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regression - Stepwise selection for many models in R

I try to calculate a stepwise selection for a vast amount of Poisson regressions based on Chapter 20 "Many models with purrr and broom" in the book “R for Data Science” by Wickham and Grolemund (https://r4ds.had.co.nz/). However using this approach, the calculated models lack a meaningful name, which seems to be problematic when applying the step function. A reproducible example follows below.

library(tidyverse)
library(gapminder)

# Create tibble with variables and models for each corresponding country
by_country <- gapminder %>%
  group_by(country, continent) %>%
  nest()

# model-fitting function:
country_model <- function(df) {
  lm(lifeExp ~ year + pop + gdpPercap, data = df)
}

# apply country_model to each element:
by_country <- by_country %>%
  mutate(model = map(data, country_model))

# Stepwise selection
step_select <- mutate(by_country, step_sel = map(model, step))
# This fails

# It works fine if applied to individually calculated models (in this case for Germany), but not if you extract the same model from the tibble
# Individually calculated model
ge <- filter(gapminder, country == "Germany")
ge_mod <- lm(lifeExp ~ year + pop + gdpPercap, data = ge)
step(ge_mod)
# Extract the same model from the tibble
tb_ge_mod <- by_country[[4]][[48]]
step(tb_ge_mod)

# The only difference I can spot between these models is the name of data, which is generic in the tibble, but specific in the individually calculated model:
ge_mod[["call"]] 
tb_ge_mod[["call"]]
ge_mod[["call"]][["data"]]
tb_ge_mod[["call"]][["data"]]

# If you replace these names, it works.
tb_ge_mod[["call"]][["data"]] <- ge
tb_ge_mod[["call"]] <- lm(formula = lifeExp ~ year + pop + gdpPercap, data = ge)
step(tb_ge_mod)

However, I have found no way to adjust the names automatically (and this would be only a workaround, anyway). So, is there a way how to apply the stepwise selection here?

question from:https://stackoverflow.com/questions/66045700/stepwise-selection-for-many-models-in-r

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