scheffe.test {agricolae} | R Documentation

## Multiple comparisons, scheffe

### Description

Scheffe 1959, method is very general in that all possible contrasts can be tested for significance and confidence intervals can be constructed for the corresponding linear. The test is conservative.

### Usage

    scheffe.test(y, trt, DFerror, MSerror, Fc, 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` | Degrees of freedom
`MSerror` | Mean Square Error
`Fc` | F Value
`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
`means` | Statistical summary of the study variable
`comparison` | Comparison between treatments
`groups` | Formation of treatment groups

### Author(s)

Felipe de Mendiburu

### References

Robert O. Kuehl. 2nd ed. Design of experiments. Duxbury, copyright 2000. Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach. pp189

### See Also

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

### Examples

    library(agricolae)
    data(sweetpotato)
    model<-aov(yield~virus, data=sweetpotato)
    comparison <- scheffe.test(model,"virus", group=TRUE,console=TRUE,
    main="Yield of sweetpotato\nDealt with different virus")
    # Old version scheffe.test()
    df<-df.residual(model)
    MSerror<-deviance(model)/df
    Fc<-anova(model)["virus",4]
    out <- with(sweetpotato,scheffe.test(yield, virus, df, MSerror, Fc))
    print(out)

[Package agricolae version 1.2-9 Index]