R: Friedman test and multiple comparison of treatments

friedman {agricolae} | R Documentation

## Friedman test and multiple comparison of treatments

### Description

The data consist of b-blocks mutually independent k-variate random variables Xij, i=1,..,b; j=1,..k. The random variable X is in block i and is associated with treatment j. It makes the multiple comparison of the Friedman test with or without ties. A first result is obtained by friedman.test of R.

### Usage

    friedman(judge,trt,evaluation,alpha=0.05,group=TRUE,main=NULL,console=FALSE)

### Arguments

`judge` | Identification of the judge in the evaluation
`trt` | Treatment
`evaluation` | Variable
`alpha` | Significant test
`group` | TRUE or FALSE
`main` | Title
`console` | logical, print output

### Details

The post hoc friedman test is using the criterium Fisher's least significant difference (LSD)

### 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`, `HSD.test`, `kruskal`, `LSD.test`, `Median.test`, `PBIB.test`, `REGW.test`, `scheffe.test`, `SNK.test`, `waerden.test`, `waller.test`, `plot.group`

### Examples

    library(agricolae)
    data(grass)
    out<-with(grass,friedman(judge,trt, evaluation,alpha=0.05, group=TRUE,console=TRUE,
    main="Data of the book of Conover"))
    #startgraph
    plot(out,variation="IQR")
    #endgraph

[Package agricolae version 1.2-8 Index]