将lm函数应用于R中的公式列表时，避免丢失公式

Question

我正在尝试在mtcars数据集中获取所有变量对，并使用lm函数创建线性模型。但是当我总结或绘制模型时，我的方法让我失去了公式。这是我正在使用的代码。

library(tidyverse)
my_vars <- names(mtcars)) 
pairs <- t(combn(my_vars, 2)) # Get all possible pairs of variables

# Create formulas for the lm model
fmls <- 
  as.tibble(pairs) %>%
  mutate(fml = paste(V1, V2, sep = "~")) %>%
  select(fml) %>%
  .[[1]] %>%
  sapply(as.formula)

# Create a linear model for ear pair of variables
mods <- lapply(fmls, function(v) lm(data = mtcars, formula = v))

# print the summary of all variables 
for (i in 1:length(mods)) {
  print(summary(mods[[i]]))
}

（我从这里开始使用字符串来制作公式的想法[1]：Pass a vector of variables into lm() formula。）以下是第一个模型（summary(mods[[1]])）摘要的输出：

Call:
lm(formula = v, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9814 -2.1185  0.2217  1.0717  7.5186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.206 on 30 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7171 
F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

我正在寻找（也许是元编程）技术，这样调用线看起来像lm(formula = var1 ~ var2, data = mtcars)而不是formula = v。

Answer 1

我把对子组成了一个数据框，让生活更轻松：

library(tidyverse)
my_vars <- names(mtcars) 
pairs <- t(combn(my_vars, 2)) %>% 
  as.data.frame# Get all possible pairs of variables

您可以使用评估表达式的eval（）来完成此操作。

listOfRegs <- apply(pairs, 1, function(pair) {
  V1 <- pair[[1]] %>% as.character
  V2 <- pair[[2]] %>% as.character
  fit <- eval(parse(text = paste0("lm(", pair[[1]] %>% as.character,
                                  "~",  pair[[2]] %>% as.character,
                                  ", data = mtcars)")))
  return(fit)
})

lapply(listOfRegs, summary)

然后：

> lapply(listOfRegs, summary)
[[1]]

Call:
lm(formula = mpg ~ cyl, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9814 -2.1185  0.2217  1.0717  7.5186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.206 on 30 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7171 
F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

 ... etc