R: Multiple comparisons, "Least significant difference" and...

LSD.test {agricolae} | R Documentation

## Multiple comparisons, "Least significant difference" and Adjust P-values

### Description

Multiple comparisons of treatments by means of LSD and a grouping of treatments. The level by alpha default is 0.05. Returns p-values adjusted using one of several methods

### Usage

    LSD.test(y, trt, DFerror, MSerror, alpha = 0.05, p.adj=c("none","holm","hommel",
    "hochberg", "bonferroni", "BH", "BY", "fdr"), 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 of the experimental error
`MSerror` | Means square error of the experimental
`alpha` | Level of risk for the test
`p.adj` | Method for adjusting p values (see p.adjust)
`group` | TRUE or FALSE
`main` | title of the study
`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

Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach. pp178.

### See Also

`BIB.test`, `DAU.test`, `duncan.test`, `durbin.test`, `friedman`, `HSD.test`, `kruskal`, `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 <- LSD.test(model,"virus", p.adj="bonferroni")
    #stargraph
    # Variation range: max and min
    plot(out)
    #endgraph
    # Old version LSD.test()
    df<-df.residual(model)
    MSerror<-deviance(model)/df
    out <- with(sweetpotato,LSD.test(yield,virus,df,MSerror))
    #stargraph
    # Variation interquartil range: Q75 and Q25
    plot(out,variation="IQR")
    #endgraph
    out<-LSD.test(model,"virus",p.adj="hommel",console=TRUE)
    plot(out,variation="SD") # variation standard deviation

[Package agricolae version 1.2-8 Index]