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How to perform post hoc test for Kruskal-Wallis in R?
The Kruskal-Wallis test is the non-parametric analogue of one-way analysis of variance. The non-parametric tests are used in situations when the assumptions of parametric tests are not met. If we find significant difference in Kruskal-Wallis then post hoc tests are done to find where the difference exists. For this purpose, we can perform dunn test. The function of dunn test can be accessed through FSA package.
Example1
Loading FSA package:
> library(FSA)
Consider the below data frame:
> x1<-sample(LETTERS[1:5],20,replace=TRUE) > y1<-rnorm(20,1,0.35) > df1<-data.frame(x1,y1) > df1
Output
x1 y1 1 E 1.1191117 2 D 1.1276032 3 D 1.5610692 4 E 1.1585054 5 E 1.0239322 6 C 0.8000165 7 C 1.2009313 8 B 1.1928228 9 A 0.7421504 10 B 0.7212436 11 C 1.4088902 12 B 1.3171291 13 D 0.9434812 14 A 0.7986718 15 C 1.0394762 16 A 0.9239324 17 E 1.1447561 18 D 1.0192032 19 B 0.8772467 20 A 0.5723085
Performing dunnTest:
Example
> dunnTest(y1~x1,data=df1) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
Output
Comparison Z P.unadj P.adj 1 A - B -1.61355862 0.10662320 0.7463624 2 A - C -2.21117293 0.02702386 0.2702386 3 B - C -0.59761430 0.55009732 1.0000000 4 A - D -2.09165007 0.03646983 0.2917586 5 B - D -0.47809144 0.63258512 1.0000000 6 C - D 0.11952286 0.90486113 1.0000000 7 A - E -2.15141150 0.03144373 0.2829936 8 B - E -0.53785287 0.59067863 1.0000000 9 C - E 0.05976143 0.95234564 1.0000000 10 D - E -0.05976143 0.95234564 0.9523456 Warning message: x1 was coerced to a factor.
Example2
> x2<-sample(c("India","Russia","China","Croatia"),20,replace=TRUE)
> y2<-rpois(20,5)
> df2<-data.frame(x2,y2)
> df2Output
x2 y2 1 Russia 0 2 Russia 6 3 Croatia 8 4 Croatia 5 5 Russia 5 6 Croatia 9 7 India 9 8 Croatia 6 9 India 4 10 China 1 11 Croatia 7 12 China 3 13 India 3 14 India 4 15 Croatia 6 16 China 7 17 China 8 18 Croatia 10 19 India 8 20 China 7
Example
> dunnTest(y2~x2,data=df2) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
Output
Comparison Z P.unadj P.adj 1 China - Croatia -1.18245422 0.23702552 1.0000000 2 China - India -0.08066504 0.93570834 0.9357083 3 Croatia - India 1.09532601 0.27337384 1.0000000 4 China - Russia 0.77619975 0.43763106 0.8752621 5 Croatia - Russia 1.82479827 0.06803148 0.4081889 6 India - Russia 0.84605772 0.39752054 1.0000000 Warning message: x2 was coerced to a factor.
Example3
> x3<-sample(c("G1","G2","G3","G4","G5"),20,replace=TRUE)
> y3<-rexp(20,1.34)
> df3<-data.frame(x3,y3)
> df3Output
x3 y3 1 G2 1.89169184 2 G3 2.74074462 3 G3 0.17273122 4 G2 0.34856852 5 G2 0.80544065 6 G1 0.54582070 7 G2 0.24551988 8 G5 0.02546690 9 G3 2.86315652 10 G1 0.43704405 11 G1 1.89036598 12 G5 0.02423629 13 G5 0.03848270 14 G1 1.01897322 15 G1 0.44416202 16 G5 0.96637068 17 G2 0.74919567 18 G5 3.24106689 19 G1 1.22994992 20 G3 0.84658591
Example
> dunnTest(y3~x3,data=df3) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
Output
Comparison Z P.unadj P.adj 1 G1 - G2 0.4745469 0.6351099 1.0000000 2 G1 - G3 -0.4582576 0.6467674 0.6467674 3 G2 - G3 -0.8693183 0.3846731 1.0000000 4 G1 - G5 1.0328375 0.3016800 1.0000000 5 G2 - G5 0.5345225 0.5929801 1.0000000 6 G3 - G5 1.3732709 0.1696681 1.0000000 Warning message: x3 was coerced to a factor.
Updated on: 21-Nov-2020
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