R/mrp_party_estimation.R
In tereom/quickcountmx: Estimate Election Results Mexico
Defines functions
mrp_party_estimation
model_bern_t
model_t
model_bern_t_pp
model_bern_t_nb
model_norm
model_norm_var
Documented in
mrp_party_estimation
#' Postratified estimation for the number of votes for a given candidate.
#'
#' The function fits a model using the \code{\link[R2jags]{jags}} function and
#' predicts number of votes for a given candidate in unobserved polling
#' stations. Optionally the model can be fit with a stratified random sample
#' of the data, and predict for the population.
#' @param data A \code{data.frame} with variables: ln_total, region,
#' distrito_loc_17, tamano_md, tamano_gd, casilla_ex and the column with
#' number of votes for the party.
#' @param party Unquoted variable indicating the column from the data.frame to
#' be modeled.
#' @param stratum If sampling the data, unquoted variable indicating the column
#' from the data.frame to be used as strata. The strata will also be used in
#' the hierarchical structure of the model.
#' @param frac If sampling the data, numeric value indicating the fraction
#' of the data to sample, the sample is selected using stratified sampling
#' with probability proportional to size.
#' @param n_iter,n_burnin,n_chains Number of iterations, burnin size and chains.
#' to be used in \code{\link[R2jags]{jags}}.
#' @param seed Integer value used to set the state of the random number
#' generator.
#' @param seed_jags Seed for the call \code{\link[R2jags]{jags}}.
#' @param model_string String indicating the model to be used, if NULL defaults
#' to \code{"model_bern_t"}, also available \code{"model_t"}.
#' @return A \code{list} with the object fitted using R2jags::jags and the vector
#' of simulated counts per candidate.
#' @seealso \code{\link{mrp_estimation}}
#' @examples
#' # predict number of votes for pan_na using 6% of the sample
#' mrp_gto_pan <- mrp_party_estimation(gto_2012, pan_na,
#' stratum = distrito_loc_17, frac = 0.06, n_iter = 200, n_burnin = 100,
#' n_chains = 2, seed = 19291)
#' mrp_gto_pan$fit
#' @importFrom magrittr %>%
#' @importFrom rlang !! !!! :=
#' @export
mrp_party_estimation <- function(data, party, stratum, frac = 1,
n_iter = 1000, n_burnin = 500, n_chains = 2, seed = NA, seed_jags = NA,
model_string = NULL, set_strata_na = integer(0)){
if (is.null(model_string)) {
model_string <- "model_bern_t"
}
stratum_enquo <- dplyr::enquo(stratum)
party_enquo <- dplyr::enquo(party)
party_name <- dplyr::quo_name(party_enquo)
if (is.na(seed_jags)) seed_jags <- sample(1:1000, 1)
if (is.na(seed)) seed <- sample(1:1000, 1)
if (frac >= 1){
data_model <- data
} else {
data <- data %>%
dplyr::mutate(casilla_id = 1:n())
data_sample <- select_sample_prop(data, stratum = !!stratum_enquo,
frac = frac, seed = seed)
data_model <- data %>%
dplyr::mutate(!!party_name := ifelse(casilla_id %in%
data_sample$casilla_id, !!party_enquo, NA))
}
data_district <- dplyr::distinct(data, !!stratum_enquo, region) %>%
dplyr::arrange(!!stratum_enquo)
x <- dplyr::pull(data_model, !!party_enquo)
stratum_vec <- dplyr::pull(data_model, !!stratum_enquo)
if (length(set_strata_na) > 0) {
x[stratum_vec %in% set_strata_na] <- NA
}
data_jags <- list(N = nrow(data_model), n = data_model$ln_total,
n_regiones = dplyr::n_distinct(data_district$region),
n_strata = nrow(data_district),
x = x,
rural = data_model$rural,
estrato = dplyr::pull(data_model, !!stratum_enquo),
tamano_md = data_model$tamano_md,
tamano_gd = data_model$tamano_gd, tipo_ex = data_model$casilla_ex,
region = (data_model$region == 1) * 1, # región binaria covariable
region_mod = data_district$region) # region para modelar (jerárquica)
model_function <- get(model_string)
fit <- model_function(data_jags, n_chains, n_iter, n_burnin,
seed_jags)
return(fit)
}
model_bern_t <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p * region[k])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 780)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_t <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
