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\name{plot.cld}
\alias{plot.cld}
\title{Plot a cld object}
\description{
Plot information of \code{glht}, \code{summary.glht} or \code{confint.glht}
objects stored as \code{cld} objects together with a compact
letter display of all pair-wise comparisons.
}
\usage{
\method{plot}{cld}(x, type = c("response", "lp"), ...)
}
\arguments{
\item{x}{
An object of class \code{cld}.
}
\item{type}{
Should the response or the linear predictor (lp) be plotted.
If there are any covariates, the lp is automatically used. To
use the response variable, set \code{type="response"} and \code{covar=FALSE}
of the \code{cld} object.
}
\item{...}{
Other optional print parameters which are passed to the plotting functions.
}
}
\details{
This function plots the information stored in \code{glht}, \code{summary.glht} or
\code{confint.glht} objects. Prior to plotting, these objects have to be converted to
\code{cld} objects (see \code{\link{cld}} for details).
All types of plots include a compact letter display (cld) of all pair-wise comparisons.
Equal letters indicate no significant differences. Two levels are significantly
different, in case they do not have any letters in common.
If the fitted model contains any covariates, a boxplot of the linear predictor is
generated with the cld within the upper margin. Otherwise, three different types
of plots are used depending on the class of variable \code{y} of the \code{cld} object.
In case of \code{class(y) == "numeric"}, a boxplot is generated using the response variable,
classified according to the levels of the variable used for the Tukey contrast
matrix. Is \code{class(y) == "factor"}, a mosaic plot is generated, and the cld is printed
above. In case of \code{class(y) == "Surv"}, a plot of fitted survival functions is generated
where the cld is plotted within the legend.
The compact letter display is computed using the algorithm of Piepho (2004).
Note: The user has to provide a sufficiently large upper margin which can be used to
depict the compact letter display (see examples).
}
\references{
Hans-Peter Piepho (2004), An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons,
\emph{Journal of Computational and Graphical Statistics}, \bold{13}(2), 456--466.
}
\seealso{
\code{\link{glht}}
\code{\link{cld}}
\code{\link{cld.summary.glht}}
\code{\link{cld.confint.glht}}
\code{\link{cld.glht}}
\code{\link{boxplot}}
\code{\link{mosaicplot}}
\code{\link[survival]{plot.survfit}}
}
\examples{
### multiple comparison procedures
### set up a one-way ANOVA
data(warpbreaks)
amod <- aov(breaks ~ tension, data = warpbreaks)
### specify all pair-wise comparisons among levels of variable "tension"
tuk <- glht(amod, linfct = mcp(tension = "Tukey"))
### extract information
tuk.cld <- cld(tuk)
### use sufficiently large upper margin
old.par <- par(mai=c(1,1,1.25,1), no.readonly=TRUE)
### plot
plot(tuk.cld)
par(old.par)
### now using covariates
amod2 <- aov(breaks ~ tension + wool, data = warpbreaks)
tuk2 <- glht(amod2, linfct = mcp(tension = "Tukey"))
tuk.cld2 <- cld(tuk2)
old.par <- par(mai=c(1,1,1.25,1), no.readonly=TRUE)
### use different colors for boxes
plot(tuk.cld2, col=c("green", "red", "blue"))
par(old.par)
### get confidence intervals
ci.glht <- confint(tuk)
### plot them
plot(ci.glht)
old.par <- par(mai=c(1,1,1.25,1), no.readonly=TRUE)
### use 'confint.glht' object to plot all pair-wise comparisons
plot(cld(ci.glht), col=c("white", "blue", "green"))
par(old.par)
### set up all pair-wise comparisons for count data
data(Titanic)
mod <- glm(Survived ~ Class, data = as.data.frame(Titanic),
weights = Freq, family = binomial())
### specify all pair-wise comparisons among levels of variable "Class"
glht.mod <- glht(mod, mcp(Class = "Tukey"))
### extract information
mod.cld <- cld(glht.mod)
### use sufficiently large upper margin
old.par <- par(mai=c(1,1,1.5,1), no.readonly=TRUE)
### plot
plot(mod.cld)
par(old.par)
### set up all pair-wise comparisons of a Cox-model
if (require("survival") && require("MASS")) {
### construct 4 classes of age
Melanoma$Cage <- factor(sapply(Melanoma$age, function(x){
if( x <= 25 ) return(1)
if( x > 25 & x <= 50 ) return(2)
if( x > 50 & x <= 75 ) return(3)
if( x > 75 & x <= 100) return(4) }
))
### fit Cox-model
cm <- coxph(Surv(time, status == 1) ~ Cage, data = Melanoma)
### specify all pair-wise comparisons among levels of "Cage"
cm.glht <- glht(cm, mcp(Cage = "Tukey"))
# extract information & plot
old.par <- par(no.readonly=TRUE)
### use mono font family
if (dev.interactive())
old.par <- par(family = "mono")
plot(cld(cm.glht), col=c("black", "red", "blue", "green"))
par(old.par)
}
if (require("nlme") && require("lme4")) {
data("ergoStool", package = "nlme")
stool.lmer <- lmer(effort ~ Type + (1 | Subject),
data = ergoStool)
glme41 <- glht(stool.lmer, mcp(Type = "Tukey"))
old.par <- par(mai=c(1,1,1.5,1), no.readonly=TRUE)
plot(cld(glme41))
par(old.par)
}
}
\keyword{hplot}
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