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法律研究如何在 R 中对 Kruskal-Wallis 进行事后检验？
Kruskal-Wallis 检验是非参数的一元方差分析类似物。当不满足参数检验的假设时，将使用非参数检验。如果我们在 Kruskal-Wallis 中发现有显着差异，那么会进行事后检验以找出差异所在。为此，我们可以执行 Dunn 检验。可通过 FSA 软件包访问 Dunn 检验的功能。
示例 1
加载 FSA 软件包
> library(FSA)
考虑以下数据帧
> x1<-sample(LETTERS[1:5],20,replace=TRUE) > y1<-rnorm(20,1,0.35) > df1<-data.frame(x1,y1) > df1
输出
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
执行 dunnTest
示例
> dunnTest(y1~x1,data=df1) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
输出
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.
示例 2
> x2<-sample(c("India","Russia","China","Croatia"),20,replace=TRUE)
> y2<-rpois(20,5)
> df2<-data.frame(x2,y2)
> df2输出
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
示例
> dunnTest(y2~x2,data=df2) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
输出
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.
示例 3
> x3<-sample(c("G1","G2","G3","G4","G5"),20,replace=TRUE)
> y3<-rexp(20,1.34)
> df3<-data.frame(x3,y3)
> df3输出
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
示例
> dunnTest(y3~x3,data=df3) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
输出
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.
更新于： 2020-11-21
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