x[k] ~ dt(n[k] * theta[k], tau[estrato[k]] / n[k], nu[estrato[k]]) T(-0.01, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.25)
beta_rural ~ dnorm(0, 0.25)
beta_region ~ dnorm(0, 0.25)
beta_tamano_md ~ dnorm(0, 0.25)
beta_tamano_gd ~ dnorm(0, 0.25)
beta_tipo_ex ~ dnorm(0, 0.25)
beta_rural_tamano_md ~ dnorm(0, 0.25)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dunif(0, 10)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.25)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex", "beta_estrato",
"beta_estrato_raw", "sigma", "sigma_estrato",
"beta_rural_tamano_md", "beta_region", "nu"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_bern_t_pp <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p * region[k])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
# posterior preditive
for(k in 1:N){
x_pred[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
}
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p", "x_pred"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
# region no binaria
model_bern_t_nb <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p[region_mod[estrato[k]]])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_norm <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <-
"
model{
for(k in 1:N){
x[k] ~ dnorm(n[k] * theta[k], tau / n[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.5)
beta_rural ~ dnorm(0, 0.5)
beta_region ~ dnorm(0, 0.5)
beta_tamano_md ~ dnorm(0, 0.5)
beta_tamano_gd ~ dnorm(0, 0.5)
beta_tipo_ex ~ dnorm(0, 0.5)
beta_rural_tamano_md ~ dnorm(0, 0.5)
sigma ~ dunif(0, 1)
tau <- pow(sigma, -2)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
}
mu_estrato ~ dnorm(0, 0.5)
sigma_estrato ~ dunif(0, 1)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_norm_var <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <-
"
model{
for(k in 1:N){
x[k] ~ dnorm(n[k] * theta[k], tau[estrato[k]] / n[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.5)
beta_rural ~ dnorm(0, 0.5)
beta_region ~ dnorm(0, 0.5)
beta_tamano_md ~ dnorm(0, 0.5)
beta_tamano_gd ~ dnorm(0, 0.5)
beta_tipo_ex ~ dnorm(0, 0.5)
beta_rural_tamano_md ~ dnorm(0, 0.5)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dunif(0, 10)
}
mu_estrato ~ dnorm(0, 0.5)
sigma_estrato ~ dunif(0, 1)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
tereom/quickcountmx documentation built on Dec. 2, 2019, 9:58 p.m.
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Defines functions mrp_party_estimation model_bern_t model_t model_bern_t_pp model_bern_t_nb model_norm model_norm_var
Documented in mrp_party_estimation
#' Postratified estimation for the number of votes for a given candidate.
#'
#' The function fits a model using the \code{\link[R2jags]{jags}} function and
#' predicts number of votes for a given candidate in unobserved polling
#' stations. Optionally the model can be fit with a stratified random sample
#' of the data, and predict for the population.
#' @param data A \code{data.frame} with variables: ln_total, region,
#' distrito_loc_17, tamano_md, tamano_gd, casilla_ex and the column with
#' number of votes for the party.
#' @param party Unquoted variable indicating the column from the data.frame to
#' be modeled.
#' @param stratum If sampling the data, unquoted variable indicating the column
#' from the data.frame to be used as strata. The strata will also be used in
#' the hierarchical structure of the model.
#' @param frac If sampling the data, numeric value indicating the fraction
#' of the data to sample, the sample is selected using stratified sampling
#' with probability proportional to size.
#' @param n_iter,n_burnin,n_chains Number of iterations, burnin size and chains.
#' to be used in \code{\link[R2jags]{jags}}.
#' @param seed Integer value used to set the state of the random number
#' generator.
#' @param seed_jags Seed for the call \code{\link[R2jags]{jags}}.
#' @param model_string String indicating the model to be used, if NULL defaults
#' to \code{"model_bern_t"}, also available \code{"model_t"}.
#' @return A \code{list} with the object fitted using R2jags::jags and the vector
#' of simulated counts per candidate.
