multcomp (version 1.4-5)

cld: Set up a compact letter display of all pair-wise comparisons

Description

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.

Usage

"cld"(object, level = 0.05, decreasing = FALSE, ...)
"cld"(object, level = 0.05, decreasing = FALSE, ...)
"cld"(object, decreasing = FALSE, ...)

Arguments

object

An object of class glht, summary.glht or confint.glht.

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.

Value

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.

Details

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.

References

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.

See Also

glht 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)