Plot a cld object
Plot information of glht, summary.glht or confint.glht
objects stored as cld objects together with a compact
letter display of all pair-wise comparisons.
## S3 method for class 'cld'
plot(x, type = c("response", "lp"), ...)x |
An object of class |
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 |
... |
Other optional print parameters which are passed to the plotting functions. |
This function plots the information stored in glht, summary.glht or
confint.glht objects. Prior to plotting, these objects have to be converted to
cld objects (see 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 y of the cld object.
In case of 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 class(y) == "factor", a mosaic plot is generated, and the cld is printed
above. In case of 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).
Hans-Peter Piepho (2004), An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons, Journal of Computational and Graphical Statistics, 13(2), 456–466.
### 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)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.