[r-cran-matchit] 20/45: Import Upstream version 2.4-10
Andreas Tille
tille at debian.org
Fri Oct 20 06:17:20 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-matchit.
commit fc24d4d10b58f8bff35a12dbee067bc040d67d2d
Author: Andreas Tille <tille at debian.org>
Date: Fri Oct 20 07:40:56 2017 +0200
Import Upstream version 2.4-10
---
DESCRIPTION | 33 +-
NAMESPACE | 1 +
R/discard.R | 6 +-
R/hist.pscore.R | 41 +
R/jitter.pscore.R | 14 +-
R/match.qoi.R | 33 +-
R/matchit.R | 16 +-
R/matchit.qqplot.R | 21 +-
R/matchit2cem.R | 57 +
R/matchit2full.R | 5 +-
R/matchit2genetic.R | 6 +-
R/matchit2nearest.R | 8 +-
R/matchit2subclass.R | 23 +-
R/plot.matchit.R | 2 +
R/plot.matchit.subclass.R | 2 +
R/plot.summary.matchit.R | 42 +
R/print.summary.matchit.exact.R | 8 +-
R/summary.matchit.R | 16 +-
R/summary.matchit.exact.R | 9 +-
R/summary.matchit.full.R | 1 +
R/summary.matchit.subclass.R | 6 +-
demo/00Index | 1 +
demo/analysis.R | 21 +-
demo/cem.R | 47 +
demo/exact.R | 4 +-
demo/full.R | 8 +-
demo/genetic.R | 7 +-
demo/nearest.R | 34 +-
demo/optimal.R | 9 +-
demo/subclass.R | 7 +-
inst/doc/.latex2html-init | 244 ++
inst/doc/Makefile | 16 +
inst/doc/balance.tex | 137 +
inst/doc/faq.tex | 194 +
inst/doc/figs/f2figb.ps | 4081 +++++++++++++++++++++
inst/doc/figs/hist.pdf | 363 ++
inst/doc/figs/jitterplotnn.eps | 4085 +++++++++++++++++++++
inst/doc/figs/jitterplotnn.pdf | 3484 ++++++++++++++++++
inst/doc/figs/qqplotnn.eps | 7710 +++++++++++++++++++++++++++++++++++++++
inst/doc/figs/qqplotnn.pdf | 3007 +++++++++++++++
inst/doc/figs/qqplotnn1.eps | 1594 ++++++++
inst/doc/figs/qqplotnn1.pdf | 1555 ++++++++
inst/doc/figs/qqplotnn2.eps | 1604 ++++++++
inst/doc/figs/qqplotnn2.pdf | 1557 ++++++++
inst/doc/graphics.R | 48 +
inst/doc/index.shtml | 76 +
inst/doc/intro.tex | 85 +
inst/doc/makematchH | 18 +
inst/doc/matchit.aux | 188 +
inst/doc/matchit.bbl | 118 +
inst/doc/matchit.blg | 52 +
inst/doc/matchit.log | 636 ++++
inst/doc/matchit.out | 62 +
inst/doc/matchit.pdf | Bin 0 -> 430354 bytes
inst/doc/matchit.tex | 196 +
inst/doc/matchit.toc | 70 +
inst/doc/matchit2zelig.tex | 285 ++
inst/doc/matchitref.tex | 389 ++
inst/doc/mdataref.tex | 103 +
inst/doc/notation.tex | 151 +
inst/doc/overview.tex | 199 +
inst/doc/plotref.tex | 105 +
inst/doc/preprocess.tex | 233 ++
inst/doc/summaryref.tex | 127 +
man/help.matchit.Rd | 2 +-
man/lalonde.Rd | 6 +-
man/match.data.Rd | 32 +-
man/matchit.Rd | 102 +-
man/user.prompt.Rd | 2 +-
69 files changed, 33202 insertions(+), 202 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index f610c96..c71d404 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,21 +1,20 @@
Package: MatchIt
-Version: 2.2-11
-Date: 2006-06-12
-Title: MatchIt: Nonparametric Preprocessing for Parametric Casual Inference
+Version: 2.4-10
+Date: 2009-02-02
+Title: MatchIt
Author: Daniel Ho <daniel.e.ho at gmail.com>,
+ Elizabeth Stuart <stuart at stat.harvard.edu>,
Kosuke Imai <kimai at Princeton.Edu>,
- Gary King <king at harvard.edu>,
- Elizabeth Stuart <stuart at stat.harvard.edu>
+ Gary King <king at harvard.edu>
Maintainer: Kosuke Imai <kimai at Princeton.Edu>
-Depends: R (>= 2.2), MASS
-Suggests: optmatch, Matching, WhatIf
-Description: MatchIt selects matched samples of the
- the original treated and control groups with similar
- covariate distributions -- can be used to match exactly
- on covariates, to match on propensity scores, or perform
- a variety of other matching procedures.
-LazyLoad: yes
-LazyData: yes
-License: GPL version 2 or newer
-URL: http://gking.harvard.edu/matchit
-Packaged: Thu Jul 13 16:25:02 2006; estuart
+Depends: R (>= 2.7), MASS
+Description: MatchIt preprocesses data by selecting approximate matched samples
+ of the treated and control groups with similar covariate
+ distributions, drawing on a large variety of matching methods.
+ After preprocessing data with MatchIt, whatever standard parametric
+ technique one might have used without preprocessing can be used, but
+ the results will be far less model dependent.
+License: GPL (>= 2)
+URL: http://gking.harvard.edu/matchit/
+Suggests: cem, optmatch, Matching, WhatIf, nnet, rpart, mgcv
+Packaged: Mon Feb 2 09:44:18 2009; king
diff --git a/NAMESPACE b/NAMESPACE
index c6afeb7..d6f4433 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,6 +11,7 @@ S3method(print, summary.matchit)
S3method(print, summary.matchit.exact)
S3method(print, summary.matchit.subclass)
S3method(plot, matchit)
+S3method(plot, summary.matchit)
S3method(plot, matchit.subclass)
S3method(summary, matchit)
S3method(summary, matchit.exact)
diff --git a/R/discard.R b/R/discard.R
index a7c9527..ef1eb05 100644
--- a/R/discard.R
+++ b/R/discard.R
@@ -19,10 +19,10 @@ discard <- function(treat, pscore, option, X) {
## convext hull stuff
if (!("WhatIf" %in% .packages(all = TRUE)))
install.packages("WhatIf")
- if (!("lpSolve" %in% .packages(all = TRUE)))
- install.packages("lpSolve")
+# if (!("lpSolve" %in% .packages(all = TRUE)))
+# install.packages("lpSolve")
require(WhatIf)
- require(lpSolve)
+# require(lpSolve)
discarded <- rep(FALSE, n.obs)
if (option == "hull.control"){ # discard units not in T convex hull
wif <- whatif(cfact = X[treat==0,], data = X[treat==1,])
diff --git a/R/hist.pscore.R b/R/hist.pscore.R
new file mode 100644
index 0000000..ddc245b
--- /dev/null
+++ b/R/hist.pscore.R
@@ -0,0 +1,41 @@
+hist.pscore <- function(x, numdraws=5000){
+ treat <- x$treat
+ pscore <- x$distance
+ weights <- x$weights
+ matched <- weights!=0
+ q.cut <- x$q.cut
+ cwt <- sqrt(weights)
+ ratio <- x$call$ratio
+ if(is.null(ratio)){ratio <- 1}
+
+ ## For full or ratio matching, sample numdraws observations using the weights
+ if(identical(x$call$method,"full") | (ratio!=1)) {
+ pscore.treated.matched <- sample(names(treat)[treat==1],
+ numdraws/2, replace=TRUE,
+ prob=x$weights[treat==1])
+ pscore.treated.matched <- pscore[pscore.treated.matched]
+ pscore.control.matched <- sample(names(treat)[treat==0],
+ numdraws/2, replace=TRUE,
+ prob=x$weights[treat==0])
+ pscore.control.matched <- pscore[pscore.control.matched]
+
+ } else {
+ pscore.treated.matched <- pscore[treat==1 & weights!=0]
+ pscore.control.matched <- pscore[treat==0 & weights!=0]
+ }
+ par(mfrow=c(2,2))
+ hist(pscore[treat==1],xlim=range(na.omit(pscore)),
+ xlab="Propensity Score", freq=F,
+ main="Raw Treated")
+ hist(pscore.treated.matched,xlim=range(na.omit(pscore)),
+ xlab="Propensity Score", freq=F,
+ main="Matched Treated")
+ if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
+ hist(pscore[treat==0],xlim=range(na.omit(pscore)),
+ xlab="Propensity Score", freq=F,
+ main="Raw Control")
+ hist(pscore.control.matched,xlim=range(na.omit(pscore)),
+ xlab="Propensity Score", freq=F,
+ main="Matched Control")
+ if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
+}
diff --git a/R/jitter.pscore.R b/R/jitter.pscore.R
index 60ffc2b..f8340ea 100644
--- a/R/jitter.pscore.R
+++ b/R/jitter.pscore.R
@@ -4,9 +4,11 @@ jitter.pscore <- function(x, interactive){
weights <- x$weights
matched <- weights!=0
q.cut <- x$q.cut
- jitp <- jitter(rep(1,length(treat)),factor=6)-(treat==0)
+ jitp <- jitter(rep(1,length(treat)),factor=6)+(treat==1)*(weights==0)-(treat==0)-(weights==0)*(treat==0)
cwt <- sqrt(weights)
- plot(pscore,xlim=range(na.omit(pscore)),ylim=c(-1,2),
+ minp <- min(pscore,na.rm=T)
+ maxp <- max(pscore,na.rm=T)
+ plot(pscore,xlim=c(minp,maxp+0.1*(maxp-minp)),ylim=c(-1.5,2.5),
type="n",ylab="",xlab="Propensity Score",
axes=F,main="Distribution of Propensity Scores")
if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
@@ -19,11 +21,13 @@ jitter.pscore <- function(x, interactive){
points(pscore[treat==0&weights==0],jitp[treat==0&weights==0],
pch=5,col="grey",cex=0.5)
axis(1)
- text(sum(range(na.omit(pscore)))/2,1.5,"Treatment Units")
- text(sum(range(na.omit(pscore)))/2,-0.5,"Control Units")
+ text(sum(range(na.omit(pscore)))/2,2.5,"Unmatched Treatment Units")
+ text(sum(range(na.omit(pscore)))/2,1.5,"Matched Treatment Units")
+ text(sum(range(na.omit(pscore)))/2,0.5,"Matched Control Units")
+ text(sum(range(na.omit(pscore)))/2,-0.5,"Unmatched Control Units")
box()
if(interactive==TRUE) {
print("To identify the units, use first mouse button; to stop, use second.")
- identify(pscore,jitp,names(treat))
+ identify(pscore,jitp,names(treat),atpen=T)
}
}
diff --git a/R/match.qoi.R b/R/match.qoi.R
index 4658dbf..2d08362 100644
--- a/R/match.qoi.R
+++ b/R/match.qoi.R
@@ -1,51 +1,58 @@
## Function to calculate summary stats
qoi <- function(xx,tt,ww, t.plot=NULL, c.plot=NULL, sds=NULL,
- standardize = FALSE){
+ standardize = FALSE, std=F){
weighted.var <- function(x, w) {
sum(w * (x - weighted.mean(x,w))^2)/(sum(w) - 1)}
- xsum <- matrix(NA,2,6)
+ xsum <- matrix(NA,2,7)
xsum <- as.data.frame(xsum)
row.names(xsum) <- c("Full","Matched")
if (standardize)
- names(xsum) <- c("Means Treated","Means Control", "Std. Mean Diff.",
+ names(xsum) <- c("Means Treated","Means Control",
+ "SD Control", "Std. Mean Diff.",
"eCDF Med", "eCDF Mean", "eCDF Max")
else
- names(xsum) <- c("Means Treated","Means Control","Mean Diff", "eQQ Med",
- "eQQ Mean", "eQQ Max")
+ names(xsum) <- c("Means Treated","Means Control",
+ "SD Control", "Mean Diff",
+ "eQQ Med", "eQQ Mean", "eQQ Max")
x1 <- xx[tt==1]
x0 <- xx[tt==0]
ww1 <- ww[tt==1]
ww0 <- ww[tt==0]
xsum[1,1] <- mean(x1,na.rm=T)
xsum[1,2] <- mean(x0,na.rm=T)
+ xsum[1,3] <- sd(x0,na.rm=T)
X.t.m <- xx[tt==1][ww1>0]
X.c.m <- xx[tt==0][ww0>0]
xsum[2,1] <- weighted.mean(X.t.m, ww1[ww1>0])
xsum[2,2] <- weighted.mean(X.c.m, ww0[ww0>0])
+ xsum[2,3] <- sqrt(weighted.var(X.c.m, ww0[ww0>0]))
if(!(sum(tt==1)<2|(sum(tt==0)<2))){
xsd <- sd(x1,na.rm=T)
qqall <- qqsum(x1,x0,standardize=standardize)
- xsum[1,4:6] <- c(qqall$meddiff,qqall$meandiff,qqall$maxdiff)
+ xsum[1,5:7] <- c(qqall$meddiff,qqall$meandiff,qqall$maxdiff)
if (standardize)
if (!is.null(sds))
- xsum[1,3] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/sds
+ xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/sds
else
- xsum[1,3] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/xsd
+ xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/xsd
else
- xsum[1,3] <- mean(x1,na.rm=T)-mean(x0,na.rm=T)
+ xsum[1,4] <- mean(x1,na.rm=T)-mean(x0,na.rm=T)
if(!is.null(t.plot))
qqmat <- qqsum(xx[t.plot],xx[c.plot],standardize=standardize)
else
qqmat <- qqsum(x1[ww1>0],x0[ww0>0],standardize=standardize)
- xsum[2,4:6] <- c(qqmat$meddiff,qqmat$meandiff,qqmat$maxdiff)
+ xsum[2,5:7] <- c(qqmat$meddiff,qqmat$meandiff,qqmat$maxdiff)
if (standardize)
if (!is.null(sds))
- xsum[2,3] <- (xsum[2,1]-xsum[2,2])/sds
+ xsum[2,4] <- (xsum[2,1]-xsum[2,2])/sds
else
- xsum[2,3] <- (xsum[2,1]-xsum[2,2])/xsd
+ xsum[2,4] <- (xsum[2,1]-xsum[2,2])/xsd
else
- xsum[2,3] <- xsum[2,1]-xsum[2,2]
+ xsum[2,4] <- xsum[2,1]-xsum[2,2]
}
+ if(!std){
+ xsum <- xsum[,c(1:2,4:7)]
+ }
xsum
}
diff --git a/R/matchit.R b/R/matchit.R
index 254de57..2747883 100644
--- a/R/matchit.R
+++ b/R/matchit.R
@@ -4,12 +4,17 @@ matchit <- function(formula, data, method = "nearest", distance = "logit",
#Checking input format
#data input
+ mcall <- match.call()
if(is.null(data)) stop("Dataframe must be specified",call.=FALSE)
if(!is.data.frame(data)){
stop("Data must be a dataframe",call.=FALSE)}
if(sum(is.na(data))>0)
stop("Missing values exist in the data")
-
+ # list-wise deletion
+ # allvars <- all.vars(mcall)
+ # varsindata <- colnames(data)[colnames(data) %in% all.vars(mcall)]
+ # data <- na.omit(subset(data, select = varsindata))
+
## 7/13/06: Convert character variables to factors as necessary
ischar <- rep(0, dim(data)[2])
for (i in 1:dim(data)[2])
@@ -37,12 +42,10 @@ matchit <- function(formula, data, method = "nearest", distance = "logit",
distance <- out1 <- discarded <- NULL
if (!is.null(distance))
warning("distance is set to `NULL' when exact matching is used.")
- }
- else if (is.numeric(distance)){
+ } else if (is.numeric(distance)){
out1 <- NULL
discarded <- discard(treat, distance, discard, X)
- }
- else {
+ } else {
if (is.null(distance.options$formula))
distance.options$formula <- formula
if (is.null(distance.options$data))
@@ -51,6 +54,7 @@ matchit <- function(formula, data, method = "nearest", distance = "logit",
discarded <- discard(treat, out1$distance, discard, X)
if (reestimate) {
distance.options$data <- data[!discarded,]
+ distance.options$weights <- distance.options$weights[!discarded]
tmp <- out1
out1 <- do.call(fn1, distance.options)
tmp$distance[!discarded] <- out1$distance
@@ -63,7 +67,7 @@ matchit <- function(formula, data, method = "nearest", distance = "logit",
out2 <- do.call(fn2, list(treat, X, data, distance=distance, discarded, ...))
## putting all the results together
- out2$call <- match.call()
+ out2$call <- mcall
out2$model <- out1$model
out2$formula <- formula
out2$treat <- treat
diff --git a/R/matchit.qqplot.R b/R/matchit.qqplot.R
index 1c7ab6d..5fd52fb 100644
--- a/R/matchit.qqplot.R
+++ b/R/matchit.qqplot.R
@@ -22,9 +22,12 @@ matchit.qqplot <- function(x,discrete.cutoff,
}
treat <- x$treat
matched <- x$weights!=0
+
+ ratio <- x$call$ratio
+ if(is.null(ratio)){ratio <- 1}
- ## For full matching, sample numdraws observations using the weights
- if(x$call$method=="full") {
+ ## For full or ratio matching, sample numdraws observations using the weights
+ if(identical(x$call$method,"full") | (ratio!=1)) {
t.plot <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=x$weights[treat==1])
c.plot <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=x$weights[treat==0])
@@ -52,11 +55,7 @@ matchit.qqplot <- function(x,discrete.cutoff,
nc <- length(nn)
covariates <- data.matrix(covariates)
oma <- c(4, 4, 6, 4)
- if(interactive){
- opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma, ask=T)
- } else {
- opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma, ask=F)
- }
+ opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma)
on.exit(par(opar))
for (i in 1:nc){
xi <- covariates[,i]
@@ -94,5 +93,11 @@ matchit.qqplot <- function(x,discrete.cutoff,
abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2)
abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2)
box()
- }
+ if(interactive){
+ par(ask=T)
+ } else {
+ par(ask=F)
+ }
+ }
+ par(ask=F)
}
diff --git a/R/matchit2cem.R b/R/matchit2cem.R
new file mode 100644
index 0000000..6b7dee4
--- /dev/null
+++ b/R/matchit2cem.R
@@ -0,0 +1,57 @@
+# matchit2cem - matchit wrapper for cem matching algorithm
+#
+# 06/10/2008 - m.blackwell
+#
+# this function takes inputs from matchit() and returns the
+# strata for each observation in the subclass entry and the
+# weight for each observation in the weight entry. No match
+# matrix is returned since matches are not unique within
+# strata.
+#
+matchit2cem <- function(treat, X, data, distance, discarded,
+ ratio = 1, verbose = FALSE, k2k.method=NULL, ...) {
+
+
+ if (!("cem" %in% .packages(all = TRUE)))
+ install.packages("cem",repos="http://gking.harvard.edu/")
+ require(cem)
+
+ if (verbose)
+ cat("Coarsened exact matching...\n")
+
+ n <- length(treat)
+
+ # cem takes the data all together and wants the treatment specified
+ # with the column name of the data frame. Here we massage the matchit
+ # inputs to this format. Note that X has its proper columnames, but
+ # treat does not have the original column name.
+ cem.data <- as.data.frame(cbind(treat,X))
+
+ mat <-
+ cem(treatment="treat",data=cem.data,verbose=as.integer(verbose)+1,
+ method=k2k.method,...)
+
+ # here we create a column vector where the matched entry get its stratum
+ # and the unmatched entry gets an NA.
+ strat <- rep(NA,n)
+ names(strat) <- names(treat)
+ strat[mat$matched] <- mat$strata[mat$matched]
+
+ # here we just add the names onto the wieght from the cem output
+ wh <- mat$w
+ names(wh) <- names(treat)
+
+ # weighting functions in matchit error-out on these conditions,
+ # so we should too.
+
+ if (sum(wh)==0)
+ stop("No units were matched")
+ else if (sum(wh[treat==1])==0)
+ stop("No treated units were matched")
+ else if (sum(wh[treat==0])==0)
+ stop("No control units were matched")
+
+ res <- list(subclass = strat, weights = mat$w)
+ class(res) <- "matchit"
+ return(res)
+}
diff --git a/R/matchit2full.R b/R/matchit2full.R
index 7b297cf..896c7b9 100644
--- a/R/matchit2full.R
+++ b/R/matchit2full.R
@@ -9,10 +9,11 @@ matchit2full <- function(treat, X, data, distance, discarded,
## full matching for undiscarded units
ttt <- treat[!discarded]
+ ddd <- distance[!discarded]
n0 <- length(ttt[ttt==0])
n1 <- length(ttt[ttt==1])
- d1 <- distance[ttt==1]
- d0 <- distance[ttt==0]
+ d1 <- ddd[ttt==1]
+ d0 <- ddd[ttt==0]
d <- matrix(0, ncol=n0, nrow=n1)
rownames(d) <- names(ttt[ttt==1])
colnames(d) <- names(ttt[ttt==0])
diff --git a/R/matchit2genetic.R b/R/matchit2genetic.R
index 9aedf33..1b11283 100644
--- a/R/matchit2genetic.R
+++ b/R/matchit2genetic.R
@@ -1,8 +1,8 @@
matchit2genetic <- function(treat, X, data, distance, discarded,
ratio = 1, verbose = FALSE, ...) {
- if (!("rgenoud" %in% .packages(all = TRUE)))
- install.packages("rgenoud")
- require(rgenoud)
+ #if (!("rgenoud" %in% .packages(all = TRUE)))
+ # install.packages("rgenoud")
+ #require(rgenoud)
if (!("Matching" %in% .packages(all = TRUE)))
install.packages("Matching")
diff --git a/R/matchit2nearest.R b/R/matchit2nearest.R
index 9a3460f..dc873a4 100644
--- a/R/matchit2nearest.R
+++ b/R/matchit2nearest.R
@@ -45,7 +45,13 @@ matchit2nearest <- function(treat, X, data, distance, discarded,
clabels <- names(treat[treat==0])
in.sample <- !discarded
names(in.sample) <- labels
-
+
+ ## 10/1/07: Warning for if fewer control than ratio*treated and matching without replacement
+ if (n0 < ratio*n1 & replace==FALSE) {
+ if (ratio > 1) warning(paste("Not enough control units for ", ratio, " matches for each treated unit when matching without replacement. Not all treated units will receive", ratio, "matches"))
+ else warning(paste("Fewer control than treated units and matching without replacement. Not all treated units will receive a match. Treated units will be matched in the order specified by m.order:", m.order))
+ }
+
## Generating match matrix
match.matrix <- matrix(0, nrow=n1, ncol=ratio, dimnames=list(tlabels, 1:ratio))
diff --git a/R/matchit2subclass.R b/R/matchit2subclass.R
index b36f891..30fea01 100644
--- a/R/matchit2subclass.R
+++ b/R/matchit2subclass.R
@@ -59,12 +59,18 @@ matchit2subclass <- function(treat, X, data, distance, discarded,
if(subclass<=0){stop("Subclass must be a positive vector",call.=FALSE)}
sprobs <- seq(0,1,length=(round(subclass)+1))
sprobs <- sprobs[2:(length(sprobs)-1)]
- if(sub.by=="treat")
- q <- c(0,quantile(p1,probs=sprobs,na.rm=TRUE),1)
+ min.dist <- min(distance,na.rm=TRUE)-0.01
+ max.dist <- max(distance,na.rm=TRUE)+0.01
+ if(sub.by=="treat")
+ q <- c(min.dist,quantile(p1,probs=sprobs,na.rm=TRUE),
+ max.dist)
else if(sub.by=="control")
- q <- c(0,quantile(p0,probs=sprobs,na.rm=TRUE),1)
+ q <- c(min.dist,quantile(p0,probs=sprobs,na.rm=TRUE),
+ max.dist)
else if(sub.by=="all")
- q <- c(0,quantile(distance,probs=sprobs,na.rm=TRUE),1)
+ q <- c(min.dist,
+ quantile(distance,probs=sprobs,na.rm=TRUE),
+ max.dist)
else
stop("Must specify a valid sub.by",call.=FALSE)
}
@@ -77,7 +83,7 @@ matchit2subclass <- function(treat, X, data, distance, discarded,
q2 <- q[i+1]
psclass <- psclass+i*as.numeric(distance<q2 & distance>=q1)
}
-
+
## No subclass for discarded or unmatched units
psclass[in.sample==0] <- NA
psclass[!matched] <- NA
@@ -85,6 +91,13 @@ matchit2subclass <- function(treat, X, data, distance, discarded,
res <- list(subclass = psclass, q.cut = q,
weights = weights.subclass(psclass, treat))
+ #warning for discrete data
+ unique.classes <- unique(psclass)
+ unique.classes <- unique.classes[!is.na (unique.classes)]
+ if(length(unique.classes)!=subclass){
+ warning("Due to discreteness in data, fewer subclasses generated",call.=F)
+ }
+
class(res) <- c("matchit.subclass", "matchit")
return(res)
}
diff --git a/R/plot.matchit.R b/R/plot.matchit.R
index 8213cf9..1ecea83 100644
--- a/R/plot.matchit.R
+++ b/R/plot.matchit.R
@@ -11,6 +11,8 @@ plot.matchit <- function(x, discrete.cutoff=5, type="QQ",
which.xs = which.xs)
} else if(type=="jitter"){
jitter.pscore(x, interactive=interactive)
+ } else if(type=="hist"){
+ hist.pscore(x)
} else {
stop("Invalid type")
}
diff --git a/R/plot.matchit.subclass.R b/R/plot.matchit.subclass.R
index 01b9912..7be455a 100644
--- a/R/plot.matchit.subclass.R
+++ b/R/plot.matchit.subclass.R
@@ -37,6 +37,8 @@ plot.matchit.subclass <- function(x, discrete.cutoff=5,
}
} else if(type=="jitter"){
jitter.pscore(x, interactive=interactive)
+ } else if(type=="hist"){
+ hist.pscore(x)
} else {
stop("Invalid type")
}
diff --git a/R/plot.summary.matchit.R b/R/plot.summary.matchit.R
new file mode 100644
index 0000000..601f87a
--- /dev/null
+++ b/R/plot.summary.matchit.R
@@ -0,0 +1,42 @@
+plot.summary.matchit <- function(x, interactive = TRUE, ...) {
+ if ("matchit.exact" %in% class(x)){
+ stop("Not appropriate for exact matching. No plots generated.")
