迭代变量并在每次迭代中替换一个

Question

假设我有这个 df：

df <- structure(list(id = c(1, 2, 3, 4, 5, 6, 7, 8), q1 = c(1, 1, 4, 
5, 3, 3, 3, 2), q2 = c(5, 4, 4, 1, 1, 2, 3, 3), q3 = c(3, 3, 
2, 4, 3, 3, 2, 5), q4 = c(6, 5, 3, 3, 2, 1, 3, 4), q5 = c(2, 
1, 3, 4, 5, 4, 3, 2), v1 = c(0, 0, 1, 1, 1, 1, 0, 1), v2 = c("19-25", 
"19-25", "19-25", "26-34", "26-34", "35-44", "35-44", "35-44"
), v3 = c("abc", "def", "abc", "abc", "abc", "def", "def", "abc"
)), row.names = c(NA, -8L), class = c("tbl_df", "tbl", "data.frame"
))

> df
# A tibble: 8 x 9
     id    q1    q2    q3    q4    q5    v1 v2    v3   
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1     1     1     5     3     6     2     0 19-25 abc  
2     2     1     4     3     5     1     0 19-25 def  
3     3     4     4     2     3     3     1 19-25 abc  
4     4     5     1     4     3     4     1 26-34 abc  
5     5     3     1     3     2     5     1 26-34 abc  
6     6     3     2     3     1     4     1 35-44 def  
7     7     3     3     2     3     3     0 35-44 def  
8     8     2     3     5     4     2     1 35-44 abc  

我想运行一系列模型来估计在所有其他列（id 除外）上回归 q* 的 R^2，然后将第一个模型中的 DV 替换为另一个 q* 列，等等。输出应该是来自broom::glance的标准输出的小标题。

例如，第一个模型是：

glance(
    lm(as.numeric(q1) ~ 
           v1 +
           as.factor(v2) + 
           as.factor(v3) + 
           q2 +
           q3 +
           q4 +
           q5,
       data = df))

# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

第二个模型会将q1 与q2 交换，因此q2 成为因变量，q1 成为自变量之一。我将对所有 q* 列重复此操作，因此我最终得到 5 行的 tibble。 v* 列包含在每个模型中，但不会成为因变量。

我还希望在最后的 tibble 中有一个指示符，该指标是针对哪个因变量运行该模型的（即，当 q1 是因变量时，名为 dv 的列包含 q1，或者当那是 q2 时是因变量等）。

这可能吗？我宁愿避免复制粘贴上面的n次。

Answer 1

你可以定义

vars <- c("q1", "q2", "q3", "q4", "q5")

并对其进行迭代并创建公式：

library(broom)
library(dplyr)
library(purrr)

vars %>% 
  map_chr(~ paste0("as.numeric(", .x, ") ~ v1 + as.factor(v2) + as.factor(v3) +",
                   paste(vars[vars != .x], collapse = "+"))) %>% 
  map(~ .x %>% 
        as.formula() %>% 
        lm(data = df) %>% 
        glance())

这会产生一个包含五个 data.frames 的列表：

[[1]]
# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

[[2]]
# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

[[3]]
# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

[[4]]
# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

[[5]]
# A tibble: 1 x 12
  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance df.residual  nobs
      <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1         1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0           0     8

由于它们都相似，您可以创建一个 data.frame：

vars <- c("q1", "q2", "q3", "q4", "q5")
names(vars) <- vars

vars %>% 
  map_chr(~ paste0("as.numeric(", .x, ") ~ v1 + as.factor(v2) + as.factor(v3) +",
                   paste(vars[vars != .x], collapse = "+"))) %>% 
  map_df(~ .x %>% 
        as.formula() %>% 
        lm(data = df) %>% 
        glance(),
        .id = "dependent_var")

返回

# A tibble: 5 x 13
  dependent_var r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance
  <chr>             <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>
1 q1                    1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0
2 q2                    1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0
3 q3                    1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0
4 q4                    1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0
5 q5                    1           NaN   NaN       NaN     NaN     7    Inf  -Inf  -Inf        0
# ... with 2 more variables: df.residual <int>, nobs <int>