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

Question

这个 link 展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：.

但我的问题是，如何将相同的方法（例如 tidyverse 和 broom）应用到运行我们遇到相反情况的许多回归：相同的因变量但不同的自变量。配合前面link中的代码，是这样的：

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

但是，此代码并没有完全按照我的要求执行，而是生成：

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，您需要一些方法来创建您想要的所有不同公式。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

使用示例数据

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 函数获取 n 自变量的所有组合，然后迭代它们。比方说 n=3 这里：

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

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

Many regressions using tidyverse and broom: Same dependent variable, different independent variables

r

lm

dplyr

broom

tidyverse