将具有9个变量的lm()模型组合的所有可能的broom :: glance统计信息放入R中的数据框

问题描述

由于我只是在学习R，所以我不确定如何解决这个问题.我正在尝试获取一个显示以下内容的数据框:

As I am just learning R, I am not sure how to solve this. I am trying to get a data frame that shows me the following:

Model Number | adj.r.squared | sigma   | statistic | df
------------------------------------------------------
Model 1      | 0.465         | 0.437   |  459.0.   | 8
Model 2      | 0.0465        | 0.0437  |  659.0.   | 7

我正在使用 broom 包，以便通过glance()获取这些统计信息，并为此创建了一个函数:

I am using the broom package in order to get these statistics with glance() and created a function for it:

glancing <- function(x) {
  glance(x)[c("adj.r.squared", "sigma", "statistic", "df")]
}

我使用的数据集具有9个变量(跳舞能力"，能量"，响度"，言语"，声学"，活力"，价"，节奏"，工具性")，我需要所有可能的组合以进行线性回归来预测受欢迎程度得分

I am using a dataset that has 9 variables ("danceability","energy", "loudness", "speechiness", "acousticness", "liveness", "valence", "tempo", "instrumentalness) and I needed all the combination possible for linear regression to predict the popularity score

我找到了一种将所有公式放在列表中的方法:

I found a way to put all the formulas in a list:

characteristics <- c("popularity","danceability","energy", "loudness", "speechiness", "acousticness", "liveness", "valence", "tempo", "instrumentalness")
N <- list(1,2,3,4,5,6,7,8,9)
COMB <- sapply(N, function(m) combn(x=characteristics[2:10], m))
formulas <- list()
k=0
for(i in seq(COMB)){
  tmp <- COMB[[i]]
  for(j in seq(ncol(tmp))){
    k <- k + 1
    formulas[[k]] <- formula(paste("popularity", "~", paste(tmp[,j], collapse=" + ")))
  }
}

我还能够将列表中的每个公式分配给具有线性模型的对象:

I was also able to assign each formula in the list to an object with the linear model:

#Assign each model to a variables
for(i in 1:length(formulas)) {
  assign(paste0("model",i),lm(formulas[[i]], data=training_data))
}

这留下了511个模型(对象)，我必须手动将它们放入 glancing函数，然后合并到一个数据框中.

This leaves me with 511 models (objects), which I have to put into the glancing function manually, and then combine into a data frame.

是否有更简单的方法可以完全做到这一点?

我已经尝试过将列表转换为数据帧或向量，但是由于该类是公式"这一事实而似乎失败了.

I already tried to convert the list into a data frame or vector, but it seems to fail due to the fact the class is a "formula".

感谢您的帮助！

推荐答案

使用 assign 替换此循环:

for(i in 1:length(formulas)) {
  assign(paste0("model",i),lm(formulas[[i]], data=training_data))
}

使用列表进行此循环:

model_list = list()
for(i in 1:length(formulas)) {
  model_list[[i]] = lm(formulas[[i]], data=training_data)
}

然后，如果您想一目了然所有这些内容:

Then if you want to glance all of them:

library(dplyr)
library(broom)
glance_results = bind_rows(lapply(model_list, glance))

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