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法律研究如何在 R 中對 Kruskal-Wallis 進行事後檢驗？
Kruskal-Wallis 檢驗是非引數的一元方差分析類似物。當不滿足引數檢驗的假設時，將使用非引數檢驗。如果我們在 Kruskal-Wallis 中發現有顯著差異，那麼會進行事後檢驗以找出差異所在。為此，我們可以執行 Dunn 檢驗。可透過 FSA 軟體包訪問 Dunn 檢驗的功能。
示例 1
載入 FSA 軟體包
> library(FSA)
考慮以下資料幀
> x1<-sample(LETTERS[1:5],20,replace=TRUE) > y1<-rnorm(20,1,0.35) > df1<-data.frame(x1,y1) > df1
輸出
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
執行 dunnTest
示例
> dunnTest(y1~x1,data=df1) Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
輸出
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輸出
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.
輸出
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輸出
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.
輸出
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.
更新於： 2020-11-21
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