# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # # Developed at the University of Groningen, the Netherlands, in # # collaboration with non-profit organisations Certe Medical # # Diagnostics & Advice, and University Medical Center Groningen. # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # # We created this package for both routine data analysis and academic # # research and it was publicly released in the hope that it will be # # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. # # # # Visit our website for the full manual and a complete tutorial about # # how to conduct AMR analysis: https://msberends.github.io/AMR/ # # ==================================================================== # #' Random MIC values/disk zones/RSI generation #' #' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR analysis practice. #' @inheritSection lifecycle Maturing lifecycle #' @param size desired size of the returned vector #' @param mo any character that can be coerced to a valid microorganism code with [as.mo()] #' @param ab any character that can be coerced to a valid antimicrobial agent code with [as.ab()] #' @param prob_RSI a vector of length 3: the probabilities for R (1st value), S (2nd value) and I (3rd value) #' @param ... extension for future versions, not used at the moment #' @details The base R function [sample()] is used for generating values. #' #' Generated values are based on the latest EUCAST guideline implemented in the [rsi_translation] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` parameter. #' @return class `` for [random_mic()] (see [as.mic()]) and class `` for [random_disk()] (see [as.disk()]) #' @name random #' @rdname random #' @export #' @inheritSection AMR Read more on our website! #' @examples #' random_mic(100) #' random_disk(100) #' random_rsi(100) #' #' \donttest{ #' # make the random generation more realistic by setting a bug and/or drug: #' random_mic(100, "Klebsiella pneumoniae") # range 0.0625-64 #' random_mic(100, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16 #' random_mic(100, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4 #' #' random_disk(100, "Klebsiella pneumoniae") # range 11-50 #' random_disk(100, "Klebsiella pneumoniae", "ampicillin") # range 6-14 #' random_disk(100, "Streptococcus pneumoniae", "ampicillin") # range 16-22 #' } random_mic <- function(size, mo = NULL, ab = NULL, ...) { random_exec("MIC", size = size, mo = mo, ab = ab) } #' @rdname random #' @export random_disk <- function(size, mo = NULL, ab = NULL, ...) { random_exec("DISK", size = size, mo = mo, ab = ab) } #' @rdname random #' @export random_rsi <- function(size, prob_RSI = c(0.33, 0.33, 0.33), ...) { sample(as.rsi(c("R", "S", "I")), size = size, replace = TRUE, prob = prob_RSI) } random_exec <- function(type, size, mo = NULL, ab = NULL) { df <- rsi_translation %pm>% pm_filter(guideline %like% "EUCAST") %pm>% pm_arrange(pm_desc(guideline)) %pm>% subset(guideline == max(guideline) & method == type) if (!is.null(mo)) { mo_coerced <- as.mo(mo) mo_include <- c(mo_coerced, as.mo(mo_genus(mo_coerced)), as.mo(mo_family(mo_coerced)), as.mo(mo_order(mo_coerced))) df_new <- df %pm>% subset(mo %in% mo_include) if (nrow(df_new) > 0) { df <- df_new } else { warning_("No rows found that match mo '", mo, "', ignoring parameter `mo`", call = FALSE) } } if (!is.null(ab)) { ab_coerced <- as.ab(ab) df_new <- df %pm>% subset(ab %in% ab_coerced) if (nrow(df_new) > 0) { df <- df_new } else { warning_("No rows found that match ab '", ab, "', ignoring parameter `ab`", call = FALSE) } } if (type == "MIC") { # all valid MIC levels valid_range <- as.mic(levels(as.mic(1))) set_range_max <- max(df$breakpoint_R) if (log(set_range_max, 2) %% 1 == 0) { # return powers of 2 valid_range <- unique(as.double(valid_range)) # add one higher MIC level to set_range_max set_range_max <- 2 ^ (log(set_range_max, 2) + 1) set_range <- as.mic(valid_range[log(valid_range, 2) %% 1 == 0 & valid_range <= set_range_max]) } else { # no power of 2, return factors of 2 to left and right side valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3)))) set_range <- valid_mics[!is.na(valid_mics)] } return(as.mic(sample(set_range, size = size, replace = TRUE))) } else if (type == "DISK") { set_range <- seq(from = as.integer(min(df$breakpoint_R)), to = as.integer(max(df$breakpoint_S)), by = 1) out <- sample(set_range, size = size, replace = TRUE) out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE) out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE) return(as.disk(out)) } }