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.

It is necessary first makes a analysis of variance.
if model=y, then to apply the instruction:
LSD.test(model, "trt", alpha = 0.05, p.adj=c("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)
where the model class is aov or lm.

### 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.

`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)
#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