Kruskal Wallis test and multiple comparison of treatments.

Source: R/kruskal.R

It makes the multiple comparison with Kruskal-Wallis. The alpha parameter by default is 0.05. Post hoc test is using the criterium Fisher's least significant difference. The adjustment methods include the Bonferroni correction and others.

kruskal(
  y,
  trt,
  alpha = 0.05,
  p.adj = c("none", "holm", "hommel", "hochberg", "bonferroni", "BH", "BY", "fdr"),
  group = TRUE,
  main = NULL,
  console = FALSE
)

Arguments

y

response
trt

treatment
alpha

level signification
p.adj

Method for adjusting p values (see p.adjust)
group

TRUE or FALSE
main

Title
console

logical, print output

Value

statistics

Statistics of the model

parameters

Design parameters

means

Statistical summary of the study variable

comparison

Comparison between treatments

groups

Formation of treatment groups

Details

For equal or different repetition.
For the adjustment methods, see the function p.adjusted.
p-adj = "none" is t-student.

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

See also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples


library(agricolae)
data(corn)
str(corn)
#> 'data.frame':	34 obs. of  3 variables:
#>  $ method     : int  1 1 1 1 1 1 1 1 1 2 ...
#>  $ observation: int  83 91 94 89 89 96 91 92 90 91 ...
#>  $ rx         : num  11 23 28.5 17 17 31.5 23 26 19.5 23 ...
comparison<-with(corn,kruskal(observation,method,group=TRUE, main="corn"))
comparison<-with(corn,kruskal(observation,method,p.adj="bon",group=FALSE, main="corn"))