#' @seealso \code{\link{mrp_estimation}}
#' @examples
#' # predict number of votes for pan_na using 6% of the sample
#' mrp_gto_pan <- mrp_party_estimation(gto_2012, pan_na,
#' stratum = distrito_loc_17, frac = 0.06, n_iter = 200, n_burnin = 100,
#' n_chains = 2, seed = 19291)
#' mrp_gto_pan$fit
#' @importFrom magrittr %>%
#' @importFrom rlang !! !!! :=
#' @export
mrp_party_estimation <- function(data, party, stratum, frac = 1,
n_iter = 1000, n_burnin = 500, n_chains = 2, seed = NA, seed_jags = NA,
model_string = NULL, set_strata_na = integer(0)){
if (is.null(model_string)) {
model_string <- "model_bern_t"
}
stratum_enquo <- dplyr::enquo(stratum)
party_enquo <- dplyr::enquo(party)
party_name <- dplyr::quo_name(party_enquo)
if (is.na(seed_jags)) seed_jags <- sample(1:1000, 1)
if (is.na(seed)) seed <- sample(1:1000, 1)
if (frac >= 1){
data_model <- data
} else {
data <- data %>%
dplyr::mutate(casilla_id = 1:n())
data_sample <- select_sample_prop(data, stratum = !!stratum_enquo,
frac = frac, seed = seed)
data_model <- data %>%
dplyr::mutate(!!party_name := ifelse(casilla_id %in%
data_sample$casilla_id, !!party_enquo, NA))
}
data_district <- dplyr::distinct(data, !!stratum_enquo, region) %>%
dplyr::arrange(!!stratum_enquo)
x <- dplyr::pull(data_model, !!party_enquo)
stratum_vec <- dplyr::pull(data_model, !!stratum_enquo)
if (length(set_strata_na) > 0) {
x[stratum_vec %in% set_strata_na] <- NA
}
data_jags <- list(N = nrow(data_model), n = data_model$ln_total,
n_regiones = dplyr::n_distinct(data_district$region),
n_strata = nrow(data_district),
x = x,
rural = data_model$rural,
estrato = dplyr::pull(data_model, !!stratum_enquo),
tamano_md = data_model$tamano_md,
tamano_gd = data_model$tamano_gd, tipo_ex = data_model$casilla_ex,
region = (data_model$region == 1) * 1, # región binaria covariable
region_mod = data_district$region) # region para modelar (jerárquica)
model_function <- get(model_string)
fit <- model_function(data_jags, n_chains, n_iter, n_burnin,
seed_jags)
return(fit)
}
model_bern_t <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p * region[k])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 780)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_t <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
x[k] ~ dt(n[k] * theta[k], tau[estrato[k]] / n[k], nu[estrato[k]]) T(-0.01, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.25)
beta_rural ~ dnorm(0, 0.25)
beta_region ~ dnorm(0, 0.25)
beta_tamano_md ~ dnorm(0, 0.25)
beta_tamano_gd ~ dnorm(0, 0.25)
beta_tipo_ex ~ dnorm(0, 0.25)
beta_rural_tamano_md ~ dnorm(0, 0.25)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dunif(0, 10)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.25)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex", "beta_estrato",
"beta_estrato_raw", "sigma", "sigma_estrato",
"beta_rural_tamano_md", "beta_region", "nu"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_bern_t_pp <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p * region[k])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
# posterior preditive
for(k in 1:N){
x_pred[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
}
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p", "x_pred"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
# region no binaria
model_bern_t_nb <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <- "
model{
for(k in 1:N){
b[k] ~ dbern(p[k])
p[k] <- ilogit(beta_0_p + beta_rural_p * rural[k] +
beta_rural_tamano_md_p * rural[k] * tamano_md[k] +
beta_estrato_raw_p[estrato[k]] +
beta_tamano_md_p * tamano_md[k] +
beta_tamano_gd_p * tamano_gd[k] +
beta_tipo_ex_p * tipo_ex[k] +
beta_region_p[region_mod[estrato[k]]])
mu_mix[k] <- n[k] * theta[k] * b[k]
tau_mix[k] <- b[k] * tau[estrato[k]] / n[k] + (1 - b[k]) * 10000
nu_mix[k] <- b[k] * nu[estrato[k]] + (1 - b[k]) * 100
