计算AME，MEM，MER的最简单方法？

Question

对数logit模型，计算平均边际效应，平均边际效应和代表值的边际效应的最简单方法是什么？

我找到了这个例子，但是解释很混乱，坦率地说，我不明白：https://cran.r-project.org/web/packages/margins/vignettes/Introduction.html

我正在使用STATA数据集ANES.dta以及来自2000年美国总统大选的信息。这是数据集内容的样子：

dat <- structure(list(age = c(49, 63, 40, 47, 26, 48, 41, 18, 31, 22
), gender = c(1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L), race = c(1L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 5L, 1L), education = c(3L, 3L, 3L, 
4L, 4L, 2L, 2L, 2L, 3L, 3L), income = c(4L, 3L, 3L, 3L, 4L, 3L, 
3L, 3L, 4L, 3L), attendance = c(2L, 5L, 5L, 5L, 4L, 5L, 4L, 1L, 
0L, 3L), lib_con = c(59, 49, 94, 24, 29, 19, 39, 49, 79, 49), 
pro_choice = c(2L, 4L, 3L, 4L, 4L, 2L, 4L, 1L, 1L, 4L), vote = c(1, 
0, 1, 0, 0, 0, 0, 1, 1, 0), black = c(0, 0, 0, 0, 1, 0, 0, 
0, 0, 0)),row.names = c(NA, -10L), class = c("data.frame"))

这里是数据集的头：

age gender race education income attendance lib_con pro_choice vote black
1   49      1    1         3      4          2      59          2    1     0
4   63      1    1         3      3          5      49          4    0     0
5   40      2    1         3      3          5      94          3    1     0
8   47      2    1         4      3          5      24          4    0     0
9   26      2    2         4      4          4      29          4    0     1
10  48      2    1         2      3          5      19          2    0     0 

这是我的模型的代码：

rm(list=ls())

library(foreign)
dat <- read.dta("ANES.dta", convert.factors = FALSE)
dat_clear <- na.omit(dat)
head(dat_clear)

m1_logit <- glm(vote ~ gender + income + pro_choice ,
                data = dat_clear, family = binomial(link = "logit") , 
                na.action = na.omit)
summary(m1_logit)

Answer 1

您可以使用margins::margins计算平均边际效应：

library(margins)
margins(m1_logit)
Average marginal effects
glm(formula = vote ~ gender + income + pro_choice, family = binomial(link = "logit"),     data = dat, na.action = na.omit)

   gender  income pro_choice
 -0.08049 0.08049    -0.1607

然后您可以使用marginal_effects计算对数据或任意值的边际影响：

 marginal_effects(m1_logit)
   dydx_gender dydx_income dydx_pro_choice
1  -0.07200314  0.07200315     -0.14380458
2  -0.08333712  0.08333712     -0.16644077
3  -0.15510887  0.15510887     -0.30978309
4  -0.03829122  0.03829123     -0.07647519
5  -0.08333712  0.08333712     -0.16644077
6  -0.21028256  0.21028254     -0.41997574
7  -0.03829122  0.03829123     -0.07647519
8  -0.07215379  0.07215380     -0.14410546
9  -0.01377665  0.01377665     -0.02751471
10 -0.03829122  0.03829123     -0.07647519

See the README了解更多。