| durbin.test | R Documentation |
A multiple comparison of the Durbin test for the balanced incomplete blocks for sensorial or categorical evaluation. It forms groups according to the demanded ones for level of significance (alpha); by default, 0.05.
durbin.test(judge, trt, evaluation, alpha = 0.05, group =TRUE,
main = NULL, console=FALSE)
judge |
Identification of the judge in the evaluation |
trt |
Treatments |
evaluation |
variable |
alpha |
level of significant |
group |
TRUE or FALSE |
main |
Title |
console |
logical, print output |
The post hoc test is using the criterium Fisher's least significant difference.
statistics |
Statistics of the model |
parameters |
Design parameters |
means |
Statistical summary of the study variable |
rank |
rank table of the study variable |
comparison |
Comparison between treatments |
groups |
Formation of treatment groups |
Felipe de Mendiburu
Practical Nonparametrics Statistics. W.J. Conover, 1999 Nonparametric Statistical Methods. Myles Hollander and Douglas A. Wofe, 1999
BIB.test, DAU.test, duncan.test,
friedman, HSD.test, kruskal,
LSD.test, Median.test, PBIB.test,
REGW.test, scheffe.test, SNK.test,
waerden.test, waller.test, plot.group
library(agricolae)
# Example 1. Conover, pag 391
person<-gl(7,3)
variety<-c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
preference<-c(2,3,1,3,1,2,2,1,3,1,2,3,3,1,2,3,1,2,3,1,2)
out<-durbin.test(person,variety,preference,group=TRUE,console=TRUE,
main="Seven varieties of ice cream manufacturer")
#startgraph
bar.group(out$groups,horiz=TRUE,xlim=c(0,10),density=4,las=1)
#endgraph
# Example 2. Myles Hollander, pag 311
# Source: W. Moore and C.I. Bliss. 1942
day<-gl(7,3)
chemical<-c("A","B","D","A","C","E","C","D","G","A","F","G","B","C","F",
"B","E","G","D","E","F")
toxic<-c(0.465,0.343,0.396,0.602,0.873,0.634,0.875,0.325,0.330,0.423,0.987,
0.426,0.652,1.142,0.989,0.536,0.409,0.309,0.609,0.417,0.931)
out<-durbin.test(day,chemical,toxic,group=TRUE,console=TRUE,
main="Logarithm of Toxic Dosages")
plot(out)
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