我有一个示例数据集,如下所示:
Day<-c(1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2)
Group<-c("A","A","A","B","B","B","C","C","C","A","A","A","A","B","B","B","C","C","C")
Rain<-c(4,4,6,5,3,4,5,5,3,6,6,6,5,3,3,3,2,5,2)
UV<-c(6,6,7,8,5,6,5,6,6,6,7,7,8,8,5,6,8,5,7)
我在数据集上运行了Kruskal-Wallis测试:
library(rstatix)
library(tidyverse)
cols <- c('Rain', 'UV')
map_df(cols, ~dat %>% group_by(Day) %>% kruskal_test(reformulate('Group', .x)))
根据Kruskal-Wallis的结果,我如何结合这一点:如果来自Kruskal-Vallis的p值<0.05,然后在数据框中打印出测试组的结果和调整后的p值?谢谢
您可以对p值小于0.05的列进行子集设置,并仅对这些列应用wilcox_test
。
library(tidyverse)
library(rstatix)
result <- map_df(cols, ~dat %>%
group_by(Day) %>%
kruskal_test(reformulate('Group', .x)))
group <- unique(result$.y.[result$p < 0.05])
map_df(group, ~dat %>% group_by(Day) %>% wilcox_test(reformulate('Group', .x)))
# Day .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
# <dbl> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#1 1 Rain A B 3 3 6 0.643 1 ns
#2 1 Rain A C 3 3 5 1 1 ns
#3 1 Rain B C 3 3 3.5 0.814 1 ns
#4 2 Rain A B 4 3 12 0.036 0.107 ns
#5 2 Rain A C 4 3 11.5 0.061 0.123 ns
#6 2 Rain B C 3 3 6 0.637 0.637 ns