+ }
+
+ if (!"Std. Mean Diff."%in%names(x$sum.all)){
+ stop("Not appropriate for unstandardized summary. Run summary() with the standardize=TRUE option, and then plot.")
+ }
+
+ sd.pre <- abs(x$sum.all$"Std. Mean Diff.")
+ sd.post <- abs(x$sum.matched$"Std. Mean Diff.")
+
+ if (!is.null(x$q.table)) sd.post <- abs(x$sum.subclass$"Std. Mean Diff")
+
+ ases.dat <- data.frame(es.unw = sd.pre, es.w = sd.post)
+ par(mfrow=c(1,1))
+ plot(c(0.85, 2.15), c(0, min(3, max(unlist(ases.dat[,
+ 1:2]), na.rm = TRUE))), type = "n", xaxt = "n", ylab = "Absolute Standardized Diff in Means",
+ xlab = "", main = "")
+ abline(h = c(0.2, 0.4, 0.6, 0.8, 1.0))
+ axis(side = 1, at = 1:2, labels = c("All Data", "Matched Data"))
+ for (i in 1:nrow(ases.dat)) {
+ points(1:2, abs(ases.dat[i, c("es.unw", "es.w")]),
+ type = "b", col = "grey", pch=19)
+ }
+ temp1 <- ases.dat[abs(ases.dat$es.unw) < abs(ases.dat$es.w),]
+ for (i in 1:nrow(temp1)) {
+ points(1:2, abs(temp1[i, c("es.unw", "es.w")]), type = "b",
+ col = "black", lwd = 2, pch=19)
+ }
+ if (max(ases.dat$es.w, na.rm = TRUE) > 3)
+ mtext(text = "Some standardized diffs in means > 3 after matching!", side = 3,
+ col = "red")
+
+ if(interactive==TRUE) {
+ print("To identify the variables, use first mouse button; to stop, use second.")
+ identify(rep(1, length(sd.pre)),sd.pre,rownames(x$sum.all),atpen=T)
+ identify(rep(2, length(sd.post)),sd.post,rownames(x$sum.all),atpen=T)
+ }
+}
+
+
diff --git a/R/print.summary.matchit.exact.R b/R/print.summary.matchit.exact.R
index b48cae9..85ab5d6 100644
--- a/R/print.summary.matchit.exact.R
+++ b/R/print.summary.matchit.exact.R
@@ -3,13 +3,7 @@ print.summary.matchit.exact <- function(x, digits = max(3,
...){
cat("\nCall:", deparse(x$call), sep = "\n")
cat("\nSample sizes:\n")
- ntab <- table(factor(!is.na(x$subclass),
- levels=c("TRUE","FALSE")), x$treat)
- nn <- rbind(table(x$treat),
- ntab[c("TRUE","FALSE"),])
- dimnames(nn) <- list(c("All","Matched","Discarded"),
- c("Control","Treated"))
- print.table(nn,digits=digits)
+ print.table(x$nn,digits=digits)
cat("\nMatched sample sizes by subclass:\n")
print.data.frame(x$q.table, digits = digits)
cat("\n")
diff --git a/R/summary.matchit.R b/R/summary.matchit.R
index 7df6881..73d2974 100644
--- a/R/summary.matchit.R
+++ b/R/summary.matchit.R
@@ -19,12 +19,16 @@ summary.matchit <- function(object, interactions = FALSE,
treat <- object$treat
weights <- object$weights
nam <- dimnames(XX)[[2]]
+ dupnam <- duplicated(nam)
+ if(sum(dupnam)>0){
+ nam[dupnam] <- paste(nam[dupnam],".1",sep="")
+ }
kk <- ncol(XX)
## Summary Stats
- aa <- apply(XX,2,qoi,tt=treat,ww=weights,standardize=standardize)
- sum.all <- as.data.frame(matrix(0,kk,6))
- sum.matched <- as.data.frame(matrix(0,kk,6))
+ aa <- apply(XX,2,qoi,tt=treat,ww=weights,standardize=standardize,std=T)
+ sum.all <- as.data.frame(matrix(0,kk,7))
+ sum.matched <- as.data.frame(matrix(0,kk,7))
row.names(sum.all) <- row.names(sum.matched) <- nam
names(sum.all) <- names(sum.matched) <- names(aa[[1]])
sum.all.int <- sum.matched.int <- NULL
@@ -34,7 +38,7 @@ summary.matchit <- function(object, interactions = FALSE,
if(interactions){
for(j in i:kk){
x2 <- XX[,i]*as.matrix(XX[,j])
- jqoi <- qoi(x2,tt=treat,ww=weights,standardize=standardize)
+ jqoi <- qoi(x2,tt=treat,ww=weights,standardize=standardize,std=T)
sum.all.int <- rbind(sum.all.int,jqoi[1,])
sum.matched.int <- rbind(sum.matched.int,jqoi[2,])
row.names(sum.all.int)[nrow(sum.all.int)] <-
@@ -49,9 +53,9 @@ summary.matchit <- function(object, interactions = FALSE,
## Imbalance Reduction
stat0 <- abs(cbind(sum.all[,2]-sum.all[,1],
- sum.all[,4:6]))
+ sum.all[,5:7]))
stat1 <- abs(cbind(sum.matched[,2]-sum.matched[,1],
- sum.matched[,4:6]))
+ sum.matched[,5:7]))
reduction <- as.data.frame(100*(stat0-stat1)/stat0)
if(sum(stat0==0 & stat1==0, na.rm=T)>0){
reduction[stat0==0 & stat1==0] <- 0
diff --git a/R/summary.matchit.exact.R b/R/summary.matchit.exact.R
index ed94a96..8c66243 100644
--- a/R/summary.matchit.exact.R
+++ b/R/summary.matchit.exact.R
@@ -20,9 +20,14 @@ summary.matchit.exact <- function(object, covariates = FALSE, ...) {
length(treat[qi & !is.na(qi)]),as.numeric(XX[qi,,drop=F][1,]))
}
}
-
+ ntab <- table(factor(!is.na(object$subclass),
+ levels=c("TRUE","FALSE")), treat)
+ nn <- rbind(table(treat),
+ ntab[c("TRUE","FALSE"),])
+ dimnames(nn) <- list(c("All","Matched","Discarded"),
+ c("Control","Treated"))
## output
- res <- list(q.table = q.table, subclass = object$subclass,
+ res <- list(q.table = q.table, nn = nn, subclass = object$subclass,
treat = object$treat, call = object$call)
class(res) <- c("summary.matchit.exact", "summary.matchit")
return(res)
diff --git a/R/summary.matchit.full.R b/R/summary.matchit.full.R
index 578b4e0..6652f5f 100644
--- a/R/summary.matchit.full.R
+++ b/R/summary.matchit.full.R
@@ -29,6 +29,7 @@ summary.matchit.full <- function(object, interactions = FALSE,
if(interactions){
for(j in i:kk){
x2 <- XX[,i]*as.matrix(XX[,j])
+ names(x2) <- names(XX[,1])
jqoi <- qoi(x2,tt=treat,ww=weights, t.plot=t.plot,
c.plot=c.plot, standardize=standardize)
sum.all.int <- rbind(sum.all.int,jqoi[1,])
diff --git a/R/summary.matchit.subclass.R b/R/summary.matchit.subclass.R
index 96c59a6..3a9351f 100644
--- a/R/summary.matchit.subclass.R
+++ b/R/summary.matchit.subclass.R
@@ -4,7 +4,7 @@ summary.matchit.subclass <- function(object, interactions = FALSE,
X <- object$X
## Fix X matrix so that it doesn't have any factors
- varnames <- colnames(X)
+ varnames <- colnames(X)
for(var in varnames) {
if(is.factor(X[,var])) {
tempX <- X[,!colnames(X)%in%c(var)]
@@ -83,9 +83,9 @@ summary.matchit.subclass <- function(object, interactions = FALSE,
}
if(object$call$sub.by=="treat") {
wsub <- qn[1,]/sum(qn[1,])
- } else if(sub.by=="control") {
+ } else if(object$call$sub.by=="control") {
wsub <- qn[2,]/sum(qn[2,])
- } else if(sub.by=="all") {
+ } else if(object$call$sub.by=="all") {
wsub <- qn[3,]/sum(qn[3,])
}
sum.subclass <- sum.all
diff --git a/demo/00Index b/demo/00Index
index 5440f3e..dc64ad4 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -6,3 +6,4 @@ nearest Demos of nearest neighbor matching, using Lalonde dataset
subclass Demo of subclassification, using Lalonde dataset
optimal Demo of optimal matching, using Lalonde dataset
analysis Demo of using Zelig with MatchIt for parametric causal inference after matching
+cem Demo of coarsened exact matching
diff --git a/demo/analysis.R b/demo/analysis.R
index 61cb677..988caf5 100644
--- a/demo/analysis.R
+++ b/demo/analysis.R
@@ -4,11 +4,9 @@
## load the Lalonde data
data(lalonde)
-user.prompt()
## load Zelig package: if not already installed, try install.package("Zelig")
library(Zelig)
-user.prompt()
## propensity score matching
m.out1 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75,
@@ -31,7 +29,7 @@ s.out1 <- sim(z.out1, x = x.out1)
user.prompt()
## obtain a summary
-summary(s.out1)
+print(summary(s.out1))
user.prompt()
@@ -42,7 +40,7 @@ user.prompt()
## fit the linear model to the treatment group controlling for propensity score and
## other covariates
z.out2 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 +
- distance, data = match.data(m.out1, "control"), model = "ls")
+ distance, data = match.data(m.out1, "treat"), model = "ls")
user.prompt()
## conducting the simulation procedure for the control group
@@ -63,13 +61,13 @@ user.prompt()
## some summaries
## point estimate
-mean(ate.all)
+print(mean(ate.all))
user.prompt()
## standard error
-sd(ate.all)
+print(sd(ate.all))
user.prompt()
## 95% confidence interval
-quantile(ate.all, c(0.025, 0.975))
+print(quantile(ate.all, c(0.025, 0.975)))
user.prompt()
@@ -97,17 +95,16 @@ s.out3 <- sim(z.out3, x = x.out3, num = 100)
user.prompt()
## overall results
-summary(s.out3)
+print(summary(s.out3))
user.prompt()
## summary for each subclass
-summary(s.out3, subset = 1)
+print(summary(s.out3, subset = 1))
user.prompt()
-summary(s.out3, subset = 2)
+print(summary(s.out3, subset = 2))
user.prompt()
-summary(s.out3, subset = 3)
-user.prompt()
+print(summary(s.out3, subset = 3))
diff --git a/demo/cem.R b/demo/cem.R
new file mode 100644
index 0000000..fc473fb
--- /dev/null
+++ b/demo/cem.R
@@ -0,0 +1,47 @@
+###
+### An Example Script for Coarsened Exact Matching
+###
+
+## load the Lalonde data
+data(lalonde)
+
+## coarsened exact matching with automatic coarsening
+m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree
+ + re74 + re75, data = lalonde, method = "cem")
+user.prompt()
+
+## print a short summary
+print(m.out)
+user.prompt()
+
+## balance diagnostics through statistics; standardize = T for plotting
+s.out <- summary(m.out, covariates = T, standardize = T)
+print(s.out)
+user.prompt()
+
+## graphical balance checks
+plot(m.out)
+plot(s.out)
+
+
+## create some cutpoints for continuous variables
+re74cut <- hist(lalonde$re74, br=seq(0,max(lalonde$re74)+1000, by=1000),plot=FALSE)$breaks
+re75cut <- hist(lalonde$re75, br=seq(0,max(lalonde$re75)+1000, by=1000),plot=FALSE)$breaks
+agecut <- hist(lalonde$age, br=seq(15,55, length=14),plot=FALSE)$breaks
+mycp <- list(re75=re75cut, re74=re74cut, age=agecut)
+
+## coarsened exact matching with user-given cutpoints
+m.out2 <- matchit(treat ~ age + educ + black + hispan + married + nodegree
+ + re74 + re75, data = lalonde, method = "cem",
+ cutpoints = mycp)
+user.prompt()
+
+## print a short summary
+print(m.out2)
+user.prompt()
+
+## balance diagnostics through statistics
+s.out2 <- summary(m.out2, covariates = T)
+print(s.out2)
+user.prompt()
+
diff --git a/demo/exact.R b/demo/exact.R
index 94dfd7e..3b625db 100644
--- a/demo/exact.R
+++ b/demo/exact.R
@@ -4,7 +4,6 @@
## laod the Lalonde data
data(lalonde)
-user.prompt()
## exact matching
m.out <- matchit(treat ~ educ + black + hispan, data = lalonde,
@@ -16,6 +15,5 @@ print(m.out)
user.prompt()
## balance diagnostics through statistics
-summary(m.out, covariates = T)
-user.prompt()
+print(summary(m.out, covariates = T))
diff --git a/demo/full.R b/demo/full.R
index 3b8f1c2..fe01767 100644
--- a/demo/full.R
+++ b/demo/full.R
@@ -4,21 +4,23 @@
## load the Lalonde data
data(lalonde)
-user.prompt()
## conduct full matching using the propensity score based on logistic regression
m.out <- matchit(treat ~ age + educ + black + hispan + married +
nodegree + re74 + re75, data = lalonde,
method = "full", distance = "logit")
-user.prompt()
## print a short summary
print(m.out)
user.prompt()
## balance diagnostics through statistics
-summary(m.out)
+s.out <- summary(m.out)
+print(s.out)
+s.out <- summary(m.out, standardize=TRUE)
+print(s.out)
user.prompt()
## balance diagnostics through graphics
plot(m.out)
+plot(s.out)
diff --git a/demo/genetic.R b/demo/genetic.R
index 67262b6..d04b171 100644
--- a/demo/genetic.R
+++ b/demo/genetic.R
@@ -7,12 +7,17 @@ data(lalonde)
## using logistic propensity score as one of the covariates
m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "genetic", distance = "logit")
+user.prompt()
## printing a short summary
print(m.out)
+user.prompt()
## numerical balance diagonstics
-summary(m.out)
+s.out <- summary(m.out, standardize=TRUE)
+print(s.out)
+user.prompt()
## graphical balance diagnostics
plot(m.out)
+plot(s.out)
diff --git a/demo/nearest.R b/demo/nearest.R
index 1368aeb..a0b7d07 100644
--- a/demo/nearest.R
+++ b/demo/nearest.R
@@ -7,18 +7,24 @@ user.prompt()
## 1:1 Nearest neighbor matching
m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age,
data = lalonde, method = "nearest")
-user.prompt()
## print a short summary
print(m.out)
user.prompt()
## balance diagnostics through statistics
-summary(m.out)
+s.out <- summary(m.out, standardize=TRUE)
+print(s.out)
user.prompt()
## balance diagnostics through graphics
plot(m.out)
+user.prompt()
+plot(m.out, type="jitter")
+user.prompt()
+plot(m.out, type="hist")
+user.prompt()
+plot(s.out)
## 2:1 Nearest neighbor matching
m.out1 <- matchit(treat ~ re74+re75+age+educ, data=lalonde,
@@ -30,11 +36,13 @@ print(m.out1)
user.prompt()
## balance diagnostics through statistics
-summary(m.out1)
+print(summary(m.out1))
user.prompt()
## balance diagnostics through graphics
plot(m.out)
+user.prompt()
+plot(m.out, type="jitter")
## 1:1 Nearest neighbor matching with Mahalanobis matching on re74 and re75 and exact matching on married
m.out2 <- matchit(treat ~ re74+re75+age+educ, data=lalonde,
@@ -46,11 +54,16 @@ print(m.out2)
user.prompt()
## balance diagnostics through statistics
-summary(m.out2)
+s.out2 <- summary(m.out2, standardize=TRUE)
+print(s.out2)
user.prompt()
## balance diagnostics through graphics
plot(m.out2)
+user.prompt()
+plot(m.out2, type="jitter")
+user.prompt()
+plot(s.out2)
## 1:1 Nearest neighbor matching with units outside the common support discarded
m.out3 <- matchit(treat ~ re74+re75+age+educ, data=lalonde,
@@ -62,11 +75,12 @@ print(m.out3)
user.prompt()
## balance diagnostics through statistics
-summary(m.out3)
+print(summary(m.out3))
user.prompt()
## balance diagnostics through graphics
plot(m.out3)
+plot(m.out3, type="jitter")
## 2:1 Nearest neighbor matching with replacement
m.out4 <- matchit(treat ~ re74+re75+age+educ, data=lalonde,
@@ -78,11 +92,13 @@ print(m.out4)
user.prompt()
## balance diagnostics through statistics
-summary(m.out4)
+print(summary(m.out4))
user.prompt()
## balance diagnostics through graphics
plot(m.out4)
+plot(m.out4, type="jitter")
+plot(m.out4, type="hist")
## 1:1 Nearest neighbor matching followed by subclassification
m.out5 <- matchit(treat ~ re74+re75+age+educ, data=lalonde,
@@ -94,8 +110,12 @@ print(m.out5)
user.prompt()
## balance diagnostics through statistics
-summary(m.out5)
+print(summary(m.out5))
user.prompt()
## balance diagnostics through graphics
plot(m.out5)
+user.prompt()
+
+s.out5 <- summary(m.out5, standardize=TRUE)
+plot(s.out5)
diff --git a/demo/optimal.R b/demo/optimal.R
index 6d5eadc..98d79db 100644
--- a/demo/optimal.R
+++ b/demo/optimal.R
@@ -4,7 +4,6 @@
## load the Lalonde data
data(lalonde)
-user.prompt()
## optimal ratio matching using the propensity score based on logistic regression
m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde,
@@ -16,8 +15,14 @@ print(m.out)
user.prompt()
## balance diagnostics through statistics
-summary(m.out)
+print(summary(m.out))
user.prompt()
## balance diagnostics through graphics
plot(m.out)
+user.prompt()
+plot(m.out, type="jitter")
+
+s.out <- summary(m.out, standardize=TRUE)
+plot(s.out)
+
diff --git a/demo/subclass.R b/demo/subclass.R
index c968a9c..1dfd4a9 100644
--- a/demo/subclass.R
+++ b/demo/subclass.R
@@ -4,7 +4,6 @@
## load the Lalonde data
data(lalonde)
-user.prompt()
## sublclassification
m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age,
@@ -16,9 +15,13 @@ print(m.out)
user.prompt()
## balance diagnostics
-summary(m.out)
+print(summary(m.out))
user.prompt()
## balance diagnostics through plots
plot(m.out)
+user.prompt()
plot(m.out, type="jitter")
+
+s.out <- summary(m.out, standardize=TRUE)
+plot(s.out)
diff --git a/inst/doc/.latex2html-init b/inst/doc/.latex2html-init
new file mode 100644
index 0000000..f044168
--- /dev/null
+++ b/inst/doc/.latex2html-init
@@ -0,0 +1,244 @@
+#LaTeX2HTML Version 96.1 : dot.latex2html-init
+#
+### Command Line Argument Defaults #######################################
+
+$MAX_SPLIT_DEPTH = 8; # Stop making separate files at this depth
+
+$MAX_LINK_DEPTH = 4; # Stop showing child nodes at this depth
+
+$NOLATEX = 0; # 1 = do not pass unknown environments to Latex
+
+$EXTERNAL_IMAGES = 0; # 1 = leave the images outside the document
+
+$ASCII_MODE = 0; # 1 = do not use any icons or internal images
+
+# 1 = use links to external postscript images rather than inlined bitmap
+# images.
+$PS_IMAGES = 0;
+
+$TITLE = $default_title; # The default is "No Title"
+
+# $STYLESHEET_CASCADE = "<link rel='stylesheet' href='/gking.css' MEDIA='screen' TYPE='text/css'>";
+
+# add new UI Javascript to HEAD section. -rmesard 30-nov-2005
+# $LATEX2HTML_META = "<script language='Javascript' src='/gking.js'></script>";
+
+$DESTDIR = ''; # Put the result in this directory
+
+# When this is set, the generated HTML files will be placed in the
+# current directory. If set to 0 the default behaviour is to create (or reuse)
+# another file directory.
+$NO_SUBDIR = 0;
+
+
+# Supply your own string if you don't like the default <Name> <Date>
+$ADDRESS = "Gary King \n$address_data[1]<script language='Javascript'>displayFooter()</script>";
+
+$NO_NAVIGATION = 0; # 1 = do not put a navigation panel at the top of each page
+
+# Put navigation links at the top of each page. If the page exceeds
+# $WORDS_IN_PAGE number of words then put one at the bottom of the page.
+$AUTO_NAVIGATION = 0;
+
+# Put a link to the index page in the navigation panel
+$INDEX_IN_NAVIGATION = 1;
+
+# Put a link to the table of contents in the navigation panel
+$CONTENTS_IN_NAVIGATION = 1;
+
+# Put a link to the next logical page in the navigation panel
+$NEXT_PAGE_IN_NAVIGATION = 1;
+
+# Put a link to the previous logical page in the navigation panel
+$PREVIOUS_PAGE_IN_NAVIGATION = 1;
+
+$INFO = 1; # 0 = do not make a "About this document..." section
+
+# Reuse images generated during previous runs
+$REUSE = 2;
+
+# When this is 1, the section numbers are shown. The section numbers should
+# then match those that would have bee produced by LaTeX.
+# The correct section numbers are obtained from the $FILE.aux file generated
+# by LaTeX.
+# Hiding the seciton numbers encourages use of particular sections
+# as standalone documents. In this case the cross reference to a section
+# is shown using the default symbol rather than the section number.
+$SHOW_SECTION_NUMBERS = 0;
+
+### Other global variables ###############################################
+$CHILDLINE = "<BR> <HR>\n";
+
+# This is the line width measured in pixels and it is used to right justify
+# equations and equation arrays;
+$LINE_WIDTH = 500;
+
+# Used in conjunction with AUTO_NAVIGATION
+$WORDS_IN_PAGE = 300;
+
+# Affects ONLY the way accents are processed
+$default_language = 'english';
+#$default_language = 'japanese';
+
+# The value of this variable determines how many words to use in each
+# title that is added to the navigation panel (see below)
+#
+$WORDS_IN_NAVIGATION_PANEL_TITLES = 4;
+
+# This number will determine the size of the equations, special characters,
+# and anything which will be converted into an inlined image
+# *except* "image generating environments" such as "figure", "table"
+# or "minipage".
+# Effective values are those greater than 0.
+# Sensible values are between 0.1 - 4.
+$MATH_SCALE_FACTOR = 1.6;
+
+# This number will determine the size of
+# image generating environments such as "figure", "table" or "minipage".
+# Effective values are those greater than 0.
+# Sensible values are between 0.1 - 4.
+$FIGURE_SCALE_FACTOR = 1.6;
+
+
+# If this is set then intermediate files are left for later inspection.
+# This includes $$_images.tex and $$_images.log created during image
+# conversion.
+# Caution: Intermediate files can be *enormous*.
+$DEBUG = 0;
+
+# If both of the following two variables are set then the "Up" button
+# of the navigation panel in the first node/page of a converted document
+# will point to $EXTERNAL_UP_LINK. $EXTERNAL_UP_TITLE should be set
+# to some text which describes this external link.
