# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # # # # 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 more info: https://msberends.github.io/AMR. # # ==================================================================== # #' Predict antimicrobial resistance #' #' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min` and `se_max`. See *Examples* for a real live example. #' @inheritSection lifecycle Maturing lifecycle #' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`) #' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class #' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date` #' @param year_max highest year to use in the prediction model, defaults to 10 years after today #' @param year_every unit of sequence between lowest year found in the data and `year_max` #' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model. #' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options. #' @param I_as_S a logical to indicate whether values `I` should be treated as `S` (will otherwise be treated as `R`). The default, `TRUE`, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section *Interpretation of S, I and R* below. #' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be `NA`. #' @param info a logical to indicate whether textual analysis should be printed with the name and [summary()] of the statistical model. #' @param main title of the plot #' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars #' @param ... parameters passed on to functions #' @inheritSection as.rsi Interpretation of R and S/I #' @inheritParams first_isolate #' @inheritParams graphics::plot #' @details Valid options for the statistical model (parameter `model`) are: #' - `"binomial"` or `"binom"` or `"logit"`: a generalised linear regression model with binomial distribution #' - `"loglin"` or `"poisson"`: a generalised log-linear regression model with poisson distribution #' - `"lin"` or `"linear"`: a linear regression model #' @return A [data.frame] with extra class [`resistance_predict`] with columns: #' - `year` #' - `value`, the same as `estimated` when `preserve_measurements = FALSE`, and a combination of `observed` and `estimated` otherwise #' - `se_min`, the lower bound of the standard error with a minimum of `0` (so the standard error will never go below 0%) #' - `se_max` the upper bound of the standard error with a maximum of `1` (so the standard error will never go above 100%) #' - `observations`, the total number of available observations in that year, i.e. \eqn{S + I + R} #' - `observed`, the original observed resistant percentages #' - `estimated`, the estimated resistant percentages, calculated by the model #' #' Furthermore, the model itself is available as an attribute: `attributes(x)$model`, please see *Examples*. #' @seealso The [proportion()] functions to calculate resistance #' #' Models: [lm()] [glm()] #' @rdname resistance_predict #' @export #' @importFrom stats predict glm lm #' @inheritSection AMR Read more on our website! #' @examples #' x <- resistance_predict(example_isolates, #' col_ab = "AMX", #' year_min = 2010, #' model = "binomial") #' plot(x) #' if (require("ggplot2")) { #' ggplot_rsi_predict(x) #' } #' #' # using dplyr: #' if (require("dplyr")) { #' x <- example_isolates %>% #' filter_first_isolate() %>% #' filter(mo_genus(mo) == "Staphylococcus") %>% #' resistance_predict("PEN", model = "binomial") #' plot(x) #' #' # get the model from the object #' mymodel <- attributes(x)$model #' summary(mymodel) #' } #' #' # create nice plots with ggplot2 yourself #' \dontrun{ #' library(dplyr) #' library(ggplot2) #' #' data <- example_isolates %>% #' filter(mo == as.mo("E. coli")) %>% #' resistance_predict(col_ab = "AMX", #' col_date = "date", #' model = "binomial", #' info = FALSE, #' minimum = 15) #' #' ggplot(data, #' aes(x = year)) + #' geom_col(aes(y = value), #' fill = "grey75") + #' geom_errorbar(aes(ymin = se_min, #' ymax = se_max), #' colour = "grey50") + #' scale_y_continuous(limits = c(0, 1), #' breaks = seq(0, 1, 0.1), #' labels = paste0(seq(0, 100, 10), "%")) + #' labs(title = expression(paste("Forecast of Amoxicillin Resistance in ", #' italic("E. coli"))), #' y = "%R", #' x = "Year") + #' theme_minimal(base_size = 13) #' } resistance_predict <- function(x, col_ab, col_date = NULL, year_min = NULL, year_max = NULL, year_every = 1, minimum = 30, model = NULL, I_as_S = TRUE, preserve_measurements = TRUE, info = interactive(), ...) { stop_ifnot(is.data.frame(x), "`x` must be a data.frame") stop_if(any(dim(x) == 0), "`x` must contain rows and columns") stop_if(is.null(model), 'choose a regression model with the `model` parameter, e.g. resistance_predict(..., model = "binomial")') stop_ifnot(col_ab %in% colnames(x), "column `", col_ab, "` not found") dots <- unlist(list(...)) if (length(dots) != 0) { # backwards compatibility with old parameters dots.names <- dots %pm>% names() if ("tbl" %in% dots.names) { x <- dots[which(dots.names == "tbl")] } if ("I_as_R" %in% dots.names) { warning("`I_as_R is deprecated - use I_as_S instead.", call. = FALSE) } } # -- date if (is.null(col_date)) { col_date <- search_type_in_df(x = x, type = "date") stop_if(is.null(col_date), "`col_date` must be set") } stop_ifnot(col_date %in% colnames(x), "column `", col_date, "` not found") # no grouped tibbles x <- as.data.frame(x, stringsAsFactors = FALSE) year <- function(x) { # don't depend on lubridate or so, would be overkill for only this function if (all(grepl("^[0-9]{4}$", x))) { as.integer(x) } else { as.integer(format(as.Date(x), "%Y")) } } df <- x df[, col_ab] <- droplevels(as.rsi(df[, col_ab, drop = TRUE])) if (I_as_S == TRUE) { # then I as S df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE]) } else { # then I as R df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE]) } df[, col_ab] <- ifelse(is.na(df[, col_ab, drop = TRUE]), 0, df[, col_ab, drop = TRUE]) # remove rows with NAs df <- subset(df, !is.na(df[, col_ab, drop = TRUE])) df$year <- year(df[, col_date, drop = TRUE]) df <- as.data.frame(rbind(table(df[, c("year", col_ab)])), stringsAsFactors = FALSE) df$year <- as.integer(rownames(df)) rownames(df) <- NULL df <- subset(df, sum(df$R + df$S, na.rm = TRUE) >= minimum) df_matrix <- as.matrix(df[, c("R", "S"), drop = FALSE]) stop_if(NROW(df) == 0, "there are no observations") year_lowest <- min(df$year) if (is.null(year_min)) { year_min <- year_lowest } else { year_min <- max(year_min, year_lowest, na.rm = TRUE) } if (is.null(year_max)) { year_max <- year(Sys.Date()) + 10 } years <- list(year = seq(from = year_min, to = year_max, by = year_every)) if (model %in% c("binomial", "binom", "logit")) { model <- "binomial" model_lm <- with(df, glm(df_matrix ~ year, family = binomial)) if (info == TRUE) { cat("\nLogistic regression model (logit) with binomial distribution") cat("\n------------------------------------------------------------\n") print(summary(model_lm)) } predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE) prediction <- predictmodel$fit se <- predictmodel$se.fit } else if (model %in% c("loglin", "poisson")) { model <- "poisson" model_lm <- with(df, glm(R ~ year, family = poisson)) if (info == TRUE) { cat("\nLog-linear regression model (loglin) with poisson distribution") cat("\n--------------------------------------------------------------\n") print(summary(model_lm)) } predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE) prediction <- predictmodel$fit se <- predictmodel$se.fit } else if (model %in% c("lin", "linear")) { model <- "linear" model_lm <- with(df, lm((R / (R + S)) ~ year)) if (info == TRUE) { cat("\nLinear regression model") cat("\n-----------------------\n") print(summary(model_lm)) } predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE) prediction <- predictmodel$fit se <- predictmodel$se.fit } else { stop("no valid model selected. See ?resistance_predict.") } # prepare