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