Unnest fitted glm models - r

Question

I have a tibble with nested glm models. I nest over a variable (region) and run a function region_model that fits the model.
# toy data
test_data = data.frame(region = sample(letters[1:3], 1000, replace = TRUE),
x = sample(0:1, 1000, replace = TRUE),
y = sample(1:100, 1000, replace = TRUE),
z = sample(0:1, 1000, replace = TRUE)) %>% arrange(region)
# nest
by_region = test_data %>%
group_by(region) %>%
nest()
# glm function
region_model <- function(df) {
glm(x ~ y + z, data = df, family = "binomial")
}
# run the model
by_region = by_region %>% mutate(mod_rat = data %>% map(region_model))
The resulting tibble looks like this:
> by_region
# A tibble: 3 x 3
region data mod_rat
<fctr> <list> <list>
1 a <tibble [352 x 3]> <S3: glm>
2 b <tibble [329 x 3]> <S3: glm>
3 c <tibble [319 x 3]> <S3: glm>
My purpose is to unnest the models to calculate marginal effects. I have tried it and I have got this error:
> unnest(by_region, mod_rat)
Error: Each column must either be a list of vectors or a list of data frames [mod_rat]
I wonder whether it possible to use unnest on this type of objects (<S3: glm>) and in case not, whether there is an alternative to get these estimates.

Answer 1

As it happens, the margins package has had some recent updates which will help you do this in a tidy fashion. In particular a margins_summary() function has been added that can be mapped onto nested model objects.
This issue on GitHub has the details.
Here is some code that works with your example
Using data from above
library(tidyverse)
library(magrittr)
library(margins)
# toy data
test_data <- data.frame(region = sample(letters[1:3], 1000, replace = TRUE),
x = sample(0:1, 1000, replace = TRUE),
y = sample(1:100, 1000, replace = TRUE),
z = sample(0:1, 1000, replace = TRUE)) %>%
arrange(region)
# nest
by_region <-
test_data %>%
group_by(region) %>%
nest()
# glm function
region_model <- function(df) {
glm(x ~ y + z, data = df, family = "binomial")
}
# run the model
by_region %<>%
mutate(mod_rat = map(data, region_model))
Using the margins_summary() function via purrr:map2() to compute marginal effects (I have included both methods for calculating the marginal effects with logistic regression as described in the package vignette)
by_region %<>%
mutate(marginals = map2(mod_rat, data, ~margins_summary(.x, data = .y)),
marginals_link = map2(mod_rat, data, ~margins_summary(.x, data = .y, type = "link")))
We can now unnest either of the created list columns with the marginal effect data
by_region %>%
unnest(marginals) -> region_marginals
region_marginals
# A tibble: 6 x 8
region factor AME SE z p
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 a y -9.38e-4 9.71e-4 -0.966 0.334
2 a z 3.59e-2 5.55e-2 0.647 0.517
3 b y 1.14e-3 9.19e-4 1.24 0.215
4 b z -2.93e-2 5.38e-2 -0.545 0.586
5 c y 4.67e-4 9.77e-4 0.478 0.633
6 c z -3.32e-2 5.49e-2 -0.604 0.546
# ... with 2 more variables: lower <dbl>,
# upper <dbl>
And plot nicely
region_marginals %>%
ggplot(aes(reorder(factor, AME), AME, ymin = lower, ymax = upper)) +
geom_hline(yintercept = 0, colour = "#AAAAAA") +
geom_pointrange() +
facet_wrap(~region) +
coord_flip()