SNK.test {agricolae}

Student-Newman-Keuls (SNK)

Description

SNK is derived from Tukey, but it is less conservative (finds more differences). Tukey controls the error for all comparisons, where SNK only controls for comparisons under consideration. The level by alpha default is 0.05.

Usage

SNK.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.

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

Value

statistics

Statistics of the model

parameters

Design parameters

snk

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

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- SNK.test(model,"virus", console=TRUE, 
main="Yield of sweetpotato. Dealt with different virus")
print(SNK.test(model,"virus", group=FALSE))
# version old SNK.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,SNK.test(yield,virus,df,MSerror, group=TRUE))
print(out$groups)