agricolae (version 1.2-6)

duncan.test: 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

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

Details

It is necessary first makes a analysis of variance.

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.

See Also

LSD.test, waller.test , HSD.test , SNK.test

Examples

# NOT RUN {
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)
# }