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)

Value

statistics: Statistics of the model
parameters: Design parameters
duncan: Critical Range Table
means: Statistical summary of the study variable
comparison: Comparison between treatments
groups: Formation of treatment groups

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

Author

Felipe de Mendiburu

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
duncan.test(model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

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

Run this code

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- duncan.test(model,"virus", 
main="Yield of sweetpotato. Dealt with different virus")
plot(out,variation="IQR")
duncan.test(model,"virus",alpha=0.01,console=TRUE)
# version old duncan.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
plot(out,horiz=TRUE,las=1)
print(out$groups)

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