
-\BOOKMARK [1][-]{section.12.19}{mlogit.bayes: Bayesian Multinomial Logistic Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.20}{negbin: Negative Binomial Regression for Event Count Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.21}{netls: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.22}{netlogit: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.23}{normal: Normal Regression for Continuous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.24}{normal.bayes: Bayesian Normal Linear Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.25}{ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.26}{oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.27}{oprobit.bayes: Bayesian Ordered Probit Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.28}{poisson: Poisson Regression for Event Count Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.29}{poisson.bayes: Bayesian Poisson Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.30}{probit: Probit Regression for Dichotomous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.31}{probit.bayes: Bayesian Probit Regression}{chapter.12}
-\BOOKMARK [1][-]{section.12.32}{relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables}{chapter.12}
-\BOOKMARK [1][-]{section.12.33}{tobit: Linear Regression for a Left-Censored Dependent Variable}{chapter.12}
-\BOOKMARK [1][-]{section.12.34}{tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable}{chapter.12}
-\BOOKMARK [1][-]{section.12.35}{weibull: Weibull Regression for Duration Dependent Variables}{chapter.12}
-\BOOKMARK [0][-]{chapter.13}{Commands for Programmers and Contributors}{part.3}
-\BOOKMARK [1][-]{section.13.1}{describe: Describe a model's systematic and stochastic parameters}{chapter.13}
-\BOOKMARK [1][-]{section.13.2}{model.end: Cleaning up after optimization}{chapter.13}
-\BOOKMARK [1][-]{section.13.3}{model.frame.multiple: Extracting the ``environment'' of a model formula}{chapter.13}
-\BOOKMARK [1][-]{section.13.4}{model.matrix.multiple: Design matrix for multivariate models}{chapter.13}
-\BOOKMARK [1][-]{section.13.5}{parse.formula: Parsing the inputs}{chapter.13}
-\BOOKMARK [1][-]{section.13.6}{parse.par: Select and reshape parameter vectors}{chapter.13}
-\BOOKMARK [1][-]{section.13.7}{put.start: Set specific starting values for certain parameters}{chapter.13}
-\BOOKMARK [1][-]{section.13.8}{set.start: Set starting values for all parameters}{chapter.13}
-\BOOKMARK [1][-]{section.13.9}{tag: Constrain parameter effects across equations}{chapter.13}
-\BOOKMARK [-1][-]{part.4}{IV Appendices}{}
-\BOOKMARK [0][-]{appendix.A}{Frequently Asked Questions}{part.4}
-\BOOKMARK [1][-]{section.A.1}{For All Zelig Users}{appendix.A}
-\BOOKMARK [1][-]{section.A.2}{For Zelig Contributors}{appendix.A}
-\BOOKMARK [0][-]{appendix.B}{What's New? What's Next?}{part.4}
-\BOOKMARK [1][-]{section.B.1}{What's New: Zelig Release Notes}{appendix.B}
-\BOOKMARK [1][-]{section.B.2}{What's Next?}{appendix.B}
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trailer
<<
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%%EOF
diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index 9645c64..48540a0 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/zelig.tex
@@ -1,5 +1,8 @@
\documentclass[oneside,letterpaper,12pt]{book}
%\usepackage[ae,hyper]{Rd}
+
+\usepackage{Rd}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
\usepackage{bibentry}
\usepackage{upquote}
\usepackage{graphicx}
@@ -59,7 +62,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 2.8-3\\ \today}
+\date{Version 2.8-4\\ \today}
\begin{document}
\maketitle
diff --git a/inst/doc/zelig.toc b/inst/doc/zelig.toc
deleted file mode 100644
index 0be94c7..0000000
--- a/inst/doc/zelig.toc
+++ /dev/null
@@ -1,562 +0,0 @@
-\contentsline {chapter}{\numberline {1}Introduction}{7}{chapter.1}
-\contentsline {section}{\numberline {1.1}What Zelig and R Do}{7}{section.1.1}
-\contentsline {section}{\numberline {1.2}Getting Help}{8}{section.1.2}
-\contentsline {part}{I\hspace {1em}User's Guide}{10}{part.1}
-\contentsline {chapter}{\numberline {2}Installation}{11}{chapter.2}
-\contentsline {subsubsection}{If You Know R}{11}{section*.3}
-\contentsline {subsubsection}{If You Are New to R}{11}{section*.4}
-\contentsline {section}{\numberline {2.1}Windows}{12}{section.2.1}
-\contentsline {subsubsection}{Installing R}{12}{section*.5}
-\contentsline {subsubsection}{Installing Zelig}{12}{section*.6}
-\contentsline {subsubsection}{Updating Zelig}{13}{section*.7}
-\contentsline {section}{\numberline {2.2}MacOS X}{13}{section.2.2}
-\contentsline {subsubsection}{Installing R}{13}{section*.8}
-\contentsline {subsubsection}{Installing Zelig}{13}{section*.9}
-\contentsline {subsubsection}{Updating Zelig}{15}{section*.10}
