| HSD.test {agricolae} | R Documentation |
It makes multiple comparisons of treatments by means of Tukey. The level by alpha default is 0.05.
HSD.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,unbalanced=FALSE,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 |
unbalanced |
TRUE or FALSE. not equal replication |
console |
logical, print output |
It is necessary first makes a analysis of variance.
statistics |
Statistics of the model |
parameters |
Design parameters |
means |
Statistical summary of the study variable |
comparison |
Comparison between treatments |
groups |
Formation of treatment groups |
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.
BIB.test, DAU.test, duncan.test,
durbin.test, friedman, kruskal,
LSD.test, Median.test, PBIB.test,
REGW.test, scheffe.test, SNK.test,
waerden.test, waller.test, plot.group
library(agricolae) data(sweetpotato) model<-aov(yield~virus, data=sweetpotato) out <- HSD.test(model,"virus", group=TRUE,console=TRUE, main="Yield of sweetpotato\nDealt with different virus") #stargraph # Variation range: max and min plot(out) #endgraph out<-HSD.test(model,"virus", group=FALSE) print(out$comparison) # Old version HSD.test() df<-df.residual(model) MSerror<-deviance(model)/df with(sweetpotato,HSD.test(yield,virus,df,MSerror, group=TRUE,console=TRUE, main="Yield of sweetpotato. Dealt with different virus"))