r lm矢量化控制变量

Question

我经常必须编写一个长方程，其中控制变量不会改变。

例如，hp是我的兴趣（x）模型和vs + am + gear + carb之间的变化而变化是我控制

lm(disp ~ hp + vs + am + gear + carb, mtcars) 

然后我x是drat然后wt但我的控制是相同的。

lm(disp ~ drat + vs + am + gear + carb, mtcars) 
lm(disp ~ wt + vs + am + gear + carb, mtcars) 

我会觉得有时是相当有用的，以便能够减少方程像

y = 'disp' 
x = 'hp' 
controls = 'vs + am + gear + carb' 

lm(y ~ x + controls, mtcars) 

任何想法，我怎么能做到这一点？

Answer 1

下面的代码构造的字符串式（用小编辑到@ ZheyuanLi的评论）喂到lm和还使用map函数从purrr（一个tidyverse封装）在x载体中以产生用于每个变量的单独模型。列表models中的每个元素都包含模型对象，元素的名称是在模型公式中使用的值x。

library(tidyverse) 

y = 'disp' 
x = c('hp','wt') 
controls=c("vs","am","gear","carb") 

models = map(setNames(x,x), 
      ~ lm(paste(y, paste(c(.x, controls), collapse="+"), sep="~"), 
        data=mtcars)) 

map(models, summary) 

$hp 

Call: 
lm(formula = paste(y, paste(c(.x, controls), collapse = "+"), 
    sep = "~"), data = mtcars) 

Residuals: 
    Min  1Q Median  3Q  Max 
-85.524 -19.153 1.109 14.957 115.804 

Coefficients: 
      Estimate Std. Error t value Pr(>|t|)  
(Intercept) 261.9238 73.2477 3.576 0.0014 ** 
hp   1.2021  0.2453 4.900 4.38e-05 *** 
vs   -63.7135 26.5957 -2.396 0.0241 * 
am   -56.0468 30.7338 -1.824 0.0797 . 
gear  -31.6231 23.4816 -1.347 0.1897  
carb  -14.3237 10.1169 -1.416 0.1687  
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 47.97 on 26 degrees of freedom 
Multiple R-squared: 0.8743, Adjusted R-squared: 0.8502 
F-statistic: 36.18 on 5 and 26 DF, p-value: 6.547e-11 


$wt 

Call: 
lm(formula = paste(y, paste(c(.x, controls), collapse = "+"), 
    sep = "~"), data = mtcars) 

Residuals: 
    Min  1Q Median  3Q  Max 
-74.153 -36.993 -2.097 30.616 102.331 

Coefficients: 
      Estimate Std. Error t value Pr(>|t|)  
(Intercept) 28.875 108.220 0.267 0.79172  
wt   88.577  18.810 4.709 7.25e-05 *** 
vs   -92.669  25.186 -3.679 0.00107 ** 
am   -3.734  34.662 -0.108 0.91503  
gear   -4.688  25.271 -0.186 0.85427  
carb   -8.455  9.662 -0.875 0.38955  
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 48.88 on 26 degrees of freedom 
Multiple R-squared: 0.8695, Adjusted R-squared: 0.8445 
F-statistic: 34.66 on 5 and 26 DF, p-value: 1.056e-10