-\contentsline {section}{\numberline {2.3}UNIX and Linux}{16}{section.2.3}
-\contentsline {subsubsection}{Installing R}{16}{section*.11}
-\contentsline {subsubsection}{Installing Zelig}{16}{section*.12}
-\contentsline {subsubsection}{Updating Zelig}{17}{section*.13}
-\contentsline {section}{\numberline {2.4}Version Compatability}{18}{section.2.4}
-\contentsline {chapter}{\numberline {3}Data Analysis Commands}{19}{chapter.3}
-\contentsline {section}{\numberline {3.1}Command Syntax}{19}{section.3.1}
-\contentsline {subsection}{\numberline {3.1.1}Getting Started}{19}{subsection.3.1.1}
-\contentsline {subsection}{\numberline {3.1.2}Details}{20}{subsection.3.1.2}
-\contentsline {section}{\numberline {3.2}Data Sets}{21}{section.3.2}
-\contentsline {subsection}{\numberline {3.2.1}Data Structures}{21}{subsection.3.2.1}
-\contentsline {subsection}{\numberline {3.2.2}Loading Data}{21}{subsection.3.2.2}
-\contentsline {subsection}{\numberline {3.2.3}Saving Data}{23}{subsection.3.2.3}
-\contentsline {section}{\numberline {3.3}Variables}{24}{section.3.3}
-\contentsline {subsection}{\numberline {3.3.1}Classes of Variables}{24}{subsection.3.3.1}
-\contentsline {subsection}{\numberline {3.3.2}Recoding Variables}{25}{subsection.3.3.2}
-\contentsline {chapter}{\numberline {4}Statistical Commands}{30}{chapter.4}
-\contentsline {section}{\numberline {4.1}Zelig Commands}{30}{section.4.1}
-\contentsline {subsection}{\numberline {4.1.1}Quick Overview}{30}{subsection.4.1.1}
-\contentsline {subsection}{\numberline {4.1.2}Examples}{31}{subsection.4.1.2}
-\contentsline {subsection}{\numberline {4.1.3}Details}{33}{subsection.4.1.3}
-\contentsline {section}{\numberline {4.2}Supported Models}{38}{section.4.2}
-\contentsline {section}{\numberline {4.3}Replication Procedures}{41}{section.4.3}
-\contentsline {subsection}{\numberline {4.3.1}Saving Replication Materials}{42}{subsection.4.3.1}
-\contentsline {subsection}{\numberline {4.3.2}Replicating Analyses}{42}{subsection.4.3.2}
-\contentsline {chapter}{\numberline {5}Graphing Commands}{44}{chapter.5}
-\contentsline {section}{\numberline {5.1}Drawing Plots}{44}{section.5.1}
-\contentsline {section}{\numberline {5.2}Adding Points, Lines, and Legends to Existing Plots}{46}{section.5.2}
-\contentsline {section}{\numberline {5.3}Saving Graphs to Files}{46}{section.5.3}
-\contentsline {section}{\numberline {5.4}Examples}{48}{section.5.4}
-\contentsline {subsection}{\numberline {5.4.1}Descriptive Plots: Box-plots}{48}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Density Plots: A Histogram}{49}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Advanced Examples}{50}{subsection.5.4.3}
-\contentsline {part}{II\hspace {1em}Advanced Zelig Uses}{53}{part.2}
-\contentsline {chapter}{\numberline {6}R Objects}{54}{chapter.6}
-\contentsline {section}{\numberline {6.1}Scalar Values}{54}{section.6.1}
-\contentsline {section}{\numberline {6.2}Data Structures}{55}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Arrays}{55}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}Lists}{58}{subsection.6.2.2}
-\contentsline {subsection}{\numberline {6.2.3}Data Frames}{59}{subsection.6.2.3}
-\contentsline {subsection}{\numberline {6.2.4}Identifying Objects and Data Structures}{60}{subsection.6.2.4}
-\contentsline {chapter}{\numberline {7}Programming Statements}{61}{chapter.7}
-\contentsline {section}{\numberline {7.1}Functions}{61}{section.7.1}
-\contentsline {section}{\numberline {7.2}If-Statements}{61}{section.7.2}
-\contentsline {section}{\numberline {7.3}For-Loops}{62}{section.7.3}
-\contentsline {paragraph}{Example 1: Creating a vector with a logical statement}{62}{section*.24}
-\contentsline {paragraph}{Example 2: Creating dummy variables by hand}{63}{section*.25}
-\contentsline {paragraph}{Example 3: Weighted regression with subsets}{64}{section*.26}
-\contentsline {chapter}{\numberline {8}Writing New Models}{66}{chapter.8}
-\contentsline {section}{\numberline {8.1}Managing Statistical Model Inputs}{67}{section.8.1}
-\contentsline {subsection}{\numberline {8.1.1}Describe the Statistical Model}{67}{subsection.8.1.1}
-\contentsline {subsection}{\numberline {8.1.2}Single Response Variable Models: Normal Regression Model}{68}{subsection.8.1.2}
-\contentsline {subsection}{\numberline {8.1.3}Multivariate models: Bivariate Normal example}{71}{subsection.8.1.3}
-\contentsline {section}{\numberline {8.2}Easy Ways to Manage Matrices}{73}{section.8.2}
-\contentsline {subsection}{\numberline {8.2.1}The Intuitive Layout}{74}{subsection.8.2.1}
-\contentsline {subsection}{\numberline {8.2.2}The Computationally-Efficient Layout}{74}{subsection.8.2.2}
-\contentsline {subsection}{\numberline {8.2.3}The Memory-Efficient Layout}{75}{subsection.8.2.3}
