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The following code reproduces our analysis of data from Namibia as described in our paper.
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")))
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.