R 线性模型回归系数的相关矩阵
使用R 线性模型回归系数的相关矩阵,r,matrix,linear-regression,correlation,R,Matrix,Linear Regression,Correlation,使用cor(mtcars,method='pearson')生成一个矩阵,显示mtcars中所有变量与mtcars中所有其他变量的皮尔逊相关性。例如: head(cor(mtcars, method='pearson')) mpg cyl disp hp drat wt qsec vs am gear mpg 1.0000000 -0.852
cor(mtcars,method='pearson')
生成一个矩阵,显示mtcars
中所有变量与mtcars
中所有其他变量的皮尔逊相关性。例如:
head(cor(mtcars, method='pearson'))
mpg cyl disp hp drat wt qsec vs am gear
mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.6811719 -0.8676594 0.41868403 0.6640389 0.5998324 0.4802848
cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.6999381 0.7824958 -0.59124207 -0.8108118 -0.5226070 -0.4926866
disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.7102139 0.8879799 -0.43369788 -0.7104159 -0.5912270 -0.5555692
hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.4487591 0.6587479 -0.70822339 -0.7230967 -0.2432043 -0.1257043
drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.0000000 -0.7124406 0.09120476 0.4402785 0.7127111 0.6996101
wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.7124406 1.0000000 -0.17471588 -0.5549157 -0.6924953 -0.5832870
carb
mpg -0.5509251
cyl 0.5269883
disp 0.3949769
hp 0.7498125
drat -0.0907898
wt 0.4276059
除了每个值不是每个变量之间的皮尔逊相关性,而是线性模型中的r.squared
值之外,我如何得到上面相同的矩阵?例如,第一列第二行将与摘要(lm(mtcars$mpg~mtcars$cyl))$r.squared
相同。多谢各位
library(tidyverse)
# kepp names of dataset
names = names(mtcars)
expand.grid(names, names, stringsAsFactors = F) %>% # create pairs of names
filter(Var1 != Var2) %>% # exclude same variables (creates warnings)
rowwise() %>% # for each row
mutate(r = summary(lm(paste(Var1, "~" ,Var2), data = mtcars))$r.squared) %>% # get the r squared
spread(Var2, r) # reshape
# # A tibble: 11 x 12
# Var1 am carb cyl disp drat gear hp mpg
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 am NA 0.00331 0.273 0.350 0.508 0.631 0.0591 0.360
# 2 carb 0.00331 NA 0.278 0.156 0.00824 0.0751 0.562 0.304
# 3 cyl 0.273 0.278 NA 0.814 0.490 0.243 0.693 0.726
# 4 disp 0.350 0.156 0.814 NA 0.504 0.309 0.626 0.718
# 5 drat 0.508 0.00824 0.490 0.504 NA 0.489 0.201 0.464
# 6 gear 0.631 0.0751 0.243 0.309 0.489 NA 0.0158 0.231
# 7 hp 0.0591 0.562 0.693 0.626 0.201 0.0158 NA 0.602
# 8 mpg 0.360 0.304 0.726 0.718 0.464 0.231 0.602 NA
# 9 qsec 0.0528 0.431 0.350 0.188 0.00832 0.0452 0.502 0.175
# 10 vs 0.0283 0.324 0.657 0.505 0.194 0.0424 0.523 0.441
# 11 wt 0.480 0.183 0.612 0.789 0.508 0.340 0.434 0.753
# # ... with 3 more variables: qsec <dbl>, vs <dbl>, wt <dbl>
这将更接近您从cor(mtcars,method='pearson')
这将更接近您从
cor(mtcars,method='pearson')
中获得的输出。我创建了一个corlm函数,它用for循环填充条目
corlm <- function(df){
mat <- matrix(NA, ncol(df), ncol(df), dimnames = list(colnames(df),colnames(df)))
suppressWarnings(for(i in 1:ncol(df)){
for(j in 1:ncol(df)){
mat[i,j] = summary(lm(df[,j] ~ df[,i]))$r.squared}})
diag(mat) = NA; return(mat)
}
round(corlm(mtcars),3)
mpg cyl disp hp drat wt qsec vs am gear carb
mpg NA 0.726 0.718 0.602 0.464 0.753 0.175 0.441 0.360 0.231 0.304
cyl 0.726 NA 0.814 0.693 0.490 0.612 0.350 0.657 0.273 0.243 0.278
disp 0.718 0.814 NA 0.626 0.504 0.789 0.188 0.505 0.350 0.309 0.156
hp 0.602 0.693 0.626 NA 0.201 0.434 0.502 0.523 0.059 0.016 0.562
drat 0.464 0.490 0.504 0.201 NA 0.508 0.008 0.194 0.508 0.489 0.008
wt 0.753 0.612 0.789 0.434 0.508 NA 0.031 0.308 0.480 0.340 0.183
qsec 0.175 0.350 0.188 0.502 0.008 0.031 NA 0.554 0.053 0.045 0.431
vs 0.441 0.657 0.505 0.523 0.194 0.308 0.554 NA 0.028 0.042 0.324
am 0.360 0.273 0.350 0.059 0.508 0.480 0.053 0.028 NA 0.631 0.003
gear 0.231 0.243 0.309 0.016 0.489 0.340 0.045 0.042 0.631 NA 0.075
carb 0.304 0.278 0.156 0.562 0.008 0.183 0.431 0.324 0.003 0.075 NA
corlm我创建了一个corlm函数,它用for循环填充条目
corlm <- function(df){
mat <- matrix(NA, ncol(df), ncol(df), dimnames = list(colnames(df),colnames(df)))
suppressWarnings(for(i in 1:ncol(df)){
for(j in 1:ncol(df)){
mat[i,j] = summary(lm(df[,j] ~ df[,i]))$r.squared}})
diag(mat) = NA; return(mat)
}
round(corlm(mtcars),3)
mpg cyl disp hp drat wt qsec vs am gear carb
mpg NA 0.726 0.718 0.602 0.464 0.753 0.175 0.441 0.360 0.231 0.304
cyl 0.726 NA 0.814 0.693 0.490 0.612 0.350 0.657 0.273 0.243 0.278
disp 0.718 0.814 NA 0.626 0.504 0.789 0.188 0.505 0.350 0.309 0.156
hp 0.602 0.693 0.626 NA 0.201 0.434 0.502 0.523 0.059 0.016 0.562
drat 0.464 0.490 0.504 0.201 NA 0.508 0.008 0.194 0.508 0.489 0.008
wt 0.753 0.612 0.789 0.434 0.508 NA 0.031 0.308 0.480 0.340 0.183
qsec 0.175 0.350 0.188 0.502 0.008 0.031 NA 0.554 0.053 0.045 0.431
vs 0.441 0.657 0.505 0.523 0.194 0.308 0.554 NA 0.028 0.042 0.324
am 0.360 0.273 0.350 0.059 0.508 0.480 0.053 0.028 NA 0.631 0.003
gear 0.231 0.243 0.309 0.016 0.489 0.340 0.045 0.042 0.631 NA 0.075
carb 0.304 0.278 0.156 0.562 0.008 0.183 0.431 0.324 0.003 0.075 NA
corlm