[r-cran-matchit] 22/45: Import Upstream version 2.4-11
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 080e0a239557decbd85cfc7fe0504d3bd8266059
Author: Andreas Tille <tille at debian.org>
Date: Fri Oct 20 07:40:58 2017 +0200
Import Upstream version 2.4-11
---
DESCRIPTION | 26 +-
R/hist.pscore.R | 49 +-
R/jitter.pscore.R | 31 +-
R/matchit.qqplot.R | 20 +-
R/plot.matchit.R | 6 +-
R/plot.matchit.subclass.R | 8 +-
inst/CITATION | 20 +
inst/doc/balance.tex | 86 +-
inst/doc/figs/hist.pdf | 412 +-
inst/doc/figs/jitterplotnn.pdf | 7126 ++++++++++++++++++-----------------
inst/doc/figs/qqplotnn1.pdf | 8146 +++++++++++++++++++++++++++++++++-------
inst/doc/index.shtml | 2 +-
inst/doc/matchit.aux | 116 +-
inst/doc/matchit.log | 67 +-
inst/doc/matchit.pdf | Bin 430354 -> 662242 bytes
inst/doc/matchit.tex | 2 +
inst/doc/matchit.toc | 84 +-
17 files changed, 11110 insertions(+), 5091 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index c71d404..fe76f2e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,20 +1,22 @@
Package: MatchIt
-Version: 2.4-10
-Date: 2009-02-02
+Version: 2.4-11
+Date: 2009-06-25
Title: MatchIt
-Author: Daniel Ho <daniel.e.ho at gmail.com>,
- Elizabeth Stuart <stuart at stat.harvard.edu>,
- Kosuke Imai <kimai at Princeton.Edu>,
+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>
Maintainer: Kosuke Imai <kimai at Princeton.Edu>
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.
+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
+Packaged: 2009-06-26 03:58:18 UTC; king
+Repository: CRAN
+Date/Publication: 2009-06-26 17:42:18
diff --git a/R/hist.pscore.R b/R/hist.pscore.R
index ddc245b..851bb27 100644
--- a/R/hist.pscore.R
+++ b/R/hist.pscore.R
@@ -1,4 +1,4 @@
-hist.pscore <- function(x, numdraws=5000){
+hist.pscore <- function(x, numdraws=5000, xlab="Propensity Score", main=NULL, freq=F, xlim = NULL,...){
treat <- x$treat
pscore <- x$distance
weights <- x$weights
@@ -24,18 +24,37 @@ hist.pscore <- function(x, numdraws=5000){
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)}
+ if(!is.null(xlim)){warning("xlim may not be user specified. xlim returned to default.")}
+ xlim <- range(na.omit(pscore))
+ if(is.null(main)){
+ hist(pscore[treat==1],xlim=xlim,
+ xlab=xlab, freq=freq,
+ main="Raw Treated", ...)
+ hist(pscore.treated.matched,xlim=xlim,
+ xlab=xlab, freq=freq,
+ main="Matched Treated",...)
+ if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
+ hist(pscore[treat==0],xlim=xlim,
+ xlab=xlab, freq=freq,
+ main="Raw Control",...)
+ hist(pscore.control.matched,xlim=xlim,
+ xlab=xlab, freq=freq,
+ main="Matched Control",...)
+ if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
+ }else{
+ hist(pscore[treat==1],xlim=xlim,
+ xlab=xlab, freq=freq,
+ main=main, ...)
+ hist(pscore.treated.matched,xlim=xlim,
+ xlab=xlab, freq=freq,
+ main=main,...)
+ if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
+ hist(pscore[treat==0],xlim=xlim,
+ xlab=xlab, freq=freq,
+ main=main,...)
+ hist(pscore.control.matched,xlim=xlim,
+ xlab=xlab, freq=freq,
+ main=main,...)
