In tereom/quickcount: Estimate Election Results Mexico

library(quickcountmx)
library(tidyverse)
data("gto_2012")

We first run a single model for one party

model_fit <- mrp_party_estimation(gto_2012, party = pri_pvem, 
  stratum = distrito_loc_17, frac = 0.04, 
  seed = 211871, n_chains = 2, n_burnin = 300, n_iter = 500, 
  model_string = "model_bern_t_pp")

Now we check posterior distributions and compare with observed data at the stratum level.

pp_data <- pp_check_party(model_fit)
ggplot(pp_data, aes(x = n_votes_post/1000)) + 
  geom_histogram(alpha = 0.9, binwidth = 0.01) +
  facet_wrap(~ stratum, scales = "fixed") +
  geom_vline(aes(xintercept = observed/1000), colour = 'salmon', alpha = 0.9) +
  xlab("Thousands of votes")

mrp_gto <- mrp_estimation(gto_2012, pri_pvem:otros, frac = 0.075, 
    stratum = distrito_loc_17, n_iter = 2000, n_burnin = 1000, 
    n_chains = 2, seed = 19291, parallel = TRUE, model_string = "model_bern_t_pp")

pp_data <- pp_check(mrp_gto)
ggplot(pp_data, aes(x = prop_post)) + 
  geom_histogram(alpha = 0.9, binwidth = 0.001) +
  facet_grid(stratum ~ party, scales = 'free_x') +
  geom_vline(aes(xintercept = prop_obs), colour = 'salmon', alpha = 0.9)

ggplot(filter(pp_data, party=="pan_na"), aes(x = prop_post)) + 
  geom_histogram(alpha = 0.9, binwidth = 0.01) +
  facet_wrap(~stratum) +
  geom_vline(aes(xintercept = prop_obs), colour = 'salmon', alpha = 0.9)

ggplot(filter(pp_data, party=="prd"), aes(x = prop_post)) + 
  geom_histogram(alpha = 0.9, binwidth = 0.01) +
  facet_wrap(~stratum) +
  geom_vline(aes(xintercept = prop_obs), colour = 'salmon', alpha = 0.9)

ggplot(filter(pp_data, party=="mc"), aes(x = prop_post)) + 
  geom_histogram(alpha = 0.9, binwidth = 0.01) +
  facet_wrap(~stratum) +
  geom_vline(aes(xintercept = prop_obs), colour = 'salmon', alpha = 0.9)