HSD.test: Multiple comparisons: Tukey
In agricolae: Statistical Procedures for Agricultural Research

HSD.test

R Documentation

Multiple comparisons: Tukey

Description

It makes multiple comparisons of treatments by means of Tukey. The level by alpha default is 0.05.

Usage

HSD.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL, 
unbalanced=FALSE,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
`unbalanced`	TRUE or FALSE. not equal replication
`console`	logical, print output

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
HSD.test (model, "trt", alpha = 0.05, group = TRUE, main = NULL, unbalanced=FALSE, 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

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.

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- HSD.test(model,"virus", group=TRUE,console=TRUE,
main="Yield of sweetpotato\nDealt with different virus")
#stargraph
# Variation range: max and min
plot(out)
#endgraph
out<-HSD.test(model,"virus", group=FALSE)
print(out$comparison)
# Old version HSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
with(sweetpotato,HSD.test(yield,virus,df,MSerror, group=TRUE,console=TRUE,
main="Yield of sweetpotato. Dealt with different virus"))

agricolae documentation built on Oct. 23, 2023, 1:06 a.m.