+ 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 f8340ea..ddc3f99 100644
--- a/R/jitter.pscore.R
+++ b/R/jitter.pscore.R
@@ -1,4 +1,4 @@
-jitter.pscore <- function(x, interactive){
+jitter.pscore <- function(x, interactive,pch=1,cex=NULL,...){
treat <- x$treat
pscore <- x$distance
weights <- x$weights
@@ -10,16 +10,27 @@ jitter.pscore <- function(x, interactive){
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")
+ axes=F,main="Distribution of Propensity Scores",...)
if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)}
- points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0],
- pch=18,cex=cwt[treat==1&weights!=0])
- points(pscore[treat==1&weights==0],jitp[treat==1&weights==0],
- pch=5,col="grey",cex=0.5)
- points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0],
- pch=18,cex=cwt[treat==0&weights!=0])
- points(pscore[treat==0&weights==0],jitp[treat==0&weights==0],
- pch=5,col="grey",cex=0.5)
+ if(is.null(cex)){
+ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0],
+ pch=pch,cex=cwt[treat==1&weights!=0],...)
+ points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0],
+ pch=pch,cex=cwt[treat==0&weights!=0],...)
+ points(pscore[treat==1&weights==0],jitp[treat==1&weights==0],
+ pch=pch,cex=1,...)
+ points(pscore[treat==0&weights==0],jitp[treat==0&weights==0],
+ pch=pch,cex=1,...)
+ }else{
+ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0],
+ pch=pch,cex=cex,...)
+ points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0],
+ pch=pch,cex=cex,...)
+ points(pscore[treat==1&weights==0],jitp[treat==1&weights==0],
+ pch=pch,cex=cex,...)
+ points(pscore[treat==0&weights==0],jitp[treat==0&weights==0],
+ pch=pch,cex=cex,...)
+ }
axis(1)
text(sum(range(na.omit(pscore)))/2,2.5,"Unmatched Treatment Units")
text(sum(range(na.omit(pscore)))/2,1.5,"Matched Treatment Units")
diff --git a/R/matchit.qqplot.R b/R/matchit.qqplot.R
index 5fd52fb..c1b3f2b 100644
--- a/R/matchit.qqplot.R
+++ b/R/matchit.qqplot.R
@@ -1,6 +1,6 @@
matchit.qqplot <- function(x,discrete.cutoff,
which.subclass=NULL, numdraws=5000,
- interactive = T, which.xs = NULL){
+ interactive = T, which.xs = NULL,...){
X <- x$X
## Fix X matrix so that it doesn't have any factors
varnames <- colnames(X)
@@ -54,9 +54,13 @@ matchit.qqplot <- function(x,discrete.cutoff,
nn <- dimnames(covariates)[[2]]
nc <- length(nn)
covariates <- data.matrix(covariates)
- oma <- c(4, 4, 6, 4)
+ # oma <- c(4, 4, 6, 4)
+ oma <- c(2.25,0,3.75,1.5)
opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma)
on.exit(par(opar))
+
+ # par(oma=c(2.25,0,3.75,1.5))
+
for (i in 1:nc){
xi <- covariates[,i]
m.xi <- m.covariates[,i]
@@ -67,28 +71,28 @@ matchit.qqplot <- function(x,discrete.cutoff,
if(!is.null(which.subclass)){
htext <- paste(htext,paste(" (Subclass ",which.subclass,")",sep=""),sep="")
}
- mtext(htext, 3, 3, TRUE, 0.5, cex=1.2,font=2)
- mtext("All", 3, 1, TRUE, 0.5, cex=1.2,font = 1)
- mtext("Matched", 3, 1, TRUE, 0.83, cex=1.2,font = 1)
+ mtext(htext, 3, 2, TRUE, 0.5, cex=1.1,font=2)
+ mtext("All", 3, .25, TRUE, 0.5, cex=1,font = 1)
+ mtext("Matched", 3, .25, TRUE, 0.83, cex=1,font = 1)
mtext("Control Units", 1, 0, TRUE, 2/3, cex=1,font = 1)
mtext("Treated Units", 4, 0, TRUE, 0.5, cex=1,font = 1)
}
par(usr = c(0, 1, 0, 1))
l.wid <- strwidth(nn, "user")
- cex.labels <- max(0.8, min(2, 0.9/max(l.wid)))
+ cex.labels <- max(0.75, min(1.45, 0.85/max(l.wid)))
text(0.5,0.5,ni,cex=cex.labels)
if(length(table(xi))<=discrete.cutoff){
xi <- jitter(xi)
m.xi <- jitter(m.xi)
}
rr <- range(xi)
- eqqplot(xi[treat==0],xi[treat==1], xlim=rr,ylim=rr,axes=F,ylab="",xlab="")
+ eqqplot(xi[treat==0],xi[treat==1], xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...)