-\contentsline {subsection}{\numberline {8.2.4}Interchanging the Three Methods}{75}{subsection.8.2.4}
-\contentsline {chapter}{\numberline {9}Adding Models and Methods to Zelig}{78}{chapter.9}
-\contentsline {section}{\numberline {9.1}Making the Model Compatible with Zelig}{79}{section.9.1}
-\contentsline {subsubsection}{To Work with {\tt zelig()}}{80}{section*.27}
-\contentsline {subsubsection}{To Work with {\tt setx()}}{81}{section*.28}
-\contentsline {subsubsection}{Compatibility with {\tt sim()}}{81}{section*.29}
-\contentsline {paragraph}{Simulating Parameters}{81}{section*.30}
-\contentsline {paragraph}{Calculating Quantities of Interest}{83}{section*.31}
-\contentsline {section}{\numberline {9.2}Getting Ready for the GUI}{85}{section.9.2}
-\contentsline {section}{\numberline {9.3}Formatting Reference Manual Pages}{85}{section.9.3}
-\contentsline {part}{III\hspace {1em}Reference Manual}{88}{part.3}
-\contentsline {chapter}{\numberline {10}Main Commands}{89}{chapter.10}
-\contentsline {section}{\numberline {10.1}{\tt zelig}: Estimating a Statistical Model}{89}{section.10.1}
-\contentsline {subsubsection}{Description}{89}{section*.33}
-\contentsline {subsubsection}{Syntax}{89}{section*.34}
-\contentsline {subsubsection}{Arguments}{89}{section*.35}
-\contentsline {subsubsection}{Output Values}{91}{section*.36}
-\contentsline {subsubsection}{Examples}{91}{section*.37}
-\contentsline {subsubsection}{See Also}{92}{section*.38}
-\contentsline {subsubsection}{Contributors}{92}{section*.39}
-\contentsline {section}{\numberline {10.2}{\tt setx}: Setting Explanatory Variable Values}{93}{section.10.2}
-\contentsline {subsubsection}{Description}{93}{section*.40}
-\contentsline {subsubsection}{Syntax}{93}{section*.41}
-\contentsline {subsubsection}{Arguments}{93}{section*.42}
-\contentsline {subsubsection}{Output Values}{94}{section*.43}
-\contentsline {subsubsection}{Example: Unconditional Prediction}{94}{section*.44}
-\contentsline {subsubsection}{Example: Conditional Prediction With MatchIt Data}{94}{section*.45}
-\contentsline {subsubsection}{Example: Conditional Prediction With Multiple Analyses}{95}{section*.46}
-\contentsline {subsubsection}{See Also}{95}{section*.47}
-\contentsline {subsubsection}{Contributors}{95}{section*.48}
-\contentsline {section}{\numberline {10.3}{\tt sim}: Simulating Quantities of Interest}{96}{section.10.3}
-\contentsline {subsubsection}{Description}{96}{section*.49}
-\contentsline {subsubsection}{Syntax}{96}{section*.50}
-\contentsline {subsubsection}{Arguments}{96}{section*.51}
-\contentsline {subsubsection}{Output Values}{97}{section*.52}
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-\contentsline {subsubsection}{Contributors}{98}{section*.54}
-\contentsline {section}{\numberline {10.4}{\tt summary}: Summarizing Zelig Output}{99}{section.10.4}
-\contentsline {subsubsection}{Description}{99}{section*.55}
-\contentsline {subsubsection}{Syntax}{99}{section*.56}
-\contentsline {subsubsection}{Arguments for multiply-imputed {\tt zelig()} output}{99}{section*.57}
-\contentsline {subsubsection}{Arguments for subsetted {\tt zelig()} output}{99}{section*.58}
-\contentsline {subsubsection}{Arguments for {\tt sim()} output}{100}{section*.59}
-\contentsline {subsubsection}{Output Values}{100}{section*.60}
-\contentsline {subsubsection}{See Also}{100}{section*.61}
-\contentsline {subsubsection}{Contributors}{101}{section*.62}
-\contentsline {section}{\numberline {10.5}{\tt plot}: Graphing Quantities of Interest}{102}{section.10.5}
-\contentsline {subsubsection}{Description}{102}{section*.63}
-\contentsline {subsubsection}{Syntax}{102}{section*.64}
-\contentsline {subsubsection}{Arguments}{102}{section*.65}
-\contentsline {subsubsection}{Output Values}{102}{section*.66}
-\contentsline {subsubsection}{Examples}{102}{section*.67}
-\contentsline {subsubsection}{See Also}{103}{section*.68}
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-\contentsline {section}{\numberline {10.6}{\tt print}: Printing Quantities of Interest}{104}{section.10.6}
-\contentsline {subsubsection}{Description}{104}{section*.70}
-\contentsline {subsubsection}{Syntax}{104}{section*.71}
-\contentsline {subsubsection}{Arguments}{104}{section*.72}
-\contentsline {subsubsection}{Examples}{104}{section*.73}
-\contentsline {subsubsection}{See Also}{104}{section*.74}
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-\contentsline {section}{\numberline {10.7}{\tt repl}: Replicating Analyses}{105}{section.10.7}
-\contentsline {subsubsection}{Description}{105}{section*.76}
-\contentsline {subsubsection}{Syntax}{105}{section*.77}
-\contentsline {subsubsection}{Arguments}{105}{section*.78}
-\contentsline {subsubsection}{Output Values}{105}{section*.79}
