数据框中变量的回归 [英] Regression of variables in a dataframe

问题描述

我有一个数据框:

df = data.frame(x1 = rnorm(50), x2 = rnorm(50), x3 = rnorm(50), x4 = rnorm(50))

我想将每个变量与所有其他变量进行回归，例如:

I would like to regress each variable versus all the other variables, for instance:

fit1 <- lm(x1 ~ ., data = df)
fit2 <- lm(x2 ~ ., data = df)

等(当然，实际的数据框具有更多的变量).

etc. (Of course, the real dataframe has a lot more variables).

我尝试将它们放在一个循环中，但是没有用.我也尝试使用 lapply ，但也无法产生所需的结果.有人知道这个窍门吗?

I tried putting them in a loop, but it didn't work. I also tried using lapply but couldn't produce the desired result either. Does anyone know the trick?

推荐答案

您可以使用 reformulate 动态构建正式版

You can use reformulate to dynamically build formuals

df = data.frame(x1 = rnorm(50), x2 = rnorm(50), x3 = rnorm(50), x4 = rnorm(50))

vars <- names(df)
result <- lapply(vars, function(resp) {
    lm(reformulate(".",resp), data=df)
})

或者，您可以使用do.call在每个模型中获取更漂亮"的公式

alternatively you could use do.call to get "prettier" formauls in each of the models

vars <- names(df)
result <- lapply(vars, function(resp) {
    do.call("lm", list(reformulate(".",resp), data=quote(df)))
})

这些方法中的每一个都返回一个列表.您可以使用 result [[1]] ， result [[2]] 等

each of these methods returns a list. You can extract individual models with result[[1]], result[[2]], etc

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