abline(a=0,b=1)
abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2)
abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2)
axis(2)
box()
- eqqplot(m.xi[m.treat==0],m.xi[m.treat==1],xlim=rr,ylim=rr,axes=F,ylab="",xlab="")
+ eqqplot(m.xi[m.treat==0],m.xi[m.treat==1],xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...)
abline(a=0,b=1)
abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2)
abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2)
diff --git a/R/plot.matchit.R b/R/plot.matchit.R
index 1ecea83..53e1ecb 100644
--- a/R/plot.matchit.R
+++ b/R/plot.matchit.R
@@ -8,11 +8,11 @@ plot.matchit <- function(x, discrete.cutoff=5, type="QQ",
if(type=="QQ"){
matchit.qqplot(x=x,discrete.cutoff=discrete.cutoff,
numdraws=numdraws, interactive=interactive,
- which.xs = which.xs)
+ which.xs = which.xs, ...)
} else if(type=="jitter"){
- jitter.pscore(x, interactive=interactive)
+ jitter.pscore(x, interactive=interactive,...)
} else if(type=="hist"){
- hist.pscore(x)
+ hist.pscore(x,...)
} else {
stop("Invalid type")
}
diff --git a/R/plot.matchit.subclass.R b/R/plot.matchit.subclass.R
index 7be455a..ba13b23 100644
--- a/R/plot.matchit.subclass.R
+++ b/R/plot.matchit.subclass.R
@@ -28,17 +28,17 @@ plot.matchit.subclass <- function(x, discrete.cutoff=5,
if(ans!=0)
{
matchit.qqplot(x,discrete.cutoff,which.subclass=ans,
- interactive = interactive, which.xs=which.xs)
+ interactive = interactive, which.xs=which.xs,...)
}
}
} else {
matchit.qqplot(x,discrete.cutoff,which.subclass=subclass,
- interactive=interactive, which.xs=which.xs)
+ interactive=interactive, which.xs=which.xs,...)
}
} else if(type=="jitter"){
- jitter.pscore(x, interactive=interactive)
+ jitter.pscore(x, interactive=interactive,...)
} else if(type=="hist"){
- hist.pscore(x)
+ hist.pscore(x,...)
} else {
stop("Invalid type")
}
diff --git a/inst/CITATION b/inst/CITATION
new file mode 100644
index 0000000..6afb261
--- /dev/null
+++ b/inst/CITATION
@@ -0,0 +1,20 @@
+citHeader("To cite MatchIt in publications use:")
+
+citEntry(entry = "Article",
+ title = "{MatchIt}: Nonparametric Preprocessing for Parametric Causal Inference",
+ author = personList(as.person("Daniel E. Ho"),
+ as.person("Kosuke Imai"),
+ as.person("Gary King"),
+ as.person("Elizabeth A. Stuart")),
+ journal = "Journal of Statistical Software",
+ year = "2009",
+ note = "Forthcoming",
+ url = "http://www.jstatsoft.org/",
+
+ textVersion =
+ paste("Daniel E. Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart (2009).",
+ "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.",
+ "Journal of Statistical Software, Forthcoming.",
+ "URL http://www.jstatsoft.org/.")
+)
+
diff --git a/inst/doc/balance.tex b/inst/doc/balance.tex
index c78c7dd..15515cf 100644
--- a/inst/doc/balance.tex
+++ b/inst/doc/balance.tex
@@ -81,55 +81,53 @@ subclass are shown.
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
+distance measure, Q-Q plots of each covariate, and histograms of the
+distance measure. For subclassification, separate Q-Q plots can be
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}
+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.
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}
+in Figure~\ref{fig:plotcommandoutput}. 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 (lower left panel of
+Figure~\ref{fig:plotcommandoutput}). Deviations from the 45 degree
+line indicate differences in the empirical distribution. The jitter
+plot (top panel) shows the overall distribution of propensity scores
+in the treated and control groups. In the jitter plot, which can be
+created by setting \texttt{type = "jitter"}, the size of each point is
+proportional to the weight given to that unit. Observation names can
+be interactively identified by clicking the first mouse button near
+the units. The histograms (lower right panel) can be plotted by
+setting \texttt{type = "hist"}.