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-\contentsline {subsubsection}{See Also}{106}{section*.81}
-\contentsline {subsubsection}{Contributors}{106}{section*.82}
-\contentsline {chapter}{\numberline {11}Supplementary Commands}{107}{chapter.11}
-\contentsline {section}{\numberline {11.1}{\tt matchit}: Create matched data}{107}{section.11.1}
-\contentsline {subsubsection}{Description}{107}{section*.83}
-\contentsline {subsubsection}{Syntax}{107}{section*.84}
-\contentsline {subsubsection}{Arguments}{107}{section*.85}
-\contentsline {paragraph}{Arguments for All Matching Methods}{107}{section*.86}
-\contentsline {paragraph}{Additional Arguments for Specification of Distance Measures}{108}{section*.87}
-\contentsline {paragraph}{Additional Arguments for Subclassification}{110}{section*.88}
-\contentsline {paragraph}{Additional Arguments for Nearest Neighbor Matching}{110}{section*.89}
-\contentsline {paragraph}{Additional Arguments for Optimal Matching}{111}{section*.90}
-\contentsline {paragraph}{Additional Arguments for Full Matching}{111}{section*.91}
-\contentsline {paragraph}{Additional Arguments for Genetic Matching}{111}{section*.92}
-\contentsline {subsubsection}{Output Values}{112}{section*.93}
-\contentsline {subsubsection}{Contributors}{113}{section*.94}
-\contentsline {section}{\numberline {11.2}{\tt mi}: Create a list of multiply imputed data frames}{115}{section.11.2}
-\contentsline {subsubsection}{Description}{115}{section*.95}
-\contentsline {subsubsection}{Syntax}{115}{section*.96}
-\contentsline {subsubsection}{Arguments}{115}{section*.97}
-\contentsline {subsubsection}{Output Values}{115}{section*.98}
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-\contentsline {subsubsection}{Contributors}{115}{section*.100}
-\contentsline {subsection}{\numberline {11.2.1} \texttt {network}: Format Individual Matricies into a Data Frame for Social Network Analysis }{116}{subsection.11.2.1}
-\contentsline {subsubsection}{Description}{116}{section*.101}
-\contentsline {subsubsection}{Usage}{116}{section*.102}
-\contentsline {subsubsection}{Arguments}{116}{section*.103}
-\contentsline {subsubsection}{Details}{116}{section*.104}
-\contentsline {subsubsection}{Example}{116}{section*.105}
-\contentsline {subsubsection}{Contributors}{116}{section*.106}
-\contentsline {section}{\numberline {11.3}{\tt plot.ci}: Plotting Vertical confidence Intervals}{117}{section.11.3}
-\contentsline {subsubsection}{Description}{117}{section*.107}
-\contentsline {subsubsection}{Syntax}{117}{section*.108}
-\contentsline {subsubsection}{Arguments}{117}{section*.109}
-\contentsline {subsubsection}{Output Values}{117}{section*.110}
-\contentsline {subsubsection}{Example}{117}{section*.111}
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-\contentsline {section}{\numberline {11.4}{\tt rocplot}: Receiver Operator Characteristic Plots}{119}{section.11.4}
-\contentsline {subsubsection}{Description}{119}{section*.114}
-\contentsline {subsubsection}{Syntax}{119}{section*.115}
-\contentsline {subsubsection}{Arguments}{119}{section*.116}
-\contentsline {subsubsection}{Output Values}{120}{section*.117}
-\contentsline {subsubsection}{Example}{120}{section*.118}
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-\contentsline {section}{\numberline {11.5}{\tt ternaryplot}: Ternary Diagram for 3D Data}{121}{section.11.5}
-\contentsline {subsubsection}{Description}{121}{section*.121}
-\contentsline {subsubsection}{Syntax}{121}{section*.122}
-\contentsline {subsubsection}{Arguments}{121}{section*.123}
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-\contentsline {subsubsection}{See Also}{122}{section*.125}
-\contentsline {subsubsection}{Contributors}{122}{section*.126}
-\contentsline {section}{\numberline {11.6}{\tt ternarypoints}: Adding Points to Ternary Diagrams}{123}{section.11.6}
-\contentsline {subsubsection}{Description}{123}{section*.127}
-\contentsline {subsubsection}{Syntax}{123}{section*.128}
-\contentsline {subsubsection}{Arguments}{123}{section*.129}
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-\contentsline {subsubsection}{See Also}{123}{section*.132}
-\contentsline {subsubsection}{Contributors}{123}{section*.133}
-\contentsline {chapter}{\numberline {12}Models Zelig Can Run}{124}{chapter.12}
-\contentsline {section}{\numberline {12.1}{\tt ARIMA} : ARIMA Models for Time Series Data}{126}{section.12.1}
-\contentsline {section}{\numberline {12.2}{\tt blogit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{132}{section.12.2}
-\contentsline {subsubsection}{Syntax}{132}{section*.143}
-\contentsline {subsubsection}{Input Values}{132}{section*.144}
-\contentsline {subsubsection}{Examples}{132}{section*.145}
-\contentsline {subsubsection}{Model}{134}{section*.146}
