R 中具有不同变量组合的线性模型 [英] Linear models in R with different combinations of variables

问题描述

我是 R 的新手，但遇到了一个问题.我正在尝试读取表中的一组数据，并且想要执行线性建模.以下是我读取数据和变量名称的方式:

I am new to R and I am stuck with a problem. I am trying to read a set of data in a table and I want to perform linear modeling. Below is how I read my data and my variables names:

>data =read.table(datafilename,header=TRUE)
>names(data)
[1] "price"     "model"     "size"   "year"   "color"

我想要做的是使用变量的不同组合(价格是目标)创建几个线性模型，例如:

What I want to do is create several linear models using different combinations of the variables (price being the target ), such as:

> attach(data)
> model1 = lm(price~model+size)
> model2 = lm(price~model+year)
> model3 = lm(price~model+color)
> model4 = lm(price~model+size)
> model4 = lm(price~size+year+color)
#... and so on for all different combination...

我的主要目的是比较不同的模型.有没有更聪明的方法来生成这些模型而不是对变量进行硬编码，尤其是在某些情况下我的变量数量会增加到 13 个左右.

My main aim is to compare the different models. Is there a more clever way to generate these models instead of hard coding the variables, especially that the number of my variables in some cases will increase to 13 or so.

推荐答案

这是使用 combn 函数获取所有变量组合的一种方法.它有点乱，并且使用了一个循环(也许有人可以使用 mapply 对此进行改进):

Here's one way to get all of the combinations of variables using the combn function. It's a bit messy, and uses a loop (perhaps someone can improve on this with mapply):

vars <- c("price","model","size","year","color")
N <- list(1,2,3,4)
COMB <- sapply(N, function(m) combn(x=vars[2:5], m))
COMB2 <- list()
k=0
for(i in seq(COMB)){
    tmp <- COMB[[i]]
    for(j in seq(ncol(tmp))){
        k <- k + 1
        COMB2[[k]] <- formula(paste("price", "~", paste(tmp[,j], collapse=" + ")))
    }
}

然后，您可以调用这些公式并使用 list 存储模型对象，或者可能使用 assign 函数给出唯一名称:

Then, you can call these formulas and store the model objects using a list or possibly give unique names with the assign function:

res <- vector(mode="list", length(COMB2))
for(i in seq(COMB2)){
    res[[i]] <- lm(COMB2[[i]], data=data)
}

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