+
+\begin{figure}
+ \begin{center}
+ \includegraphics[height=2.7in, keepaspectratio=true]{figs/jitterplotnn.pdf}
+ \includegraphics[height=3in,keepaspectratio=true]{figs/qqplotnn1.pdf}
+ \includegraphics[height=3in,keepaspectratio=true]{figs/hist.pdf}
+ \caption{Examples of the three types of output from the
+ \texttt{plot} command resulting from matching on the
+ /texttt{lalonde} data set based on real earnings in 1974
+ (\texttt{re74}) divided by 1000, real earnings in 1975
+ (\texttt{re75}) divided by 1000, years of education
+ (\texttt{educ}), Hispanic (\texttt{hispan}) and marital status
+ (\texttt{married}). Observations in both the treated and the
+ control groups outside the support of the distance measure were
+ discarded. The upper plot shows the jitter plot of the distance
+ measure. The lower left plot shows the QQ plots for the first
+ three covariates (\texttt{I(re74/1000)}, \texttt{I(re75/1000)},
+ \texttt{educ}). The lower right plot shows the histograms of the
+ density of propensity scores for observations before and after
+ matching.}
+ \label{fig:plotcommandoutput}
\end{center}
+\end{figure}
+
%%% Local Variables:
%%% mode: latex
diff --git a/inst/doc/figs/hist.pdf b/inst/doc/figs/hist.pdf
index 33c2f42..1183ae2 100644
--- a/inst/doc/figs/hist.pdf
+++ b/inst/doc/figs/hist.pdf
@@ -1,11 +1,11 @@
-%PDF-1.1
+%PDF-1.4
%���ρ�\r
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-/CreationDate (D:20071010113947)
-/ModDate (D:20071010113947)
+/CreationDate (D:20090625200302)
+/ModDate (D:20090625200302)
/Title (R Graphics Output)
-/Producer (R 2.4.1)
+/Producer (R 2.7.0)
/Creator (R)
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@@ -37,12 +37,12 @@ endobj
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0.000 0.000 0.000 RG
0.75 w
[] 0 d
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BT
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ET
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S
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-0.000 0.000 0.000 RG
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0.75 w
[] 0 d
1 J
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[] 0 d
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S
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work better."
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diff --git a/inst/doc/matchit.log b/inst/doc/matchit.log
index 9208903..5970efd 100644
--- a/inst/doc/matchit.log
+++ b/inst/doc/matchit.log
@@ -1,4 +1,4 @@
-This is pdfTeX, Version 3.14159-1.10b (Web2C 7.4.5) (format=pdflatex 2007.11.9) 2 FEB 2009 09:43
+This is pdfTeX, Version 3.14159-1.10b (Web2C 7.4.5) (format=pdflatex 2007.11.9) 25 JUN 2009 23:57
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File: figs/jitterplotnn.pdf Graphic file (type pdf)
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File: figs/qqplotnn1.pdf Graphic file (type pdf)
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Overfull \hbox (4.2199pt too wide) in paragraph at lines 104--104
[]/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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[] /cmtt12/married + re74 + re75, data = match.data(m.out1, "
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Chapter 4.
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[]
-[25]
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[]/cmtt12/.../cmr12/: Ad-di-tional in-puts that can be passed to the /cmtt12/fu
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@@ -523,7 +522,7 @@ Overfull \hbox (40.20618pt too wide) in paragraph at lines 334--338
ce mea-sure. /cmtt12/summary(m.out$model)
[]
-[26]) (./summaryref.tex
+[27]) (./summaryref.tex
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[]
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[]/cmtt12/> unmatched.data <- lalonde[!row.names(lalonde)%in%row.names(match.da
ta(m.out1)),][]
[]
-[32]) (./faq.tex [33]
+[33]) (./faq.tex [34]
Chapter 5.
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[]/cmtt12/cbind(lalonde[row.names(foo$match.matrix),"re78"], lalonde[foo$match.
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Chapter 6.
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[]/cmbx12/2.4-7 /cmr12/(Au-gust 4, 2008): Fixed mi-nor bug in sub-clas-si-fi-ca
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[]
-[38
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-] [39] (./matchit.bbl
+] [40] (./matchit.bbl
Underfull \hbox (badness 3029) in paragraph at lines 22--25
[]/cmr12/Diamond, A. and Sekhon, J. (2005), ``Ge-netic Match-ing for Es-ti-mat-
ing Causal
@@ -595,28 +594,28 @@ Underfull \hbox (badness 10000) in paragraph at lines 58--60
k-ing,''
[]
-[40
+[41
])
Package natbib Warning: There were undefined citations.