-\contentsline {subsubsection}{Quantities of Interest}{135}{section*.147}
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-\contentsline {subsubsection}{Contributors}{137}{section*.149}
-\contentsline {section}{\numberline {12.3}{\tt bprobit}: Bivariate Probit Regression for Two Dichotomous Dependent Variables}{138}{section.12.3}
-\contentsline {subsubsection}{Syntax}{138}{section*.150}
-\contentsline {subsubsection}{Input Values}{138}{section*.151}
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-\contentsline {subsubsection}{Model}{140}{section*.153}
-\contentsline {subsubsection}{Quantities of Interest}{141}{section*.154}
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-\contentsline {section}{\numberline {12.4}\texttt {ei.dynamic}: Quinn's Dynamic Ecological Inference Model}{145}{section.12.4}
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-\contentsline {subsubsection}{Inputs}{145}{section*.158}
-\contentsline {subsubsection}{Additional Inputs}{145}{section*.159}
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-\contentsline {subsubsection}{Model}{148}{section*.162}
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-\contentsline {section}{\numberline {12.5}\texttt {ei.hier}: Hierarchical Ecological Inference Model for $2 \times 2$ Tables}{151}{section.12.5}
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-\contentsline {subsubsection}{Inputs}{151}{section*.166}
-\contentsline {subsubsection}{Additional Inputs}{151}{section*.167}
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-\contentsline {subsubsection}{Model}{154}{section*.170}
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-\contentsline {section}{\numberline {12.6}\texttt {ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological Inference Model for $R \times C$ Tables}{157}{section.12.6}
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-\contentsline {subsubsection}{Model}{159}{section*.176}
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-\contentsline {section}{\numberline {12.7}{\tt exp}: Exponential Regression for Duration Dependent Variables}{161}{section.12.7}
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-\contentsline {subsubsection}{Quantities of Interest}{163}{section*.183}
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-\contentsline {subsubsection}{Model}{169}{section*.191}
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-\contentsline {subsubsection}{Model}{174}{section*.199}
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-\contentsline {section}{\numberline {12.10}\texttt {factor.ord}: Ordinal Data Factor Analysis}{177}{section.12.10}
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-\contentsline {subsubsection}{Model}{180}{section*.207}
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-\contentsline {section}{\numberline {12.11}{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}{182}{section.12.11}
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-\contentsline {subsubsection}{Example}{182}{section*.212}
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-\contentsline {subsubsection}{Quantities of Interest}{183}{section*.214}
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-\contentsline {section}{\numberline {12.12}\texttt {irt1d}: One Dimensional Item Response Model}{187}{section.12.12}
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-\contentsline {subsubsection}{Inputs}{187}{section*.218}
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-\contentsline {subsubsection}{Model}{190}{section*.222}
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-\contentsline {section}{\numberline {12.13}\texttt {irtkd}: $k$-Dimensional Item Response Theory Model}{192}{section.12.13}
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-\contentsline {subsubsection}{Inputs}{192}{section*.226}
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-\contentsline {section}{\numberline {12.14}{\tt logit}: Logistic Regression for Dichotomous Dependent Variables}{197}{section.12.14}
-\contentsline {subsubsection}{Syntax}{197}{section*.233}
-\contentsline {subsubsection}{Additional Inputs}{197}{section*.234}
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-\contentsline {subsubsection}{Model}{199}{section*.236}
-\contentsline {subsubsection}{Quantities of Interest}{199}{section*.237}
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-\contentsline {section}{\numberline {12.15}\texttt {logit.bayes}: Bayesian Logistic Regression}{202}{section.12.15}
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-\contentsline {subsubsection}{Model}{204}{section*.244}
-\contentsline {subsubsection}{Quantities of Interest}{205}{section*.245}
