mirror of
https://github.com/msberends/AMR.git
synced 2025-07-09 13:42:04 +02:00
(v2.1.1.9260) fix antibiogram
This commit is contained in:
@ -32,11 +32,11 @@
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#' @description
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#' Welcome to the `AMR` package.
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#'
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#' The `AMR` package is a peer-reviewed, [free and open-source](https://amr-for-r.org/#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. [Many different researchers](https://amr-for-r.org/authors.html) from around the globe are continually helping us to make this a successful and durable project!
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#' The `AMR` package is a peer-reviewed, [free and open-source](https://amr-for-r.org/#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](https://amr-for-r.org/authors.html) from around the globe to make this a successful and durable project!
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#'
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#' This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
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#'
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#' After installing this package, R knows [**`r format_included_data_number(AMR::microorganisms)` microorganisms**](https://amr-for-r.org/reference/microorganisms.html) (updated `r format(TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r format_included_data_number(nrow(AMR::antimicrobials) + nrow(AMR::antivirals))` antibiotic, antimycotic and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the public [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
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#' After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated June 2024) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
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#'
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#' The `AMR` package is available in `r vector_and(vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
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#' @section Download Our Reference Data:
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@ -44,7 +44,7 @@
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#'
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#' For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.
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#'
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#' Visit [our website for direct download links](https://amr-for-r.org/articles/datasets.html), or explore the actual files in [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
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#' Visit [our website for direct download links](https://amr-for-r.org/articles/datasets.html), or explore the actual files in [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw/datasets).
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#' @source
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#' To cite AMR in publications use:
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#'
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@ -538,9 +538,9 @@ word_wrap <- function(...,
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txt = parts[parts %in% c("antimicrobials", "microorganisms", "microorganisms.codes", "microorganisms.groups")]
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)
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# text starting with `?` must also lead to the help page
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parts[parts %like% "^[?]"] <- font_url(
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url = paste0("ide:help:AMR::", gsub("?", "", parts[parts %like% "^[?]"], fixed = TRUE)),
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txt = parts[parts %like% "^[?]"]
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parts[parts %like% "^[?].+"] <- font_url(
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url = paste0("ide:help:AMR::", gsub("?", "", parts[parts %like% "^[?].+"], fixed = TRUE)),
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txt = parts[parts %like% "^[?].+"]
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)
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msg <- paste0(parts, collapse = "`")
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}
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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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54
R/sir.R
54
R/sir.R
@ -729,7 +729,7 @@ as.sir.data.frame <- function(x,
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# -- MO
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col_mo.bak <- col_mo
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo", info = FALSE)
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col_mo <- search_type_in_df(x = x, type = "mo", info = info)
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}
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# -- host
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@ -742,7 +742,7 @@ as.sir.data.frame <- function(x,
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}
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if (breakpoint_type == "animal") {
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if (is.null(host)) {
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host <- search_type_in_df(x = x, type = "host", add_col_prefix = FALSE)
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host <- search_type_in_df(x = x, type = "host", add_col_prefix = FALSE, info = info)
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} else if (length(host) == 1 && as.character(host) %in% colnames(x)) {
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host <- x[[as.character(host)]]
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}
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@ -753,7 +753,7 @@ as.sir.data.frame <- function(x,
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# -- UTIs
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col_uti <- uti
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if (is.null(col_uti)) {
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col_uti <- search_type_in_df(x = x, type = "uti", add_col_prefix = FALSE)
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col_uti <- search_type_in_df(x = x, type = "uti", add_col_prefix = FALSE, info = info)
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}
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if (!is.null(col_uti)) {
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if (is.logical(col_uti)) {
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@ -773,7 +773,7 @@ as.sir.data.frame <- function(x,
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}
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} else {
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# col_uti is still NULL - look for specimen column and make logicals of the urines
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col_specimen <- suppressMessages(search_type_in_df(x = x, type = "specimen"))
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col_specimen <- suppressMessages(search_type_in_df(x = x, type = "specimen", info = info))
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if (!is.null(col_specimen)) {
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uti <- x[, col_specimen, drop = TRUE] %like% "urin"
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values <- sort(unique(x[uti, col_specimen, drop = TRUE]))
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@ -846,7 +846,7 @@ as.sir.data.frame <- function(x,
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stop_if(is.null(col_mo), "`col_mo` must be set")
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# if not null, we already found it, now find again so a message will show
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if (is.null(col_mo.bak)) {
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col_mo <- search_type_in_df(x = x, type = "mo")
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col_mo <- search_type_in_df(x = x, type = "mo", info = info)
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}