-[41] (./matchit.aux)
+[42] (./matchit.aux)
LaTeX Font Warning: Some font shapes were not available, defaults substituted.
)
Here is how much of TeX's memory you used:
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- 56697 string characters out of 1190233
- 112297 words of memory out of 1000001
- 7198 multiletter control sequences out of 10000+50000
+ 4458 strings out of 95437
+ 56733 string characters out of 1190233
+ 112346 words of memory out of 1000001
+ 7199 multiletter control sequences out of 10000+50000
19072 words of font info for 74 fonts, out of 500000 for 1000
14 hyphenation exceptions out of 1000
- 34i,11n,41p,219b,459s stack positions out of 1500i,500n,5000p,200000b,5000s
- 714 PDF objects out of 300000
- 152 named destinations out of 131072
+ 34i,11n,41p,920b,459s stack positions out of 1500i,500n,5000p,200000b,5000s
+ 731 PDF objects out of 300000
+ 154 named destinations out of 131072
465 words of extra memory for PDF output out of 65536
{/usr/share/texmf/dvips/tetex/bbad153f.enc}</usr/share/texmf/fonts/type1/blue
sky/cm/cmsy6.pfb>{/usr/share/texmf/dvips/tetex/aae443f0.enc}</usr/share/texmf/f
@@ -633,4 +632,4 @@ y/cm/cmtt10.pfb></usr/share/texmf/fonts/type1/bluesky/cm/cmr8.pfb></usr/share/t
exmf/fonts/type1/bluesky/cm/cmr10.pfb></usr/share/texmf/fonts/type1/bluesky/cm/
cmr12.pfb></usr/share/texmf/fonts/type1/bluesky/cm/cmr17.pfb>{/usr/share/texmf/
dvips/tetex/0ef0afca.enc}</usr/share/texmf/fonts/type1/bluesky/cm/cmcsc10.pfb>
-Output written on matchit.pdf (42 pages, 430354 bytes).
+Output written on matchit.pdf (43 pages, 662242 bytes).
diff --git a/inst/doc/matchit.pdf b/inst/doc/matchit.pdf
index f619bce..7d0a8ef 100644
Binary files a/inst/doc/matchit.pdf and b/inst/doc/matchit.pdf differ
diff --git a/inst/doc/matchit.tex b/inst/doc/matchit.tex
index 03ae024..b9d53df 100644
--- a/inst/doc/matchit.tex
+++ b/inst/doc/matchit.tex
@@ -115,6 +115,8 @@ or most simply via \hlink{Zelig}{http://gking.harvard.edu/zelig} in R.
\chapter{What's New?}
\begin{itemize}
+\item \textbf{2.4-11} (June 25, 2009): More flexible inputs in
+ plotting.
\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
diff --git a/inst/doc/matchit.toc b/inst/doc/matchit.toc
index 8ee17e1..197b7e9 100644
--- a/inst/doc/matchit.toc
+++ b/inst/doc/matchit.toc
@@ -24,47 +24,47 @@
\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 {section}{\numberline {3.3}Conducting Analyses after Matching}{16}{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}
+\contentsline {chapter}{\numberline {4}Reference Manual}{22}{chapter.4}
+\contentsline {section}{\numberline {4.1}\texttt {matchit()}: Implementation of Matching Methods}{22}{section.4.1}
+\contentsline {subsubsection}{\numberline {4.1.0.1}Syntax}{22}{subsubsection.4.1.0.1}
+\contentsline {subsubsection}{\numberline {4.1.0.2}Arguments}{22}{subsubsection.4.1.0.2}
+\contentsline {paragraph}{\numberline {4.1.0.2.1}Arguments for All Matching Methods}{22}{paragraph.4.1.0.2.1}
+\contentsline {paragraph}{\numberline {4.1.0.2.2}Additional Arguments for Specification of Distance Measures}{23}{paragraph.4.1.0.2.2}
+\contentsline {paragraph}{\numberline {4.1.0.2.3}Additional Arguments for Subclassification}{25}{paragraph.4.1.0.2.3}
+\contentsline {paragraph}{\numberline {4.1.0.2.4}Additional Arguments for Nearest Neighbor Matching}{25}{paragraph.4.1.0.2.4}
