如何为R中的每一列运行lm回归 [英] how to run lm regression for every column in R

问题描述

我的数据框架为：

df=data.frame(x=rnorm(100),y1=rnorm(100),y2=rnorm(100),y3=...)

我想运行一个从第一列的第二列开始对每一列进行回归的循环：

I want to run a loop which regresses each column starting from the second column on the first column:

for(i in names(df[,-1])){
    model = lm(i~x, data=df)
}

但是我失败了。关键是我想对每列进行回归循环，而某些列名称只是一个数字（例如404.1）。我找不到使用上述命令为每一列运行循环的方法。

But I failed. The point is that I want to do a loop of regression for each column and some column names is just a number (e.g. 404.1). I cannot find a way to run a loop for each column using the above command.

推荐答案

您的代码看起来不错，除非您调用 lm 中的 i ，R将读取 i 作为字符串，您无法将其退缩。使用 get 将允许您拉出与 i 相对应的列。

Your code looks fine except when you call i within lm, R will read i as a string, which you can't regress things against. Using get will allow you to pull the column corresponding to i.

df=data.frame(x=rnorm(100),y1=rnorm(100),y2=rnorm(100),y3=rnorm(100))

storage <- list()
for(i in names(df)[-1]){
  storage[[i]] <- lm(get(i) ~ x, df)
}

我创建一个空列表存储，我将在循环的每次迭代中进行填充。这只是个人喜好，但我也建议您不要编写当前循环：

I create an empty list storage, which I'm going to fill up with each iteration of the loop. It's just a personal preference but I'd also advise against how you've written your current loop:

 for(i in names(df[,-1])){
    model = lm(i~x, data=df)
}

您将覆盖模型，因此仅返回最后的迭代结果。建议您将其更改为列表或可以迭代存储结果的矩阵。

You will overwrite model, thus returning only the last iteration results. I suggest you change it to a list, or a matrix where you can iteratively store results.

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