多威爾科克斯測試與R

Question

循環

我的數據集包括

cb <- data.frame(group = ("A", "B", "C", "D", "E"), 
     WC = runif(100, 0, 100), 
     Ana = runif(100, 0, 100), 
     Clo = runif(100, 0, 100)) 

str(cb) 
data.frame: 66936 obs of 89 variables: 
$group: Factor w/ 5 levels "A", "B", "C" ... 
$WC: int 19 28 35 92 10 23... 
$Ana: num 17.2 48 35.4 84.2 
$ Clo: num 37.2 12.1 45.4 38.9 
.... 

現在我想在$組執行多個威爾科克斯測試，因此它看起來像這到底：

commands: 
wilcox.test(cb$WC[cb$group == "A"], cb$WC[cb$group == "B"]) 
wilcox.test(cb$WC[cb$group == "A"], cb$WC[cb$group == "C"]) 
wilcox.test(cb$WC[cb$group == "A"], cb$WC[cb$group == "D"]) 
wilcox.test(cb$WC[cb$group == "A"], cb$WC[cb$group == "E"]) 
.... 

inserting the p-value: 
WC A  B C  D E 
A 1  0.12 0.03 0.2 0.42 
B 0.12 1 0.1 0.07 0.1 
C 0.03 0.1 1  0.2 0.3 
D 0.2 0.07 0.2 1 0.1 
E 0.42 0.1 0.3 0.1 1 

Ana A  B C  D E 
A 1  0.12 0.2 0.39 0.1 
B 0.12 1  0.1 0.07 0.1 
C ... 
D 
E 

... 

我有一個for循環的前面的問題，multiple t-tests，但我努力使它適應這個任務，因爲Wilcox-Test在設計上是如此不同。 下面是for循環我用的t檢驗：

res <- matrix(NA, ncol=5, 
dimnames=list(NULL, c("group", "col", "statistic", "estimate", "p.value"))) 

gr <- levels(cb$group) 

for(cl in 2:ncol(cb)){ 
    for(grp in gr){ 
     temp <- cb[cb$group == grp, cl] 
     res <- rbind(res, c(grp, colnames(cb)[cl], 
      unlist(t.test(temp, mu = mean(cb[,cl]), alternative="two.sided"))[c(1, 5, 3)])) 
    } 
} 

你有一個想法如何改變這種for循環執行威爾科克斯測試？

Answer 1

原始數據：

set.seed(1L) 
cb <- data.frame(group = factor(c("A", "B", "C", "D", "E")), 
       WC = runif(100, 0, 100), 
       Ana = runif(100, 0, 100), 
       Clo = runif(100, 0, 100)) 

代碼：

library(purrr) 

combins <- combn(levels(cb$group), 2) 

params_list <- split(as.vector(combins), rep(1:ncol(combins), each = nrow(combins))) 

model_wc <- map(.x = params_list, 
       .f = ~ wilcox.test(formula = WC ~ group, 
            data = subset(cb, group %in% .x))) 

model_ana <- map(.x = params_list, 
       .f = ~ wilcox.test(formula = Ana ~ group, 
            data = subset(cb, group %in% .x))) 

model_clo <- map(.x = params_list, 
       .f = ~ wilcox.test(formula = Clo ~ group, 
            data = subset(cb, group %in% .x))) 

wilcox_pvals <- do.call(cbind, list(t(data.frame(map(.x = model_wc, .f = "p.value"))), 
            t(data.frame(map(.x = model_ana, .f = "p.value"))), 
            t(data.frame(map(.x = model_clo, .f = "p.value"))))) 

row.names(wilcox_pvals) <- unlist(map(.x = params_list, .f = ~ paste0(.x, collapse = ""))) 

colnames(wilcox_pvals) <- names(cb)[2:4] 

輸出：

> wilcox_pvals 
#   WC  Ana  Clo 
# AB 0.7380622 0.52909692 0.75835096 
# AC 0.9466955 0.41352631 0.32726184 
# AD 0.6395139 0.79940719 0.30125264 
# AE 0.8619871 0.34078485 0.04595423 
# BC 0.9680024 0.63951388 0.18263084 
# BD 0.8410127 0.38341328 0.12741907 
# BE 0.7994072 0.10807707 0.01809358 
# CD 0.7994072 0.21096433 0.94669547 
# CE 0.7179503 0.03751918 0.38341328 
# DE 0.7788036 0.63951388 0.30125264 

Answer 2

一種方法是生成組值的組合和運行測試，如下所示：

apply(combn(unique(cb$group), 2), 2, 
     function(x) 
     wilcox.test(cb$WC[cb$group == x[1]], cb$WC[cb$group == x[2]]) 
) 

輸出如下：

[[1]] 

    Wilcoxon rank sum test 

data: cb$WC[cb$group == x[1]] and cb$WC[cb$group == x[2]] 
W = 205, p-value = 0.9042 
alternative hypothesis: true location shift is not equal to 0 


[[2]] 

    Wilcoxon rank sum test 

data: cb$WC[cb$group == x[1]] and cb$WC[cb$group == x[2]] 
W = 153, p-value = 0.211 
alternative hypothesis: true location shift is not equal to 0 

如果你只是想給p值，你可以這樣得到它們：

apply(combn(unique(cb$group), 2), 2, 
     function(x) { 
     fit <- wilcox.test(cb$WC[cb$group == x[1]], cb$WC[cb$group == x[2]]) 
     fit$p.value 
     } 
) 
[1] 0.904208038 0.210964327 0.820148096 0.564831637 0.012165581 0.799407187 0.231498716 0.021076794 0.004681199 
[10] 0.242269621 

這些對應於10對成對比較：

combn(unique(cb$group), 2) 
    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] 
[1,] "A" "A" "A" "A" "B" "B" "B" "C" "C" "D" 
[2,] "B" "C" "D" "E" "C" "D" "E" "D" "E" "E" 

Answer 3

如果你只是想要p值這應該工作。我只是從矩陣中所有可能的組合中提取p值。也要小心多次比較，你可能需要調整你的alpha值。

gr <- levels(cb$group) 
res <- matrix(NA, nrow= length(gr), ncol = length(gr), dimnames = list(gr,gr)) 

for (i in 1:ncol(res)){ 
    for (j in 1:nrow(res)){ 
    x<- wilcox.test(cb$WC[cb$group == gr[i]], cb$WC[cb$group == gr[j]]) 
    res[i,j] <- x$p.value 
    } 
}