+$EXTERNAL_UP_LINK = "";
+$EXTERNAL_UP_TITLE = "";
+
+# If this is set then the resulting HTML will look marginally better if viewed
+# with Netscape.
+$NETSCAPE_HTML = 0;
+
+# Valid paper sizes are "letter", "legal", "a4","a3","a2" and "a0"
+# Paper sizes has no effect other than in the time it takes to create inlined
+# images and in whether large images can be created at all ie
+# - larger paper sizes *MAY* help with large image problems
+# - smaller paper sizes are quicker to handle
+$PAPERSIZE = "a4";
+
+# Replace "english" with another language in order to tell LaTeX2HTML that you
+# want some generated section titles (eg "Table of Contents" or "References")
+# to appear in a different language. Currently only "english" and "french"
+# is supported but it is very easy to add your own. See the example in the
+# file "latex2html.config"
+$TITLES_LANGUAGE = "english";
+
+### Navigation Panel ##########################################################
+#
+# The navigation panel is constructed out of buttons and section titles.
+# These can be configured in any combination with arbitrary text and
+# HTML tags interspersed between them.
+# The buttons available are:
+# $PREVIOUS - points to the previous section
+# $UP - points up to the "parent" section
+# $NEXT - points to the next section
+# $NEXT_GROUP - points to the next "group" section
+# $PREVIOUS_GROUP - points to the previous "group" section
+# $CONTENTS - points to the contents page if there is one
+# $INDEX - points to the index page if there is one
+#
+# If the corresponding section exists the button will contain an
+# active link to that section. If the corresponding section does
+# not exist the button will be inactive.
+#
+# Also for each of the $PREVIOUS $UP $NEXT $NEXT_GROUP and $PREVIOUS_GROUP
+# buttons there are equivalent $PREVIOUS_TITLE, $UP_TITLE, etc variables
+# which contain the titles of their corresponding sections.
+# Each title is empty if there is no corresponding section.
+#
+# The subroutine below constructs the navigation panels in each page.
+# Feel free to mix and match buttons, titles, your own text, your logos,
+# and arbitrary HTML (the "." is the Perl concatenation operator).
+sub top_navigation_panel {
+
+ # call script func from gking.js to open new UI
+ # this should be the 1st thing after BODY tag -rmesard 30-nov-2005
+ "<script language='Javascript'>displayHeader()</script>\n" .
+
+ # Now add a few buttons with a space between them
+ # "$NEXT $UP $PREVIOUS $CONTENTS $INDEX $CUSTOM_BUTTONS" .
+
+ # "<BR>\n" . # Line break
+ "<table border='0' cellpadding='0' cellspacing='0' class='latexnav'>" .
+ "<tr><td class='latexnavhome'>" .
+ "<a href='/'>" .
+ "<img src='/images/gking_name_sm.gif' height='30' width='119' border='0' alt='Gary King Homepage' />" .
+ "</a></td>" .
+
+ # try for the contents link
+ # ($CONTENTS_TITLE ? "<td>c: $CONTENTS_TITLE </td>\n" : undef) .
+
+ # ... and the ``previous'' title
+ ($PREVIOUS_TITLE ? "<td> Previous: $PREVIOUS_TITLE </td>\n" : undef) .
+
+ # Similarly with the ``up'' title ...
+ ($UP_TITLE ? "<td> Up: $UP_TITLE </td>\n" : undef) .
+
+ # If ``next'' section exists, add its title to the navigation panel
+ ($NEXT_TITLE ? "<td> Next: $NEXT_TITLE </td>\n" : undef) .
+
+
+
+ # Line Break, horizontal rule (3-d dividing line) and new paragraph
+ "</tr></table>\n" .
+ "<BR> <P>\n"
+}
+
+sub bot_navigation_panel {
+
+ # Start with a horizontal rule (3-d dividing line)
+ "<HR>"
+
+ # Now add a few buttons with a space between them
+ # "$NEXT $UP $PREVIOUS $CONTENTS $INDEX $CUSTOM_BUTTONS" .
+
+ # "<BR>\n" . # Line break
+
+ # If ``next'' section exists, add its title to the navigation panel
+ # ($NEXT_TITLE ? "<B> Next:</B> $NEXT_TITLE\n" : undef) .
+
+ # Similarly with the ``up'' title ...
+ # ($UP_TITLE ? "<B>Up:</B> $UP_TITLE\n" : undef) .
+
+ # ... and the ``previous'' title
+ # ($PREVIOUS_TITLE ? "<B> Previous:</B> $PREVIOUS_TITLE\n" : undef)
+
+}
+
+sub meta_information {
+ local($_) = @_;
+ # Cannot have nested HTML tags...
+ do { s/<[^>]*>//g;
+ "<SCRIPT LANGUAGE=\"Javascript\" SRC=\"/gking.js\"></SCRIPT>\n" .
+ "<META NAME=\"description\" CONTENT=\"$_\">\n" .
+ "<META NAME=\"keywords\" CONTENT=\"$FILE\">\n" .
+ "<META NAME=\"resource-type\" CONTENT=\"document\">\n" .
+ "<META NAME=\"distribution\" CONTENT=\"global\">\n"
+ } if $_;
+}
+
+ our(%custom_filenames);
+ sub custom_title_hook {
+ my($sec_name) = shift;
+ $sec_name=~tr/[a-z][A-Z][0-9] /_/sc;
+ my(@words)= split(/\s+/,$sec_name);
+ my($fn)= join("_", at words[0..2]);
+ $fn=substr($fn,0,22);
+ $fn=~s/(.*?)_*$/$1/;
+ $custom_filenames{"$fn"}++;
+ if ($custom_filenames{"$fn"}>1) {
+ $fn=$fn . $custom_filenames{"$fn"};
+ }
+ return($fn);
+ }
+ $CUSTOM_TITLES=1;
+
+1; # This must be the last line
diff --git a/inst/doc/Makefile b/inst/doc/Makefile
new file mode 100644
index 0000000..66f2fb9
--- /dev/null
+++ b/inst/doc/Makefile
@@ -0,0 +1,16 @@
+
+# stub makefile, does nothing, but causes R CMD build not to force rebuild pdf file
+
+REVAL=TRUE
+
+all: MatchIt/inst/doc/matchit.pdf
+
+%.tex: %.Rnw
+ echo "Sweave("$<", debug=TRUE, eval=$(REVAL))" | R --slave
+
+%.pdf: %.tex
+ pdflatex $*
+ bibtex $*
+ pdflatex $*
+ pdflatex $*
+ pdflatex $*
diff --git a/inst/doc/balance.tex b/inst/doc/balance.tex
new file mode 100644
index 0000000..c78c7dd
--- /dev/null
+++ b/inst/doc/balance.tex
@@ -0,0 +1,137 @@
+\section{Checking Balance}
+\label{sec:balance}
+
+\subsection{Quick Overview}
+
+To check balance, use \texttt{summary(m.out)} for numerical summaries
+and \texttt{plot(m.out)} for graphical summaries.
+
+\subsection{Details}
+
+\subsubsection{The {\tt summary()} Command}
+
+The \texttt{summary()} command gives measures of the balance between
+the treated and control groups in the full (original) data set, and
+then in the matched data set. If the matching worked well, the
+measures of balance should be smaller in the matched data set (smaller
+values of the measures indicate better balance).
+
+The \texttt{summary()} output for subclassification is the same as
+that for other types of matching, except that the balance statistics
+are shown separately for each subclass, and the overall balance in the
+matched samples is calculated by aggregating across the subclasses,
+where each subclass is weighted by the number of units in the
+subclass. For exact matching, the covariate values within each
+subclass are guaranteed to be the same, and so the measures of balance
+are not output for exact matching; only the sample sizes in each
+subclass are shown.
+
+\begin{itemize}
+\item {\bf Balance statistics:} The statistics the \texttt{summary()}
+ command provides include means, the original control group standard deviation (where applicable),
+ mean differences, standardized mean
+ differences, and (median, mean and maximum) Quantile-Quantile (Q-Q)
+ plot differences. In addition, the \texttt{summary()} command will
+ report (a) the matched call, (b) how many units were matched,
+ unmatched, or discarded due to the \texttt{discard} option
+ (described below), and (c) the percent improvement in balance for
+ each of the balance measures, defined as $100((|a|-|b|)/|a|)$, where
+ $a$ is the balance before and $b$ is the balance after matching.
+ For each set of units (original and matched data sets, with weights
+ used as appropriate in the matched data sets), the
+ following statistics are provided:
+\begin{enumerate}
+ \item ``Means Treated'' and ``Means Control'' show the weighted
+ means in the treated and control groups
+ \item ``SD Control" is the standard deviation calculated in the control group (where applicable)
+ \item ``Mean Diff'' is the difference in means between the groups
+ \item The final three columns of the summary output give summary
+ statistics of a Q-Q plot (see below for more information on these
+ plots). Those columns give the median, mean, and maximum distance
+ between the two empirical quantile functions (treated and control
+ groups). Values greater than 0 indicate deviations between the
+ groups in some part of the empirical distributions. The plots of
+ the two empirical quantile functions themselves, described below,
+ can provide further insight into which part of the covariate
+ distribution has differences between the two groups.
+\end{enumerate}
+
+\item {\bf Additional options:} Three options to the \texttt{summary()}
+ command can also help with assessing balance and respecifying the
+ propensity score model, as necessary. First, the {\tt interactions
+ = TRUE} option with {\tt summary()} shows the balance of all
+ squares and interactions of the covariates used in the matching
+ procedure. Large differences in higher order interactions usually
+ are a good indication that the propensity score model (the distance measure) needs to be
+ respecified. Similarly, the {\tt addlvariables} option with {\tt
+ summary()} will provide balance measures on additional variables
+ not included in the original matching procedure. If a variable (or
+ interaction of variables) not included in the original propensity score model
+ has large imbalances in the matched groups, including that
+ variable in the next model specification may improve the resulting
+ balance on that variable. Because the outcome variable is not used
+ in the matching procedure, a variety of matching methods can be
+ tried, and the one that leads to the best resulting balance chosen. Finally,
+ the {\tt standardize = TRUE} option will print out standardized versions of the
+ balance measures, where the mean difference is standardized (divided) by the standard deviation
+ in the original treated group.
+\end{itemize}
+
+\subsubsection{The \texttt{plot()} Command}
+
+We can also examine the balance graphically using the \texttt{plot()}
+command, which provides three types of plots: jitter plots of the
+distance measure, Q-Q plots of each covariate, and histograms of the
+distance measure. For subclassification, separate Q-Q plots are
+printed for each subclass. The jitter plot for subclassification is
+the same as that for other types of matching, with the addition of
+vertical lines indicating the subclass cut-points. With the histogram option,
+4 histograms are provided: the original treated and control groups and the matched
+treated and control groups. For the Q-Q plots and the histograms, the weights that result
+after matching are used to create the plots.
+
+%\begin{figure}[tbp]
+% \begin{center}
+% \includegraphics{figs/qqplotnn1}
+% \includegraphics{figs/qqplotnn2}
+% \hfill
+% \caption{Sample diagnostic QQ plots with nearest neighbor matching}
+% \label{diagqqnn}
+% \end{center}
+%\end{figure}
+
+%\begin{figure}[tbp]
+% \begin{center}
+% \includegraphics{figs/jitterplotnn}
+% \hfill
+% \caption{Sample diagnostic jitter plot: Matched units shown in
+% black, unmatched units shown in grey.}
+% \label{diagjitternn}
+% \end{center}
+%\end{figure}
+
+Three examples of the output from the {\tt plot()} command are shown
+below. If the empirical distributions are the same in the treated and
+control groups, the points in the Q-Q plots would all lie on the 45 degree line.
+Deviations from the 45 degree line indicate differences in the
+empirical distribution. The jitter plot shows the overall
+distribution of propensity scores in the treated and control groups.
+In the jitter plot, the size of each diamond is proportional to the
+weight given to that unit; matched units are in black while unmatched
+units are in grey. In the jitter
+plot, which can be created by setting \texttt{type = "jitter"}, you
+may identify units by observation name by clicking the first mouse
+button near the units. The histograms can be plotted by setting \texttt{type = "hist"}.
+
+
+\hspace{-0.75in}
+\begin{center}
+\includegraphics[height=3in]{figs/jitterplotnn}\\
+\includegraphics[height=3in]{figs/qqplotnn1}
+\includegraphics[height=3in]{figs/hist}
+\end{center}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/faq.tex b/inst/doc/faq.tex
new file mode 100644
index 0000000..2202806
--- /dev/null
+++ b/inst/doc/faq.tex
@@ -0,0 +1,194 @@
+\chapter{Frequently Asked Questions}
+
+\section{How do I Cite this Work?}
+
+If you use \MatchIt, please cite\nocite{HoImaKin07,HoImaKin07a}
+\begin{verse}
+ Daniel Ho; Kosuke Imai; Gary King; and Elizabeth Stuart (2007),
+ ``Matching as Nonparametric Preprocessing for Reducing Model
+ Dependence in Parametric Causal Inference,'' \emph{Political
+ Analysis} 15(3): 199-236,
+ \url{http://gking.harvard.edu/files/abs/matchp-abs.shtml}.
+
+and
+
+Daniel Ho; Kosuke Imai; Gary King; and Elizabeth Stuart (2007b)
+``Matchit: Nonparametric Preprocessing for Parametric Causal
+Inference,'' \emph{Journal of Statistical Software},
+\url{http://gking.harvard.edu/matchit/}.
+\end{verse}
+
+In addition, the {\tt convex.hull} discard option is implemented via
+the {\tt WhatIf} package \citep{KinZen06,KinZen07,StoKinZen05}.
+Generalized linear distance measures are implemented via the {\tt
+ stats} package \citep{VenRip02}. Generalized additive distance
+measures are implemented via the {\tt mcgv} package \citep{HasTib90}.
+The neural network distance measure is implemented via the {\tt nnet}
+package \citep{Ripley96}. The classification trees distance measure
+is implemented via the {\tt rpart} package \citep{BreFriOls84}. Full
+and optimal matching are implemented via the {\tt optmatch} package
+\citep{Hansen04}. Genetic matching is implemented via the {\tt
+ Matching} package \citep{DiaSek05}. Coarsened exact matching is
+implemented via the \texttt{cem} package
+\citep{IacKinPor08,IacKinPor08b}.
+
+\section{What if My datasets Are Big and Are Taking Up
+ Too Much Memory?}
+
+{\tt matchit()} does not save the data set in its output object, but
+it does save a matrix of the covariates. {\tt match.data()} will
+create a matched data set. One can eliminate the original data set to
+save memory in R by {\tt rm(name)}, where {\tt name} is the name of
+the data set, after calling {\tt match.data()}.
+
+%\section{Can I use a Difference-in-Difference Estimator for Matched
+% Data?}
+%
+%A difference-in-differences (DID) analysis can be easily conducted
+%with \MatchIt. If we were interested in the DID matching estimate in
+%the Lalonde data, we could simply include {\texttt re75} as a
+%covariate in the preprocessing step. Then the analysis can be
+%performed on the change in income from 1975 to 1978: {\tt re78}-{\tt
+% re75}. Time-varying covariates (of which none exist in the Lalonde
+%data) should of course also be differenced for the DID estimator.
+%** we should show how to do this with zelig
+
+\section{How Exactly are the Weights Created?}
+\label{subsec:weights}
+
+Each type of matching method can be thought of as creating groups of
+units with at least one treated unit and at least one control unit in
+each. In exact matching, subclassification, or full matching, these
+groups are the subclasses formed, and the number of treated and
+control units will vary quite a bit across subclasses. In nearest
+neighbor or optimal matching, the groups are the pairs (or sets) of
+treated and control units matched. In 1:1 nearest neighbor matching
+there will be one treated unit and one control unit in each group. In
+2:1 nearest neighbor matching there will be one treated unit and two
+control units in each group. Unmatched units receive a weight of 0.
+All matched treated units receive a weight of 1. These weights are constructed
+to estimate the average treatment effect on the treated, with the control group
+essentially weighted to look like the treated group.
+
+The weights for matched control units are formed as follows:
+\begin{enumerate}
+\item Within each group, each control unit is given a preliminary
+ weight of $n_{ti}/n_{ci}$, where $n_{ti}$ and $n_{ci}$ are the
+ number of treated and control units in group $i$, respectively.
+\item If matching is done with replacement, each control unit's weight
+ is added up across the groups in which it was matched.
+\item The control group weights are scaled to sum to the number of
+ uniquely matched control units.
+\end{enumerate}
+
+With subclassification, when the analysis is done separately within
+each subclass and then aggregated up across the subclasses, these
+weights will generally not be used, but they may be used for full
+matching or nearest neighbor matching if the number of control units
+matched to each treated unit varies.
+
+
+
+\section{How Do I Create Observation Names?}
+\label{rnames}
+
+Since the diagnostics often make use of the observation names of the
+data frame, you may find it helpful to specify observation names for
+the data input. Use the \texttt{row.names} command to achieve this.
+For example, to assign the names ``Dan'', ``Kosuke'', ``Liz'' and
+``Gary'' to a data frame with the first four observations in the
+Lalonde data, type:
+
+
+\begin{verbatim}
+> test <- lalonde[1:4, ]
+> row.names(test) <- c("Dan", "Kosuke", "Liz", "Gary")
+> print(test)
+ age educ black hisp married nodegr re74 re75 re78 u74 u75 treat
+Dan 37 11 1 0 1 1 0 0 9930 1 1 1
+Kosuke 22 9 0 1 0 1 0 0 3596 1 1 1
+Liz 30 12 1 0 0 0 0 0 24910 1 1 1
+Gary 27 11 1 0 0 1 0 0 7506 1 1 1
+\end{verbatim}
+
+\section{How Can I See Outcomes of Matched Pairs?}
+
+To obtain outcomes of matched pairs, recall that the original dataset has unique row names corresponding to each of
+the observations. The row names of \texttt{match.matrix} correspond to the names of the treated, and each
+of the cells corresponds to a name of matched controls. So to obtain matched outcomes, you can use:
+
+\begin{verbatim}
+cbind(lalonde[row.names(foo$match.matrix),"re78"], lalonde[foo$match.matrix,"re78"])
+\end{verbatim}
+
+\section{How Do I Ensure Replicability As \MatchIt\ Versions Develop?}
+\label{subsec:vercontrol}
+
+As the literature on matching techniques is rapidly evolving,
+\MatchIt\ will strive to incorporate new developments. \MatchIt\ is
+thereby an evolving program. Users may be concerned that analysis
+written in a particular version may not be compatible with newer
+versions of the program. The primary way to ensure that replication
+archives remain valid is to record the version of \MatchIt\ that was
+used in the analysis. Our website maintains binaries of all public
+release versions, so that researchers can replicate results exactly
+with the appropriate version (for Unix-based platforms, see
+\hlink{http://gking.harvard.edu/src/contrib/}{http://gking.harvard.edu/src/contrib/};
+for windows, see
+\hlink{http://gking.harvard.edu/bin/windows/contrib/}{http://gking.harvard.edu/bin/windows/contrib/}).
+
+In addition, users may find it helpful to install packages with
+version control, using the {\tt installWithVers} command with {\tt
+install.packages}. So for example, in the windows R console, users
+may download the appropriate version from our website and install the
+package with version control by:
+
+\begin{verbatim}
+install.packages(choose.files('',filters=Filters[c('zip','All'),]),
+ .libPaths()[1],installWithVers=T,CRAN=NULL)
+\end{verbatim}
+
+{\tt R CMD INSTALL} similarly permits users to specify this version
+using the \\ {\tt --with-package-versions} option. After having
+specified version control, different versions of the program may be
+called as necessary. Similar advice may also be appropriate for
+version control for R more generally.
+
+\section{How Do I Use My Own Distance Measure with \MatchIt\,?}
+
+A vector of your own distance measure can be used by specifying it as
+the input for {\tt distance} option in {\tt matchit()}.
+
+\section{What Do I Do about Missing Data?}
+
+\MatchIt\ requires complete data sets, with no missing values (other
+than potential outcomes of course). If there are missing values in
+the data set, imputation techniques should be used first to fill in
+(``impute'') the missing values (both covariates and outcomes), or the
+analysis should be done using only complete cases (which we do not in
+general recommend). For imputation software, see Amelia at
+(\hlink{http://gking.harvard.edu/stats.shtml}{http://gking.harvard.edu/stats.shtml})
+or other programs at
+\hlink{http://www.multiple-imputation.com}{http://www.multiple-imputation.com}.
+For more information on missing data and imputation methods, see
+\cite{KinHonJos01}.
+
+\section{Why Preprocessing?}
+
+The purpose of matching is to approximate an experimental template,
+where the matching procedure approximates blocking prior to random
+treatment assignment in order to balance covariates between treatment
+and control groups. Separation of the estimation procedure into two
+steps simulates the research design of an experiment, where no
+information on outcomes is known at the point of experimental design
+and randomization. The separation of the balancing process in
+\MatchIt\ from the analysis process afterward helps keep clear the
+goal of balancing control and treatment groups and makes it less
+likely that the user will inadvertently cook the books in his or her
+favor.
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/figs/f2figb.ps b/inst/doc/figs/f2figb.ps
new file mode 100644
index 0000000..38d8200
--- /dev/null
+++ b/inst/doc/figs/f2figb.ps
@@ -0,0 +1,4081 @@
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diff --git a/inst/doc/graphics.R b/inst/doc/graphics.R
new file mode 100644
index 0000000..2dc558a
--- /dev/null
+++ b/inst/doc/graphics.R
@@ -0,0 +1,48 @@
+# File to create sample graphics and output for documentation
+
+library(MatchIt)
+library(lattice)
+data(lalonde)
+m.out <- matchit(treat ~ re74+re75+educ+black+hispan+age, data=lalonde, method="nearest")
+
+print(summary(m.out))
+
+ps.options(family = c("Times"), pointsize = 8)
+
+postscript(file="figs/qqplotnn1.eps", horizontal=FALSE, paper="special", width=2.75, height=2.75)
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2))
+plot(m.out, which.xs=c("re74", "re75", "educ"), interactive=FALSE)
+dev.off()
+
+pdf(file="figs/qqplotnn1.pdf", width=2.75, height=2.75, pointsize=8, family="Times")
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2))
+plot(m.out, which.xs=c("re74", "re75", "educ"), interactive=FALSE)
+dev.off()
+
+postscript(file="figs/qqplotnn2.eps", horizontal=FALSE, paper="special", width=2.75, height=2.75)
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2))
+plot(m.out, which.xs=c("black", "hispan", "educ"), interactive=FALSE)
+dev.off()
+
+pdf(file="figs/qqplotnn2.pdf", width=2.75, height=2.75, pointsize=8, family="Times")
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2))
+plot(m.out, which.xs=c("black", "hispan", "educ"), interactive=FALSE)
+dev.off()
+
+postscript(file="figs/jitterplotnn.eps", horizontal=FALSE, paper="special", width=5.5, height=3.5)
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white")
+plot(m.out, type="jitter", interactive=FALSE)
+dev.off()
+
+pdf(file="figs/jitterplotnn.pdf", width=5.5, height=3.5, pointsize=8, family="Times")
+par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5,
+ mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white")
+plot(m.out, type="jitter", interactive=FALSE)
+dev.off()
+
+
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
new file mode 100755
index 0000000..865e724
--- /dev/null
+++ b/inst/doc/index.shtml
@@ -0,0 +1,76 @@
+<html>
+<head>
+<title>MatchIt Software Website</title>
+<script language='Javascript' src='/gking.js'></script></HEAD>
+
+<BODY><script language="Javascript">displayHeader()</script>
+
+<h1>MatchIt: Nonparametric Preprocessing for Parametric Causal Inference</h1>
+
+<p><a href="http://people.iq.harvard.edu/~dho/" target="gkingext">Daniel Ho</a>,
+<a href="http://www.princeton.edu/~kimai/" target="gkingext">Kosuke Imai</a>,
+<a href="http://GKing.Harvard.edu">Gary King</a>,
+<a href="http://people.iq.harvard.edu/~estuart/" target="gkingext">Elizabeth Stuart</a>
+
+<p>"At MatchIt, we don't make parametric models, we make parametric models
+work better."
+
+<!-- rbuild: replace 'Version:' '</b>' version -->
+<p><b>Version:2.4-10</b>
+
+ <table width="90%" border="0" cellpadding="10">
+ <tr>
+ <td><img src="face_off.jpg"></a></td>
+
+<!-- width="285" height="425"> -->
+
+ <td valign="top"><br>MatchIt implements the suggestions of <a
+ href="/files/abs/matchp-abs.shtml">Ho, Imai, King, and Stuart
+ (2007)</a> for improving parametric statistical
+ models by
+ preprocessing data with nonparametric matching methods. MatchIt
+ implements a wide range of sophisticated matching methods,
+ making it possible to greatly reduce the dependence of causal
+ inferences on hard-to-justify, but commonly made, statistical
+ modeling assumptions. The software also easily fits into
+ existing research practices since, after preprocessing data with
+ MatchIt, researchers can use whatever parametric model they
+ would have used without MatchIt, but produce inferences with
+ substantially more robustness and less sensitivity to modeling
+ assumptions. MatchIt is an <a
+ href="http://www.r-project.org" target="gkingext">R</a> program, and also works
+ seamlessly with <a href="/zelig/">Zelig</a>.
