Kruskal - 用於R的數據子集的Wallis p值矩陣

Question

考慮一個數據集Data ，它有幾個因子和幾個數值連續變量。 這些變量中的一些，比如slice_by_1 （類“Male”，“Female”）和slice_by_2 （類“Sad”，“Neutral”，“Happy”），用於將數據“切片”成子集。 對於每個子集，Kruskal-Wallis測試應該在變量length ， preasure ， pulse上運行，每個變量由另一個因子變量compare_by分組。 R中是否有快速方法來完成此任務並將計算出的p值放入矩陣中？

我使用dplyr包來准備數據。

樣本數據集：

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)

我們來看看數據：

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
..        ...        ...        ...    ...       ...          ...

所需輸出的示例：

    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

你有大部分的group_by ，現在你只需do ：

Data %>%
    do({
        data.frame(
            Data_subsets=paste(.$slice_by_1[[1]], .$slice_by_2[[1]], sep='_'),
            length=kruskal.test(.$length, .$compare_by)$p.value,
            preasure=kruskal.test(.$preasure, .$compare_by)$p.value,
            pulse=kruskal.test(.$pulse, .$compare_by)$p.value,
            stringsAsFactors=FALSE)
    }) %>%
    ungroup() %>%
    select(-starts_with("slice_"))
## Source: local data frame [6 x 4]
##     Data_subsets    length  preasure     pulse
## 1   Female_Happy 0.4369918 0.1937327 0.8767561
## 2 Female_Neutral 0.3750688 0.8588069 0.2858796
## 3     Female_Sad 0.7958502 0.6274940 0.5801208
## 4     Male_Happy 0.3099704 0.6929493 0.3796494
## 5   Male_Neutral 0.4953853 0.2986860 0.2418708
## 6       Male_Sad 0.7159970 0.8528201 0.5686672

您必須執行ungroup()來刪除slice*列，因為group_by列不會被刪除（我想說“從不丟棄”，但我不確定）。

Answer 2

我們可以使用Map kruskal.test do來執行多列kruskal.test ，然后使用unite from library(tidyr)將'slice_by_1'和'slice_by_2'列連接到單個列'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

---

或者我們可以使用data.table來做到這data.table 。 我們將'data.frame'轉換為'data.table'（ setDT(Data) ），通過paste 'slice_by_1'和'slice_by_2'列創建分組變量（'Data_subsets'），然后我們對數據集的列進行子集化並傳遞輸入到Map ，做krusal.test並提取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