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x_mo <- as.mo(x[, col_mo, drop = TRUE], info = info)
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}
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@ -854,10 +854,17 @@ as.sir.data.frame <- function(x,
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# set up parallel computing
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n_cores <- get_n_cores(max_cores = max_cores)
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n_cores <- min(n_cores, length(ab_cols)) # never more cores than variables required
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if (isTRUE(parallel) && .Platform$OS.type != "windows" && getRversion() < "4.0.0") {
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n_cores <- 1
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if (isTRUE(info)) {
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warning("Parallel computing is not available on unix in R < 4.0", call. = FALSE)
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if (isTRUE(parallel) && (.Platform$OS.type == "windows" || getRversion() < "4.0.0")) {
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cl <- tryCatch(parallel::makeCluster(n_cores, type = "PSOCK"),
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error = function(e) {
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if (isTRUE(info)) {
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message_("Could not create parallel cluster, using single-core computation. Error message: ", e$message, add_fn = font_red)
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}
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return(NULL)
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}
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)
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if (is.null(cl)) {
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n_cores <- 1
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}
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}
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@ -959,10 +966,10 @@ as.sir.data.frame <- function(x,
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if (isTRUE(parallel) && n_cores > 1 && length(ab_cols) > 1) {
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if (isTRUE(info)) {
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message()
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message_("Running in parallel mode using ", n_cores, " out of ", get_n_cores(Inf), " cores, on columns ", vector_and(font_bold(ab_cols, collapse = NULL), quotes = "'", sort = FALSE), "...", as_note = FALSE, appendLF = FALSE, add_fn = font_red)
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message_("Running in parallel mode using ", n_cores, " out of ", get_n_cores(Inf), " cores, on columns ", vector_and(font_bold(ab_cols, collapse = NULL), quotes = "'", sort = FALSE), "...", as_note = FALSE, appendLF = FALSE)
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}
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if (.Platform$OS.type == "windows" || getRversion() < "4.0.0") {
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cl <- parallel::makeCluster(n_cores, type = "PSOCK")
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# `cl` has been created in the part above before the `run_as_sir_column` function
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on.exit(parallel::stopCluster(cl), add = TRUE)
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parallel::clusterExport(cl, varlist = c(
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"x", "x.bak", "x_mo", "ab_cols", "types",
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@ -974,12 +981,13 @@ as.sir.data.frame <- function(x,
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), envir = environment())
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result_list <- parallel::parLapply(cl, seq_along(ab_cols), run_as_sir_column)
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} else {
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# R>=4.0 on unix
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result_list <- parallel::mclapply(seq_along(ab_cols), run_as_sir_column, mc.cores = n_cores)
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}
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if (isTRUE(info)) {
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message_(" Done.", appendLF = TRUE, as_note = FALSE, add_fn = font_red)
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message_(font_green_bg(" DONE "), as_note = FALSE)
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message()
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message_("Run `sir_interpretation_history()` to retrieve a logbook with all the details of the breakpoint interpretations.", add_fn = font_green)
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message_("Run `sir_interpretation_history()` to retrieve a logbook with all details of the breakpoint interpretations.", add_fn = font_green)
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}
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} else {
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# sequential mode (non-parallel)
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@ -1116,7 +1124,7 @@ as_sir_method <- function(method_short,
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if (isTRUE(info) && message_not_thrown_before("as.sir", "sir_interpretation_history")) {
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message()
|
||||
message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all the details of the breakpoint interpretations.\n\n", add_fn = font_green)
|
||||
message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all details of the breakpoint interpretations.\n\n", add_fn = font_green)
|
||||
}
|
||||
|
||||
current_df <- tryCatch(get_current_data(NA, 0), error = function(e) NULL)
|
||||
@ -1200,7 +1208,7 @@ as_sir_method <- function(method_short,
|
||||
mo <- NULL
|
||||
try(
|
||||
{
|
||||
mo <- suppressMessages(search_type_in_df(df, "mo", add_col_prefix = FALSE))
|
||||
mo <- suppressMessages(search_type_in_df(df, "mo", add_col_prefix = FALSE, info = info))
|
||||
},
|
||||
silent = TRUE
|
||||
)
|
||||
@ -1236,7 +1244,7 @@ as_sir_method <- function(method_short,
|
||||
uti <- NULL
|
||||
try(
|
||||
{
|
||||
uti <- suppressMessages(search_type_in_df(df, "uti", add_col_prefix = FALSE))
|
||||
uti <- suppressMessages(search_type_in_df(df, "uti", add_col_prefix = FALSE, info = info))
|
||||
},
|
||||
silent = TRUE
|
||||
)
|
||||
@ -1441,14 +1449,7 @@ as_sir_method <- function(method_short,
|
||||
if (nrow(breakpoints) == 0) {
|
||||
# apparently no breakpoints found
|
||||
if (isTRUE(info)) {
|
||||
message(
|
||||
paste0(font_rose_bg(" WARNING "), "\n"),
|
||||
font_black(paste0(
|
||||
" ", AMR_env$bullet_icon, " No ", method_coerced, " breakpoints available for ",
|
||||
suppressMessages(suppressWarnings(ab_name(unique(ab_coerced), language = NULL, tolower = TRUE, info = info))),
|
||||
" (", unique(ab_coerced), ")."
|
||||
), collapse = "\n")
|
||||
)
|
||||
message(font_grey_bg(font_black(" NO BREAKPOINTS ")))
|
||||
}
|
||||
|
||||
load_mo_uncertainties(metadata_mo)
|
||||
@ -1829,12 +1830,13 @@ as_sir_method <- function(method_short,
|
||||
message(font_yellow_bg(" NOTE "))
|
||||
}
|
||||
notes <- unique(notes)
|
||||
if (isTRUE(verbose) || length(notes) == 1 || NROW(AMR_env$sir_interpretation_history) == 0) {
|
||||
# if (isTRUE(verbose) || length(notes) == 1 || NROW(AMR_env$sir_interpretation_history) == 0) {
|
||||
if (isTRUE(verbose)) {
|
||||
for (i in seq_along(notes)) {
|
||||
message(word_wrap(" ", AMR_env$bullet_icon, " ", notes[i], add_fn = font_black))
|
||||
}
|
||||
} else {
|
||||
message(word_wrap(" ", AMR_env$bullet_icon, " There were multiple notes. Print or View `sir_interpretation_history()` to examine them, or use `as.sir(..., verbose = TRUE)` next time to directly print them here.", add_fn = font_black))
|
||||
# message(word_wrap(" ", AMR_env$bullet_icon, " There were multiple notes. Print or View `sir_interpretation_history()` to examine them, or use `as.sir(..., verbose = TRUE)` next time to directly print them here.", add_fn = font_black))
|
||||
}
|
||||
} else {
|
||||
message(font_green_bg(" OK "))
|
||||
|
||||
Reference in New Issue
Block a user