Load your library

library(mcmsupply)
set.seed(1209)

Get your data

cleaned_subnatdata <- get_data(national=FALSE)

Get the JAGS model inputs from the cleaned data

pkg_data <- get_modelinputs(startyear=1990, endyear=2025.5, nsegments=12, 
                            raw_data = cleaned_subnatdata)

Run JAGS model and get posterior point estimates with uncertainty.

mod <- run_jags_model(jagsdata = pkg_data, jagsparams = NULL,
                      n_iter = 80000, n_burnin = 10000, n_thin = 35)

Check the model diagnostics

We use this to evaluate the convergence of the model parameters. We should expect to see R-hat values of approximately 1.05. The plot function will give you a visual summary for each parameter monitored.

plot(mod$JAGS)

print(mod$JAGS)

Using the ggplot2 and tidybayes R packages, we will check the trace plots to assess the convergence of individual parameters. We expect to see a ‘caterpillar’ like appearance of the chains over the iterations.

sample_draws <- tidybayes::tidy_draws(mod$JAGS$BUGSoutput$sims.matrix)

var <- sample_draws %>% dplyr::select(.chain, .iteration, .draw,`P[1,2,1,1]`) %>%
  dplyr::mutate(chain = rep(1:2, each=mod$JAGS$BUGSoutput$n.keep)) %>%
  dplyr::mutate(iteration = rep(1:mod$JAGS$BUGSoutput$n.keep, 2))

ggplot2::ggplot(data=var) +
  ggplot2::geom_line(ggplot2::aes(x=iteration, y=`P[1,2,1,1]`, color=as.factor(chain)))

Plot posterior point estimates with uncertainty

plots <- plot_estimates(jagsdata = pkg_data, model_output = mod)

Pull out estimates that you are particularly interested in

estimates_2018 <- pull_estimates(model_output = mod, country = 'Nepal', year=2018)

head(estimates_2018)