# Running a subnational level multi-country model

## 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)