## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center"
)

## ----namibia, eval=FALSE, include=TRUE----------------------------------------
# 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")))
# }