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-\contentsline {section}{\numberline {12.16}{\tt lognorm}: Log-Normal Regression for Duration Dependent Variables}{207}{section.12.16}
-\contentsline {subsubsection}{Syntax}{207}{section*.248}
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-\contentsline {subsubsection}{Quantities of Interest}{209}{section*.252}
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-\contentsline {section}{\numberline {12.17}{\tt ls}: Least Squares Regression for Continuous Dependent Variables}{212}{section.12.17}
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-\contentsline {subsubsection}{Quantities of Interest}{214}{section*.259}
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-\contentsline {section}{\numberline {12.18}{\tt mlogit}: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}{217}{section.12.18}
-\contentsline {subsubsection}{Syntax}{217}{section*.262}
-\contentsline {subsubsection}{Input Values}{217}{section*.263}
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-\contentsline {subsubsection}{Quantities of Interest}{219}{section*.266}
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-\contentsline {subsubsection}{Further Information}{221}{section*.268}
-\contentsline {section}{\numberline {12.19}\texttt {mlogit.bayes}: Bayesian Multinomial Logistic Regression}{222}{section.12.19}
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-\contentsline {subsubsection}{Additional Inputs}{222}{section*.270}
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-\contentsline {subsubsection}{Model}{225}{section*.273}
-\contentsline {subsubsection}{Quantities of Interest}{225}{section*.274}
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-\contentsline {section}{\numberline {12.20}{\tt negbin}: Negative Binomial Regression for Event Count Dependent Variables}{228}{section.12.20}
-\contentsline {subsubsection}{Syntax}{228}{section*.277}
-\contentsline {subsubsection}{Additional Inputs}{228}{section*.278}
-\contentsline {subsubsection}{Example}{228}{section*.279}
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-\contentsline {subsubsection}{Quantities of Interest}{230}{section*.281}
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-\contentsline {section}{\numberline {12.21}{\tt netls}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}{233}{section.12.21}
-\contentsline {subsubsection}{Syntax}{233}{section*.284}
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-\contentsline {subsubsection}{Model}{234}{section*.286}
-\contentsline {subsubsection}{Quantities of Interest}{234}{section*.287}
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-\contentsline {section}{\numberline {12.22}{\tt netlogit}: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}{236}{section.12.22}
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-\contentsline {subsubsection}{Quantities of Interest}{237}{section*.293}
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-\contentsline {section}{\numberline {12.23}{\tt normal}: Normal Regression for Continuous Dependent Variables}{240}{section.12.23}
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-\contentsline {subsubsection}{Additional Inputs}{240}{section*.297}
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-\contentsline {subsubsection}{Quantities of Interest}{242}{section*.300}
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-\contentsline {section}{\numberline {12.24}\texttt {normal.bayes}: Bayesian Normal Linear Regression}{245}{section.12.24}
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-\contentsline {subsubsection}{Additional Inputs}{245}{section*.304}
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-\contentsline {section}{\numberline {12.25}{\tt ologit}: Ordinal Logistic Regression for Ordered Categorical Dependent Variables}{250}{section.12.25}
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-\contentsline {subsubsection}{Example}{250}{section*.312}
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-\contentsline {subsubsection}{Quantities of Interest}{252}{section*.314}
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-\contentsline {section}{\numberline {12.26}{\tt oprobit}: Ordinal Probit Regression for Ordered Categorical Dependent Variables}{255}{section.12.26}
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-\contentsline {subsubsection}{Example}{255}{section*.318}
-\contentsline {subsubsection}{Model}{256}{section*.319}
-\contentsline {subsubsection}{Quantities of Interest}{257}{section*.320}
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-\contentsline {section}{\numberline {12.27}\texttt {oprobit.bayes}: Bayesian Ordered Probit Regression}{260}{section.12.27}
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-\contentsline {subsubsection}{Quantities of Interest}{300}{section*.384}
