Set up a compact letter display of all pair-wise comparisons
Extract information from glht, summary.glht or
confint.glht objects which is required to create
and plot compact letter displays of all pair-wise comparisons.
## S3 method for class 'summary.glht' cld(object, level = 0.05, decreasing = FALSE, ...) ## S3 method for class 'glht' cld(object, level = 0.05, decreasing = FALSE, ...) ## S3 method for class 'confint.glht' cld(object, decreasing = FALSE, ...)
object |
An object of class |
level |
Significance-level to be used to term a specific pair-wise comparison significant. |
decreasing |
logical. Should the order of the letters be increasing or decreasing? |
... |
additional arguments. |
This function extracts all the information from glht,
summary.glht or 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 "Tukey", an error
is issued. In case of 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 "level".
Once, this information is extracted, plotting of all pair-wise
comparisons can be carried out.
An object of class cld, a list with items:
y |
Values of the response variable of the original model. |
yname |
Name of the response variable. |
x |
Values of the variable used to compute Tukey contrasts. |
weights |
Weights used in the fitting process. |
lp |
Predictions from the fitted model. |
covar |
A logical indicating whether the fitted model contained covariates. |
signif |
Vector of logicals indicating significant differences with hyphenated names that identify pair-wise comparisons. |
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
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.