Illustrate the separation package

illustrate-separation-package.R

# install packages (if necessary)
install.packages("devtools")
install.packages("arm")

# install separation and compactr from GitHub
devtools::install_github("carlislerainey/compactr")
devtools::install_github("carlislerainey/separation")

# load packages
library(separation)
library(arm)  # for rescale()

# load and recode data
data(politics_and_need) 
d <- politics_and_need
d$dem_governor <- 1 - d$gop_governor
d$st_percent_uninsured <- rescale(d$percent_uninsured)

# formula to use throughout
f <- oppose_expansion ~ dem_governor + 
  percent_favorable_aca + gop_leg +
  st_percent_uninsured + bal2012 + 
  multiplier + percent_nonwhite + 
  percent_metro

# informative prior
prior_sims_4.5 <- rnorm(10000, 0, 4.5)
pppd <- calc_pppd(formula = f, 
                  data = d, 
                  prior_sims = prior_sims_4.5, 
                  sep_var_name = "dem_governor",
                  prior_label = "Normal(0, 4.5)")
print(pppd)
plot(pppd)
plot(pppd, log_scale = TRUE)

# mcmc estimation
post <- sim_post_normal(f, d, sep_var = "dem_governor",
                        sd = 4.5,
                        n_sims = 10000,
                        n_burnin = 1000,
                        n_chains = 4)
print(post)

# compute quantities of interest

## dem_governor
X_pred_list <- set_at_median(f, d)
x <- c(0, 1)
X_pred_list$dem_governor <- x
qi <- calc_qi(post, X_pred_list, qi_name = "fd")
plot(qi, xlim = c(-1, 1),
     xlab = "First Difference",
     ylab = "Posterior Density",
     main = "The Effect of Democratic Partisanship on Opposing the Expansion")

## st_percent_uninsured
X_pred_list <- set_at_median(f, d)
x <- seq(min(d$st_percent_uninsured),
         max(d$st_percent_uninsured),
         by = 0.1)
X_pred_list$st_percent_uninsured <- x
qi <- calc_qi(post, X_pred_list, qi_name = "pr")
plot(qi, x,
     xlab = "Percent Uninsured (Std.)",
     ylab = "Predicted Probability",
     main = "The Probability of Opposition as the Percent Uninsured (Std.) Varies")