# Running a subnational level single-country model

## Load your library

    library(mcmsupply)
    set.seed(1209)

## Load the data

    cleaned_data <- get_data(national=FALSE, local=TRUE,
                             mycountry="Nepal")

## Get the JAGS model inputs and the cleaned data

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

## Run JAGS model and get posterior point estimates with uncertainty

For speed and illustration purposes, we will use 10 iterations, with no burn in period and taking every third sample. This leaves only 9 samples. We DO NOT recommend this setting. The recommended settings are 40000 iterations, with 10000 burn in period and taking every 15th sample. This is commented out and listed underneath the below R code.

    mod <- run_jags_model(jagsdata = pkg_data, jagsparams = NULL,
                           n_iter = 10, n_burnin = 1, n_thin = 3)
                          # n_iter = 40000, n_burnin = 10000, n_thin = 15)

## 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.

    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 = 1, # rep(1:mod$JAGS$BUGSoutput$n.chains, each=mod$JAGS$BUGSoutput$n.sims)),
                    iteration = 3) # rep(1:mod$JAGS$BUGSoutput$n.sims, mod$JAGS$BUGSoutput$n.chains))

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

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.

    head(mod$JAGS$BUGSoutput$summary)

## Plot posterior point estimates with uncertainty

    plots <- plot_estimates(jagsdata = pkg_data, model_output = mod)
    plots[[1]]

## Pull out estimates that you are particularly interested in

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

    head(estimates_2018)