+\contentsline {paragraph}{\numberline {4.1.0.2.5}Additional Arguments for Optimal Matching}{26}{paragraph.4.1.0.2.5}
+\contentsline {paragraph}{\numberline {4.1.0.2.6}Additional Arguments for Full Matching}{26}{paragraph.4.1.0.2.6}
+\contentsline {paragraph}{\numberline {4.1.0.2.7}Additional Arguments for Genetic Matching}{27}{paragraph.4.1.0.2.7}
+\contentsline {paragraph}{\numberline {4.1.0.2.8}Additional Arguments for Coarsened Exact Matching}{27}{paragraph.4.1.0.2.8}
+\contentsline {subsubsection}{\numberline {4.1.0.3}Output Values}{27}{subsubsection.4.1.0.3}
+\contentsline {section}{\numberline {4.2}\texttt {summary()}: Numerical Summaries of Balance}{28}{section.4.2}
+\contentsline {subsubsection}{\numberline {4.2.0.1}Syntax}{28}{subsubsection.4.2.0.1}
+\contentsline {subsubsection}{\numberline {4.2.0.2}Arguments}{29}{subsubsection.4.2.0.2}
+\contentsline {subsubsection}{\numberline {4.2.0.3}Output Values}{29}{subsubsection.4.2.0.3}
+\contentsline {section}{\numberline {4.3}\texttt {plot()}: Graphical Summaries of Balance}{30}{section.4.3}
+\contentsline {subsection}{\numberline {4.3.1}Plot options for the matchit object}{30}{subsection.4.3.1}
+\contentsline {subsubsection}{\numberline {4.3.1.1}Syntax}{31}{subsubsection.4.3.1.1}
+\contentsline {subsubsection}{\numberline {4.3.1.2}Arguments}{31}{subsubsection.4.3.1.2}
+\contentsline {subsubsection}{\numberline {4.3.1.3}Output Values}{31}{subsubsection.4.3.1.3}
+\contentsline {subsection}{\numberline {4.3.2}Plot options for the matchit summary object}{32}{subsection.4.3.2}
+\contentsline {subsubsection}{\numberline {4.3.2.1}Syntax}{32}{subsubsection.4.3.2.1}
+\contentsline {subsubsection}{\numberline {4.3.2.2}Arguments}{32}{subsubsection.4.3.2.2}
+\contentsline {subsubsection}{\numberline {4.3.2.3}Output Values}{32}{subsubsection.4.3.2.3}
+\contentsline {section}{\numberline {4.4}\texttt {match.data()}: Extracting the Matched Data Set}{32}{section.4.4}
+\contentsline {subsection}{\numberline {4.4.1}Usage}{32}{subsection.4.4.1}
+\contentsline {subsection}{\numberline {4.4.2}Arguments}{32}{subsection.4.4.2}
+\contentsline {subsection}{\numberline {4.4.3}Examples}{33}{subsection.4.4.3}
+\contentsline {chapter}{\numberline {5}Frequently Asked Questions}{35}{chapter.5}
+\contentsline {section}{\numberline {5.1}How do I Cite this Work?}{35}{section.5.1}
+\contentsline {section}{\numberline {5.2}What if My datasets Are Big and Are Taking Up Too Much Memory?}{35}{section.5.2}
+\contentsline {section}{\numberline {5.3}How Exactly are the Weights Created?}{36}{section.5.3}
+\contentsline {section}{\numberline {5.4}How Do I Create Observation Names?}{36}{section.5.4}
+\contentsline {section}{\numberline {5.5}How Can I See Outcomes of Matched Pairs?}{37}{section.5.5}
+\contentsline {section}{\numberline {5.6}How Do I Ensure Replicability As \textsc {MatchIt}\ Versions Develop?}{37}{section.5.6}
+\contentsline {section}{\numberline {5.7}How Do I Use My Own Distance Measure with \textsc {MatchIt}\tmspace +\thinmuskip {.1667em}?}{37}{section.5.7}
+\contentsline {section}{\numberline {5.8}What Do I Do about Missing Data?}{38}{section.5.8}
+\contentsline {section}{\numberline {5.9}Why Preprocessing?}{38}{section.5.9}
+\contentsline {chapter}{\numberline {6}What's New?}{39}{chapter.6}
--
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