如何在 R 中找到相关矩阵的 p 值?
How to find p values for correlation matrix in R?
我已经能够使用以下方法生成以下相关矩阵:
attach(iris)
library(corrplot)
library(Hmisc)
library(Formula)
library(survival)
#FOR SETOSA:
m<-levels(Species)
setosaCor=cor(iris[Species==m[1],1:4],method = "pearson")
corrplot(setosaCor,method="number",mar=c(0,0,1,0),tl.col="black")
但是,很难获得此矩阵的 p 值。我需要矩阵形式的 p 值。
这是我试过但没有任何运气
p.value<-rcorr(as.matrix(iris[c(1,2,3,4)]), type=c("pearson"))
cor_mat(iris, vars = NULL, method = "pearson", alternative = "two.sided",conf.level = 0.95)
这么喜欢?
library("Hmisc")
res2 <- rcorr(as.matrix(mtcars))
res2$P
输出:
mpg cyl disp hp drat wt qsec vs
mpg NA 6.112688e-10 9.380328e-10 1.787835e-07 1.776240e-05 1.293958e-10 1.708199e-02 3.415937e-05
cyl 6.112688e-10 NA 1.803002e-12 3.477861e-09 8.244636e-06 1.217567e-07 3.660533e-04 1.843018e-08
disp 9.380328e-10 1.803002e-12 NA 7.142679e-08 5.282022e-06 1.222311e-11 1.314404e-02 5.235012e-06
hp 1.787835e-07 3.477861e-09 7.142679e-08 NA 9.988772e-03 4.145827e-05 5.766253e-06 2.940896e-06
drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 NA 4.784260e-06 6.195826e-01 1.167553e-02
wt 1.293958e-10 1.217567e-07 1.222311e-11 4.145827e-05 4.784260e-06 NA 3.388683e-01 9.798492e-04
qsec 1.708199e-02 3.660533e-04 1.314404e-02 5.766253e-06 6.195826e-01 3.388683e-01 NA 1.029669e-06
vs 3.415937e-05 1.843018e-08 5.235012e-06 2.940896e-06 1.167553e-02 9.798492e-04 1.029669e-06 NA
am 2.850207e-04 2.151207e-03 3.662114e-04 1.798309e-01 4.726790e-06 1.125440e-05 2.056621e-01 3.570439e-01
gear 5.400948e-03 4.173297e-03 9.635921e-04 4.930119e-01 8.360110e-06 4.586601e-04 2.425344e-01 2.579439e-01
carb 1.084446e-03 1.942340e-03 2.526789e-02 7.827810e-07 6.211834e-01 1.463861e-02 4.536949e-05 6.670496e-04
am gear carb
mpg 2.850207e-04 5.400948e-03 1.084446e-03
cyl 2.151207e-03 4.173297e-03 1.942340e-03
disp 3.662114e-04 9.635921e-04 2.526789e-02
hp 1.798309e-01 4.930119e-01 7.827810e-07
drat 4.726790e-06 8.360110e-06 6.211834e-01
wt 1.125440e-05 4.586601e-04 1.463861e-02
qsec 2.056621e-01 2.425344e-01 4.536949e-05
vs 3.570439e-01 2.579439e-01 6.670496e-04
am NA 5.834043e-08 7.544526e-01
gear 5.834043e-08 NA 1.290291e-01
carb 7.544526e-01 1.290291e-01 NA
输出还具有矩阵格式,您可以使用 [] 符号进行子集化
我已经能够使用以下方法生成以下相关矩阵:
attach(iris)
library(corrplot)
library(Hmisc)
library(Formula)
library(survival)
#FOR SETOSA:
m<-levels(Species)
setosaCor=cor(iris[Species==m[1],1:4],method = "pearson")
corrplot(setosaCor,method="number",mar=c(0,0,1,0),tl.col="black")
但是,很难获得此矩阵的 p 值。我需要矩阵形式的 p 值。 这是我试过但没有任何运气
p.value<-rcorr(as.matrix(iris[c(1,2,3,4)]), type=c("pearson"))
cor_mat(iris, vars = NULL, method = "pearson", alternative = "two.sided",conf.level = 0.95)
这么喜欢?
library("Hmisc")
res2 <- rcorr(as.matrix(mtcars))
res2$P
输出:
mpg cyl disp hp drat wt qsec vs
mpg NA 6.112688e-10 9.380328e-10 1.787835e-07 1.776240e-05 1.293958e-10 1.708199e-02 3.415937e-05
cyl 6.112688e-10 NA 1.803002e-12 3.477861e-09 8.244636e-06 1.217567e-07 3.660533e-04 1.843018e-08
disp 9.380328e-10 1.803002e-12 NA 7.142679e-08 5.282022e-06 1.222311e-11 1.314404e-02 5.235012e-06
hp 1.787835e-07 3.477861e-09 7.142679e-08 NA 9.988772e-03 4.145827e-05 5.766253e-06 2.940896e-06
drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 NA 4.784260e-06 6.195826e-01 1.167553e-02
wt 1.293958e-10 1.217567e-07 1.222311e-11 4.145827e-05 4.784260e-06 NA 3.388683e-01 9.798492e-04
qsec 1.708199e-02 3.660533e-04 1.314404e-02 5.766253e-06 6.195826e-01 3.388683e-01 NA 1.029669e-06
vs 3.415937e-05 1.843018e-08 5.235012e-06 2.940896e-06 1.167553e-02 9.798492e-04 1.029669e-06 NA
am 2.850207e-04 2.151207e-03 3.662114e-04 1.798309e-01 4.726790e-06 1.125440e-05 2.056621e-01 3.570439e-01
gear 5.400948e-03 4.173297e-03 9.635921e-04 4.930119e-01 8.360110e-06 4.586601e-04 2.425344e-01 2.579439e-01
carb 1.084446e-03 1.942340e-03 2.526789e-02 7.827810e-07 6.211834e-01 1.463861e-02 4.536949e-05 6.670496e-04
am gear carb
mpg 2.850207e-04 5.400948e-03 1.084446e-03
cyl 2.151207e-03 4.173297e-03 1.942340e-03
disp 3.662114e-04 9.635921e-04 2.526789e-02
hp 1.798309e-01 4.930119e-01 7.827810e-07
drat 4.726790e-06 8.360110e-06 6.211834e-01
wt 1.125440e-05 4.586601e-04 1.463861e-02
qsec 2.056621e-01 2.425344e-01 4.536949e-05
vs 3.570439e-01 2.579439e-01 6.670496e-04
am NA 5.834043e-08 7.544526e-01
gear 5.834043e-08 NA 1.290291e-01
carb 7.544526e-01 1.290291e-01 NA
输出还具有矩阵格式,您可以使用 [] 符号进行子集化