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https://github.com/msberends/AMR.git
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(v2.1.1.9260) fix antibiogram
This commit is contained in:
139
R/antibiogram.R
139
R/antibiogram.R
@ -682,9 +682,8 @@ antibiogram.default <- function(x,
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wisca_parameters <- data.frame()
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# WISCA START
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if (wisca == TRUE) {
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# WISCA ----
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if (isTRUE(has_syndromic_group)) {
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colnames(out)[1] <- "syndromic_group"
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out_wisca <- out %pm>%
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@ -708,9 +707,6 @@ antibiogram.default <- function(x,
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warning_("Number of tested isolates should exceed ", minimum, " for each regimen (and group). WISCA coverage estimates might be inaccurate.", call = FALSE)
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}
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out_wisca$p_susceptible <- out_wisca$n_susceptible / out_wisca$n_tested
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out_wisca$p_susceptible[is.nan(out_wisca$p_susceptible)] <- 0
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if (isTRUE(has_syndromic_group)) {
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out$group <- paste(out$syndromic_group, out$ab)
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out_wisca$group <- paste(out_wisca$syndromic_group, out_wisca$ab)
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@ -719,31 +715,6 @@ antibiogram.default <- function(x,
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out_wisca$group <- out_wisca$ab
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}
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# create the WISCA parameters, including our priors/posteriors
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out$gamma_posterior <- NA_real_
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out$beta_posterior_1 <- NA_real_
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out$beta_posterior_2 <- NA_real_
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for (i in seq_len(NROW(out))) {
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out_current <- out[i, , drop = FALSE]
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## calculate priors ----
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# pathogen incidence (Dirichlet distribution)
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gamma_prior <- rep(1, length(unique(out_current$mo))) # Dirichlet prior
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gamma_posterior <- gamma_prior + out_current$n_total # Posterior parameters
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# regimen susceptibility (Beta distribution)
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beta_prior <- rep(1, length(unique(out_current$mo))) # Beta prior
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r <- out_current$n_susceptible
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n <- out_current$n_tested
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beta_posterior_1 <- beta_prior + r # Posterior alpha
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beta_posterior_2 <- beta_prior + (n - r) # Posterior beta
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out$gamma_posterior[i] <- gamma_posterior
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out$beta_posterior_1[i] <- beta_posterior_1
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out$beta_posterior_2[i] <- beta_posterior_2
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}
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wisca_parameters <- out
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progress <- progress_ticker(
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@ -754,42 +725,28 @@ antibiogram.default <- function(x,
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)
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on.exit(close(progress))
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# run WISCA
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# run WISCA per group
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for (group in unique(wisca_parameters$group)) {
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params_current <- wisca_parameters[which(wisca_parameters$group == group), , drop = FALSE]
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params_current <- wisca_parameters[wisca_parameters$group == group, , drop = FALSE]
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if (sum(params_current$n_tested, na.rm = TRUE) == 0) {
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next
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}
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# Monte Carlo simulation
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coverage_simulations <- replicate(simulations, {
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progress$tick()
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# prepare priors
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priors_current <- create_wisca_priors(params_current)
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# simulate pathogen incidence
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# = Dirichlet (Gamma) parameters
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random_incidence <- stats::runif(n = 1, min = 0, max = 1)
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simulated_incidence <- stats::qgamma(
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p = random_incidence,
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shape = params_current$gamma_posterior,
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scale = 1
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)
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# Monte Carlo simulations
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coverage_simulations <- vapply(
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FUN.VALUE = double(1),
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seq_len(simulations), function(i) {
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progress$tick()
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simulate_coverage(priors_current)
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}
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)
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# normalise
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simulated_incidence <- simulated_incidence / sum(simulated_incidence, na.rm = TRUE)
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# simulate susceptibility
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# = Beta parameters
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random_susceptibity <- stats::runif(n = 1, min = 0, max = 1)
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simulated_susceptibility <- stats::qbeta(
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p = random_susceptibity,
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shape1 = params_current$beta_posterior_1,
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shape2 = params_current$beta_posterior_2
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)
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sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
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})
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# calculate coverage statistics
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# summarise results
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coverage_mean <- mean(coverage_simulations)
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if (interval_side == "two-tailed") {
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probs <- c((1 - conf_interval) / 2, 1 - (1 - conf_interval) / 2)
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} else if (interval_side == "left") {
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@ -797,17 +754,20 @@ antibiogram.default <- function(x,
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} else if (interval_side == "right") {
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probs <- c(1 - conf_interval, 1)
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}
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coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
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out_wisca$coverage[which(out_wisca$group == group)] <- coverage_mean
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out_wisca$lower_ci[which(out_wisca$group == group)] <- coverage_ci[1]
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out_wisca$upper_ci[which(out_wisca$group == group)] <- coverage_ci[2]
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out_wisca$coverage[out_wisca$group == group] <- coverage_mean
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out_wisca$lower_ci[out_wisca$group == group] <- coverage_ci[1]
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out_wisca$upper_ci[out_wisca$group == group] <- coverage_ci[2]
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}
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# remove progress bar from console
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close(progress)
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# prepare for definitive output
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# final output preparation
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out <- out_wisca
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wisca_parameters <- wisca_parameters[, colnames(wisca_parameters)[!colnames(wisca_parameters) %in% c(levels(NA_sir_), "lower_ci", "upper_ci", "group")], drop = FALSE]
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if (isTRUE(has_syndromic_group)) {
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long_numeric <- out_wisca %pm>%
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pm_ungroup() %pm>%
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@ -1346,3 +1306,56 @@ knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.ka
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out <- paste(c("", "", knitr::kable(x, ..., output = FALSE)), collapse = "\n")
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knitr::asis_output(out)
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}
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create_wisca_priors <- function(data) {
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pathogens <- unique(data$mo)
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n_pathogens <- length(pathogens)
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# Dirichlet prior (gamma parameters)
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gamma_prior <- rep(1, times = n_pathogens)
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multinomial_obs <- data$n_total
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gamma_posterior <- gamma_prior + multinomial_obs
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# beta priors
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beta_prior_alpha <- rep(1, times = n_pathogens)
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beta_prior_beta <- rep(1, times = n_pathogens)
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r <- data$n_susceptible
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n <- data$n_tested
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diff_nr <- n - r
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beta_posterior_1 <- beta_prior_alpha + r
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beta_posterior_2 <- beta_prior_beta + diff_nr
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list(
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gamma_posterior = gamma_posterior,
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beta_posterior_1 = beta_posterior_1,
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beta_posterior_2 = beta_posterior_2
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)
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}
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simulate_coverage <- function(params) {
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n_pathogens <- length(params$gamma_posterior)
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# random draws per pathogen
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random_incidence <- runif(n = n_pathogens)
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random_susceptibility <- runif(n = n_pathogens)
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simulated_incidence <- stats::qgamma(
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p = random_incidence,
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shape = params$gamma_posterior,
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scale = 1
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)
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# normalise incidence
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simulated_incidence <- simulated_incidence / sum(simulated_incidence, na.rm = TRUE)
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simulated_susceptibility <- stats::qbeta(
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p = random_susceptibility,
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shape1 = params$beta_posterior_1,
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shape2 = params$beta_posterior_2
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)
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# weighted coverage
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sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
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}
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