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Last data update: 2014.03.03

R: Duncan's new multiple range test

duncan.test

R Documentation

Duncan's new multiple range test

Description

This test is adapted from the Newman-Keuls method. Duncan's test does not control family wise error rate at the specified alpha level. It has more power than the other post tests, but only because it doesn't control the error rate properly. The Experimentwise Error Rate at: 1-(1-alpha)^(a-1); where "a" is the number of means and is the Per-Comparison Error Rate. Duncan's procedure is only very slightly more conservative than LSD. The level by alpha default is 0.05.

Usage

duncan.test(y, trt, DFerror, MSerror, 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`	Degree free
`MSerror`	Mean Square Error
`alpha`	Significant level
`group`	TRUE or FALSE
`main`	Title
`console`	logical, print output

Details

It is necessary first makes a analysis of variance.

Value

`y`	class (aov or lm) or vector numeric
`trt`	constant (only y=model) or vector alfanumeric
`DFerror`	Numeric
`MSerror`	Numeric
`alpha`	Numeric
`group`	Logic
`main`	Text

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)
comparison <- duncan.test(model,"virus", 
main="Yield of sweetpotato. Dealt with different virus")
duncan.test(model,"virus",alpha=0.01,console=TRUE)
# version old duncan.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
comparison <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
print(comparison$groups)