Code to illustrate separation

br-separation-illustration.R

# load packages
library(modelsummary)

# get latest version of {crdata} package, if updated
remotes::install_github("carlislerainey/crdata")

# load (and slightly wrangle) data from Barrilleaux and Rainey (2014)
br <- crdata::br2014 %>%
  # recode from centered GOP gov. indicator to 0/1 Dem. gov. indicator
  # (explanatory variables are centered about mean)
  mutate(dem_governor = 1*(gop_governor < 0)) 

# formula to reproduce Figure 2 on p. 446 of Barrilleaux and Rainey (2014).
# (I changed the GOP gov. indicator to a Dem. gov. indicator)
f <- oppose_expansion ~ dem_governor +  percent_favorable_aca +  gop_leg +
  percent_uninsured + bal2012 + multiplier + percent_nonwhite + percent_metro

# logit model with separation (default precision)
fit <- glm(f, data = br, family = binomial)

# logit model with separation (default precision)
fit_maxprec <- glm(f, data = br, family = binomial, 
                   control = glm.control(maxit = 1000, 
                                         epsilon = .Machine$double.eps))

# make a table!
modelsummary(list("Default Precision" = fit, 
                  "Max. Precision" = fit_maxprec),
             shape = term ~ model + statistic, 
             fmt = fmt_sprintf("%.1f"))