1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
|
\name{cld}
\alias{cld}
\alias{cld.glht}
\alias{cld.summary.glht}
\alias{cld.confint.glht}
\title{Set up a compact letter display of all pair-wise comparisons}
\description{
Extract information from \code{glht}, \code{summary.glht} or
\code{confint.glht} objects which is required to create
and plot compact letter displays of all pair-wise comparisons.
}
\usage{
\method{cld}{summary.glht}(object, level = 0.05, decreasing = FALSE, ...)
\method{cld}{glht}(object, level = 0.05, decreasing = FALSE, ...)
\method{cld}{confint.glht}(object, decreasing = FALSE, ...)
}
\arguments{
\item{object}{
An object of class \code{glht}, \code{summary.glht} or \code{confint.glht}.
}
\item{level}{
Significance-level to be used to term a specific pair-wise
comparison significant.
}
\item{decreasing}{
logical. Should the order of the letters be increasing or decreasing?
}
\item{...}{additional arguments.}
}
\details{
This function extracts all the information from \code{glht},
\code{summary.glht} or \code{confint.glht} objects that is required
to create a compact letter display of all pair-wise comparisons.
In case the contrast matrix is not of type \code{"Tukey"}, an error
is issued. In case of \code{confint.glht} objects, a pair-wise comparison
is termed significant whenever a particular confidence interval contains 0.
Otherwise, p-values are compared to the value of \code{"level"}.
Once, this information is extracted, plotting of all pair-wise
comparisons can be carried out.
}
\value{
An object of class \code{cld}, a list with items:
\item{y}{
Values of the response variable of the original model.
}
\item{yname}{
Name of the response variable.
}
\item{x}{
Values of the variable used to compute Tukey contrasts.
}
\item{weights}{
Weights used in the fitting process.
}
\item{lp}{
Predictions from the fitted model.
}
\item{covar}{
A logical indicating whether the fitted model contained covariates.
}
\item{signif}{
Vector of logicals indicating significant differences with
hyphenated names that identify pair-wise comparisons.
}
}
\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{plot.cld}}
}
\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
data(warpbreaks)
amod2 <- aov(breaks ~ tension + wool, data = warpbreaks)
### specify all pair-wise comparisons among levels of variable "tension"
tuk2 <- glht(amod2, linfct = mcp(tension = "Tukey"))
### extract information
tuk.cld2 <- cld(tuk2)
### use sufficiently large upper margin
old.par <- par(mai=c(1,1,1.25,1), no.readonly = TRUE)
### plot using different colors
plot(tuk.cld2, col=c("black", "red", "blue"))
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)
}
|
