R中具有p值的相关矩阵 [英] correlation matrix with p-value in R

问题描述

假设我想要行为相关矩阵

Suppose I want conduct correlation matrix

library(dplyr)

data(iris)
iris %>% 
  select_if(is.numeric) %>%
  cor(y =iris$Petal.Width, method = "spearman") %>%  round(2)

现在我们看到了

              [,1]
Sepal.Length  0.83
Sepal.Width  -0.29
Petal.Length  0.94
Petal.Width   1.00

我希望统计显着相关性由*
标记，其中

i want that statistical significant correlation were marked by * where

*<0,05
**<0,01
*** <0,001

该怎么做？

推荐答案

使用 tidyverse 。我们可以将数据框转换为长格式，使用嵌套创建列表列，然后使用 map 执行<$每个子集c $ c> cor.test 。之后， map_dbl 可以通过指定名称 p.value 来提取P值。 dat1 是最终输出。

A solution using tidyverse. We can convert the data frame to long format, create list column using nest, and then use map to perform cor.test for each subset. After that, map_dbl can extract the P value by specifying the name "p.value". dat1 is the final output.

library(tidyverse)

data(iris)
dat1 <- iris %>% 
  select_if(is.numeric) %>%
  gather(Column, Value, -Petal.Width) %>%
  group_by(Column) %>%
  nest() %>%
  mutate(Cor = map(data, ~cor.test(.x$Value, .x$Petal.Width, method = "spearman"))) %>%
  mutate(Estimate = round(map_dbl(Cor, "estimate"), 2), 
         P_Value = map_dbl(Cor, "p.value"))

dat1
# # A tibble: 3 x 5
#   Column       data               Cor         Estimate  P_Value
#   <chr>        <list>             <list>         <dbl>    <dbl>
# 1 Sepal.Length <tibble [150 x 2]> <S3: htest>    0.83  4.19e-40
# 2 Sepal.Width  <tibble [150 x 2]> <S3: htest>   -0.290 3.34e- 4
# 3 Petal.Length <tibble [150 x 2]> <S3: htest>    0.94  8.16e-70

如果不需要列表列，则可以使用 select 删除它们。

If you don't need the list columns, you can use select to remove them.

dat1 %>% select(-data, -Cor)
# # A tibble: 3 x 3
#   Column       Estimate  P_Value
#   <chr>           <dbl>    <dbl>
# 1 Sepal.Length    0.83  4.19e-40
# 2 Sepal.Width    -0.290 3.34e- 4
# 3 Petal.Length    0.94  8.16e-70

现在我们可以使用 mutate 和 case_when 添加标签以显示重要性。

Now we can use mutate and case_when to add the label to show significance.

dat2 <- dat1 %>%
  select(-data, -Cor) %>%
  mutate(Significance = case_when(
    P_Value < 0.001  ~ "*** <0,001",
    P_Value < 0.01   ~ "** <0,01",
    P_Value < 0.05   ~ "*<0,05",
    TRUE             ~ "Not Significant"
  ))
dat2
# # A tibble: 3 x 4
#   Column       Estimate  P_Value Significance
#   <chr>           <dbl>    <dbl> <chr>       
# 1 Sepal.Length    0.83  4.19e-40 *** <0,001  
# 2 Sepal.Width    -0.290 3.34e- 4 *** <0,001  
# 3 Petal.Length    0.94  8.16e-70 *** <0,001 

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