R: Multiple comparisons, Waller-Duncan

waller.test {agricolae} | R Documentation

## Multiple comparisons, Waller-Duncan

### Description

The Waller-Duncan k-ratio t test is performed on all main effect means in the MEANS statement. See the K-RATIO option for information on controlling details of the test.

### Usage

    waller.test(y, trt, DFerror, MSerror, Fc, K = 100, 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 unit
`DFerror` | Degrees of freedom
`MSerror` | Mean Square Error
`Fc` | F Value
`K` | K-RATIO
`group` | TRUE or FALSE
`main` | Title
`console` | logical, print output

### Details

It is necessary first makes a analysis of variance.

K-RATIO (K): value specifies the Type 1/Type 2 error seriousness ratio for the Waller-Duncan test. Reasonable values for KRATIO are 50, 100, and 500, which roughly correspond for the two-level case to ALPHA levels of 0.1, 0.05, and 0.01. By default, the procedure uses the default value of 100.

### 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

Waller, R. A. and Duncan, D. B. (1969). A Bayes Rule for the Symmetric Multiple Comparison Problem, Journal of the American Statistical Association 64, pages 1484-1504.

Waller, R. A. and Kemp, K. E. (1976) Computations of Bayesian t-Values for Multiple Comparisons, Journal of Statistical Computation and Simulation, 75, pages 169-172.

Steel & Torry & Dickey. Third Edition 1997 Principles and procedures of statistics a biometrical approach

### See Also

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

### Examples

    library(agricolae)
    data(sweetpotato)
    model<-aov(yield~virus, data=sweetpotato)
    out <- waller.test(model,"virus", group=TRUE)
    #startgraph
    par(mfrow=c(2,2))
    # variation: SE is error standard
    # variation: range is Max - Min
    bar.err(out$means,variation="SD",horiz=TRUE,xlim=c(0,45),bar=FALSE,
    col=colors()[25],space=2, main="Standard deviation",las=1)
    bar.err(out$means,variation="SE",horiz=FALSE,ylim=c(0,45),bar=FALSE,
    col=colors()[15],space=2,main="SE",las=1)
    bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col="green",
    space=3,main="Range = Max - Min",las=1)
    bar.group(out$groups,horiz=FALSE,ylim=c(0,45),density=8,col="red",
    main="Groups",las=1)
    #endgraph
    # Old version HSD.test()
    df<-df.residual(model)
    MSerror<-deviance(model)/df
    Fc<-anova(model)["virus",4]
    out <- with(sweetpotato,waller.test(yield, virus, df, MSerror, Fc, group=TRUE))
    print(out)

[Package agricolae version 1.2-8 Index]