| SNK.test {agricolae} | R Documentation |
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.
SNK.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)
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 |
It is necessary first makes a analysis of variance.
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 |
Felipe de Mendiburu
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.
LSD.test, waller.test , HSD.test ,
duncan.test
library(agricolae) data(sweetpotato) model<-aov(yield~virus,data=sweetpotato) comparison <- SNK.test(model,"virus", main="Yield of sweetpotato. Dealt with different virus") SNK.test(model,"virus", group=FALSE) # version old SNK.test() df<-df.residual(model) MSerror<-deviance(model)/df comparison <- with(sweetpotato,SNK.test(yield,virus,df,MSerror, group=TRUE)) print(comparison$groups)