+
+<ul>
+ <li>Documentation: <a href="docs/">HTML</a> or <a
+href="/matchit/docs/matchit.pdf">PDF</a>
+ <li><a
+href="http://gking.harvard.edu/matchit/docs/Installing_M_SMALL_ATC.html">Installation
+and Downloads</a>
+
+<li>All questions, bugs and requests: MatchIt Mailing List: <a
+href="http://lists.hmdc.harvard.edu/index.cgi?info=matchit"
+target="gkingext">[Un]Subscribe</a>, or <a
+href="http://lists.hmdc.harvard.edu/lists/matchit/"
+target="gkingext">Browse/Search Archives</a>
+
+</ul>
+
+We're pleased to report that the <a
+href="/files/abs/matchp-abs.shtml">article</a> on which MatchIt is
+based won the <em>Warren Miller Prize</em> for the best paper in
+<em>Political Analysis</em> that year and, separately, has been named a
+ <em>Fast Breaking Paper</em> by Thomson Reuters' ScienceWatch, for
+ being the article with the largest
+ percentage increase in citations among those in the top 1% of
+ total citations across the social sciences in the last two years.
+ (You may be interested in this interview: <a
+ href="http://sciencewatch.com/dr/fbp/2008/08octfbp/08octfbpHoET/">HTML</a> |
+ <a href="/files/fbp08.pdf">PDF</a>)
+
+</td>
+ </tr>
+ </table>
+
+<script language="Javascript">displayFooter()</script></BODY>
+</html>
diff --git a/inst/doc/intro.tex b/inst/doc/intro.tex
new file mode 100644
index 0000000..60f32e6
--- /dev/null
+++ b/inst/doc/intro.tex
@@ -0,0 +1,85 @@
+
+\section{What \MatchIt\ Does}
+
+\MatchIt\ implements the suggestions of \citet*{HoImaKin07} for
+improving parametric statistical models and reducing model dependence
+by preprocessing data with semi-parametric and non-parametric matching
+methods. After appropriately preprocessing with \MatchIt, researchers
+can use whatever parametric model and software they would have used
+without \MatchIt, without other modification, and produce inferences
+that are more robust and less sensitive to modeling assumptions. (In
+addition, you may wish to use Zelig
+(\hlink{\url{http://gking.harvard.edu/zelig/}})
+{http://gking.harvard.edu/zelig/}; \citealt{ImaKinLau06} for
+subsequent parametric analyses, as it is designed to be convenient in
+analyzing \MatchIt\ data sets.) \MatchIt\ reduces the dependence of
+causal inferences on commonly made, but hard-to-justify, statistical
+modeling assumptions via the largest range of sophisticated matching
+methods of any software we know of. The program includes most
+existing approaches to matching and even enables users to access
+methods implemented in other programs through its single, unified, and
+easy-to-use interface. In addition, we have written \MatchIt\ so
+that adding new matching methods to the software is as easy for anyone
+with the inclination as it is for us.
+
+\section{Software Requirements}
+\label{sec:require}
+
+\MatchIt\ works in conjunction with the R programming language and
+statistical software, and will run on any platform where R is
+installed (Windows, Unix, or Mac OS X). R is available free for
+download at the Comprehensive R Archive Network (CRAN) at
+\hlink{http://cran.r-project.org/}{http://cran.r-project.org/}.
+\MatchIt\ has been tested on the most recent version of R. A good way
+to learn R, if you don't know it already, is to learn Zelig (available
+at
+\hlink{http://gking.harvard.edu/zelig}{http://gking.harvard.edu/zelig})
+which includes a self-contained introduction to R and can be used to
+analyze the matched data after running \MatchIt.
+
+\section{Installing \MatchIt}
+\label{sec:install}
+
+To install \MatchIt\ for all platforms, type at the R command prompt,
+\begin{verbatim}
+> install.packages("MatchIt")
+\end{verbatim}
+and \MatchIt\ will install itself onto your system automatically.
+(During the installation process you may either decide to keep or
+discard the installation files, which will not affect the way
+\MatchIt\ runs.)
+
+\section{Loading \MatchIt} \label{sec:load}
+
+You need to install \MatchIt\ only once, but you must load it prior to
+each use. You can do this at the R prompt:
+\begin{verbatim}
+> library(MatchIt)
+\end{verbatim}
+
+Alternatively, you can specify R to load \MatchIt\ automatically at
+launch by editing the {\tt Rprofile} file located in the R program
+subdirectory, e.g. \texttt{C:/R/rw2011/etc/}, for Windows systems or
+the {\tt .Rprofile} file located in the home directory for Unix/Linux
+and Mac OS X systems, and adding this line:
+\begin{verbatim}
+options(defaultPackages = c(getOption("defaultPackages"), "MatchIt"))
+\end{verbatim}
+For this change to take effect, you need to restart R.
+
+\section{Updating \MatchIt}
+
+We recommend that you periodically update \MatchIt\ at the R prompt by typing:
+\begin{verbatim}
+> update.packages()
+> library(MatchIt)
+\end{verbatim}
+which will update all the libraries including \MatchIt\ and load the
+new version of \MatchIt.
+
+
+%%% Local Variables:
+%%% mode: pdflatex
+%%% TeX-master: "matchit"
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/makematchH b/inst/doc/makematchH
new file mode 100755
index 0000000..b2e6c93
--- /dev/null
+++ b/inst/doc/makematchH
@@ -0,0 +1,18 @@
+#!/bin/tcsh
+source ~/.aliases
+latex matchit
+bibtex matchit
+latex matchit
+latex matchit
+latex matchit
+latex2html -noauto_link -info "" -contents "http://gking.harvard.edu/matchit/docs/matchit.html" -t "matchit" -local_icons -toc_depth 3 -link 3 -dir ~/www/matchit/docs/ matchit.tex
+
+dp matchit
+cp matchit.pdf ~/www/matchit/docs/.
+
+#rm -rf matchit.Rcheck
+cp ~/www/matchit/docs/matchit.html ~/www/matchit/docs/index.html
+echo "A new version of MatchIt has been posted"|mail -s "MatchIt" kimai at Princeton.Edu
+echo "A new version of MatchIt has been posted"|mail -s "MatchIt" stuart at stat.harvard.edu
+echo "A new version of MatchIt has been posted"|mail -s "MatchIt" daniel.ho at yale.edu
+
diff --git a/inst/doc/matchit.aux b/inst/doc/matchit.aux
new file mode 100644
index 0000000..9350245
--- /dev/null
+++ b/inst/doc/matchit.aux
@@ -0,0 +1,188 @@
+\relax
+\ifx\hyper at anchor\@undefined
+\global \let \oldcontentsline\contentsline
+\gdef \contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
+\global \let \oldnewlabel\newlabel
+\gdef \newlabel#1#2{\newlabelxx{#1}#2}
+\gdef \newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
+\AtEndDocument{\let \contentsline\oldcontentsline
+\let \newlabel\oldnewlabel}
+\else
+\global \let \hyper at last\relax
+\fi
+
+\citation{HoImaKin07}
+\citation{ImaKinLau06}
+\@writefile{toc}{\contentsline {chapter}{\numberline {1}Introduction}{3}{chapter.1}}
+\@writefile{lof}{\addvspace {10\p@ }}
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+\newlabel{sec:install}{{1.3}{4}{Installing \MatchIt \relax }{section.1.3}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {1.4}Loading \textsc {MatchIt}}{4}{section.1.4}}
+\newlabel{sec:load}{{1.4}{4}{Loading \MatchIt \relax }{section.1.4}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {1.5}Updating \textsc {MatchIt}}{4}{section.1.5}}
+\citation{ImaDyk04}
+\citation{HoImaKin07}
+\@writefile{toc}{\contentsline {chapter}{\numberline {2}Statistical Overview}{5}{chapter.2}}
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
+\citation{HoImaKin07}
+\@writefile{toc}{\contentsline {section}{\numberline {2.1}Preprocessing via Matching}{6}{section.2.1}}
+\citation{ImaKinStu08}
+\@writefile{toc}{\contentsline {section}{\numberline {2.2}Checking Balance}{7}{section.2.2}}
+\newlabel{sec:balance-sum}{{2.2}{7}{Checking Balance\relax }{section.2.2}{}}
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+\citation{HoImaKin07}
+\citation{lalonde86}
+\citation{DehWah99}
+\@writefile{toc}{\contentsline {chapter}{\numberline {3}User's Guide to \textsc {MatchIt}}{9}{chapter.3}}
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+\@writefile{toc}{\contentsline {subsection}{\numberline {3.1.1}Quick Overview}{9}{subsection.3.1.1}}
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+\citation{GuRos93}
+\citation{Hansen04}
+\citation{Rosenbaum02}
+\citation{Hansen04}
+\citation{Hansen04}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2.3}Nearest Neighbor Matching}{11}{subsubsection.3.1.2.3}}
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+\citation{DiaSek05}
+\citation{AbaImb07}
+\citation{Sekhon04}
+\citation{IacKinPor08}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2.6}Genetic Matching}{12}{subsubsection.3.1.2.6}}
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+\citation{Ripley96}
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+\citation{KinHonJos01}
+\@writefile{toc}{\contentsline {section}{\numberline {5.5}How Can I See Outcomes of Matched Pairs?}{36}{section.5.5}}
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+\bibstyle{asa}
+\bibdata{gk,gkpubs}
+\bibcite{AbaImb07}{{1}{2007}{{Abadie and Imbens}}{{}}}
+\bibcite{BreFriOls84}{{2}{1984}{{Breiman et~al.}}{{Breiman, Friedman, Olshen, and Stone}}}
+\bibcite{DehWah99}{{3}{1999}{{Dehejia and Wahba}}{{}}}
+\bibcite{DiaSek05}{{4}{2005}{{Diamond and Sekhon}}{{}}}
+\bibcite{GuRos93}{{5}{1993}{{Gu and Rosenbaum}}{{}}}
+\bibcite{Hansen04}{{6}{2004}{{Hansen}}{{}}}
+\bibcite{HasTib90}{{7}{1990}{{Hastie and Tibshirani}}{{}}}
+\bibcite{HoImaKin07}{{8}{2007}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
+\bibcite{HoImaKin07a}{{9}{Forthcoming}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
+\bibcite{IacKinPor08b}{{10}{2008{a}}{{Iacus et~al.}}{{Iacus, King, and Porro}}}
+\bibcite{IacKinPor08}{{11}{2008{b}}{{Iacus et~al.}}{{Iacus, King, and Porro}}}
+\bibcite{Imai05}{{12}{2005}{{Imai}}{{}}}
+\bibcite{ImaKinLau06}{{13}{2006}{{Imai et~al.}}{{Imai, King, and Lau}}}
+\bibcite{ImaKinStu08}{{14}{2008}{{Imai et~al.}}{{Imai, King, and Stuart}}}
+\bibcite{ImaDyk04}{{15}{2004}{{Imai and van Dyk}}{{}}}
+\bibcite{KinHonJos01}{{16}{2001}{{King et~al.}}{{King, Honaker, Joseph, and Scheve}}}
+\bibcite{KinZen06}{{17}{2006}{{King and Zeng}}{{}}}
+\bibcite{KinZen07}{{18}{2007}{{King and Zeng}}{{}}}
+\bibcite{lalonde86}{{19}{1986}{{Lalonde}}{{}}}
+\bibcite{Ripley96}{{20}{1996}{{Ripley}}{{}}}
+\bibcite{Rosenbaum02}{{21}{2002}{{Rosenbaum}}{{}}}
+\bibcite{StoKinZen05}{{22}{2005}{{Stoll et~al.}}{{Stoll, King, and Zeng}}}
+\bibcite{VenRip02}{{23}{2002}{{Venables and Ripley}}{{}}}
diff --git a/inst/doc/matchit.bbl b/inst/doc/matchit.bbl
new file mode 100644
index 0000000..4490ab4
--- /dev/null
+++ b/inst/doc/matchit.bbl
@@ -0,0 +1,118 @@
+\begin{thebibliography}{23}
+\newcommand{\enquote}[1]{``#1''}
+\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi
+
+\bibitem[{Abadie and Imbens(2007)}]{AbaImb07}
+Abadie, A. and Imbens, G.~W. (2007), \enquote{Bias-Corrected Matching
+ Estimators for Average Treatment Effects,}
+ {http://ksghome.harvard.edu/~aabadie/research.html}.
+
+\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[{Dehejia and Wahba(1999)}]{DehWah99}
+Dehejia, R.~H. and Wahba, S. (1999), \enquote{Causal Effects in Nonexperimental
+ Studies: Re-Evaluating the Evaluation of Training Programs,} \textit{Journal
+ of the American Statistical Association}, 94, 1053--62.
+
+\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[{Gu and Rosenbaum(1993)}]{GuRos93}
+Gu, X. and Rosenbaum, P.~R. (1993), \enquote{Comparison of multivariate
+ matching methods: structures, distances, and algorithms,} \textit{Journal of
+ Computational and Graphical Statistics}, 2, 405--420.
+
+\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.(2007)Ho, Imai, King, and Stuart}]{HoImaKin07}
+Ho, D., Imai, K., King, G., and Stuart, E. (2007), \enquote{Matching as
+ Nonparametric Preprocessing for Reducing Model Dependence in Parametric
+ Causal Inference,} \textit{Political Analysis}, 15, 199--236,
+ {http://gking.harvard.edu/files/abs/matchp-abs.shtml}.
+
+\bibitem[{Ho et~al.(Forthcoming)Ho, Imai, King, and Stuart}]{HoImaKin07a}
+Ho, D.~E., Imai, K., King, G., and Stuart, E.~A. (Forthcoming),
+ \enquote{MatchIt: Nonparametric Preprocessing for Parametric Causal
+ Inference,} \textit{Journal of Statistical Software},
+ {http://gking.harvard.edu/matchit}.
+
+\bibitem[{Iacus et~al.(2008{\natexlab{a}})Iacus, King, and
+ Porro}]{IacKinPor08b}
+Iacus, S.~M., King, G., and Porro, G. (2008{\natexlab{a}}), \enquote{CEM:
+ Coarsened Exact Matching Software,} {http://gking.harvard.edu/cem}.
+
+\bibitem[{Iacus et~al.(2008{\natexlab{b}})Iacus, King, and Porro}]{IacKinPor08}
+--- (2008{\natexlab{b}}), \enquote{Matching for Causal Inference Without
+ Balance Checking,} {http://gking.harvard.edu/files/abs/cem-abs.shtml}.
+
+\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[{Imai et~al.(2006)Imai, King, and Lau}]{ImaKinLau06}
+Imai, K., King, G., and Lau, O. (2006), \enquote{Zelig: Everyone's Statistical
+ Software,} {http://gking.harvard.edu/zelig}.
+
+\bibitem[{Imai et~al.(2008)Imai, King, and Stuart}]{ImaKinStu08}
+Imai, K., King, G., and Stuart, E. (2008), \enquote{Misunderstandings Among
+ Experimentalists and Observationalists about Causal Inference,}
+ \textit{Journal of the Royal Statistical Society, {S}eries {A}}, 171, part 2,
+ 481--502, {http://gking.harvard.edu/files/abs/matchse-abs.shtml}.
+
+\bibitem[{Imai and van Dyk(2004)}]{ImaDyk04}
+Imai, K. and van Dyk, D.~A. (2004), \enquote{Causal Inference with General
+ Treatment Treatment Regimes: Generalizing the Propensity Score,}
+ \textit{Journal of the American Statistical Association}, 99, 854--866.
+
+\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 and Zeng(2006)}]{KinZen06}
+King, G. and Zeng, L. (2006), \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(2007)}]{KinZen07}
+--- (2007), \enquote{When Can History Be Our Guide? The Pitfalls of
+ Counterfactual Inference,} \textit{International Studies Quarterly},
+ 183--210, {http://gking.harvard.edu/files/abs/counterf-abs.shtml}.
+
+\bibitem[{Lalonde(1986)}]{lalonde86}
+Lalonde, R. (1986), \enquote{Evaluating the Econometric Evaluations of Training
+ Programs,} \textit{American Economic Review}, 76, 604--620.
+
+\bibitem[{Ripley(1996)}]{Ripley96}
+Ripley, B. (1996), \textit{Pattern Recognition and Neural Networks}, Cambridge
+ Univeristy Press.
+
+\bibitem[{Rosenbaum(2002)}]{Rosenbaum02}
+Rosenbaum, P.~R. (2002), \textit{Observational Studies, 2nd Edition}, New York,
+ NY: Springer Verlag.
+
+\bibitem[{Stoll et~al.(2005)Stoll, King, and Zeng}]{StoKinZen05}
+Stoll, H., King, G., and Zeng, L. (2005), \enquote{WhatIf: Software for
+ Evaluating Counterfactuals,} \textit{Journal of Statistical Software}, 15,
+ {http://www.jstatsoft.org/index.php?vol=15}.
+
+\bibitem[{Venables and Ripley(2002)}]{VenRip02}
+Venables, W.~N. and Ripley, B.~D. (2002), \textit{Modern Applied Statistics
+ with S}, Springer-Verlag, 4th ed.
+
+\end{thebibliography}
diff --git a/inst/doc/matchit.blg b/inst/doc/matchit.blg
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+Chapter 1.
+(./intro.tex [3
+
+
+]) [4]
+Chapter 2.
+(./overview.tex [5
+
+] [6] [7]) [8]
+Chapter 3.
+(./preprocess.tex [9
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+]
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+
+
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 197--197
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+degree +[]
+ []
+
+) (./balance.tex [12]
+
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+/qqplotnn1.pdf> <./figs/hist.pdf>] [16]
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+ []/cmtt12/> z.out <- zelig(re78 ~ treat + age + educ + black + hispan + nodegr
+ee +
+ []
+
+
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+ [] /cmtt12/married + re74 + re75, method = "nearest", data
+= lalonde)[]
+ []
+
+[17]
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+ []
+
+
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+TRUE)
+ []
+
+
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+ [] /cmtt12/married + re74 + re75, data = match.data(m.out1, "
+treat"),[]
+ []
+
+
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+ []/cmtt12/> x.out2 <- setx(z.out2, data = match.data(m.out1, "control"), cond
+= TRUE)
+ []
+
+[18] [19]
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+ []/cmtt12/> weighted.mean(mdata$re78[mdata$treat == 1], mdata$weights[mdata$tr
+eat==1])[]
+ []
+
+
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+ [] /cmtt12/- weighted.mean(mdata$re78[mdata$treat==0], mdata$weights[md
+ata$treat==0])[]
+ []
+
+
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+ []/cmtt12/> zelig(re78 ~ treat + black + hispan + educ, data = m.data, model =
+ "ls",[]
+ []
+
+) [20]
+Chapter 4.
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+ich.edu/~bbh/optmatch.html[]
+ []
+
+[25]
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+
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+ce mea-sure. /cmtt12/summary(m.out$model)
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+
+) (./plotref.tex [29] [30]) (./mdataref.tex [31]
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+[]/cmtt12/> unmatched.data <- lalonde[!row.names(lalonde)%in%row.names(match.da
+ta(m.out1)),][]
+ []
+
+[32]) (./faq.tex [33]
+Chapter 5.
+[34
+
+] [35]
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+[]/cmtt12/cbind(lalonde[row.names(foo$match.matrix),"re78"], lalonde[foo$match.
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+
+
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+
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+
+
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diff --git a/inst/doc/matchit.out b/inst/doc/matchit.out
new file mode 100644
index 0000000..de0cbe5
--- /dev/null
+++ b/inst/doc/matchit.out
@@ -0,0 +1,62 @@
+\BOOKMARK [0][-]{chapter.1}{Introduction}{}
+\BOOKMARK [1][-]{section.1.1}{What MatchIt Does}{chapter.1}
+\BOOKMARK [1][-]{section.1.2}{Software Requirements}{chapter.1}
+\BOOKMARK [1][-]{section.1.3}{Installing MatchIt}{chapter.1}
+\BOOKMARK [1][-]{section.1.4}{Loading MatchIt}{chapter.1}
+\BOOKMARK [1][-]{section.1.5}{Updating MatchIt}{chapter.1}
+\BOOKMARK [0][-]{chapter.2}{Statistical Overview}{}
+\BOOKMARK [1][-]{section.2.1}{Preprocessing via Matching}{chapter.2}
+\BOOKMARK [1][-]{section.2.2}{Checking Balance}{chapter.2}
+\BOOKMARK [1][-]{section.2.3}{Conducting Analyses after Matching}{chapter.2}
+\BOOKMARK [0][-]{chapter.3}{User's Guide to MatchIt}{}
+\BOOKMARK [1][-]{section.3.1}{Preprocessing via Matching}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.1.1}{Quick Overview}{section.3.1}
+\BOOKMARK [2][-]{subsection.3.1.2}{Examples}{section.3.1}
+\BOOKMARK [3][-]{subsubsection.3.1.2.1}{Exact Matching}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.2}{Subclassification}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.3}{Nearest Neighbor Matching}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.4}{Optimal Matching}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.5}{Full Matching}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.6}{Genetic Matching}{subsection.3.1.2}
+\BOOKMARK [3][-]{subsubsection.3.1.2.7}{Coarsened Exact Matching}{subsection.3.1.2}
+\BOOKMARK [1][-]{section.3.2}{Checking Balance}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.2.1}{Quick Overview}{section.3.2}
+\BOOKMARK [2][-]{subsection.3.2.2}{Details}{section.3.2}
+\BOOKMARK [3][-]{subsubsection.3.2.2.1}{The summary\(\) Command}{subsection.3.2.2}
+\BOOKMARK [3][-]{subsubsection.3.2.2.2}{The plot\(\) Command}{subsection.3.2.2}
+\BOOKMARK [1][-]{section.3.3}{Conducting Analyses after Matching}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.3.1}{Quick Overview}{section.3.3}
+\BOOKMARK [2][-]{subsection.3.3.2}{Examples}{section.3.3}
+\BOOKMARK [0][-]{chapter.4}{Reference Manual}{}
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+\BOOKMARK [3][-]{subsubsection.4.1.0.3}{Output Values}{subsubsection.4.1.0.1}
+\BOOKMARK [1][-]{section.4.2}{summary\(\): Numerical Summaries of Balance}{chapter.4}
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+\BOOKMARK [2][-]{subsection.4.3.1}{Plot options for the matchit object}{section.4.3}
+\BOOKMARK [3][-]{subsubsection.4.3.1.1}{Syntax}{subsection.4.3.1}
+\BOOKMARK [3][-]{subsubsection.4.3.1.2}{Arguments}{subsection.4.3.1}
+\BOOKMARK [3][-]{subsubsection.4.3.1.3}{Output Values}{subsection.4.3.1}
+\BOOKMARK [2][-]{subsection.4.3.2}{Plot options for the matchit summary object}{section.4.3}
+\BOOKMARK [3][-]{subsubsection.4.3.2.1}{Syntax}{subsection.4.3.2}
+\BOOKMARK [3][-]{subsubsection.4.3.2.2}{Arguments}{subsection.4.3.2}
+\BOOKMARK [3][-]{subsubsection.4.3.2.3}{Output Values}{subsection.4.3.2}
+\BOOKMARK [1][-]{section.4.4}{match.data\(\): Extracting the Matched Data Set}{chapter.4}
+\BOOKMARK [2][-]{subsection.4.4.1}{Usage}{section.4.4}
+\BOOKMARK [2][-]{subsection.4.4.2}{Arguments}{section.4.4}
+\BOOKMARK [2][-]{subsection.4.4.3}{Examples}{section.4.4}
+\BOOKMARK [0][-]{chapter.5}{Frequently Asked Questions}{}
+\BOOKMARK [1][-]{section.5.1}{How do I Cite this Work?}{chapter.5}
+\BOOKMARK [1][-]{section.5.2}{What if My datasets Are Big and Are Taking Up Too Much Memory?}{chapter.5}
+\BOOKMARK [1][-]{section.5.3}{How Exactly are the Weights Created?}{chapter.5}
+\BOOKMARK [1][-]{section.5.4}{How Do I Create Observation Names?}{chapter.5}
+\BOOKMARK [1][-]{section.5.5}{How Can I See Outcomes of Matched Pairs?}{chapter.5}
+\BOOKMARK [1][-]{section.5.6}{How Do I Ensure Replicability As MatchIt Versions Develop?}{chapter.5}
+\BOOKMARK [1][-]{section.5.7}{How Do I Use My Own Distance Measure with MatchIt?}{chapter.5}
+\BOOKMARK [1][-]{section.5.8}{What Do I Do about Missing Data?}{chapter.5}
+\BOOKMARK [1][-]{section.5.9}{Why Preprocessing?}{chapter.5}
+\BOOKMARK [0][-]{chapter.6}{What's New?}{}
diff --git a/inst/doc/matchit.pdf b/inst/doc/matchit.pdf
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+\documentclass[oneside,letterpaper,12pt]{book}
+\usepackage{bibentry}
+\usepackage{graphicx}
+\usepackage{natbib}
+\usepackage[reqno]{amsmath}
+\usepackage{amssymb}
+\usepackage{verbatim}
+\usepackage{epsf}
+\usepackage{url}
+\usepackage{html}
+\usepackage{dcolumn}
+\usepackage{fullpage}
+\bibpunct{(}{)}{;}{a}{}{,}
+\newcolumntype{.}{D{.}{.}{-1}}
+\newcolumntype{d}[1]{D{.}{.}{#1}}
+%\pagestyle{myheadings}
+\htmladdtonavigation{
+ \htmladdnormallink{%
+ \htmladdimg{http://gking.harvard.edu/pics/home.gif}}
+ {http://gking.harvard.edu/}}
+\newcommand{\hlink}{\htmladdnormallink}
+
+\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.jpg"}
+\setcounter{tocdepth}{3}
+\setcounter{secnumdepth}{4}
+
+\newcommand{\MatchIt}{\textsc{MatchIt}}
+
+\title{\MatchIt: Nonparametric Preprocessing for Parametric Causal
+ Inference\thanks{We thank Olivia Lau for helpful suggestions about
+ incorporating \MatchIt\, into Zelig.}}
+
+\author{Daniel E. Ho,\thanks{Assistant Professor of Law
+\& Robert E.\ Paradise Faculty Scholar, Stanford Law
+ School (559 Nathan Abbott Way, Stanford CA 94305;
+ \texttt{http://dho.stanford.edu}, \texttt{dho at law.stanford.edu},
+ (650) 723-9560).} \and %
+ Kosuke Imai,\thanks{Assistant Professor, Department of Politics,
+ Princeton University (Corwin Hall 041, Department of Politics,
+ Princeton University, Princeton NJ 08544, USA;
+ \texttt{http://imai.princeton.edu},
+ \texttt{kimai at Princeton.Edu}).} \and %
+ Gary King,\thanks{David Florence Professor of Government, Harvard
+ University (Institute for Quantitative Social Science, 1737
+ Cambridge Street, Harvard University, Cambridge MA 02138;
+ \texttt{http://GKing.Harvard.Edu}, \texttt{King at Harvard.Edu},
+ (617) 495-2027).} \and %
+Elizabeth A. Stuart\thanks{Assistant Professor, Departments of Mental
+Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health
+(624 N Broadway, Room 804, Baltimore, MD 21205;
+ \texttt{http://www.biostat.jhsph.edu/$\sim$estuart},
+ \texttt{estuart at jhsph.edu}).}}
+
+%\makeindex
+
+\begin{document}
+\maketitle
+
+\begin{rawhtml}
+ <p> [Also available is a downloadable <a
+ href="/matchit/docs/matchit.pdf">PDF</a> version of this entire
+ document]
+\end{rawhtml}
+
+\tableofcontents
+
+\nobibliography*
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\clearpage
+
+\chapter{Introduction}
+\input{intro}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\chapter{Statistical Overview}
+\input{overview}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\chapter{User's Guide to \MatchIt}
+\label{methods}
+
+\input{preprocess}
+\input{balance}
+\input{matchit2zelig}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\chapter{Reference Manual}
+\label{chap:reference}
+
+\section{\texttt{matchit()}: Implementation of Matching Methods}
+\label{sec:matchit}
+Use \texttt{matchit()} to implement a variety of matching procedures
+including exact matching, nearest neighbor matching,
+subclassification, optimal matching, genetic matching, and full
+matching. The output of {\tt matchit()} can be analyzed via any
+standard R package, by exporting the data for use in another program,
+or most simply via \hlink{Zelig}{http://gking.harvard.edu/zelig} in R.
