R: Kruskal Wallis test and multiple comparison of treatments.

kruskal {agricolae} | R Documentation

## Kruskal Wallis test and multiple comparison of treatments.

### Description

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.

### Usage

    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

### Details

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

### 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

### Author(s)

Felipe de Mendiburu

### 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)
    comparison<-with(corn,kruskal(observation,method,group=TRUE, main="corn"))
    comparison<-with(corn,kruskal(observation,method,p.adj="bon",group=FALSE, main="corn"))

[Package agricolae version 1.2-8 Index]