-\contentsline {subsubsection}{Output Values}{301}{section*.385}
-\contentsline {subsubsection}{Contributors}{302}{section*.386}
-\contentsline {section}{\numberline {12.35}{\tt weibull}: Weibull Regression for Duration Dependent Variables}{303}{section.12.35}
-\contentsline {subsubsection}{Syntax}{303}{section*.387}
-\contentsline {subsubsection}{Input Values}{303}{section*.388}
-\contentsline {subsubsection}{Example}{304}{section*.389}
-\contentsline {subsubsection}{Model}{304}{section*.390}
-\contentsline {subsubsection}{Quantities of Interest}{305}{section*.391}
-\contentsline {subsubsection}{Output Values}{306}{section*.392}
-\contentsline {subsubsection}{Contributors}{307}{section*.393}
-\contentsline {chapter}{\numberline {13}Commands for Programmers and Contributors}{308}{chapter.13}
-\contentsline {section}{\numberline {13.1}{\tt describe}: Describe a model's systematic and stochastic parameters}{308}{section.13.1}
-\contentsline {subsubsection}{Description}{308}{section*.394}
-\contentsline {subsubsection}{Syntax}{308}{section*.395}
-\contentsline {subsubsection}{Arguments}{308}{section*.396}
-\contentsline {subsubsection}{Output Values}{308}{section*.397}
-\contentsline {subsubsection}{Examples}{310}{section*.398}
-\contentsline {subsubsection}{See Also}{312}{section*.399}
-\contentsline {subsubsection}{Contributors}{312}{section*.400}
-\contentsline {section}{\numberline {13.2}{\tt model.end}: Cleaning up after optimization}{313}{section.13.2}
-\contentsline {subsubsection}{Description}{313}{section*.401}
-\contentsline {subsubsection}{Syntax}{313}{section*.402}
-\contentsline {subsubsection}{Arguments}{313}{section*.403}
-\contentsline {subsubsection}{Output Values}{313}{section*.404}
-\contentsline {subsubsection}{See Also}{313}{section*.405}
-\contentsline {subsubsection}{Contributors}{313}{section*.406}
-\contentsline {section}{\numberline {13.3}{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula}{314}{section.13.3}
-\contentsline {subsubsection}{Description}{314}{section*.407}
-\contentsline {subsubsection}{Syntax}{314}{section*.408}
-\contentsline {subsubsection}{Arguments}{314}{section*.409}
-\contentsline {subsubsection}{Output Values}{314}{section*.410}
-\contentsline {subsubsection}{Examples}{314}{section*.411}
-\contentsline {subsubsection}{See Also}{315}{section*.412}
-\contentsline {subsubsection}{Contributors}{315}{section*.413}
-\contentsline {section}{\numberline {13.4}{\tt model.matrix.multiple}: Design matrix for multivariate models}{316}{section.13.4}
-\contentsline {subsubsection}{Description}{316}{section*.414}
-\contentsline {subsubsection}{Syntax}{316}{section*.415}
-\contentsline {subsubsection}{Arguments}{316}{section*.416}
-\contentsline {subsubsection}{Output Values}{316}{section*.417}
-\contentsline {subsubsection}{Examples}{317}{section*.418}
-\contentsline {subsubsection}{See Also}{317}{section*.419}
-\contentsline {subsubsection}{Contributors}{317}{section*.420}
-\contentsline {section}{\numberline {13.5}{\tt parse.formula}: Parsing the inputs}{318}{section.13.5}
-\contentsline {subsubsection}{Description}{318}{section*.421}
-\contentsline {subsubsection}{Syntax}{318}{section*.422}
-\contentsline {subsubsection}{Arguments}{318}{section*.423}
-\contentsline {subsubsection}{Output Values}{318}{section*.424}
-\contentsline {subsubsection}{Examples}{318}{section*.425}
-\contentsline {subsubsection}{See Also}{318}{section*.426}
-\contentsline {subsubsection}{Contributors}{319}{section*.427}
-\contentsline {section}{\numberline {13.6}{\tt parse.par}: Select and reshape parameter vectors}{321}{section.13.6}
-\contentsline {subsubsection}{Description}{321}{section*.428}
-\contentsline {subsubsection}{Syntax}{321}{section*.429}
-\contentsline {subsubsection}{Arguments}{321}{section*.430}
-\contentsline {subsubsection}{Output Values}{321}{section*.431}
-\contentsline {subsubsection}{Examples}{321}{section*.432}
-\contentsline {subsubsection}{See Also}{322}{section*.433}
-\contentsline {subsubsection}{Contributors}{322}{section*.434}
-\contentsline {section}{\numberline {13.7}{\tt put.start}: Set specific starting values for certain parameters}{323}{section.13.7}
-\contentsline {subsubsection}{Description}{323}{section*.435}
-\contentsline {subsubsection}{Syntax}{323}{section*.436}
-\contentsline {subsubsection}{Arguments}{323}{section*.437}
-\contentsline {subsubsection}{Output Values}{323}{section*.438}
-\contentsline {subsubsection}{See Also}{323}{section*.439}
-\contentsline {subsubsection}{Contributors}{323}{section*.440}
-\contentsline {section}{\numberline {13.8}{\tt set.start}: Set starting values for all parameters}{324}{section.13.8}
-\contentsline {subsubsection}{Description}{324}{section*.441}
