r - margins 包返回错误 eval 中的错误(model[ ["call"]][ ["data"]], env) : object '.' not found

Question

我尝试使用 margins 包来获取简单线性模型的边际效应，但它返回错误:

Error in eval(model[["call"]][["data"]], env) : object '.' not found

此数据可用于重现问题:

forty_rows <- structure(list(wk_dist_eff_nov16 = structure(c(18, -24, -35, 
-30, 18, 18, 4, -56, -41, 31, 18, -20, 36, 18, -15, 18, 35, 18, 
18, -58, -52, -21, -47, 19, 18, 23, -38, 4, -50, -63, 31, -2, 
-27, 2, 18, 18, -8, -12, 14, 19), class = "difftime", units = "days"), 
    election_2016_11 = c(NA, NA, "0", NA, "0", "0", "1", NA, 
    NA, "0", "0", NA, "1", "0", "0", "0", "1", "0", "0", NA, 
    NA, "0", "0", "0", "1", "1", "1", "1", NA, NA, "0", NA, "0", 
    NA, "1", "0", NA, NA, "0", "1")), class = "data.frame", row.names = c(NA, 
-40L))

library(tidyverse)

model <- forty_rows %>% 
             filter(!is.na(election_2016_11), 
                    wk_dist_eff_nov16 %in% -36:0) %>% 
    lm(as.numeric(election_2016_11) ~ as.factor(wk_dist_eff_nov16), data = .)

这会返回一些无意义的值，但这只是我创建一个小型可重现示例的人为因素(从 > 500k 的 df 中仅采样 40 行)。然后我可以在 model 对象上调用 tidy() 没有问题，但 margins() 返回错误:

tidy(model)
# A tibble: 4 x 5
  term                            estimate std.error statistic p.value
  <chr>                              <dbl>     <dbl>     <dbl>   <dbl>
1 (Intercept)                            0       NaN       NaN     NaN
2 as.factor(wk_dist_eff_nov16)-27        0       NaN       NaN     NaN
3 as.factor(wk_dist_eff_nov16)-21        0       NaN       NaN     NaN
4 as.factor(wk_dist_eff_nov16)-15        0       NaN       NaN     NaN

library(margins)
margins(model)
Error in eval(model[["call"]][["data"]], env) : object '.' not found

有谁知道这里出了什么问题吗？我该如何解决它？

Answer 1

margins 内部使用一些 find_data(model,parent.frame()) 内容来查找数据，并且由于管道的原因只找到 . .

您可以尝试显式定义可在 model$model 中找到的数据。

library(dplyr)

model <- mtcars %>%
  mutate(foo='bar') %>%
  lm(mpg ~ hp, data=.)

margins(model)  
# Error in eval(model[["call"]][["data"]], env) : object '.' not found

margins(model, data=model$model)  
#       hp
# -0.06823

适用于mtcars，但不适用于您的示例数据。