the output dataframe df_prediction <- data.frame(year = unlist(years), value = prediction, se_min = prediction - se, se_max = prediction + se, stringsAsFactors = FALSE) if (model == "poisson") { df_prediction$value <- as.integer(format(df_prediction$value, scientific = FALSE)) df_prediction$se_min <- as.integer(df_prediction$se_min) df_prediction$se_max <- as.integer(df_prediction$se_max) } else { # se_max not above 1 df_prediction$se_max <- ifelse(df_prediction$se_max > 1, 1, df_prediction$se_max) } # se_min not below 0 df_prediction$se_min <- ifelse(df_prediction$se_min < 0, 0, df_prediction$se_min) df_observations <- data.frame(year = df$year, observations = df$R + df$S, observed = df$R / (df$R + df$S), stringsAsFactors = FALSE) df_prediction <- df_prediction %pm>% pm_left_join(df_observations, by = "year") df_prediction$estimated <- df_prediction$value if (preserve_measurements == TRUE) { # replace estimated data by observed data df_prediction$value <- ifelse(!is.na(df_prediction$observed), df_prediction$observed, df_prediction$value) df_prediction$se_min <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_min) df_prediction$se_max <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_max) } df_prediction$value <- ifelse(df_prediction$value > 1, 1, ifelse(df_prediction$value < 0, 0, df_prediction$value)) df_prediction <- df_prediction[order(df_prediction$year), ] structure( .Data = df_prediction, class = c("resistance_predict", "data.frame"), I_as_S = I_as_S, model_title = model, model = model_lm, ab = col_ab ) } #' @rdname resistance_predict #' @export rsi_predict <- resistance_predict #' @method plot resistance_predict #' @export #' @importFrom graphics axis arrows points #' @rdname resistance_predict plot.resistance_predict <- function(x, main = paste("Resistance Prediction of", x_name), ...) { x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")") if (attributes(x)$I_as_S == TRUE) { ylab <- "%R" } else { ylab <- "%IR" } # get plot() generic; this was moved from the 'graphics' pkg to the 'base' pkg in R 4.0.0 if (as.integer(R.Version()$major) >= 4) { plot <- import_fn("plot", "base") } else { plot <- import_fn("plot", "graphics") } plot(x = x$year, y = x$value, ylim = c(0, 1), yaxt = "n", # no y labels pch = 19, # closed dots ylab = paste0("Percentage (", ylab, ")"), xlab = "Year", main = main, sub = paste0("(n = ", sum(x$observations, na.rm = TRUE), ", model: ", attributes(x)$model_title, ")"), cex.sub = 0.75) axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%")) # hack for error bars: https://stackoverflow.com/a/22037078/4575331 arrows(x0 = x$year, y0 = x$se_min, x1 = x$year, y1 = x$se_max, length = 0.05, angle = 90, code = 3, lwd = 1.5) # overlay grey points for prediction points(x = subset(x, is.na(observations))$year, y = subset(x, is.na(observations))$value, pch = 19, col = "grey40") } #' @rdname resistance_predict #' @export ggplot_rsi_predict <- function(x, main = paste("Resistance Prediction of", x_name), ribbon = TRUE, ...) { stop_ifnot_installed("ggplot2") stop_ifnot(inherits(x, "resistance_predict"), "`x` must be a resistance prediction model created with resistance_predict()") x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")") if (attributes(x)$I_as_S == TRUE) { ylab <- "%R" } else { ylab <- "%IR" } p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) + ggplot2::geom_point(data = subset(x, !is.na(observations)), size = 2) + scale_y_percent(limits = c(0, 1)) + ggplot2::labs(title = main, y = paste0("Percentage (", ylab, ")"), x = "Year", caption = paste0("(n = ", sum(x$observations, na.rm = TRUE), ", model: ", attributes(x)$model_title, ")")) if (ribbon == TRUE) { p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25) } else { p <- p + ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5) } p <- p + # overlay grey points for prediction ggplot2::geom_point(data = subset(x, is.na(observations)), size = 2, colour = "grey40") p }