duncan.test: Duncan's new multiple range test

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/duncan.test.R

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

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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

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

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.

See Also

BIB.test, DAU.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

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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)

Example output

Study: model ~ "virus"

Duncan's new multiple range test
for yield 

Mean Square Error:  22.48917 

virus,  means

      yield      std r  Min  Max
cc 24.40000 3.609709 3 21.7 28.5
fc 12.86667 2.159475 3 10.6 14.9
ff 36.33333 7.333030 3 28.0 41.8
oo 36.90000 4.300000 3 32.1 40.4

Alpha: 0.01 ; DF Error: 8 

Critical Range
       2        3        4 
12.99223 13.52267 13.84424 

Means with the same letter are not significantly different.

      yield groups
oo 36.90000      a
ff 36.33333      a
cc 24.40000     ab
fc 12.86667      b
      yield groups
oo 36.90000      a
ff 36.33333      a
cc 24.40000      b
fc 12.86667      c

agricolae documentation built on Sept. 13, 2017, 1:03 a.m.