+
+\input{matchitref}
+\input{summaryref}
+\input{plotref}
+\input{mdataref}
+\input{faq}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+\chapter{What's New?}
+
+\begin{itemize}
+\item \textbf{2.4-10} (February 2, 2009): Minor documentation fixes
+\item \textbf{2.4-8,2.4-9} (January 29, 2009): Minor documentation fixes
+\item \textbf{2.4-7} (August 4, 2008): Fixed minor bug in subclassification
+(thanks to Ben Domingue)
+\item \textbf{2.4-6} (July 21, 2008): Improved summary object for
+ exact matching (thanks to Andrew Stokes)
+\item \textbf{2.4-5} (July 20, 2008): Fixed a minor bug.
+\item \textbf{2.4-4} (July 18, 2008): Fixed another bug with regard to the discard option (thanks to Ben Dominique).
+\item \textbf{2.4-3} (July 18, 2008): Fixed a bug in full matching
+ regarding the discard option (thanks to Ben Dominique). Some updates
+ of documentation regarding coarsened exact matching (2.4-1 and
+ 2.4-2).
+\item \textbf{2.4} (June 12, 2008): Included coarsened exact matching;
+ documentation bug fixes (thanks to Will Lowe)
+\item \textbf{2.3-1} (October 11, 2007): Stable release for R
+ 2.6. Documentation improved. Some minor bug fixes and improvements.
+\item \textbf{2.2-14} (September 2, 2007): Stable release for R 2.5.
+ Documentation improved for full matching. (Thanks to Langche Zeng)
+\item \textbf{2.2-13} (April 10, 2007): Stable release for R
+ 2.4. Additional fix to package dependencies. Bug fix for summary().
+\item \textbf{2.2-12} (April 6, 2007): Stable release for R 2.4. Fix
+ to package dependencies.
+\item \textbf{2.2-11} (July 13, 2006): Stable release for R 2.3.
+ Fix to ensure summary() command works with character variables in dataframe (thanks to Daniel Gerlanc).
+\item \textbf{2.2-10} (May 9, 2006): Stable release for R 2.3.
+ A bug fix in {\tt demo(analysis)} (thanks to Julia Gray).
+\item \textbf{2.2-9} (May 3, 2006): Stable release for R 2.3.
+ A minor change to DESCRIPTION file.
+\item \textbf{2.2-8} (May 1, 2006): Stable release for R 2.3.
+ Removed dependency on Zelig (thanks to Dave Kane).
+\item \textbf{2.2-7} (April 11, 2006): Stable release for R 2.2.
+ Error message for missing values in the data frame added
+ (thanks to Olivia Lau).
+\item \textbf{2.2-6} (April 4, 2006): Stable release for R 2.2.
+ Bug fixes related to {\tt reestimate} in {\tt matchit()} and {\tt
+ match.data()} (thanks to Ani Ruhil and Claire Aussems).
+\item \textbf{2.2-5} (December 7, 2005): Stable release for R 2.2.
+ Changed URL of {\tt WhatIf} to CRAN.
+\item \textbf{2.2-4} (December 3, 2005): Stable release for R 2.2.
+ User's own distance measure can be used with \MatchIt\, (thanks to
+ Nelson Lim).
+\item \textbf{2.2-3} (November 18, 2005): Stable release for R 2.2.
+ standardize option added to full matching and subclass (thanks to
+ Jeronimo Cortina).
+\item \textbf{2.2-2} (November 9, 2005): Stable release for R 2.2.
+ {\tt optmatch} package now on CRAN. Changed URL for that package.
+\item \textbf{2.2-1} (November 1, 2005): Stable release for R 2.2.
+ balance measures based on empirical CDF are added as a new option
+ {\tt standardize} in {\tt summary()}.
+\item \textbf{2.1-4} (October 14, 2005): Stable release for R 2.2.
+ strictly empirical (no interpolation) quantile-quantile functions
+ and plots are used.
+\item \textbf{2.1-3} (September 27, 2005): Stable release for R 2.1.
+ automated the installation of optional packages. fixed a coding
+ error in {\tt summary()}, the documentation edited.
+\item \textbf{2.1-2} (September 27, 2005): Stable release for R 2.1.
+ minor changes to file names, the option {\tt "whichxs"} added to the
+ {\tt plot()}, major editing of the documentation.
+\item \textbf{2.1-1} (September 16, 2005): Stable release for R
+ 2.1. Genetic matching added.
+\item \textbf{2.0-1} (August 29, 2005): Stable release for R 2.1.
+ Major revisions including some syntax changes. Statistical tests are
+ no longer used for balance checking, which are now based on the
+ empirical covariate distributions (e.g., quantile-quantile plot).
+\item \textbf{1.0-2} (August 10, 2005): Stable release for R
+ 2.1. Minor bug fixes (Thanks to Bart Bonikowski).
+\item \textbf{1.0-1} (January 3, 2005): Stable release for R 2.0. The
+ first official version of \MatchIt
+\end{itemize}
+
+
+
+\clearpage
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+
+\end{document}
+
diff --git a/inst/doc/matchit.toc b/inst/doc/matchit.toc
new file mode 100644
index 0000000..8ee17e1
--- /dev/null
+++ b/inst/doc/matchit.toc
@@ -0,0 +1,70 @@
+\contentsline {chapter}{\numberline {1}Introduction}{3}{chapter.1}
+\contentsline {section}{\numberline {1.1}What \textsc {MatchIt}\ Does}{3}{section.1.1}
+\contentsline {section}{\numberline {1.2}Software Requirements}{3}{section.1.2}
+\contentsline {section}{\numberline {1.3}Installing \textsc {MatchIt}}{4}{section.1.3}
+\contentsline {section}{\numberline {1.4}Loading \textsc {MatchIt}}{4}{section.1.4}
+\contentsline {section}{\numberline {1.5}Updating \textsc {MatchIt}}{4}{section.1.5}
+\contentsline {chapter}{\numberline {2}Statistical Overview}{5}{chapter.2}
+\contentsline {section}{\numberline {2.1}Preprocessing via Matching}{6}{section.2.1}
+\contentsline {section}{\numberline {2.2}Checking Balance}{7}{section.2.2}
+\contentsline {section}{\numberline {2.3}Conducting Analyses after Matching}{7}{section.2.3}
+\contentsline {chapter}{\numberline {3}User's Guide to \textsc {MatchIt}}{9}{chapter.3}
+\contentsline {section}{\numberline {3.1}Preprocessing via Matching}{9}{section.3.1}
+\contentsline {subsection}{\numberline {3.1.1}Quick Overview}{9}{subsection.3.1.1}
+\contentsline {subsection}{\numberline {3.1.2}Examples}{9}{subsection.3.1.2}
+\contentsline {subsubsection}{\numberline {3.1.2.1}Exact Matching}{10}{subsubsection.3.1.2.1}
+\contentsline {subsubsection}{\numberline {3.1.2.2}Subclassification}{10}{subsubsection.3.1.2.2}
+\contentsline {subsubsection}{\numberline {3.1.2.3}Nearest Neighbor Matching}{11}{subsubsection.3.1.2.3}
+\contentsline {subsubsection}{\numberline {3.1.2.4}Optimal Matching}{11}{subsubsection.3.1.2.4}
+\contentsline {subsubsection}{\numberline {3.1.2.5}Full Matching}{11}{subsubsection.3.1.2.5}
+\contentsline {subsubsection}{\numberline {3.1.2.6}Genetic Matching}{12}{subsubsection.3.1.2.6}
+\contentsline {subsubsection}{\numberline {3.1.2.7}Coarsened Exact Matching}{12}{subsubsection.3.1.2.7}
+\contentsline {section}{\numberline {3.2}Checking Balance}{13}{section.3.2}
+\contentsline {subsection}{\numberline {3.2.1}Quick Overview}{13}{subsection.3.2.1}
+\contentsline {subsection}{\numberline {3.2.2}Details}{13}{subsection.3.2.2}
+\contentsline {subsubsection}{\numberline {3.2.2.1}The {\tt summary()} Command}{13}{subsubsection.3.2.2.1}
+\contentsline {subsubsection}{\numberline {3.2.2.2}The \texttt {plot()} Command}{14}{subsubsection.3.2.2.2}
+\contentsline {section}{\numberline {3.3}Conducting Analyses after Matching}{15}{section.3.3}
+\contentsline {subsection}{\numberline {3.3.1}Quick Overview}{16}{subsection.3.3.1}
+\contentsline {subsection}{\numberline {3.3.2}Examples}{17}{subsection.3.3.2}
+\contentsline {chapter}{\numberline {4}Reference Manual}{21}{chapter.4}
+\contentsline {section}{\numberline {4.1}\texttt {matchit()}: Implementation of Matching Methods}{21}{section.4.1}
+\contentsline {subsubsection}{\numberline {4.1.0.1}Syntax}{21}{subsubsection.4.1.0.1}
+\contentsline {subsubsection}{\numberline {4.1.0.2}Arguments}{21}{subsubsection.4.1.0.2}
+\contentsline {paragraph}{\numberline {4.1.0.2.1}Arguments for All Matching Methods}{21}{paragraph.4.1.0.2.1}
+\contentsline {paragraph}{\numberline {4.1.0.2.2}Additional Arguments for Specification of Distance Measures}{22}{paragraph.4.1.0.2.2}
+\contentsline {paragraph}{\numberline {4.1.0.2.3}Additional Arguments for Subclassification}{24}{paragraph.4.1.0.2.3}
+\contentsline {paragraph}{\numberline {4.1.0.2.4}Additional Arguments for Nearest Neighbor Matching}{24}{paragraph.4.1.0.2.4}
+\contentsline {paragraph}{\numberline {4.1.0.2.5}Additional Arguments for Optimal Matching}{25}{paragraph.4.1.0.2.5}
+\contentsline {paragraph}{\numberline {4.1.0.2.6}Additional Arguments for Full Matching}{25}{paragraph.4.1.0.2.6}
+\contentsline {paragraph}{\numberline {4.1.0.2.7}Additional Arguments for Genetic Matching}{26}{paragraph.4.1.0.2.7}
+\contentsline {paragraph}{\numberline {4.1.0.2.8}Additional Arguments for Coarsened Exact Matching}{26}{paragraph.4.1.0.2.8}
+\contentsline {subsubsection}{\numberline {4.1.0.3}Output Values}{26}{subsubsection.4.1.0.3}
+\contentsline {section}{\numberline {4.2}\texttt {summary()}: Numerical Summaries of Balance}{27}{section.4.2}
+\contentsline {subsubsection}{\numberline {4.2.0.1}Syntax}{27}{subsubsection.4.2.0.1}
+\contentsline {subsubsection}{\numberline {4.2.0.2}Arguments}{28}{subsubsection.4.2.0.2}
+\contentsline {subsubsection}{\numberline {4.2.0.3}Output Values}{28}{subsubsection.4.2.0.3}
+\contentsline {section}{\numberline {4.3}\texttt {plot()}: Graphical Summaries of Balance}{29}{section.4.3}
+\contentsline {subsection}{\numberline {4.3.1}Plot options for the matchit object}{29}{subsection.4.3.1}
+\contentsline {subsubsection}{\numberline {4.3.1.1}Syntax}{30}{subsubsection.4.3.1.1}
+\contentsline {subsubsection}{\numberline {4.3.1.2}Arguments}{30}{subsubsection.4.3.1.2}
+\contentsline {subsubsection}{\numberline {4.3.1.3}Output Values}{30}{subsubsection.4.3.1.3}
+\contentsline {subsection}{\numberline {4.3.2}Plot options for the matchit summary object}{31}{subsection.4.3.2}
+\contentsline {subsubsection}{\numberline {4.3.2.1}Syntax}{31}{subsubsection.4.3.2.1}
+\contentsline {subsubsection}{\numberline {4.3.2.2}Arguments}{31}{subsubsection.4.3.2.2}
+\contentsline {subsubsection}{\numberline {4.3.2.3}Output Values}{31}{subsubsection.4.3.2.3}
+\contentsline {section}{\numberline {4.4}\texttt {match.data()}: Extracting the Matched Data Set}{31}{section.4.4}
+\contentsline {subsection}{\numberline {4.4.1}Usage}{31}{subsection.4.4.1}
+\contentsline {subsection}{\numberline {4.4.2}Arguments}{31}{subsection.4.4.2}
+\contentsline {subsection}{\numberline {4.4.3}Examples}{32}{subsection.4.4.3}
+\contentsline {chapter}{\numberline {5}Frequently Asked Questions}{34}{chapter.5}
+\contentsline {section}{\numberline {5.1}How do I Cite this Work?}{34}{section.5.1}
+\contentsline {section}{\numberline {5.2}What if My datasets Are Big and Are Taking Up Too Much Memory?}{34}{section.5.2}
+\contentsline {section}{\numberline {5.3}How Exactly are the Weights Created?}{35}{section.5.3}
+\contentsline {section}{\numberline {5.4}How Do I Create Observation Names?}{35}{section.5.4}
+\contentsline {section}{\numberline {5.5}How Can I See Outcomes of Matched Pairs?}{36}{section.5.5}
+\contentsline {section}{\numberline {5.6}How Do I Ensure Replicability As \textsc {MatchIt}\ Versions Develop?}{36}{section.5.6}
+\contentsline {section}{\numberline {5.7}How Do I Use My Own Distance Measure with \textsc {MatchIt}\tmspace +\thinmuskip {.1667em}?}{36}{section.5.7}
+\contentsline {section}{\numberline {5.8}What Do I Do about Missing Data?}{37}{section.5.8}
+\contentsline {section}{\numberline {5.9}Why Preprocessing?}{37}{section.5.9}
+\contentsline {chapter}{\numberline {6}What's New?}{38}{chapter.6}
diff --git a/inst/doc/matchit2zelig.tex b/inst/doc/matchit2zelig.tex
new file mode 100644
index 0000000..ef2f1fc
--- /dev/null
+++ b/inst/doc/matchit2zelig.tex
@@ -0,0 +1,285 @@
+\section{Conducting Analyses after Matching}
+\label{sec:analysis}
+
+Any software package may be used for parametric analysis following
+\MatchIt. This includes any of the relevant R packages, or other
+statistical software by exporting the resulting matched data sets
+using R commands such as {\tt write.csv()} and {\tt write.table()} for
+ASCII files or {\tt write.dta()} in the {\tt foreign} package for a
+STATA binary file.
+
+When variable numbers of treated and control units have been matched
+to each other (e.g., through exact matching, full matching, or k:1
+matching with replacement), the weights created by MatchIt should be
+used (e.g., in a weighted regression) to ensure that the matched
+treated and control groups are weighted up to be similar. Users
+should also remember that the weights created by MatchIt estimate the
+average treatment effect on the treated, with the control units
+weighted to resemble the treated units. See below for more detail on
+the weights. With subclassification, estimates should be obtained
+within each subclass and then aggregated across subclasses. When it
+is not possible to calculate an effect within each subclass, again the
+weights can be used to weight the matched units.
+
+In this section, we show how to use
+\hlink{Zelig}{http://gking.harvard.edu/zelig/} with \MatchIt. Zelig
+\citep{ImaKinLau06} is an R package that implements a large variety of
+statistical models (using numerous existing R packages) with a single
+easy-to-use interface, gives easily interpretable results by
+simulating quantities of interest, provides numerical and graphical
+summaries, and is easily extensible to include new methods.
+
+\subsection{Quick Overview}
+
+The general syntax is as follows. First, we use \texttt{match.data()}
+to create the matched data from the \MatchIt\ output object
+(\texttt{m.out}) by excluding unmatched units from the original data,
+and including information produced by the particular matching
+procedure (i.e., primarily a new data set, but also information that
+may result such as weights, subclasses, or the distance measure).
+\begin{verbatim}
+> m.data <- match.data(m.out)
+\end{verbatim}
+where {\tt m.data} is the resulting matched data. Zelig analyses all
+use three commands --- \texttt{zelig}, \texttt{setx}, and
+\texttt{sim}. For example, the basic statistical analysis is
+performed first:
+\begin{verbatim}
+> z.out <- zelig(Y ~ treat + x1 + x2, model = mymodel, data = m.data)
+\end{verbatim}
+where {\tt Y} is the outcome variable, {\tt mymodel} is the selected
+model, and {\tt z.out} is the output object from {\tt zelig}. This
+output object includes estimated coefficients, standard errors, and
+other typical outputs from your chosen statistical model. Its
+contents can be examined via \texttt{summary(z.out)} or
+\texttt{plot(z.out)}, but the idea of Zelig is that these statistical
+results are typically only intermediate quantities needed to compute
+your ultimate quantities of interest, which in the case of matching
+are usually causal inferences. To get these causal quantities, we use
+Zelig's other two commands. Thus, we can set the explanatory
+variables at their means (the default) and change the treatment
+variable from a 0 to a 1:
+\begin{verbatim}
+> x.out0 <- setx(z.out0, treat=0)
+> x1.out0 <- setx(z.out0, treat=1)
+\end{verbatim}
+and finally compute the resulting estimates of the causal effects and
+examine a summary:
+\begin{verbatim}
+> s.out0 <- sim(z.out1, x = x.out1)
+> summary(s.out0)
+\end{verbatim}
+
+\subsection{Examples}
+
+We now give four examples using the Lalonde data. They are meant to
+be read sequentially. You can run these example commands by typing
+{\tt demo(analysis)}. Although we use the linear least squares model
+in these examples, a wide range of other models are available in Zelig
+(for the list of supported models, see
+\hlink{\url{http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html}}
+{http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html}.
+
+To load the Zelig package after installing it, type
+\begin{verbatim}
+> library(Zelig)
+\end{verbatim}
+
+\begin{description}
+\item[Model-Based Estimates] In our first example, we conduct a
+ standard parametric analysis and compute quantities of interest in
+ the most common way. We begin with nearest neighbor matching with a
+ logistic regression-based propensity score, discarding control units
+ outside the convex hull of the treated units
+ \citep{KinZen06,KinZen07}:
+\begin{verbatim}
+> m.out <- matchit(treat ~ age + educ + black + hispan + nodegree +
+ married + re74 + re75, method = "nearest", discard
+ = "hull.control", data = lalonde)
+\end{verbatim}
+ Then we check balance using the summary and plot procedures (which
+ we don't show here). When the best balance is achieved,
+ we run the parametric analysis:
+\begin{verbatim}
+> z.out <- zelig(re78 ~ treat + age + educ + black + hispan + nodegree +
+ married + re74 + re75, data = match.data(m.out),
+ model = "ls")
+\end{verbatim}
+ and then set the explanatory variables at their means (the default)
+ and change the treatment variable from a 0 to a 1:
+\begin{verbatim}
+> x.out <- setx(z.out, treat=0)
+> x1.out <- setx(z.out, treat=1)
+\end{verbatim}
+and finally compute the result and examine a summary:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out, x1 = x1.out)
+> summary(s.out)
+\end{verbatim}
+
+\item[Average Treatment Effect on the Treated] We illustrate now how
+ to estimate the average treatment effect on the treated in a way
+ that is quite robust. We do this by estimating the coefficients in
+ the control group alone.
+
+ We begin by conducting nearest neighbor matching with a logistic
+ regression-based propensity score:
+\begin{verbatim}
+> m.out1 <- matchit(treat ~ age + educ + black + hispan + nodegree +
+ married + re74 + re75, method = "nearest", data = lalonde)
+\end{verbatim}
+ Then we check balance using the summary and plot procedures (which
+ we don't show here). We reestimate the matching procedure until we
+ achieve the best balance possible. (The running examples here are
+ meant merely to illustrate, not to suggest that we've achieved the
+ best balance.) Then we go to Zelig, and in this case choose to fit
+ a linear least squares model to the control group only:
+\begin{verbatim}
+> z.out1 <- zelig(re78 ~ age + educ + black + hispan + nodegree +
+ married + re74 + re75, data = match.data(m.out1, "control"),
+ model = "ls")
+\end{verbatim}
+ where the {\tt "control"} option in {\tt match.data()} extracts only
+ the matched control units and {\tt ls} specifies least squares
+ regression. In a smaller data set, this example should probably be
+ changed to include all the data in this estimation (using
+ \texttt{data = match.data(m.out1)} for the data) and by including
+ the treatment indicator (which is excluded in the example since its
+ a constant in the control group.) Next, we use the coefficients
+ estimated in this way from the control group, and combine them with
+ the values of the covariates set to the values of the treated units.
+ We do this by choosing conditional prediction (which means use the
+ observed values) in \texttt{setx()}. The {\tt sim()} command does
+ the imputation.