x[k] ~ dt(mu_mix[k], tau_mix[k] , nu_mix[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] +
beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0_p ~ dnorm(0, 0.1)
beta_rural_p ~ dnorm(0, 0.1)
beta_region_p ~ dnorm(0, 0.1)
beta_tamano_md_p ~ dnorm(0, 0.1)
beta_rural_tamano_md_p ~ dnorm(0, 0.1)
beta_tamano_gd_p ~ dnorm(0, 0.1)
beta_tipo_ex_p ~ dnorm(0, 0.1)
beta_0 ~ dnorm(0, 0.1)
beta_rural ~ dnorm(0, 0.1)
beta_region ~ dnorm(0, 0.1)
beta_tamano_md ~ dnorm(0, 0.1)
beta_tamano_gd ~ dnorm(0, 0.1)
beta_tipo_ex ~ dnorm(0, 0.1)
beta_rural_tamano_md ~ dnorm(0, 0.1)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
beta_0_p_adj <- beta_0_p + mean(beta_estrato_raw_p[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
beta_estrato_p[j] <- beta_estrato_raw_p[j] -
mean(beta_estrato_raw_p[])
beta_estrato_raw_p[j] ~ dnorm(mu_estrato_p, tau_estrato_p)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dexp(1)
nu[j] ~ dgamma(2, 0.1)
}
mu_estrato ~ dnorm(0, 0.1)
sigma_estrato ~ dunif(0, 5)
tau_estrato <- pow(sigma_estrato, -2)
mu_estrato_p ~ dnorm(0, 0.1)
sigma_estrato_p ~ dunif(0, 5)
tau_estrato_p <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
data_jags[["b"]] <- (data_jags[["x"]] > 0) * 1
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region", "beta_0_p", "beta_rural_p", "nu",
"beta_tamano_md_p", "beta_tamano_gd_p", "beta_tipo_ex_p",
"beta_estrato_p", "beta_estrato_raw_p", "beta_0_p_adj",
"sigma_estrato_p",
"beta_region_p", "p"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_norm <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <-
"
model{
for(k in 1:N){
x[k] ~ dnorm(n[k] * theta[k], tau / n[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.5)
beta_rural ~ dnorm(0, 0.5)
beta_region ~ dnorm(0, 0.5)
beta_tamano_md ~ dnorm(0, 0.5)
beta_tamano_gd ~ dnorm(0, 0.5)
beta_tipo_ex ~ dnorm(0, 0.5)
beta_rural_tamano_md ~ dnorm(0, 0.5)
sigma ~ dunif(0, 1)
tau <- pow(sigma, -2)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
}
mu_estrato ~ dnorm(0, 0.5)
sigma_estrato ~ dunif(0, 1)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
model_norm_var <- function(data_jags, n_chains, n_iter, n_burnin, seed_jags){
model_string <-
"
model{
for(k in 1:N){
x[k] ~ dnorm(n[k] * theta[k], tau[estrato[k]] / n[k]) T(0, 750)
theta[k] <- ilogit(beta_0 + beta_rural * rural[k] +
beta_rural_tamano_md * rural[k] * tamano_md[k] +
beta_estrato_raw[estrato[k]] + beta_tamano_md * tamano_md[k] +
beta_tamano_gd * tamano_gd[k] + beta_tipo_ex * tipo_ex[k] +
beta_region * region[k])
}
beta_0 ~ dnorm(0, 0.5)
beta_rural ~ dnorm(0, 0.5)
beta_region ~ dnorm(0, 0.5)
beta_tamano_md ~ dnorm(0, 0.5)
beta_tamano_gd ~ dnorm(0, 0.5)
beta_tipo_ex ~ dnorm(0, 0.5)
beta_rural_tamano_md ~ dnorm(0, 0.5)
beta_0_adj <- beta_0 + mean(beta_estrato_raw[])
for(j in 1:n_strata){
beta_estrato[j] <- beta_estrato_raw[j] - mean(beta_estrato_raw[])
beta_estrato_raw[j] ~ dnorm(mu_estrato, tau_estrato)
tau[j] <- pow(sigma[j], -2)
sigma[j] ~ dunif(0, 10)
}
mu_estrato ~ dnorm(0, 0.5)
sigma_estrato ~ dunif(0, 1)
tau_estrato <- pow(sigma_estrato, -2)
}
"
temp_file <- tempfile(pattern = "model_string", fileext = ".txt")
cat(model_string, file = temp_file)
data_jags[["n_regiones"]] <- NULL
data_jags[["region_mod"]] <- NULL
fit_jags <- R2jags::jags(
# inits = jags_inits,
data = data_jags,
parameters.to.save = c("x", "beta_0", "beta_0_adj", "beta_rural",
"beta_tamano_md", "beta_tamano_gd", "beta_tipo_ex",
"beta_estrato", "beta_estrato_raw",
"sigma", "sigma_estrato", "beta_rural_tamano_md",
"beta_region"),
model.file = temp_file,
n.chains = n_chains,
n.iter = n_iter,
n.burnin = n_burnin,
jags.seed = seed_jags
)
n_votes <- apply(fit_jags$BUGSoutput$sims.list$x, 1, sum)
return(list(fit = fit_jags, n_votes = n_votes))
}
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