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我试图制作一个p值矩阵，该矩阵对应于我通过获取相关值获得的矩阵
以下是我的数据（为简单起见，仅做5行，对于具有50行的每个数据框，我的实际数据是3列）。
FG_Smooth <- data.frame(FS_1 = c(0.43, 0.33, 3.47, 5.26, 1.09), FS2 = c(0.01, 0.02, 6.86, 3.27, 0.86), FS_3 = c(0.07, 0.36, 1.91, 5.61, 0.84), row.names = c("Group_3", "Thermo", "Embryophyta", "Flavo", "Cyclo"))
FMG_Smooth <- data.frame(GS_1 = c(1.13, 1.20, 0.52, 2.81, 0.70), GS_2 = c(1.18, 1.7, 0.42, 2.93, 0.78), GS_3 = c(1.17, 1.11, 0.60, 3.10, 0.87), row.names = c("Proline", "Trigonelline", "L-Lysine", "Nioctine", "Caffeate"))
library(Hmisc)
rcorr(t(FG_Smooth), t(FMG_Smooth), type = "pearson")
但我收到此错误：
rcorr（t（FG_Smooth），t（FMG_Smooth），type =“ pearson”）中的错误： 必须有> 4个观察结果
我每个人只有3个生物样本-因此，我无法在多个帖子中使用建议多花费时间的rcorr命令。 rcorr命令为您提供1）相关矩阵； 2）相关的p值。
因此，为了回避此问题，我已经执行了以下操作：如其他帖子中所建议：
library(stats)
cor(t(FG_Smooth), t(FMG_Smooth), method = "pearson")
这有效并且给出了我所有相关性的矩阵。
我的下一步是在相关矩阵中找到与每个值关联的p值。函数cor.test仅给出总体p值，这不是我所需要的。
仔细阅读多个帖子后-我碰到了这个帖子： rcorr() function for correlations
我按照说明进行操作：
tblcols <- expand.grid(1:ncol(FG_Smooth), 1:ncol(FMG_Smooth))
cfunc <- function(var1, var2) {
cor.test(FG_Smooth[,var1], FMG_Smooth[,var2], method="pearson")
}
res <- mapply(function(a,b) {
cfunc(var1 = a, var2 = b)
}, tblcols$Var1, tblcols$Var2)
head(res)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
statistic 1.324125 -0.1022017 2.422883 0.9131595 -0.3509424 1.734178 1.53494
parameter 3 3 3 3 3 3 3
p.value 0.2773076 0.9250449 0.09392613 0.4284906 0.74883 0.1812997 0.2223626
estimate 0.6073388 -0.05890371 0.8135079 0.4663678 -0.1985814 0.7075406 0.663238
null.value 0 0 0 0 0 0 0
alternative "two.sided" "two.sided" "two.sided" "two.sided" "two.sided" "two.sided" "two.sided"
[,8] [,9]
statistic -0.009291327 2.880821
parameter 3 3
p.value 0.99317 0.06348644
estimate -0.005364273 0.8570256
null.value 0 0
alternative "two.sided" "two.sided"
这仅给我9个p值，而不是与cor命令获得的每个相关值相对应的p值矩阵。在此示例中，它将是p值的5x5矩阵，因为cor命令会生成5x5的相关值矩阵。
有没有区别。做到这一点的方法？
答案 0 :(得分：1)
这是一个tidyverse解决方案，它创建所有感兴趣的对，然后为每个对执行一个cor.test，并提取相关值和相应的p值。
# example data
FG_Smooth <- data.frame(FS_1 = c(0.43, 0.33, 3.47, 5.26, 1.09), FS2 = c(0.01, 0.02, 6.86, 3.27, 0.86), FS_3 = c(0.07, 0.36, 1.91, 5.61, 0.84), row.names = c("Group_3", "Thermo", "Embryophyta", "Flavo", "Cyclo"))
FMG_Smooth <- data.frame(GS_1 = c(1.13, 1.20, 0.52, 2.81, 0.70), GS_2 = c(1.18, 1.7, 0.42, 2.93, 0.78), GS_3 = c(1.17, 1.11, 0.60, 3.10, 0.87), row.names = c("Proline", "Trigonelline", "L-Lysine", "Nioctine", "Caffeate"))
library(tidyverse)
expand.grid(v1 = row.names(FG_Smooth), # create combinations of names
