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

## 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, ...)

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:
yValues of the response variable of the original model.
ynameName of the response variable.
xValues of the variable used to compute Tukey contrasts.
weightsWeights used in the fitting process.
lpPredictions from the fitted model.
covarA logical indicating whether the fitted model contained covariates.
signifVector 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.

Examples

Run this code

### 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))
  ### 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))
  ### 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))
  ### plott
  plot(mod.cld)
  par(old.par)

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