+\begin{verbatim}
+> x.out1 <- setx(z.out1, data = match.data(m.out1, "treat"), cond = TRUE)
+> s.out1 <- sim(z.out1, x = x.out1)
+\end{verbatim}
+Finally, we obtain a summary of the results by
+\begin{verbatim}
+> summary(s.out1)
+\end{verbatim}
+
+\item[Average Treatment Effect (Overall)] To estimate the average
+ treatment effect, we continue with the previous example and fit the
+ linear model to the {\it treatment group}:
+\begin{verbatim}
+> z.out2 <- zelig(re78 ~ age + educ + black + hispan + nodegree +
+ married + re74 + re75, data = match.data(m.out1, "treat"),
+ model = "ls")
+\end{verbatim}
+We then conduct the same simulation procedure in order to impute the
+counterfactual outcome for the {\it control group},
+\begin{verbatim}
+> x.out2 <- setx(z.out2, data = match.data(m.out1, "control"), cond = TRUE)
+> s.out2 <- sim(z.out2, x = x.out2)
+\end{verbatim}
+In this calculation, Zelig is computing the difference between
+observed and the expected values. This means that the treatment
+effect for the control units is the effect of control (observed
+control outcome minus the imputed outcome under treatment from the
+model). Hence, to combine treatment effects just reverse the signs of
+the estimated treatment effect of controls.
+\begin{verbatim}
+> ate.all <- c(s.out1$qi$att.ev, -s.out2$qi$att.ev)
+\end{verbatim}
+The point estimate, its standard error, and the $95\%$ confidence
+interval is given by
+\begin{verbatim}
+> mean(ate.all)
+> sd(ate.all)
+> quantile(ate.all, c(0.025, 0.975))
+\end{verbatim}
+
+\item[Subclassification] In subclassification, the average treatment
+ effect estimates are obtained separately for each subclass, and then
+ aggregated for an overall estimate. Estimating the treatment
+ effects separately for each subclass, and then aggregating across
+ subclasses, can increase the robustness of the ultimate results
+ since the parametric analysis within each subclass requires only
+ local rather than global assumptions. However, fewer observations
+ are obviously available within each subclass, and so this option is
+ normally chosen for larger data sets.
+
+ We begin this example by conducting subclassification with four
+ subclasses,
+\begin{verbatim}
+> m.out2 <- matchit(treat ~ age + educ + black + hispan + nodegree +
+ married + re74 + re75, data = lalonde,
+ method = "subclass", subclass = 4)
+\end{verbatim}
+ When balance is as good as we can get it, we then fit a linear
+ regression within each subclass by controlling for the estimated
+ propensity score (called \texttt{distance}) and other covariates.
+ In most software, this would involve running four separate
+ regressions and then combining the results. In Zelig, however, all
+ we need to do is to use the {\tt by} option:
+\begin{verbatim}
+> z.out3 <- zelig(re78 ~ re74 + re75 + distance,
+ data = match.data(m.out2, "control"),
+ model = "ls", by = "subclass")
+\end{verbatim}
+ The same set of commands as in the first example are used to do the
+ imputation of the counterfactual outcomes for the treated units:
+\begin{verbatim}
+> x.out3 <- setx(z.out3, data = match.data(m.out2, "treat"), fn = NULL,
+ cond = TRUE)
+> s.out3 <- sim(z.out3, x = x.out3)
+> summary(s.out3)
+\end{verbatim}
+It is also possible to get the summary result for each subclass. For
+example, the following command summarizes the result for the second
+subclass.
+\begin{verbatim}
+> summary(s.out3, subset = 2)
+\end{verbatim}
+
+\item[How Adjustment After Exact Matching Has No Effect] Regression
+adjustment after exact one-to-one exact matching gives the identical
+answer as a simple, unadjusted difference in means. General exact
+matching, as implemented in MatchIt, allows one-to-many matches, so to see
+the same result we must weight when adjusting. In other words: weighted
+regression adjustment after general exact matching gives the identical
+answer as a simple, unadjusted weighted difference in means. For
+example:
+
+\begin{verbatim}
+> m.out <- matchit(treat ~ educ + black + hispan, data = lalonde,
+ method = "exact")
+> m.data <- match.data(m.out)
+> ## weighted diff in means
+> weighted.mean(mdata$re78[mdata$treat == 1], mdata$weights[mdata$treat==1])
+ - weighted.mean(mdata$re78[mdata$treat==0], mdata$weights[mdata$treat==0])
+[1] 807
+> ## weighted least squares without covariates
+> zelig(re78 ~ treat, data = m.data, model = "ls", weights = "weights")
+
+Call:
+zelig(formula = re78 ~ treat, model = "ls", data = m.data, weights =
+"weights")
+
+Coefficients:
+(Intercept) treat
+ 5524 807
+
+> ## weighted least squares with covariates
+> zelig(re78 ~ treat + black + hispan + educ, data = m.data, model = "ls",
+ weights = "weights")
+
+Call:
+zelig(formula = re78 ~ treat + black + hispan + educ, model = "ls",
+data = m.data, weights = "weights")
+
+Coefficients:
+(Intercept) treat black hispan educ
+ 314 807 -1882 258 657
+\end{verbatim}
+
+
+ \end{description}
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/matchitref.tex b/inst/doc/matchitref.tex
new file mode 100644
index 0000000..4f3c30a
--- /dev/null
+++ b/inst/doc/matchitref.tex
@@ -0,0 +1,389 @@
+\subsubsection{Syntax}
+\begin{verbatim}
+> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\paragraph{Arguments for All Matching Methods}
+
+\begin{itemize}
+
+\item \texttt{formula}: formula used to calculate the distance measure
+ for matching (e.g., the propensity score model). It takes the usual syntax of R formulas, {\tt treat
+ \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
+ and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
+ treatment indicator and pre-treatment covariates must be contained
+ in the same data frame, which is specified as {\tt data} (see
+ below). All of the usual R syntax for formulas work here. For
+ example, {\tt x1:x2} represents the first order interaction term
+ between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
+ square term of {\tt x1}. See {\tt help(formula)} for details.
+
+\item \texttt{data}: the data frame containing the variables called in
+ {\tt formula}.
+
+\item \texttt{method}: the matching method (default =
+ \texttt{"nearest"}, nearest neighbor matching). Currently,
+ \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
+ \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
+ (optimal matching), \texttt{"subclass"} (subclassification),
+ \texttt{"genetic"} (genetic matching), and \texttt{"cem"} (coarsened
+ exact matching) are available. Note that within each of these
+ matching methods, \MatchIt\ offers a variety of options. See below
+ for more details.
+
+\item \texttt{verbose}: a logical value indicating whether to print
+ the status of the matching algorithm (default = \texttt{FALSE}).
+\end{itemize}
+
+
+\paragraph{Additional Arguments for Specification of
+ Distance Measures}
+\label{subsubsec:inputs-all}
+
+The following arguments specify distance measures that are used for
+matching methods. These arguments apply to all matching methods {\it
+ except exact matching}.
+
+\begin{itemize}
+
+\item \texttt{distance}: the method used to estimate the distance
+ measure (default = {\tt "logit"}, logistic regression) or a
+ numerical vector of user's own distance measure. Before using any
+ of these techniques, it is best to understand the theoretical
+ groundings of these techniques and to evaluate the results. Most of
+ these methods (such as logistic or probit regression) define the
+ distance by first estimating the propensity score, defined as the
+ probability of receiving treatment, conditional on the covariates.
+ Available methods include:
+ \begin{itemize}
+ \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
+ \item binomial generalized linear models with one of the following
+ link functions:
+ \begin{itemize}
+ \item \texttt{"logit"}: logistic link
+ \item {\tt "linear.logit"}: logistic link with linear propensity
+ score\footnote{The linear propensity scores are obtained by
+ transforming back onto a linear scale.}
+ \item \texttt{"probit"}: probit link
+ \item {\tt "linear.probit"}: probit link with linear propensity
+ score
+ \item {\tt "cloglog"}: complementary log-log link
+ \item {\tt "linear.cloglog"}: complementary log-log link with linear
+ propensity score
+ \item {\tt "log"}: log link
+ \item {\tt "linear.log"}: log link with linear propensity score
+ \item {\tt "cauchit"} Cauchy CDF link
+ \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
+ score
+ \end{itemize}
+ \item Choose one of the following generalized additive models (see
+ {\tt help(gam)} for more options).
+ \begin{itemize}
+ \item \texttt{"GAMlogit"}: logistic link
+ \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
+ score
+ \item \texttt{"GAMprobit"}: probit link
+ \item {\tt "GAMlinear.probit"}: probit link with linear propensity
+ score
+ \item {\tt "GAMcloglog"}: complementary log-log link
+ \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
+ linear propensity score
+ \item {\tt "GAMlog"}: log link
+ \item {\tt "GAMlinear.log"}: log link with linear propensity score,
+ \item {\tt "GAMcauchit"}: Cauchy CDF link
+ \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
+ propensity score
+\end{itemize}
+\item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for
+ more options.
+\item \texttt{"rpart"}: classification trees. See {\tt help(rpart)}
+ for more options.
+ \end{itemize}
+
+\item \texttt{distance.options}: optional arguments for estimating the
+ distance measure. The input to this argument should be a list. For
+ example, if the distance measure is estimated with a logistic
+ regression, users can increase the maximum IWLS iterations by
+ \texttt{distance.options = list(maxit = 5000)}. Find additional
+ options for general linear models using {\tt help(glm)} or {\tt
+ help(family)}, for general additive models using {\tt help(gam)},
+ for neutral network models {\tt help(nnet)}, and for classification
+ trees {\tt help(rpart)}.
+
+\item \texttt{discard}: specifies whether to discard units that fall
+ outside some measure of support of the distance measure (default =
+ \texttt{"none"}, discard no units). Discarding units may change the
+ quantity of interest being estimated by changing the observations left in the analysis.
+ Enter a logical vector
+ indicating which unit should be discarded or choose from the
+ following options:
+ \begin{itemize}
+ \item \texttt{"none"}: no units will be discarded before matching.
+ Use this option when the units to be matched are substantially
+ similar, such as in the case of matching treatment and control
+ units from a field experiment that was close to (but not fully)
+ randomized (e.g., \citealt{Imai05}), when caliper matching will
+ restrict the donor pool, or when you do not wish to change the
+ quantity of interest and the parametric methods to be used
+ post-matching can be trusted to extrapolate.
+ \item \texttt{"hull.both"}: all units that are not within the convex
+ hull will be discarded. See \citet{KinZen06,KinZen07} for
+ information about the convex hull in this context and as a measure
+ of model dependence.
+ \item \texttt{"both"}: all units (treated and control) that are
+ outside the support of the distance measure will be discarded.
+ \item \texttt{"hull.control"}: only control units that are not
+ within the convex hull of the treated units will be discarded.
+ \item \texttt{"control"}: only control units outside the support of
+ the distance measure of the treated units will be discarded. Use
+ this option when the average treatment effect on the treated is of
+ most interest and when you are unwilling to discard
+ non-overlapping treatment units (which would change the quantity
+ of interest).
+ \item \texttt{"hull.treat"}: only treated units that are not within
+ the convex hull of the control units will be discarded.
+ \item \texttt{"treat"}: only treated units outside the support of
+ the distance measure of the control units will be discarded. Use
+ this option when the average treatment effect on the control units
+ is of most interest and when unwilling to discard control units.
+ \end{itemize}
+
+\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
+ distance measure will not be re-estimated after units are discarded.
+ The input must be a logical value. Re-estimation may be desirable
+ for efficiency reasons, especially if many units were discarded and
+ so the post-discard samples are quite different from the original
+ samples.
+
+\end{itemize}
+
+\paragraph{Additional Arguments for Subclassification}
+\label{subsubsec:inputs-subclass}
+
+\begin{itemize}
+\item \texttt{sub.by}: criteria for subclassification. Choose from:
+ \texttt{"treat"} (default), the number of treatment units;
+ \texttt{"control"}, the number of control units; or \texttt{"all"},
+ the total number of units. Changing the default will likely also
+ signal a change in your quantity of interest from the average
+ treatment effect on the treated to other quantities.
+\item \texttt{subclass}: either a scalar specifying the number of
+ subclasses, or a vector of probabilities bounded between 0 and 1,
+ which create quantiles of the distance measure using the units in
+ the group specified by \texttt{sub.by} (default = \texttt{subclass =
+ 6}).
+\end{itemize}
+
+\paragraph{Additional Arguments for Nearest Neighbor Matching}
+\label{subsubsec:inputs-nearest}
+
+\begin{itemize}
+\item \texttt{m.order}: the order in which to match treatment units
+ with control units.
+ \begin{itemize}
+ \item {\tt "largest"} (default): matches from the largest value of
+ the distance measure to the smallest.
+ \item {\tt "smallest"}: matches from the smallest value of the
+ distance measure to the largest.
+ \item {\tt "random"}: matches in random order.
+ \end{itemize}
+\item \texttt{replace}: logical value indicating whether each control
+ unit can be matched to more than one treated unit (default = {\tt
+ replace = FALSE}, each control unit is used at most once -- i.e.,
+ sampling without replacement). For matching with replacement, use
+ \texttt{replace = TRUE}. After matching with replacement, the weights
+ can be used to reflect the frequency with which each control unit was matched.
+\item \texttt{ratio}: the number of control units to match to each
+ treated unit (default = {\tt 1}). If matching is done without
+ replacement and there are fewer control units than {\tt ratio} times
+ the number of eligible treated units (i.e., there are not enough
+ control units for the specified method), then the higher ratios will
+ have \texttt{NA} in place of the matching unit number in
+ \texttt{match.matrix}.
+\item \texttt{exact}: variables on which to perform exact matching
+ within the nearest neighbor matching (default = {\tt NULL}, no exact
+ matching). If \texttt{exact} is specified, only matches that
+ exactly match on the covariates in \texttt{exact} will be allowed.
+ Within the matches that match on the variables in \texttt{exact},
+ the match with the closest distance measure will be chosen.
+ \texttt{exact} should be entered as a vector of variable names
+ (e.g., \texttt{exact = c("X1", "X2")}).
+\item \texttt{caliper}: the number of standard deviations of the
+ distance measure within which to draw control units (default = {\tt
+ 0}, no caliper matching). If a caliper is specified, a control
+ unit within the caliper for a treated unit is randomly selected as
+ the match for that treated unit. If \texttt{caliper != 0}, there
+ are two additional options:
+ \begin{itemize}
+ \item \texttt{calclosest}: whether to take the nearest available
+ match if no matches are available within the \texttt{caliper}
+ (default = {\tt FALSE}).
+ \item \texttt{mahvars}: variables on which to perform
+ Mahalanobis-metric matching within each caliper (default = {\tt
+ NULL}). Variables should be entered as a vector of variable
+ names (e.g., \texttt{mahvars = c("X1", "X2")}). If
+ \texttt{mahvars} is specified without \texttt{caliper}, the
+ caliper is set to 0.25.
+ \end{itemize}
+\item \texttt{subclass} and \texttt{sub.by}: See the options for
+ subclassification for more details on these options. If a
+ \texttt{subclass} is specified within \texttt{method = "nearest"},
+ the matched units will be placed into subclasses after the nearest
+ neighbor matching is completed.
+\end{itemize}
+
+\paragraph{Additional Arguments for Optimal Matching}
+\label{subsubsec:inputs-optimal}
+
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+ treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional inputs that can be passed to the {\tt
+ fullmatch()} function in the {\tt optmatch} package. See {\tt
+ help(fullmatch)} or
+ \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
+ for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Full Matching}
+\label{subsubsec:inputs-full}
+
+See {\tt help(fullmatch)} (part of this information is copied below)
+or
+\hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
+for details.
+
+\begin{itemize}
+\item {\tt min.controls}: The minimum ratio of controls to treatments
+ that is to be permitted within a matched set: should be nonnegative
+ and finite. If {\tt min.controls} is not a whole number, the
+ reciprocal of a whole number, or zero, then it is rounded down to
+ the nearest whole number or reciprocal of a whole number.
+
+\item {\tt max.controls}: The maximum ratio of controls to treatments
+ that is to be permitted within a matched set: should be positive and
+ numeric. If {\tt max.controls} is not a whole number, the reciprocal
+ of a whole number, or {\tt Inf}, then it is rounded up to the
+ nearest whole number or reciprocal of a whole number.
+
+\item {\tt omit.fraction}: Optionally, specify what fraction of
+ controls or treated subjects are to be rejected. If {\tt
+ omit.fraction} is a positive fraction less than one, then {\tt
+ fullmatch()} leaves up to that fraction of the control reservoir
+ unmatched. If {\tt omit.fraction} is a negative number greater than
+ $-1$, then {\tt fullmatch()} leaves up to $|{\rm omit.fraction}|$ of
+ the treated group unmatched. Positive values are only accepted if
+ ${\rm max.controls} >= 1$; negative values, only if ${\rm
+ min.controls} <= 1$. If {\tt omit.fraction} is not specified,
+ then only those treated and control subjects without permissible
+ matches among the control and treated subjects, respectively, are
+ omitted.
+
+\item {\tt ...}: Additional inputs that can be passed to the {\tt
+ fullmatch()} function in the {\tt optmatch} package.
+\end{itemize}
+
+\paragraph{Additional Arguments for Genetic Matching}
+\label{subsubsec:inputs-genetic}
+
+The available options are listed below.
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+ treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional minor inputs that can be passed to the
+ {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
+ help(GenMatch)} or\\
+ \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
+ for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Coarsened Exact Matching}
+\label{subsubsec:inputs-cem}
+
+The available options are listed here:
+\begin{itemize}
+\item{cutpoints} named list each describing the cutpoints for the
+ variables. Each list element is either a vector of cutpoints, a
+ number of cutpoints, a method for automatic bin contruction.
+\item{k2k} return k-to-k matching?
+\item{verbose} controls level of verbosity
+\item{dist} user defined distance function
+\item {\tt ...}: additional minor inputs that can be passed to the
+ {\tt cem()} function in the {\tt cem} package. See {\tt
+ help(cem)} or
+ \hlink{http://gking.harvard.edu/cem}{http://gking.harvard.edu/cem}
+ for details.
+\end{itemize}
+
+\subsubsection{Output Values}
+\label{sec:outputs}
+
+Regardless of the type of matching performed, the \texttt{matchit}
+output object contains the following elements:\footnote{When
+ inapplicable or unnecessary, these elements may equal {\tt NULL}.
+ For example, when exact matching, {\tt match.matrix = NULL}.}
+
+\begin{itemize}
+\item \texttt{call}: the original {\tt matchit()} call.
+
+\item \texttt{formula}: the formula used to specify the model for
+ estimating the distance measure.
+
+\item \texttt{model}: the output of the model used to estimate
+ the distance measure. \texttt{summary(m.out\$model)} will give the
+ summary of the model where \texttt{m.out} is the output object from
+ \texttt{matchit()}.
+
+\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
+ where:
+ \begin{itemize}
+ \item the row names represent the names of the treatment units
+ (which match the row names of the data frame specified in
+ \texttt{data}).
+ \item each column stores the name(s) of the control unit(s) matched
+ to the treatment unit of that row. For example, when the
+ \texttt{ratio} input for nearest neighbor or optimal matching is
+ specified as 3, the three columns of \texttt{match.matrix}
+ represent the three control units matched to one treatment unit).
+ \item \texttt{NA} indicates that the treatment unit was not matched.
+ \end{itemize}
+
+\item \texttt{discarded}: a vector of length $n$ that displays whether
+ the units were ineligible for matching due to common support
+ restrictions. It equals \texttt{TRUE} if unit $i$ was discarded,
+ and it is set to \texttt{FALSE} otherwise.
+
+\item \texttt{distance}: a vector of length $n$ with the estimated
+ distance measure for each unit.
+
+\item \texttt{weights}: a vector of length $n$ with the weights
+ assigned to each unit in the matching process. Unmatched units have
+ weights equal to $0$. Matched treated units have weight $1$. Each
+ matched control unit has weight proportional to the number of
+ treatment units to which it was matched, and the sum of the control
+ weights is equal to the number of uniquely matched control units.
+
+\item \texttt{subclass}: the subclass index in an ordinal scale from 1
+ to the total number of subclasses as specified in \texttt{subclass}
+ (or the total number of subclasses from full or exact matching).
+ Unmatched units have \texttt{NA}.
+
+\item \texttt{q.cut}: the subclass cut-points that classify the
+ distance measure.
+
+\item \texttt{treat}: the treatment indicator from \texttt{data} (the
+ left-hand side of \texttt{formula}).
+
+\item \texttt{X}: the covariates used for estimating the distance
+ measure (the right-hand side of \texttt{formula}). When applicable,
+ \texttt{X} is augmented by covariates contained in \texttt{mahvars}
+ and \texttt{exact}.
+\end{itemize}
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/mdataref.tex b/inst/doc/mdataref.tex
new file mode 100644
index 0000000..975d076
--- /dev/null
+++ b/inst/doc/mdataref.tex
@@ -0,0 +1,103 @@
+\section{\texttt{match.data()}: Extracting the Matched Data Set}
+\label{sec:match.data}
+
+\subsection{Usage}
+
+To extract the matched data set for subsequent analyses from the
+output object (see Section~\ref{sec:analysis}), we provide the
+function {\tt match.data()}. This is used as follows:
+
+\begin{verbatim}
+> m.data <- match.data(object, group = "all", distance = "distance",
+ weights = "weights", subclass = "subclass")
+\end{verbatim}
+
+The output of the function {\tt match.data()} is the original data
+frame where additional information about matching (i.e., distance
+measure as well as resulting weights and subclasses) is added,
+restricted to units that were matched.
+
+\subsection{Arguments}
+
+{\tt match.data()} takes the following inputs:
+\begin{enumerate}
+\item {\tt object} is the output object from {\tt matchit()}. This is
+ a required input.
+\item {\tt group} specifies for which matched group the user wants to
+ extract the data. Available options are {\tt "all"} (all matched
+ units), {\tt "treat"} (matched units in the treatment group), and
+ {\tt "control"} (matched units in the control group). The default is
+ {\tt "all"}.
+\item {\tt distance} specifies the variable name used to store the
+ distance measure. The default is {\tt "distance"}.
+\item {\tt weights} specifies the variable name used to store the
+ resulting weights from matching. The default is {\tt "weights"}. See
+ Section~\ref{subsec:weights} for more details on the weights.
+\item {\tt subclass} specifies the variable name used to store the
+ subclass indicator. The default is {\tt "subclass"}.
+\end{enumerate}
+
+\subsection{Examples}
+
+Here, we present examples for using {\tt match.data()}. Users can run
+these commands by typing {\tt demo(match.data)} at the R
+prompt. First, we load the Lalonde data,
+
+\begin{verbatim}
+> data(lalonde)
+\end{verbatim}
+
+The next line performs nearest neighbor matching based on the
+estimated propensity score from the logistic regression,
+
+\begin{verbatim}
+> m.out1 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde,
++ method = "nearest", distance = "logit")
+\end{verbatim}
+
+To obtain matched data, type the following command,
+
+\begin{verbatim}
+> m.data1 <- match.data(m.out1)
+\end{verbatim}
+
+It is easy to summarize the resulting matched data,
+
+\begin{verbatim}
+> summary(m.data1)
+\end{verbatim}
+
+To obtain matched data for the treatment or control group, specify the option
+{\tt group} as follows,
+
+\begin{verbatim}
+> m.data2 <- match.data(m.out1, group = "treat")
+> summary(m.data2)
+> m.data3 <- match.data(m.out1, group = "control")
+> summary(m.data3)
+\end{verbatim}
+
+We can also use the function to return unmatched data:
+
+\begin{verbatim}
+> unmatched.data <- lalonde[!row.names(lalonde)%in%row.names(match.data(m.out1)),]
+\end{verbatim}
+
+We can also specify different names for the subclass indicator, the
+weight variable, and the estimated distance measure. The following
+example first does a subclassification method, obtains the
+matched data with specified names for those three variables, and then
+print out the names of all variables in the resulting matched data.
+
+\begin{verbatim}
+> m.out2 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde,
++ method = "subclass")
+> m.data4 <- match.data(m.out2, subclass = "block", weights = "w",
++ distance = "pscore")
+> names(m.data4)
+\end{verbatim}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/notation.tex b/inst/doc/notation.tex
new file mode 100644
index 0000000..4aaf294
--- /dev/null
+++ b/inst/doc/notation.tex
@@ -0,0 +1,151 @@
+\documentclass[oneside,letterpaper,titlepage,12pt]{article}
+%\usepackage[ae,hyper]{/usr/lib/R/share/texmf/Rd}
+\usepackage{makeidx}
+\usepackage{graphicx}
+\usepackage{natbib}
+\usepackage[reqno]{amsmath}
+\usepackage{amssymb}
+\usepackage{verbatim}
+\usepackage{epsf}
+\usepackage{url}
+\usepackage{html}
+\usepackage{dcolumn}
+\usepackage{longtable}
+\usepackage{vmargin}
+\setpapersize{USletter}
+\newcolumntype{.}{D{.}{.}{-1}}
+\newcolumntype{d}[1]{D{.}{.}{#1}}
+%\pagestyle{myheadings}
+\htmladdtonavigation{
+ \htmladdnormallink{%
+ \htmladdimg{http://gking.harvard.edu/pics/home.gif}}
+ {http://gking.harvard.edu/}}
+\newcommand{\hlink}{\htmladdnormallink}
+
+\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.setcounter"}
+\setcounter{tocdepth}{3}
+
+\parindent=0cm
+\newcommand{\MatchIt}{\textsc{MatchIt}}
+
+\begin{document}
+
+\begin{center}
+Notation for \MatchIt \\
+Elizabeth Stuart \\
+\end{center}
+
+This document details the notation to be used in the matching paper to accompany \MatchIt,
+as discussed in conference call on April 27, 2004 and emails on April 27-29, 2004. \\
+
+We first provide a general idea of the notation and ideas. Formal notation follows.
