每个独立变量的线性回归循环单独与依赖 [英] Linear Regression loop for each independent variable individually against dependent

问题描述

我想弄清楚如何创建一个循环或使用其中一个应用函数来针对因变量获取数据集中每个变量的单独 1:1 回归信息.

I want to figure out how to create a loop or using one of the apply functions to get individual 1:1 regression information for each variable in a dataset against the dependent variable.

假设我正在使用 mtcars.我将如何在 R 代码中编写获取数据帧中的每个变量并将其回归 MPG 的代码?

Lets say I am using mtcars. How would I write in R code that takes each variable in the data frame and regresses it against MPG?

更好的是获得每个自变量的摘要，并具有某种名称分配，例如 x1=、x2=etc

Even better would be getting a summary of each independent variable with and having some sort of name assignment such as x1=, x2=etc

summary(lm(mpg~eachvar,data=mtcars))

推荐答案

试试这样的:

models <- lapply(paste("mpg", names(mtcars)[-1], sep = "~"), formula)
res.models <- lapply(models, FUN = function(x) {summary(lm(formula = x, data = mtcars))})
names(res.models) <- paste("mpg", names(mtcars)[-1], sep = "~")
res.models[["mpg~disp"]]


# Call:
# lm(formula = x, data = mtcars)

# Residuals:
#     Min      1Q  Median      3Q     Max 
# -4.8922 -2.2022 -0.9631  1.6272  7.2305 

# Coefficients:
#              Estimate Std. Error t value Pr(>|t|)    
# (Intercept) 29.599855   1.229720  24.070  < 2e-16 ***
# disp        -0.041215   0.004712  -8.747 9.38e-10 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 3.251 on 30 degrees of freedom
# Multiple R-squared:  0.7183,  Adjusted R-squared:  0.709 
# F-statistic: 76.51 on 1 and 30 DF,  p-value: 9.38e-10

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