duncan.test {agricolae} | R Documentation

## Duncan's new multiple range test

### Description

This test is adapted from the Newman-Keuls method. Duncan's test does not control family wise error rate at the specified alpha level. It has more power than the other post tests, but only because it doesn't control the error rate properly. The Experimentwise Error Rate at: 1-(1-alpha)^(a-1); where "a" is the number of means and is the Per-Comparison Error Rate. Duncan's procedure is only very slightly more conservative than LSD. The level by alpha default is 0.05.

### Usage

    duncan.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)

### Arguments

`y` | model(aov or lm) or answer of the experimental unit
`trt` | Constant( only y=model) or vector treatment applied to each experimental unit
`DFerror` | Degree free
`MSerror` | Mean Square Error
`alpha` | Significant level
`group` | TRUE or FALSE
`main` | Title
`console` | logical, print output

### Details

It is necessary first makes a analysis of variance.

### Value

`statistics` | Statistics of the model
`parameters` | Design parameters
`duncan` | Critical Range Table
`means` | Statistical summary of the study variable
`comparison` | Comparison between treatments
`groups` | Formation of treatment groups

### Author(s)

Felipe de Mendiburu

### References

1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997 2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

### See Also

`BIB.test`, `DAU.test`, `durbin.test`, `friedman`, `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(sweetpotato)
    model<-aov(yield~virus,data=sweetpotato)
    out <- duncan.test(model,"virus",
    main="Yield of sweetpotato. Dealt with different virus")
    plot(out,variation="IQR")
    duncan.test(model,"virus",alpha=0.01,console=TRUE)
    # version old duncan.test()
    df<-df.residual(model)
    MSerror<-deviance(model)/df
    out <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
    plot(out,horiz=TRUE,las=1)
    print(out$groups)

[Package agricolae version 1.2-9 Index]