+
+\begin{enumerate}
+\item There exists in nature fixed values, $\theta_{1i}$ and $\theta_{0i}$, the
+potential outcomes under treatment and control, respectively. They, or
+more usually their difference, are our quantities of interest and hence
+the inferential target in this exercise. They are not generally known.
+There also exist in nature a vector of fixed values $X_i$ that are known and
+will play the role of covariates to condition on. Note that the use of $X_i$ in matching assumes that they are not
+affected by treatment assignment: $X_{1i}=X_{0i}=X_i$. If a researcher is interested in adjusting for a variable potentially affected by treatment
+assignment, then methods such as principal stratification should be used. [We can write a paragraph on that somewhere.]
+
+\item Then (i.e., only after step 1) $T_i$ is created, preferably by the
+experimenter, who assigns its values randomly, but in some cases $T_i$ is
+created by the world and we hope that it was created effectively randomly
+(or randomly conditional on the observed $X_i$). This is what is known as the assumption of
+unconfounded treatment assignment: That treatment assignment is independent of
+the potential outcomes given the covariates $X_i$.
+
+\item Finally, the observed outcome variable $Y_i$ is calculated
+deterministically as $Y_i = \theta_{1i}T_i + \theta_{0i}(1-T_i)$. This is
+an accounting identity, not a regression equation with an error term;
+i.e., it is just true, not really an assumption.
+\end{enumerate}
+
+The accounting identity in 3 implies that we observe $\theta_{1i}=Y_i$ (but
+not $\theta_{0i}$) when $T_i=1$, and we observe $\theta_{0i}=Y_i$ (but not
+$\theta_{1i}$) when $T_i=0$. Since the fundamental problem of causal
+inference indicates that either $\theta_{0i}$ or $\theta_{1i}$ will be
+unobserved for each $i$, we will need to infer their values, for which we
+define $\tilde\theta_{1i} \sim p(\theta_{1i})$ and $\tilde\theta_{0i} \sim
+p(\theta_{0i})$ as draws of the potential outcomes under treatment and
+control, respectively, from their respective predictive posterior
+distributions.\\
+
+We now detail the notation utilized. Fixed, but sometimes unknown values are represented by Greek letters. Draws from the posterior
+distribution of these unknown values are represented by a tilde over the variable of interest. Fixed, but always known values are represented
+by capital Roman letters.
+We first define notation for individual $i$:
+\begin{itemize}
+\item $\theta_{1i}=$ individual $i$'s potential outcome under treatment
+\item $\theta_{0i}=$ individual $i$'s potential outcome under control
+\item $T_i=$ individual $i$'s observed treatment assignment
+\begin{itemize} \item $T_i=1$ means individual $i$ receives treatment
+ \item $T_i=0$ means individual $i$ receives control
+\end{itemize}
+\item $\theta_{0i}$ and $\theta_{1i}$ are considered fixed quantities. Which one is observed depends on the random variable $T_i$.
+\item $Y_i=T_i \theta_{1i} + (1-T_i) \theta_{0i}$ is individual $i$'s observed outcome
+\item Let $Y_{1i}=(\theta_{1i}|T_i=1)$. Similarly, $Y_{0i}=(\theta_{0i}|T_i=0)$. One of these is observed for individual $i$. [Do we actually want this
+notation? It's not quite right since the capital Roman letter would imply that $Y_{1i}$ is always observed, but the notation may be handy (see below).]
+\item Let $\tilde{\theta}_{0i}$ be a draw from the posterior distribution of $\theta_{0i}$: $\tilde{\theta}_{0i} \sim p(\theta_{0i})$.
+Similarly, let $\tilde{\theta}_{1i}$ be a draw from the posterior distribution of $\theta_{1i}$: $\tilde{\theta}_{1i} \sim p(\theta_{1i})$.
+\item Consider variables $X_i$. If $X_{0i}=X_{1i}=X_i$ then $X$ is a ``proper covariate'' in that it is not affected by treatment assignment. Only proper
+covariates should be used in the matching procedure. That $X_i$ is not affected by treatment assignment
+is always an assumption (sometimes more reasonable than other times)--we never observe $X_{0i}$ and $X_{1i}$ for individual $i$.
+\end{itemize}
+
+For individual $i$, the potentially observed values can be represented by the following 2x2 table:
+
+\begin{center}
+\begin{tabular}{cc|c|c|}
+\multicolumn{4}{c}{\hspace*{4cm} Actual treatment assignment} \\
+\\
+\multicolumn{2}{c}{} & \multicolumn{2}{c}{Control \hfill Treatment} \\
+\cline{3-4}
+& & & \phantom{abcd} \\
+& Control & $\theta_{0i}$, $X_i$ & $\tilde{\theta}_{0i}$, $X_i$ \\
+Potential & & & \phantom{abcd} \\
+\cline{3-4}
+outcomes under & & & \phantom{abcd} \\
+& Treatment & $\tilde{\theta}_{1i}$, $X_i$ & $\theta_{1i}$, $X_i$ \\
+& & & \phantom{abcd} \\
+\cline{3-4}
+\end{tabular}
+\end{center}
+
+For each individual, we are in either Column 1 or Column 2 (depending on treatment assignment). Within each column, 1 number will be observed and 1 will be a drawn
+value from the posterior distribution (i.e., the true value for that cell is missing). Thus, for each individual we are in the situation of one of the
+following two vectors of potential outcomes:
+
+\begin{center}
+\begin{tabular}{rl}
+If $T_i=1$: & $(\tilde\theta_{0i}, \theta_{1i})$ \\
+If $T_i=0$: & $(\theta_{0i}, \tilde\theta_{1i})$ \\
+\end{tabular}
+\end{center}
+
+Now consider $n$ individuals observed, with $n_1$ in the treated group and $n_0$ in the control group ($n=n_0+n_1$). Then we have the following notation:
+\begin{itemize}
+\item $n_1=\sum_{i=1}^n T_i$, $n_0=\sum_{i=1}^n (1-T_i)$
+\item ${\bf Y_0} = \{Y_{i}|T_i=0\}$. ${\bf Y_0}$ is of length $n_0$.
+\item ${\bf Y_1} = \{Y_{i}|T_i=1\}$. ${\bf Y_1}$ is of length $n_1$.
+\item ${\bf Y} = \{ {\bf Y_0}, {\bf Y_1}\}$. ${\bf Y}$ is of length $n$.
+\item $\overline{Y}_1 = \frac{\sum_{i=1}^n T_i \theta_{1i}}{\sum_{i=1}^n T_i}=\frac{\sum_{i=1}^{n_1} Y_{1i}}{n_1}$
+\item $\overline{Y}_0 = \frac{\sum_{i=1}^n (1-T_i) \theta_{0i}}{\sum_{i=1}^n (1-T_i)}=\frac{\sum_{i=1}^{n_0} Y_{0i}}{n_0}$
+\item Observed sample variances would be calculated in a similar way (using ${\bf Y_0}$ and ${\bf Y_1}$).
+\end{itemize}
+
+Throughout, we will use $p()$ to represent pdf's and $g()$ for functional forms of models.
+
+One point to make sure we mention in the write-up is to say what OLS assumes. OLS with a treatment indicator does:
+$$Y_i|X_i, T_i \sim N(\alpha + \beta_0 T_i + {\boldsymbol \beta} {\bf X_i}, \sigma^2)$$
+which assumes that the models of the potential outcomes follow parallel lines with equal residual variance:
+$$\theta_{1i}|X_i \sim N(\alpha + \beta_0 + {\boldsymbol \beta} {\bf X_i}, \sigma^2)$$
+$$\theta_{0i}|X_i \sim N(\alpha + {\boldsymbol \beta} {\bf X_i}, \sigma^2).$$
+We can also have a graphical representation of this, perhaps with some examples of outcomes that clearly don't have that nice parallel linear relationship,
+and show that this would be a very special case of what we're advocating.
+
+
+
+\end{document}
diff --git a/inst/doc/overview.tex b/inst/doc/overview.tex
new file mode 100644
index 0000000..d44adc2
--- /dev/null
+++ b/inst/doc/overview.tex
@@ -0,0 +1,199 @@
+
+\MatchIt\ is designed for causal inference with a dichotomous treatment
+variable and a set of pretreatment control variables. Any number or
+type of dependent variables can be used. (If you are interested in
+the causal effect of more than one variable in your data set, run
+\MatchIt\ separately for each one; it is unlikely in any event that
+any one parametric model will produce valid causal inferences for more
+than one treatment variable at a time.) \MatchIt\ can be used for
+other types of causal variables by dichotomizing them, perhaps in
+multiple ways \citep[see also][]{ImaDyk04}. \MatchIt\ works for
+experimental data, but is usually used for observational studies where
+the treatment variable is not randomly assigned by the investigator,
+or the random assignment goes awry.
+
+We adopt the same notation as in \citet*{HoImaKin07}. Unless
+otherwise noted, let $i$ index the $n$ units in the data set, $n_1$
+denote the number of treated units, $n_0$ denote the number of control
+units (such that $n=n_0+n_1$), and $x_i$ indicate a vector of
+pretreatment (or control) variables for unit $i$. Let $t_i=1$ when
+unit $i$ is assigned treatment, and $t_i=0$ when unit $i$ is assigned
+control. (The labels ``treatment'' and ``control'' and values 1 and 0
+respectively are arbitrary and can be switched for convenience, except
+that some methods of matching are keyed to the definition of the
+treated group.) Denote $y_i(1)$ as the potential outcome of unit $i$
+under treatment --- the value the outcome variable would take if $t_i$
+were equal to 1, whether or not $t_i$ in fact is 0 or 1 -- and
+$y_i(0)$ the potential outcome of unit $i$ under control --- the value
+the outcome variable would take if $t_i$ were equal to 0, regardless
+of its value in fact. The variables $y_i(1)$ and $y_i(0)$ are jointly
+unobservable, and for each $i$, we observe one
+$y_i=t_iy_i(1)+(1-t_i)y_i(0)$, and not the other.
+
+Also denote a fixed vector of exogenous, pretreatment measured
+confounders as $X_i$. These variables are defined in the hope or
+under the assumption that conditioning on them appropriately will make
+inferences ignorable. Measures of balance should be computed with
+respect to all of $X$, even if some methods of matching only use some
+components.
+
+\section{Preprocessing via Matching}
+
+If $t_i$ and $X_i$ were independent, we would not need to control for
+$X_i$, and any parametric analysis would effectively reduce to a
+difference in means of $Y$ for the treated and control groups. The
+goal of matching is to preprocess the data prior to the parametric
+analysis so that the actual relationship between $t_i$ and $X_i$ is
+eliminated or reduced without introducing bias and or increasing
+inefficiency too much.
+
+When matching we select, duplicate, or selectively drop observations
+from our data, and we do so without inducing bias as long as we use a
+rule that is a function only of $t_i$ and $X_i$ and does not depend on
+the outcome variable $Y_i$. Many methods that offer this
+preprocessing are included here, including exact, subclassification,
+nearest neighbor, optimal, and genetic
+matching. For many of these methods the propensity score--defined as the
+probability of receiving the treatment given the covariates--is a key tool.
+In order to avoid changing the quantity of interest, most
+\MatchIt\ routines work by retaining all treated units and selecting (or weighting)
+control units to include in the final data set; this enables
+one to estimate the average treatment effect on the treated (the purpose
+of which is described in Section \ref{s:qoi}).
+
+\MatchIt\ implements and evaluates the choice of the rules for
+matching. Matching sometimes increases efficiency by eliminating
+heterogeneity or deleting observations outside of an area where a
+model can reasonably be used to extrapolate, but one needs to be
+careful not to lose too many observations in matching or efficiency
+will drop more than the reduction in bias that is achieved.
+
+The simplest way to obtain good matches (as defined above) is to use
+one-to-one exact matching, which pairs each treated unit with one
+control unit for which the values of $X_i$ are identical. However,
+with many covariates and finite numbers of potential matches,
+sufficient exact matches often cannot be found. Indeed, many of the
+other methods implemented in \MatchIt\ attempt to balance the overall
+covariate distributions as much as possible, when sufficient
+one-to-one exact matches are not available.
+
+A key point in \citet*{HoImaKin07} is that matching methods by
+themselves are not methods of estimation: Every use of matching in the
+literature involves an analysis step following the matching procedure,
+but almost all analyses use a simple difference in means. This
+procedure is appropriate only if exact matching was conducted. In
+almost all other cases, some adjustment is required, and there is no
+reason to degrade your inferences by using an inferior method of
+analysis such as a difference in means even when improving your
+inferences via preprocessing. Thus, with \MatchIt, you can improve
+your analyses in two ways. \MatchIt\ analyses are ``doubly
+robust'' in that if \emph{either} the matching analysis \emph{or} the
+analysis model is correct (but not necessarily both) your inferences
+will be statistically consistent. In practice, the modeling choices
+you make at the analysis stage will be much less consequential if you
+match first.
+
+\section{Checking Balance}
+\label{sec:balance-sum}
+
+The goal of matching is to create a data set that looks closer to one
+that would result from a perfectly blocked (and possibly randomized)
+experiment. When we get close, we break the link between the treatment
+variable and the pretreatment controls, which makes the parametric
+form of the analysis model less relevant or irrelevant entirely. To
+break this link, we need the distribution of covariates to be the same
+within the matched treated and control groups.
+
+A crucial part of any matching procedure is, therefore, to assess how
+close the (empirical) covariate distributions are in the two groups,
+which is known as ``balance.'' Because the outcome variable is not
+used in the matching procedure, any number of matching methods can be
+tried and evaluated, and the one matching procedure that leads to the
+best balance can be chosen. \MatchIt\ provides a number of ways to
+assess the balance of covariates after matching, including numerical
+summaries such as the ``mean Diff.'' (difference in means) or the
+difference in means divided by the treated group standard deviation,
+and summaries based on quantile-quantile plots that compare the
+empirical distributions of each covariate. The widely used procedure
+of doing t-tests of the difference in means is highly misleading and
+should never be used to assess balance; see \citet{ImaKinStu08}.
+
+These balance diagnostics should be performed on all variables in $X$,
+even if some are excluded from one of the matching procedures.
+
+\section{Conducting Analyses after Matching}\label{s:qoi}
+
+The most common way that parametric analyses are used to compute
+quantities of interest (without matching) is by (statistically)
+holding constant some explanatory variables, changing others, and
+computing predicted or expected values and taking the difference or
+ratio, all by using the parametric functional form. In the case of
+causal inference, this would mean looking at the effect on the
+expected value of the outcome variable when changing $T$ from 0 to 1,
+while holding constant the pretreatment control variables $X$ at their
+means or medians. This, and indeed any other appropriate analysis
+procedure, would be a perfectly reasonable way to proceed with
+analysis after matching. If it is the chosen way to proceed, then
+either treated or control units may be deleted during the matching
+stage, since the same parametric structure is assumed to apply to all
+observations.
+
+In other instances, researchers wish to reduce the assumptions
+inherent in their statistical model and so want to allow for the
+possibility that their treatment effect to vary over observations. In
+this situation, one popular quantity of interest used is the
+\emph{average treatment effect on the treated} (ATT). For example,
+for the treated group, the potential outcomes under control, $Y_i(0)$,
+are missing, whereas the outcomes under treatment, $Y_i(1)$, are
+observed, and the goal of the analysis is to impute the missing
+outcomes, $Y_i(0)$ for observations with $T_i=1$. We do this via
+simulation using a parametric statistical model such as regression,
+logit, or others (as described below). Once those potential outcomes
+are imputed from the model, the estimate of individual $i$'s treatment
+effect is $Y_i(1)-\widehat{Y}_i(0)$ where $\widehat{Y}_i(0)$ is a
+predicted value of the dependent variable for unit $i$ under the
+counterfactual condition where $T_i=0$. The in-sample average
+treatment effect for the treated individuals can then be obtained by
+averaging this difference over all observations $i$ where in fact
+$T_i=1$. Most \MatchIt\ algorithms retain all treated units, and
+choose some subset of or repeated units from the control group, so
+that estimating the ATT is straightforward. If one chooses options
+that allow matching with replacement, or any solution that has
+different numbers of controls (or treateds) within each subclass or strata (such
+as full matching),
+then the parametric analysis following matching must accomodate these
+procedures, such as by using fixed effects or weights, as appropriate.
+(Similar procedures can also be used to estimate various other
+quantities of interest such as the average treatment effect by
+computing it for all observations, but then one must be aware that the
+quantity of interest may change during the matching procedure
+as some control units may be dropped.)
+
+The imputation from the model can be done in at least two ways.
+Recall that the model is used to impute \emph{the value that the
+ outcome variable would take among the treated units if those treated
+ units were actually controls}. Thus, one reasonable approach would
+be to fit a model to the matched data and create simulated predicted
+values of the dependent variable for the treated units with $T_i$
+switched counterfactually from 1 to 0. An alternative approach would
+be to fit a model without $T$ by using only the outcomes of the
+matched control units (i.e., using only observations where $T_i=0$).
+Then, given this fitted model, the missing outcomes $Y_i(0)$ are
+imputed for the matched treated units by using the values of the
+explanatory variables for the treated units. The first approach will
+usually have lower variance, since all observations are used, and the
+second may have less bias, since no assumption of constant parameters
+across the models of the potential outcomes under treatment and control is needed.
+See \citet*{HoImaKin07} for more details.
+
+Other quantities of interest can also be computed at the parametric
+stage, following any procedures you would have followed in the absence
+of matching. The advantage is that if matching is done well your
+answers will be more robust to many small changes in parametric
+specification.
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/plotref.tex b/inst/doc/plotref.tex
new file mode 100644
index 0000000..f674ece
--- /dev/null
+++ b/inst/doc/plotref.tex
@@ -0,0 +1,105 @@
+\section{\texttt{plot()}: Graphical Summaries of Balance}
+
+
+\subsection{Plot options for the matchit object}
+The \texttt{plot()} command allows you to check the distributions of
+propensity scores and covariates in the assignment model, squares, and
+interactions, and within each subclasses if specified.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> plot(m.out, discrete.cutoff = 5, type = "QQ",
+ numdraws = 5000, interactive = TRUE, which.xs = NULL, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt type}: type of output graph. \texttt{type = "QQ"}
+ (default) outputs empirical quantile-quantile plots of each
+ covariate to check balance of marginal distributions. Alternatively,
+ \texttt{type = "jitter"} outputs jitter plots of the propensity
+ score for treated and control units. Finally, \texttt{type="hist"} outputs
+ histograms of the propensity score in the original treated and control groups
+ and weighted histograms of the propensity score in the matched treated and control groups.
+
+\item {\tt discrete.cutoff}: For quantile-quantile plots, discrete
+ covariates that take 5 or fewer values are jittered for visibility.
+ This may be changed by setting this argument to any other positive
+ integer.
+
+\item {\tt interactive}: If \texttt{TRUE} (default), users can
+ identify individual units by clicking on the graph with the left
+ mouse button, and (when applicable) choose subclasses to plot.
+
+\item {\tt which.xs}: For quantitle-quantile plots, specifies particular covariate names in a
+ character vector to plot only a subset of the covariates.
+
+\item {\tt subclass}: If \texttt{interactive = FALSE}, users can
+ specify which subclass to plot.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+\begin{itemize}
+\item Empirical quantile-quantile plot: This graph plots covariate
+ values that fall in (approximately) the same quantile of treated and
+ control distributions. Control unit quantile values are plotted on
+ the x-axis, and treated unit quantile values are plotted on the
+ y-axis. If values fall below the 45 degree line, control units
+ generally take lower values of the covariate. Data points that fall
+ exactly on the 45 degree line indicate that the marginal
+ distributions are identical.
+
+\item Jitter plots: This graph plots jittered estimated propensity
+ scores of treated and control units. Dark diamonds indicate matched
+ units and grey diamonds indicate unmatched or discarded units. The
+ area of the diamond is proportional to the weights. Vertical lines
+ are plotted if subclassification is used.
+
+\item Histograms: This graph plots histograms of the estimated propensity
+scores in the original treated and control groups and weighted histograms of the estimated
+propensity scores in the matched treated and control groups. Plots can be compared vertically to
+quickly check the balance before and after matching.
+
+\end{itemize}
+
+\subsection{Plot options for the matchit summary object}
+You can also send a matchit summary object to the \texttt{plot()} command, to obtain
+a summary of the balance on each covariate before and after matching. The summary() object
+must have been created using the option {\texttt standardize=TRUE}. The idea for this plot
+came from the ``twang" package by McCaffrey, Ridgeway, and Morral.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> s.out <- summary(object, standardize=TRUE, ...)
+
+> plot(s.out, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt interactive}: If \texttt{TRUE} (default), users can
+ identify individual variables by clicking on the graph with the left
+ mouse button.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+\begin{itemize}
+\item Line plot of standardized differences in means before and after matching.
+Numbers plotted are those output by the summary() command in the sum.all and sum.matched
+objects.
+\end{itemize}
+
+
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
+
diff --git a/inst/doc/preprocess.tex b/inst/doc/preprocess.tex
new file mode 100644
index 0000000..1023b0d
--- /dev/null
+++ b/inst/doc/preprocess.tex
@@ -0,0 +1,233 @@
+\section{Preprocessing via Matching}
+\label{sec:matching}
+
+\subsection{Quick Overview}
+
+The main command \texttt{matchit()} implements the matching
+procedures. A general syntax is:
+\begin{verbatim}
+> m.out <- matchit(treat ~ x1 + x2, data = mydata)
+\end{verbatim}
+where {\tt treat} is the dichotomous treatment variable, and {\tt x1}
+and {\tt x2} are pre-treatment covariates, all of which are contained
+in the data frame {\tt mydata}. The dependent variable (or variables)
+may be included in \texttt{mydata} for convenience but is never used
+by \MatchIt\ or included in the formula. This command creates the
+\MatchIt\ object called \texttt{m.out}. Name the output object to see
+a quick summary of the results:
+\begin{verbatim}
+> m.out
+\end{verbatim}
+
+\subsection{Examples}
+
+To run any of the examples below, you first must load the library and
+and data:
+\begin{verbatim}
+> library(MatchIt)
+> data(lalonde)
+\end{verbatim}
+
+Our example data set is a subset of the job training program analyzed
+in \citet{lalonde86} and \citet{DehWah99}. \MatchIt\ includes a
+subsample of the original data consisting of the National Supported
+Work Demonstration (NSW) treated group and the comparison sample from
+the Population Survey of Income Dynamics (PSID).\footnote{This data
+ set, \texttt{lalonde}, was created using NSWRE74$\_$TREATED.TXT and
+ CPS3$\_$CONTROLS.TXT from
+ http://www.columbia.edu/$\sim$rd247/nswdata.} The variables in this
+data set include participation in the job training program
+(\texttt{treat}, which is equal to 1 if participated in the program,
+and 0 otherwise), age ({\tt age}), years of education ({\tt educ}),
+race (\texttt{black} which is equal to 1 if black, and 0 otherwise;
+\texttt{hispan} which is equal to 1 if hispanic, and 0 otherwise),
+marital status (\texttt{married}, which is equal to 1 if married, 0
+otherwise), high school degree (\texttt{nodegree}, which is equal to 1
+if no degree, 0 otherwise), 1974 real earnings (\texttt{re74}), 1975
+real earnings (\texttt{re75}), and the main outcome variable, 1978
+real earnings (\texttt{re78}).
