使用 tidyverse 和 broom 的许多回归：相同的因变量，不同的自变量

Question

此链接显示了在我们有相同的自变量但可能有许多不同的因变量的情况下如何回答我的问题：Use broom and tidyverse to run regressions on different dependent variables。

但我的问题是，我如何应用相同的方法（例如，tidyverse 和 broom）来运行许多回归，而我们有相反的情况：相同的因变量但不同的自变量。与上一个链接中的代码一致，类似于：

mod = lm(health ~ cbind(sex,income,happiness) + faculty, ds) %>% tidy()

但是，这段代码并不完全符合我的要求，而是产生：

Call:
lm(formula = income ~ cbind(sex, health) + faculty, data = ds)

Coefficients:
             (Intercept)     cbind(sex, health)sex  
                 945.049                   -47.911  
cbind(sex, health)health                   faculty  
                   2.342                     1.869 

相当于：

lm(formula = income ~ sex + health + faculty, data = ds)

Answer 1

基本上，您需要某种方法来创建所需的所有不同公式。这是一种方法

qq <- expression(sex,income,happiness)
formulae <- lapply(qq, function(v) bquote(health~.(v)+faculty))
# [[1]]
# health ~ sex + faculty
# [[2]]
# health ~ income + faculty
# [[3]]
# health ~ happiness + faculty

获得所有公式后，您可以将它们映射到lm，然后映射到 tidy()

library(purrr)
library(broom)

formulae %>% map(~lm(.x, ds)) %>% map_dfr(tidy, .id="model")
# A tibble: 9 x 6
#   model term         estimate std.error statistic  p.value
#   <chr> <chr>           <dbl>     <dbl>     <dbl>    <dbl>
# 1 1     (Intercept) 19.5        0.504     38.6    1.13e-60
# 2 1     sex          0.755      0.651      1.16   2.49e- 1
# 3 1     faculty     -0.00360    0.291     -0.0124 9.90e- 1
# 4 2     (Intercept) 19.8        1.70      11.7    3.18e-20
# 5 2     income      -0.000244   0.00162   -0.150  8.81e- 1
# 6 2     faculty      0.143      0.264      0.542  5.89e- 1
# 7 3     (Intercept) 18.4        1.88       9.74   4.79e-16
# 8 3     happiness    0.205      0.299      0.684  4.96e- 1
# 9 3     faculty      0.141      0.262      0.539  5.91e- 1

使用样本数据

set.seed(11)
ds <- data.frame(income = rnorm(100, mean=1000,sd=200),
             happiness = rnorm(100, mean = 6, sd=1),
             health = rnorm(100, mean=20, sd = 3),
             sex = c(0,1),
             faculty = c(0,1,2,3))

Answer 2

您可以使用combn 函数来获取n 个自变量的所有组合，然后对其进行迭代。让我们在这里说 n=3：

library(tidyverse)

ds <- data.frame(income = rnorm(100, mean=1000,sd=200),
                 happiness = rnorm(100, mean = 6, sd=1),
                 health = rnorm(100, mean=20, sd = 3),
                 sex = c(0,1),
                 faculty = c(0,1,2,3))

ivs = combn(names(ds)[names(ds)!="income"], 3, simplify=FALSE)
# Or, to get all models with 1 to 4 variables:
# ivs = map(1:4, ~combn(names(ds)[names(ds)!="income"], .x, simplify=FALSE)) %>% 
#         flatten()

names(ivs) = map(ivs, ~paste(.x, collapse="-"))

models = map(ivs, 
             ~lm(as.formula(paste("income ~", paste(.x, collapse="+"))), data=ds))

map_df(models, broom::tidy, .id="model")

   model                    term        estimate std.error statistic  p.value
 * <chr>                    <chr>          <dbl>     <dbl>     <dbl>    <dbl>
 1 happiness-health-sex     (Intercept)  1086.      201.      5.39   5.00e- 7
 2 happiness-health-sex     happiness     -25.4      21.4    -1.19   2.38e- 1
 3 happiness-health-sex     health          3.58      6.99    0.512  6.10e- 1
 4 happiness-health-sex     sex            11.5      41.5     0.277  7.82e- 1
 5 happiness-health-faculty (Intercept)  1085.      197.      5.50   3.12e- 7
 6 happiness-health-faculty happiness     -25.8      20.9    -1.23   2.21e- 1
 7 happiness-health-faculty health          3.45      6.98    0.494  6.23e- 1
 8 happiness-health-faculty faculty         7.86     18.2     0.432  6.67e- 1
 9 happiness-sex-faculty    (Intercept)  1153.      141.      8.21   1.04e-12
10 happiness-sex-faculty    happiness     -25.9      21.4    -1.21   2.28e- 1
11 happiness-sex-faculty    sex             3.44     46.2     0.0744 9.41e- 1
12 happiness-sex-faculty    faculty         7.40     20.2     0.366  7.15e- 1
13 health-sex-faculty       (Intercept)   911.      143.      6.35   7.06e- 9
14 health-sex-faculty       health          3.90      7.03    0.554  5.81e- 1
15 health-sex-faculty       sex            15.6      45.6     0.343  7.32e- 1
16 health-sex-faculty       faculty         7.02     20.4     0.345  7.31e- 1