v2 = row.names(FMG_Smooth)) %>%
tbl_df() %>% # use for visualisation purpose
mutate(cor_test = map2(v1, v2, ~cor.test(unlist(FG_Smooth[.x,]), # perform the correlation test for each pair and store it
unlist(FMG_Smooth[.y,]))),
cor_value = map_dbl(cor_test, "estimate"), # get the correlation value from the test
cor_p_value = map_dbl(cor_test, "p.value")) # get the p value from the test
# # A tibble: 25 x 5
# v1 v2 cor_test cor_value cor_p_value
# <fct> <fct> <list> <dbl> <dbl>
# 1 Group_3 Proline <S3: htest> -0.998 0.0367
# 2 Thermo Proline <S3: htest> -0.592 0.596
# 3 Embryophyta Proline <S3: htest> 0.390 0.745
# 4 Flavo Proline <S3: htest> -0.544 0.634
# 5 Cyclo Proline <S3: htest> -0.966 0.167
# 6 Group_3 Trigonelline <S3: htest> -0.492 0.673
# 7 Thermo Trigonelline <S3: htest> -0.998 0.0396
# 8 Embryophyta Trigonelline <S3: htest> 0.985 0.109
# 9 Flavo Trigonelline <S3: htest> -1.000 0.00188
#10 Cyclo Trigonelline <S3: htest> -0.305 0.803
# # ... with 15 more rows
v1和v2是将为相关性测试创建对的数据集的行名，cor_test列具有每对相关性测试对象，{{1} }具有提取的相关系数，cor_value具有提取的p值。
如果将以上输出另存为数据框，则可以轻松调整形状。例如，如果将其另存为cor_p_value，则可以得到一个p值为5x5的数据帧，如下所示：
d也使用d %>%
select(v1, v2, cor_p_value) %>%
spread(v2, cor_p_value)
# # A tibble: 5 x 6
# v1 Caffeate `L-Lysine` Nioctine Proline Trigonelline
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Cyclo 0.309 0.995 0.351 0.167 0.803
# 2 Embryophyta 0.779 0.0931 0.737 0.745 0.109
# 3 Flavo 0.890 0.204 0.848 0.634 0.00188
# 4 Group_3 0.439 0.875 0.481 0.0367 0.673
# 5 Thermo 0.928 0.242 0.886 0.596 0.0396
软件包的替代版本是：
broom，它为您提供了library(tidyverse)
library(broom)
expand.grid(v1 = row.names(FG_Smooth),
v2 = row.names(FMG_Smooth)) %>%
tbl_df() %>%
mutate(cor_test = map2(v1, v2, ~tidy(cor.test(unlist(FG_Smooth[.x,]),
unlist(FMG_Smooth[.y,]))))) %>%
unnest()
# # A tibble: 25 x 8
# v1 v2 estimate statistic p.value parameter method alternative
# <fct> <fct> <dbl> <dbl> <dbl> <int> <chr> <chr>
# 1 Group_3 Proline -0.998 -17.3 0.0367 1 Pearson's product-moment correlation two.sided
# 2 Thermo Proline -0.592 -0.735 0.596 1 Pearson's product-moment correlation two.sided
# 3 Embryophyta Proline 0.390 0.423 0.745 1 Pearson's product-moment correlation two.sided
# 4 Flavo Proline -0.544 -0.648 0.634 1 Pearson's product-moment correlation two.sided
# 5 Cyclo Proline -0.966 -3.73 0.167 1 Pearson's product-moment correlation two.sided
# 6 Group_3 Trigonelline -0.492 -0.565 0.673 1 Pearson's product-moment correlation two.sided
# 7 Thermo Trigonelline -0.998 -16.0 0.0396 1 Pearson's product-moment correlation two.sided
# 8 Embryophyta Trigonelline 0.985 5.78 0.109 1 Pearson's product-moment correlation two.sided
# 9 Flavo Trigonelline -1.000 -339. 0.00188 1 Pearson's product-moment correlation two.sided
#10 Cyclo Trigonelline -0.305 -0.320 0.803 1 Pearson's product-moment correlation two.sided
