Application to Namibia

full_dat <- moire::namibia_data

epi_dat <- full_dat |>
  dplyr::select(sample_id, HealthFacility, HealthDistrict, Region, Country) |>
  dplyr::distinct()

all_hfs <- epi_dat |>
  dplyr::pull(HealthFacility) |>
  unique()

verbose <- FALSE
allow_relatedness <- TRUE
burnin <- 5e3
num_samples <- 1e4
r_alpha <- 1
r_beta <- 1
eps_pos_alpha <- 1
eps_pos_beta <- 1
eps_neg_alpha <- 1
eps_neg_beta <- 1
num_threads <- parallelly::availableCores() - 1

for (hf in all_hfs) {
  hf_dat <- full_dat |>
    dplyr::filter(HealthFacility == hf) |>
    dplyr::select(sample_id, locus, allele) |>
    moire::load_long_form_data()

  hf_res <- moire::run_mcmc(
    hf_dat, hf_dat$is_missing,
    allow_relatedness = allow_relatedness,
    burnin = burnin, samples_per_chain = num_samples,
    pt_chains = 40, pt_num_threads = num_threads, thin = 10,
    verbose = verbose, adapt_temp = TRUE, r_alpha = r_alpha, r_beta = r_beta,
    eps_pos_alpha = eps_pos_alpha, eps_pos_beta = eps_pos_beta,
    eps_neg_alpha = eps_neg_alpha, eps_neg_beta = eps_neg_beta
  )

  # create an output directory
  dir.create("mcmc_output", showWarnings = FALSE)

  # format path name
  hf <- gsub(" ", "_", hf)

  # save the results
  saveRDS(hf_res, file.path("mcmc_output", paste0(hf, ".rds")))
}