+
+\subsubsection{Exact Matching}
+\label{subsubsec:exact}
+
+The simplest version of matching is exact. This technique matches
+\emph{each} treated unit to \emph{all} possible control units with
+exactly the same values on all the covariates, forming subclasses such
+that within each subclass all units (treatment and control) have the
+same covariate values. Exact matching is implemented in \MatchIt\
+using \texttt{method = "exact"}. Exact matching will be done on all
+covariates included on the right-hand side of the \texttt{formula}
+specified in the \MatchIt\ call. There are no additional options for
+exact matching. (Exact restrictions on a subset of covariates can
+also be specified in nearest neighbor matching; see
+Section~\ref{subsubsec:nearest}.) The following example can be
+run by typing {\tt demo(exact)} at the R prompt,
+\begin{verbatim}
+> m.out <- matchit(treat ~ educ + black + hispan, data = lalonde,
+ method = "exact")
+\end{verbatim}
+
+\subsubsection{Subclassification}
+\label{subsubsec:subclass}
+
+When there are many covariates (or some covariates can take a large
+number of values), finding sufficient exact matches will often be
+impossible. The goal of subclassification is to form subclasses, such
+that in each the distribution (rather than the exact values) of
+covariates for the treated and control groups are as similar as
+possible. Various subclassification schemes exist, including the one
+based on a scalar distance measure such as the propensity score
+estimated using the \texttt{distance} option (see
+Section~\ref{subsubsec:inputs-all}). Subclassification is implemented
+in \MatchIt\ using \texttt{method = "subclass"}.
+
+The following example script can be run by typing {\tt demo(subclass)}
+at the R prompt,
+\begin{verbatim}
+> m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age,
+ data = lalonde, method = "subclass")
+\end{verbatim}
+The above syntax forms 6 subclasses, which is the default number
+of subclasses, based on a distance measure (the propensity score) estimated using logistic
+regression. By default, each subclass will have approximately the
+same number of treated units.
+
+Subclassification may also be used in conjunction with nearest
+neighbor matching described below, by leaving the default of
+\texttt{method = "nearest"} but adding the option \texttt{subclass}.
+When you choose this option, \MatchIt\ selects matches using nearest neighbor
+matching, but
+after the nearest neighbor matches are chosen it places them into
+subclasses, and adds a variable to the output object indicating subclass membership.
+
+
+\subsubsection{Nearest Neighbor Matching}
+\label{subsubsec:nearest}
+
+Nearest neighbor matching selects the $r$ (default=1) best control
+matches for each individual in the treatment group (excluding those
+discarded using the \texttt{discard} option). Matching is done
+using a distance measure specified by the {\tt distance} option
+(default=logit). Matches are chosen for each treated unit one at a
+time, with the order specified by the \texttt{m.order} command (default=largest to smallest).
+At each matching step we choose the control unit that is not
+yet matched but is closest to the treated unit on the distance
+measure.
+
+Nearest neighbor matching is implemented in \MatchIt\ using the
+\texttt{method = "nearest"} option. The following example script can
+be run by typing {\tt demo(nearest)}:
+\begin{verbatim}
+> m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age,
+ data = lalonde, method = "nearest")
+\end{verbatim}
+
+\subsubsection{Optimal Matching}
+\label{subsubsec:optimal}
+
+The default nearest neighbor matching method in \MatchIt\ is
+``greedy'' matching, where the closest control match for each treated
+unit is chosen one at a time, without trying to minimize a global
+distance measure. In contrast, ``optimal'' matching finds the matched
+samples with the smallest average absolute distance across all the
+matched pairs. \citet{GuRos93} find that greedy and optimal matching
+approaches generally choose the same sets of controls for the overall
+matched samples, but optimal matching does a better job of minimizing
+the distance within each pair. In addition, optimal matching can be
+helpful when there are not many appropriate control matches for the
+treated units.
+
+Optimal matching is performed with \MatchIt\ by setting \texttt{method
+ = "optimal"}, which automatically loads an add-on package called
+\texttt{optmatch} \citep{Hansen04}. The following example can also be
+run by typing {\tt demo(optimal)} at the R prompt. We conduct 2:1 optimal
+ratio matching based on the propensity score from the logistic
+regression.
+\begin{verbatim}
+> m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde,
+ method = "optimal", ratio = 2)
+\end{verbatim}
+
+\subsubsection{Full Matching}
+\label{subsubsec:full}
+
+Full matching is a particular type of subclassification that forms the
+subclasses in an optimal way \citep{Rosenbaum02, Hansen04}. A fully
+matched sample is composed of matched sets, where each matched set
+contains one treated unit and one or more controls (or one control
+unit and one or more treated units). As with subclassification, the
+only units not placed into a subclass will be those discarded (if a
+\texttt{discard} option is specified) because they are outside the
+range of common support. Full matching is optimal in terms of
+minimizing a weighted average of the estimated distance measure
+between each treated subject and each control subject within each
+subclass.
+
+Full matching can be performed with \MatchIt\ by setting
+\texttt{method = "full"}. Just as with optimal matching, we use the
+\texttt{optmatch} package \citep{Hansen04}, which automatically loads
+when needed. The following example with full matching (using the
+default propensity score based on logistic regression) can also be run
+by typing {\tt demo(full)} at the R prompt:
+\begin{verbatim}
+> m.out <- matchit(treat ~ age + educ + black + hispan + married +
+ nodegree + re74 + re75, data = lalonde, method = "full")
+\end{verbatim}
+
+\subsubsection{Genetic Matching}
+\label{subsub:genetic}
+
+Genetic matching automates the process of finding a good matching
+solution \citep{DiaSek05}. The idea is to use a genetic search
+algorithm to find a set of weights for each covariate such that the a
+version of optimal balance is achieved after matching. As currently
+implemented, matching is done with replacement using the matching
+method of \citet{AbaImb07} and balance is determined by two univariate
+tests, paired t-tests for dichotomous variables and a
+Kolmogorov-Smirnov test for multinomial and continuous variables, but
+these options can be changed.
+
+Genetic matching can be performed with \MatchIt\ by setting
+\texttt{method = "genetic"}, which automatically loads the
+\texttt{Matching} \citep{Sekhon04} package. The following example of
+genetic matching (using the estimated propensity score based on
+logistic regression as one of the covariates) can also be run by
+typing {\tt demo(genetic)}:
+\begin{verbatim}
+> m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree +
+ re74 + re75, data = lalonde, method = "genetic")
+\end{verbatim}
+
+
+\subsubsection{Coarsened Exact Matching}
+\label{subsub:cem}
+
+Coarsened Exact Matching (CEM) is a Monotonoic Imbalance Bounding
+(MIB) matching method --- which means that the balance between the
+treated and control groups is chosen by the user ex ante rather than
+discovered through the usual laborious process of checking after the
+fact and repeatedly reestimating, and so that adjusting the imbalance
+on one variable has no effect on the maximum imbalance of any other.
+CEM also strictly bounds through ex ante user choice both the degree
+of model dependence and the average treatment effect estimation error,
+eliminates the need for a separate procedure to restrict data to
+common empirical support, meets the congruence principle, is robust to
+measurement error, works well with multiple imputation methods for
+missing data, and is extremely fast computationally even with very
+large data sets. CEM also works well for multicategory treatments,
+determining blocks in experimental designs, and evaluating extreme
+counterfactuals \citep{IacKinPor08}.
+
+CEM can be performed with \MatchIt\ by setting \texttt{method =
+ "cem"}, which automatically loads the \texttt{cem} package. The
+following examples of CEM (with automatic coarsening) can also be run
+by typing \texttt{demo(cem)}:
+\begin{verbatim}
+m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree
+ + re74 + re75, data = lalonde, method = "cem")
+\end{verbatim}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/inst/doc/summaryref.tex b/inst/doc/summaryref.tex
new file mode 100644
index 0000000..7b8be8f
--- /dev/null
+++ b/inst/doc/summaryref.tex
@@ -0,0 +1,127 @@
+\section{\texttt{summary()}: Numerical Summaries of Balance}
+
+The \texttt{summary()} command returns numerical summaries of balance
+diagnostics.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+summary(object, interactions = FALSE, addlvariables = NULL, standardize = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item \texttt{object}: the output from {\tt matchit()}.
+\item \texttt{interactions}: an option to calculate summary statistics
+ in \texttt{sum.all} and \texttt{sum.matched} for all covariates,
+ their squares, and two-way interactions when \texttt{interactions =
+ TRUE} and only the covariates themselves when \texttt{interactions
+ = FALSE}, (DEFAULT = {\tt FALSE}).
+\item \texttt{addlvariables}: additional variables on which to
+ calculate the diagnostic statistics (in addition to the variables
+ included in the matching procedure) (DEFAULT = {\tt NULL}).
+ \texttt{addlvariables}: a data frame containing additional variables
+ whose balance is examined. The data should come with the same number
+ of units and units in the same order as in the data set used for
+ {\tt matchit()}.
+\item \texttt{standardize}: a logical variable indicating whether to
+ standardize balance measures, i.e., whether the difference in means should be divided
+ by the standard deviation in the original treated group. (DEFAULT = {\tt FALSE})
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output from the \texttt{summary()} command includes the following
+elements, when applicable:
+\begin{itemize}
+\item The original assignment model call.
+\item \texttt{sum.all}: a data frame that contains variable names and
+ interactions down the row names, and summary statistics on \emph{all
+ observations} in each of the columns. The columns in
+ \texttt{sum.all} contain:
+%\footnote{The output for full matching is
+% slightly different from that described here; see Section
+% \ref{subsubsec:full} for details.}
+ \begin{itemize}
+ \item means of all covariates $X$ for treated and control units,
+ where \texttt{Means Treated}$= \mu_{X|T=1} = \frac{1}{n_1}
+ \sum_{T=1} X_i$ and \texttt{Means Control}$= \mu_{X|T=0} =
+ \frac{1}{n_0} \sum_{T=0} X_i$,
+ \item standard deviation in the control group for all covariates $X$, where applicable,
+ $$\quad s_{x|T=0} = \sqrt{\frac{\sum_{i \in \{i: T_i=0\}}
+ (X_i - \mu_{X|T=0})^2}{n_0-1} }.$$
+ \item balance statistics of the original data (before matching),
+ which compare treated and control covariate distributions. If {\tt
+ standardize = FALSE}, balance measures will be presented on the
+ original scale. Specifically, mean differences (\texttt{Mean
+ Diff.}) as well as the median, mean, and maximum value of
+ differences in empirical quantile functions for each covariate
+ will be given (\texttt{eQQ Med}, \texttt{eQQ Mean}, and
+ \texttt{eQQ Max}, respectively). If {\tt standardize = TRUE}, the
+ balance measures will be standardized. Standardized mean
+ differences (\texttt{Std.\ Mean Diff.}), defined as
+ $\frac{\mu_{X|T=1} - \mu_{X|T=0}}{s_{x|T=1}}$, as well as the
+ median, mean, and maximum value of differences in empirical
+ cumulative distribution functions for each covariate will be given
+ (\texttt{eCDF Med}, \texttt{eCDF Mean}, and \texttt{eCDF Max},
+ respectively).
+ \end{itemize}
+
+\item \texttt{sum.matched}: a data frame which contains variable names
+ down the row names, and summary statistics on only the \emph{matched
+ observations} in each of the columns. Specifically, the columns
+ in \texttt{sum.matched} contain the following elements:
+%\footnote{The
+% values output for full matching are slightly different from that
+% described here; see Section \ref{subsubsec:full} for details}:
+ \begin{itemize}
+ \item weighted means for matched treatment units and matched control units of all covariates
+ $X$ and their interactions, where \texttt{Means Treated}$=
+ \mu_{wX|T=1} = \frac{1}{n_1} \sum_{T=1} w_iX_i$ and \texttt{Means
+ Control}$=\mu_{wX|T=0} = \frac{1}{n_0} \sum_{T=0} w_iX_i$,
+ \item weighted standard deviations in the matched control group for
+ all covariates $X$, where applicable, where \texttt{SD} $= s_{wX} =
+ \sqrt{\frac{1}{n} \sum_{i} (w_iX_i - \overline{X}^*)^2}$, where
+ $\overline{X}^*$ is the weighted mean of $X$ in the matched
+ control group, and
+ \item balance statistics of the matched data (after matching), which
+ compare treated and control covariate distributions. If {\tt
+ standardize = FALSE}, balance measures will be presented on the
+ original scale. Specifically, mean differences (\texttt{Mean
+ Diff.}) as well as the median, mean, and maximum value of
+ differences in empirical quantile functions for each covariate
+ will be given (\texttt{eQQ Med}, \texttt{eQQ Mean}, and
+ \texttt{eQQ Max}, respectively). If {\tt standardize = TRUE}, the
+ balance measures will be standardized. Standardized mean
+ differences (\texttt{Std.\ Mean Diff.}), defined as
+ $\frac{\mu_{wX|T=1} - \mu_{wX|T=0}}{s_{x|T=1}}$, as well as the
+ median, mean, and maximum value of differences in empirical
+ cumulative distribution functions for each covariate will be given
+ (\texttt{eCDF Med}, \texttt{eCDF Mean}, and \texttt{eCDF Max},
+ respectively).
+ \end{itemize}
+ where $w$ represents the vector of \texttt{weights}.
+
+\item \texttt{reduction}: the percent reduction in the difference in
+ means achieved in each of the balance measures in \texttt{sum.all}
+ and \texttt{sum.matched}, defined as $100(|a|-|b|)/|a|$, where $a$
+ was the value of the balance measure before matching and $b$ is the
+ value of the balance measure after matching.
+
+\item \texttt{nn}: the sample sizes in the full and matched samples
+ and the number of discarded units, by treatment and control.
+
+\item \texttt{q.table}: an array that contains the same information
+ as \texttt{sum.matched} by subclass.
+
+\item \texttt{qn}: the sample sizes in the full and matched
+ samples and the number of discarded units, by subclass and by
+ treatment and control.
+\item \texttt{match.matrix}: the same object is contained in the
+ output of {\tt matchit()}.
+\end{itemize}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End:
diff --git a/man/help.matchit.Rd b/man/help.matchit.Rd
index a2d1176..9809210 100644
--- a/man/help.matchit.Rd
+++ b/man/help.matchit.Rd
@@ -30,7 +30,7 @@ help.matchit(object)
\author{
Daniel Ho <\email{daniel.ho at yale.edu}>; Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
- <\email{king at harvard.edu}>; Elizabeth Stuart<\email{stuart at stat.harvard.edu}>
+ <\email{king at harvard.edu}>; Elizabeth Stuart<\email{estuart at jhsph.edu}>
}
\keyword{documentation}
diff --git a/man/lalonde.Rd b/man/lalonde.Rd
index 1df0821..b597909 100644
--- a/man/lalonde.Rd
+++ b/man/lalonde.Rd
@@ -21,11 +21,11 @@ dollars. "re78" is income in 1978, in U.S. dollars. }
\references{
Lalonde, R. (1986). Evaluating the econometric evaluations of training programs
- with experimental data. {\it American Economic Review} 76: 604-620. \\
+ with experimental data. American Economic Review 76: 604-620.
Dehejia, R.H. and Wahba, S. (1999). Causal Effects in Nonexperimental Studies:
- Re-Evaluating the Evaluation of Training Programs. {\it Journal of the American
- Statistical Association} 94: 1053-1062.
+ Re-Evaluating the Evaluation of Training Programs. Journal of the American
+ Statistical Association 94: 1053-1062.
}
\source{\url{http://www.columbia.edu/~rd247/nswdata.html}}
\keyword{datasets}
diff --git a/man/match.data.Rd b/man/match.data.Rd
index 6f1b0dd..79910f0 100644
--- a/man/match.data.Rd
+++ b/man/match.data.Rd
@@ -5,7 +5,8 @@
\title{Output Matched Data Sets}
\description{\code{match.data} outputs matched data sets from
-\code{matchit()}.}
+\code{matchit()}.
+}
\usage{
match.data <- match.data(object, group="all", distance = "distance",
@@ -13,31 +14,23 @@ weights = "weights", subclass = "subclass")
}
\arguments{
-
- \item{object}{The output object from {\tt matchit()}. This is
- a required input.
- }
+ \item{object}{The output object from \code{matchit}. This is
+ a required input.}
\item{group}{This argument specifies for which matched group the user
wants to extract the data. Available options are \code{"all"} (all
matched units), \code{"treat"} (matched units in the treatment
group), and \code{"control"} (matched units in the control
- group). The default is \code{"all"}.
- }
+ group). The default is \code{"all"}.}
\item{distance}{This argument specifies the variable name used to
- store the distance measure. The default is \code{"distance"}.
- }
+ store the distance measure. The default is \code{"distance"}.}
\item{weights}{This argument specifies the variable name used to store
- the resulting weights from matching. The default is
- \code{"weights"}.
- }
+ the resulting weights from matching. The default is \code{"weights"}.}
\item{subclass}{This argument specifies the variable name used to store the
- subclass indicator. The default is \code{"subclass"}.
- }
+ subclass indicator. The default is \code{"subclass"}.}
}
\value{
-
- \item{Returns a subset of the original data set sent to
+ Returns a subset of the original data set sent to
\code{matchit()}, with just the matched units. The data set also
contains the additional variables \code{distance}, \code{weights},
and \code{subclass}. The variable \code{distance} gives
@@ -48,7 +41,6 @@ weights = "weights", subclass = "subclass")
See the \url{http://gking.harvard.edu/matchit/} for the complete
documentation and type \code{demo(match.data)} at the R prompt to
see a demonstration of the code.
- }
}
\seealso{Please use \code{help.matchit} to access the matchit reference
@@ -56,9 +48,9 @@ weights = "weights", subclass = "subclass")
\url{http://gking.harvard.edu/matchit}.}
\author{
- Daniel Ho <\email{daniel.ho at yale.edu}>; Kosuke Imai
- <\email{kimai at princeton.edu}>; Gary King
- <\email{king at harvard.edu}>; Elizabeth Stuart<\email{stuart at stat.harvard.edu}>
+ Daniel Ho \email{daniel.ho at yale.edu}; Kosuke Imai
+ \email{kimai at princeton.edu}; Gary King
+ \email{king at harvard.edu}; Elizabeth Stuart \email{estuart at jhsph.edu}
}
\keyword{methods}
diff --git a/man/matchit.Rd b/man/matchit.Rd
index 2d39262..2e9b0f5 100644
--- a/man/matchit.Rd
+++ b/man/matchit.Rd
@@ -53,11 +53,9 @@ numerically using \code{summary(matchitobject)}.
works. For example, \code{x1:x2} represents the first order
interaction term between \code{x1} and \code{x2}, and \code{I(x1^2)}
represents the square term of \code{x1}. See \code{help(formula)}
- for details.
- }
+ for details.}
\item{data}{This argument specifies the data frame containing the
- variables called in \code{formula}.
- }
+ variables called in \code{formula}.}
\item{method}{This argument specifies a matching method. Currently,
\code{"exact"} (exact matching), \code{"full"} (full matching),
\code{"genetic"} (genetic matching), \code{"nearest"} (nearest
@@ -66,83 +64,60 @@ represents the square term of \code{x1}. See \code{help(formula)}
\code{"nearest"}. Note that within each of these matching methods,
\emph{MatchIt} offers a variety of options. See
\url{http://gking.harvard.edu/matchit/docs/Inputs.html} for the
-complete list
- of supported options.
- }
+ complete list of supported options.}
\item{distance}{This argument specifies the method used to estimate the
distance measure. The default is logistic regression,
\code{"logit"}. A variety of other methods are available. See
\url{http://gking.harvard.edu/matchit/docs/All_Matching_Methods.html}
-for the complete list of
- supported methods.
- }
+ for the complete list of supported methods.}
\item{distance.options}{ This optional argument specifies the optional
arguments that are passed to the model for estimating the distance
- measure. The input to this argument should be a list.
- }
+ measure. The input to this argument should be a list.}
\item{discard}{This argument specifies whether to discard units that
fall outside some measure of support of the distance score before
matching, and not allow them to be used at all in the matching
procedure. Note that discarding units may change the quantity of
- interest being estimated.
-
- \item{none}{(default) discards no units before matching.}
- \item{both}{discards all units (treated and control) that are
- outside the support of the distance measure.
- }
- \item{control}{discards only control units outside the
- support of the distance measure of the treated units.
- }
- \item{treat}{discards only treated units outside the support
- of the distance measure of the control units.
- }
- }
+ interest being estimated. The options are: \code{"none"} (default), which discards no units before matching,
+ \code{"both"}, which discards all units (treated and control) that are
+ outside the support of the distance measure,
+ \code{"control"}, which discards only control units outside the
+ support of the distance measure of the treated units, and
+ \code{"treat"}, which discards only treated units outside the support
+ of the distance measure of the control units.}
\item{reestimate}{This argument specifies whether the model for
distance measure should be re-estimated after units are
discarded. The input must be a logical value. The default is
- \code{FALSE}.
- }
+ \code{FALSE}.}
\item{...}{Additional arguments to be passed to a variety of matching
methods. See \url{http://gking.harvard.edu/matchit/??} for the
- complete list of options.
- }
+ complete list of options.}
}
\value{
- \item{call}{The original \code{matchit} call.
- }
+ \item{call}{The original \code{matchit} call.}
\item{formula}{The formula used to specify the model for
- estimating the distance measure.
- }
+ estimating the distance measure.}
\item{model}{The output of the model used to estimate
the distance measure. \code{summary(m.out$model)} will give the
summary of the model where \code{m.out} is the output object from
- \code{matchit}.
- }
+ \code{matchit}.}
\item{match.matrix}{An \eqn{n_1} by \code{ratio} matrix
- where\cr\cr
- the row names, which can be obtained through
+ where the row names, which can be obtained through
\code{row.names(match.matrix)}, represent the names of the
treatment units, which come from the data frame specified in
- \code{data}.\cr\cr
-
- each column stores the name(s) of the control unit(s) matched
+ \code{data}. Each column stores the name(s) of the control unit(s) matched
to the treatment unit of that row. For example, when the
\code{ratio} input for nearest neighbor or optimal matching is
specified as 3, the three columns of
\code{match.matrix} represent the three control units matched to
- one treatment unit).\cr\cr
-
- \code{NA} indicates that the treatment unit was not matched.
- }
+ one treatment unit).
+ \code{NA} indicates that the treatment unit was not matched.}
\item{discarded}{A vector of length $n$ that displays
whether the units were ineligible for matching due to common
support restrictions. It equals \code{TRUE} if unit \eqn{i} was
- discarded, and it is set to \code{FALSE} otherwise.
- }
+ discarded, and it is set to \code{FALSE} otherwise.}
\item{distance}{A vector of length \eqn{n} with the estimated
- distance measure for each unit.
- }
+ distance measure for each unit.}
\item{weights}{A vector of length \eqn{n} that provides the
weights assigned to each unit in the matching process. Unmatched
units have weights equal to \code{0}. Matched treated units have
@@ -150,24 +125,18 @@ for the complete list of
to the number of treatment units to which it was matched, and the sum of
the control weights is equal to the number of uniquely matched
control units. See
-\url{http://gking.harvard.edu/matchit/docs/How_Exactly_are.html} for
-more
- details.
- }
+ \url{http://gking.harvard.edu/matchit/docs/How_Exactly_are.html} for
+ more details.}
\item{subclass}{The subclass index in an ordinal
scale from 1 to the total number of subclasses as specified in
\code{subclass} (or the total number of subclasses from full or
- exact matching). Unmatched units have \code{NA}.
- }
+ exact matching). Unmatched units have \code{NA}.}
\item{q.cut}{The subclass cut-points that classify the
- distance measure.
- }
+ distance measure.}
\item{treat}{The treatment indicator from
- \code{data} (the left-hand side of \code{formula}).
- }
+ \code{data} (the left-hand side of \code{formula}).}
\item{X}{The covariates used for estimating the
- distance measure (the right-hand side of \code{formula}).
- }
+ distance measure (the right-hand side of \code{formula}).}
}
\seealso{Please use \code{help.matchit} to access the matchit reference
@@ -176,14 +145,15 @@ more
}
\references{Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart
- (2004) `Matching as Nonparametric Preprocessing for Improving Parametric
- Causal Inference,'' preprint available at
- \url{http://gking.harvard.edu/files/abs/matchp-abs.shtml} }
+ (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric
+ Causal Inference. Political Analysis 15(3): 199-236.
+ \url{http://gking.harvard.edu/files/abs/matchp-abs.shtml}
+}
\author{
- Daniel Ho <\email{daniel.ho at yale.edu}>; Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
- <\email{king at harvard.edu}>; Elizabeth
- Stuart<\email{stuart at stat.harvard.edu}> }
+ Daniel Ho \email{daniel.ho at yale.edu}; Kosuke Imai \email{kimai at princeton.edu}; Gary King
+ \email{king at harvard.edu}; Elizabeth
+ Stuart\email{estuart at jhsph.edu} }
\keyword{environment}
diff --git a/man/user.prompt.Rd b/man/user.prompt.Rd
index 277b4fb..d7576df 100644
--- a/man/user.prompt.Rd
+++ b/man/user.prompt.Rd
@@ -15,7 +15,7 @@ user.prompt()
\seealso{\code{readline}}
-\author{Olivia Lau <\email{olau at fas.harvard.edu}>
+\author{Olivia Lau \email{olau at fas.harvard.edu}
}
\examples{
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/r-cran-matchit.git
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