# # ... with 15 more rows
格式的相关性测试对象。您需要使用列tidy（相关系数）和estimate。
答案 1 :(得分：0)
当前设置会引起一些问题：
首先，您的cor.test必须使用转置版本和t()：
cor.test(t(FG_Smooth)[,var1], t(FMG_Smooth)[,var2], method="pearson")
第二，cor.test返回一个元素列表，您只需要提取p.value项：
cor.test(t(FG_Smooth)[,var1], t(FMG_Smooth)[,var2], method="pearson")$p.value
因此，考虑将mapply结果绑定到具有所需colnames和rownames（即dimnames）的5 X 5矩阵中：
P值矩阵
# COMBINATION PAIRS
tblcols <- expand.grid(1:ncol(t(FG_Smooth)), 1:ncol(t(FMG_Smooth)))
# COR TEST FUNCTION
cfunc <- function(var1, var2) {
cor.test(t(FG_Smooth)[,var1], t(FMG_Smooth)[,var2], method="pearson")$p.value
}
# P-VALUE MATRIX BUILD
matrix(mapply(cfunc, tblcols$Var1, tblcols$Var2),
ncol = ncol(t(FG_Smooth)), nrow = ncol(t(FMG_Smooth)),
dimnames = list(colnames(t(FG_Smooth)), colnames(t(FMG_Smooth))))
# Proline Trigonelline L-Lysine Nioctine Caffeate
# Group_3 0.0367145 0.672775387 0.87489349 0.4808196 0.4392690
# Thermo 0.5964129 0.039648033 0.24176614 0.8860530 0.9276037
# Embryophyta 0.7450881 0.109027230 0.09309087 0.7373778 0.7789284
# Flavo 0.6341827 0.001878145 0.20399625 0.8482831 0.8898338
# Cyclo 0.1669023 0.802963162 0.99491874 0.3506318 0.3090812
相关矩阵
并且可以肯定的是，如果我们使用$estimate，则矩阵构建将复制cor()调用：
t_FG <- t(FG_Smooth)
t_FMG <- t(FMG_Smooth)
cfunc <- function(var1, var2) {
cor.test(t_FG[,var1], t_FMG[,var2], method="pearson")$estimate
}
# COR MATRIX BUILD
m <- matrix(mapply(cfunc, tblcols$Var1, tblcols$Var2),
ncol = ncol(t_FG), nrow = ncol(t_FMG),
dimnames = list(colnames(t_FG), colnames(t_FMG)))
cor(t(FG_Smooth), t(FMG_Smooth), method = "pearson")
# Proline Trigonelline L-Lysine Nioctine Caffeate
# Group_3 -0.9983375 -0.4916671 0.195254411 -0.7280867 -0.7712447
# Thermo -0.5923344 -0.9980613 0.928751644 0.1780333 0.1134750
# Embryophyta 0.3898002 0.9853709 -0.989327898 -0.4009247 -0.3403212
# Flavo -0.5435196 -0.9999956 0.949098001 0.2360668 0.1721863
# Cyclo -0.9658300 -0.3045869 -0.007981547 -0.8521212 -0.8844400
m
# Proline Trigonelline L-Lysine Nioctine Caffeate
# Group_3 -0.9983375 -0.4916671 0.195254411 -0.7280867 -0.7712447
# Thermo -0.5923344 -0.9980613 0.928751644 0.1780333 0.1134750
# Embryophyta 0.3898002 0.9853709 -0.989327898 -0.4009247 -0.3403212
# Flavo -0.5435196 -0.9999956 0.949098001 0.2360668 0.1721863
# Cyclo -0.9658300 -0.3045869 -0.007981547 -0.8521212 -0.8844400