Kruskal - 带有 R 的数据子集的 Wallis p 值矩阵

Question

考虑一个Data具有多个因子和多个数值连续变量的数据集。这些变量中的一些，比方说slice_by_1（类别为“男性”、“女性”）和slice_by_2（类别为“悲伤”、“中性”、“快乐”）用于将数据“切片”为子集。对于每个子集，Kruskal-Wallis 测试都应该在变量length,上运行preasure，pulse每个变量都由另一个称为 的因子变量分组compare_by。R 中是否有一种快速方法来完成此任务并将计算出的 p 值放入矩阵？

我使用dplyr包来准备数据。

示例数据集：

library(dplyr)
set.seed(123)
Data <- tbl_df(
   data.frame(
       slice_by_1 = as.factor(rep(c("Male", "Female"), times = 120)),
       slice_by_2 = as.factor(rep(c("Happy", "Neutral", "Sad"), each = 80)),
       compare_by = as.factor(rep(c("blue", "green", "brown"), times = 80)),
       length   = c(sample(1:10, 120, replace=T), sample(5:12, 120, replace=T)),
       pulse    = runif(240, 60, 120),
       preasure = c(rnorm(80,1,2),rnorm(80,1,2.1),rnorm(80,1,3))
   )
   ) %>%
group_by(slice_by_1, slice_by_2)

我们来看数据：

Source: local data frame [240 x 6]
Groups: slice_by_1, slice_by_2

   slice_by_1 slice_by_2 compare_by length     pulse     preasure
1        Male      Happy       blue     10  69.23376  0.508694601
2      Female      Happy      green      1  68.57866 -1.155632020
3        Male      Happy      brown      8 112.72132  0.007031799
4      Female      Happy       blue      3 116.61283  0.383769524
5        Male      Happy      green      7 110.06851 -0.717791526
6      Female      Happy      brown      8 117.62481  2.938658488
7        Male      Happy       blue      9 105.59749  0.735831389
8      Female      Happy      green      2  83.44101  3.881268679
9        Male      Happy      brown      5 101.48334  0.025572561
10     Female      Happy       blue     10  62.87331 -0.715108893
..        ...        ...        ...    ...       ...          ...

所需输出的示例：

    Data_subsets    length  preasure     pulse
1     Male_Happy <p-value> <p-value> <p-value>
2   Female_Happy <p-value> <p-value> <p-value>
3   Male_Neutral <p-value> <p-value> <p-value>
4 Female_Neutral <p-value> <p-value> <p-value>
5       Male_Sad <p-value> <p-value> <p-value>
6     Female_Sad <p-value> <p-value> <p-value>

Answer 1

我们可以使用Mapinsidedo执行多列操作kruskal.test，然后使用unitefromlibrary(tidyr)将“slice_by_1”和“slice_by_2”列连接到单个列“Data_subsets”。

library(dplyr)
library(tidyr)
nm1 <- names(Data)[4:6]
f1 <- function(x,y) kruskal.test(x~y)$p.value

Data %>% 
     do({data.frame(Map(f1, .[nm1], list(.$compare_by)))}) %>% 
     unite(Data_subsets, slice_by_1, slice_by_2, sep="_")
#     Data_subsets    length     pulse  preasure
#1   Female_Happy 0.4369918 0.8767561 0.1937327
#2 Female_Neutral 0.3750688 0.2858796 0.8588069
#3     Female_Sad 0.7958502 0.5801208 0.6274940
#4     Male_Happy 0.3099704 0.3796494 0.6929493
#5   Male_Neutral 0.4953853 0.2418708 0.2986860
#6       Male_Sad 0.7159970 0.5686672 0.8528201

或者我们可以使用 来做到这一点data.table。我们将“data.frame”转换为“data.table”（setDT(Data)），通过“slice_by_1”和“slice_by_2”列创建分组变量（“Data_subsets”）paste，然后对数据集的列进行子集化并将其作为输入传递Map，执行并krusal.test提取p.value。

library(data.table)    
setDT(Data)[, Map(f1, .SD[, nm1, with=FALSE], list(compare_by)) ,
             by = .(Data_subsets= paste(slice_by_1, slice_by_2, sep='_'))]
#     Data_subsets    length     pulse  preasure
#1:     Male_Happy 0.3099704 0.3796494 0.6929493
#2:   Female_Happy 0.4369918 0.8767561 0.1937327
#3:   Male_Neutral 0.4953853 0.2418708 0.2986860
#4: Female_Neutral 0.3750688 0.2858796 0.8588069
#5:       Male_Sad 0.7159970 0.5686672 0.8528201
#6:     Female_Sad 0.7958502 0.5801208 0.6274940