-\contentsline {subsubsection}{Syntax}{324}{section*.442}
-\contentsline {subsubsection}{Arguments}{324}{section*.443}
-\contentsline {subsubsection}{Output Values}{324}{section*.444}
-\contentsline {subsubsection}{Example}{324}{section*.445}
-\contentsline {subsubsection}{See Also}{324}{section*.446}
-\contentsline {subsubsection}{Contributors}{324}{section*.447}
-\contentsline {section}{\numberline {13.9}{\tt tag}: Constrain parameter effects across equations}{325}{section.13.9}
-\contentsline {subsubsection}{Description}{325}{section*.448}
-\contentsline {subsubsection}{Syntax}{325}{section*.449}
-\contentsline {subsubsection}{Arguments}{325}{section*.450}
-\contentsline {subsubsection}{Output Values}{325}{section*.451}
-\contentsline {subsubsection}{Examples}{325}{section*.452}
-\contentsline {subsubsection}{See Also}{325}{section*.453}
-\contentsline {subsubsection}{Contributors}{325}{section*.454}
-\contentsline {part}{IV\hspace {1em}Appendices}{326}{part.4}
-\contentsline {chapter}{\numberline {A}Frequently Asked Questions}{327}{appendix.A}
-\contentsline {section}{\numberline {A.1}For All Zelig Users}{327}{section.A.1}
-\contentsline {section}{\numberline {A.2}For Zelig Contributors}{331}{section.A.2}
-\contentsline {chapter}{\numberline {B}What's New? What's Next?}{333}{appendix.B}
-\contentsline {section}{\numberline {B.1}What's New: Zelig Release Notes}{333}{section.B.1}
-\contentsline {section}{\numberline {B.2}What's Next?}{339}{section.B.2}
diff --git a/inst/doc/zinput.tex b/inst/doc/zinput.tex
new file mode 100644
index 0000000..00f140f
--- /dev/null
+++ b/inst/doc/zinput.tex
@@ -0,0 +1,38 @@
+\documentclass[oneside,letterpaper,12pt]{book}
+%\usepackage[ae,hyper]{Rd}
+\usepackage{bibentry}
+\usepackage{upquote}
+\usepackage{graphicx}
+\usepackage{natbib}
+\usepackage[reqno]{amsmath}
+\usepackage{amssymb}
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{verbatim}
+\usepackage{epsf}
+\usepackage{url}
+\usepackage{html}
+\usepackage{dcolumn}
+\usepackage{multirow}
+\usepackage{fullpage}
+\usepackage{lscape}
+\usepackage[all]{xy}
+% \usepackage[pdftex, bookmarksopen=true,bookmarksnumbered=true,
+% linkcolor=webred]{hyperref}
+\bibpunct{(}{)}{;}{a}{}{,}
+\newcolumntype{.}{D{.}{.}{-1}}
+\newcolumntype{d}[1]{D{.}{.}{#1}}
+\htmladdtonavigation{
+ \htmladdnormallink{%
+ \htmladdimg{http://gking.harvard.edu/pics/home.gif}}
+ {http://gking.harvard.edu/}}
+\newcommand{\MatchIt}{{\sc MatchIt}}
+\newcommand{\hlink}{\htmladdnormallink}
+\newcommand{\Sref}[1]{Section~\ref{#1}}
+\newcommand{\fullrvers}{2.4.1}
+\newcommand{\rvers}{2.4}
+\newcommand{\rwvers}{R-2.4.1}
+%\renewcommand{\bibentry}{\citealt}
+
+\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.jpg"}
+\setcounter{tocdepth}{2}
diff --git a/inst/unitTests/runit.multiple.R b/inst/unitTests/runit.multiple.R
index d15d9f9..9f87cdf 100644
--- a/inst/unitTests/runit.multiple.R
+++ b/inst/unitTests/runit.multiple.R
@@ -83,8 +83,8 @@ test.terms.multiple<-function(){
fml<-listMI[[i]]
objname <- paste("fml",i,sep="")
this.terms <- Zelig:::terms.multiple(fml)
- ##parsed[[objname]]<-this.terms
- ##save(parsed,file="test.terms.multiple.RData" )
+ ####parsed[[objname]]<-this.terms
+ ####save(parsed,file="test.terms.multiple.RData" )
load("test.terms.multiple.RData")
checkEquals(this.terms,parsed[[objname]])
}
diff --git a/inst/zideal/zideal.RData b/inst/zideal/zideal.RData
index 66af9c9..5f5c75e 100644
Binary files a/inst/zideal/zideal.RData and b/inst/zideal/zideal.RData differ
diff --git a/inst/zideal/zvcServer.R b/inst/zideal/zvcServer.R
index 04256ed..ee23686 100644
--- a/inst/zideal/zvcServer.R
+++ b/inst/zideal/zvcServer.R
@@ -1,3 +1,4 @@
+
### DESCRIPTION: Reads all functions describe.#.R that are part of
### Zelig package and gets all direct dependencies of Zelig
### packages names and url's to installed them
@@ -1287,3 +1288,9 @@ return(res)
### > dim(zmatnull)= 50 6
### > dim(zmathigh)= 9 6
### > dim(zmatNA) = 38 6
+
+
+## file is soruced when R CMD build Zelig
+
+zideal <- create.zelig.all.packages("Zelig")
+save(zideal, file="inst/zideal/zideal.RData")
diff --git a/tests/check.describe.R b/tests/check.describe.R
index 9198026..eee6b26 100644
--- a/tests/check.describe.R
+++ b/tests/check.describe.R
@@ -64,8 +64,8 @@ zeligListModels<-function() {
## describe function for this models does not exists
## just for testing purpuses i'm excluding them. <FIXME> urgent
- #nonexist <- c("arima","beta", "ei.dynamic","ei.hier","ei.RxC","mloglm","netlogit","netls")
- #setdiff(sub("zelig2","", tmp),nonexist)
+ nonexist <- c("arima","beta", "ei.dynamic","ei.hier","ei.RxC","mloglm","netlogit","netls")
+ setdiff(sub("zelig2","", tmp),nonexist)
}
###
--
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