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414 lines
17 KiB
R
Executable File
414 lines
17 KiB
R
Executable File
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Predict Antimicrobial Resistance
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#'
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#' 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.
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#' @param object model data to be plotted
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#' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
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#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already - the default is the first column of with a date class
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#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date`
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#' @param year_max highest year to use in the prediction model - the default is 10 years after today
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#' @param year_every unit of sequence between lowest year found in the data and `year_max`
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#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
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#' @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.
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#' @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 interpretation of I (increased exposure) in 2019, see section *Interpretation of S, I and R* below.
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#' @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`.
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#' @param info a [logical] to indicate whether textual analysis should be printed with the name and [summary()] of the statistical model.
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#' @param main title of the plot
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#' @param ribbon a [logical] to indicate whether a ribbon should be shown (default) or error bars
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#' @param ... arguments passed on to functions
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#' @inheritSection as.sir Interpretation of SIR
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#' @inheritParams first_isolate
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#' @inheritParams graphics::plot
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#' @details Valid options for the statistical model (argument `model`) are:
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#' - `"binomial"` or `"binom"` or `"logit"`: a generalised linear regression model with binomial distribution
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#' - `"loglin"` or `"poisson"`: a generalised log-linear regression model with poisson distribution
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#' - `"lin"` or `"linear"`: a linear regression model
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#' @return A [data.frame] with extra class [`resistance_predict`] with columns:
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#' - `year`
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#' - `value`, the same as `estimated` when `preserve_measurements = FALSE`, and a combination of `observed` and `estimated` otherwise
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#' - `se_min`, the lower bound of the standard error with a minimum of `0` (so the standard error will never go below 0%)
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#' - `se_max` the upper bound of the standard error with a maximum of `1` (so the standard error will never go above 100%)
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#' - `observations`, the total number of available observations in that year, i.e. \eqn{S + I + R}
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#' - `observed`, the original observed resistant percentages
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#' - `estimated`, the estimated resistant percentages, calculated by the model
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#'
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#' Furthermore, the model itself is available as an attribute: `attributes(x)$model`, see *Examples*.
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#' @seealso The [proportion()] functions to calculate resistance
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#'
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#' Models: [lm()] [glm()]
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#' @rdname resistance_predict
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#' @export
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#' @importFrom stats predict glm lm
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#' @examples
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#' x <- resistance_predict(example_isolates,
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#' col_ab = "AMX",
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#' year_min = 2010,
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#' model = "binomial"
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#' )
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#' plot(x)
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#' \donttest{
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#' if (require("ggplot2")) {
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#' ggplot_sir_predict(x)
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#' }
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#'
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#' # using dplyr:
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#' if (require("dplyr")) {
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#' x <- example_isolates %>%
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#' filter_first_isolate() %>%
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#' filter(mo_genus(mo) == "Staphylococcus") %>%
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#' resistance_predict("PEN", model = "binomial")
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#' print(plot(x))
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#'
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#' # get the model from the object
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#' mymodel <- attributes(x)$model
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#' summary(mymodel)
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#' }
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#'
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#' # create nice plots with ggplot2 yourself
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#' if (require("dplyr") && require("ggplot2")) {
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#' data <- example_isolates %>%
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#' filter(mo == as.mo("E. coli")) %>%
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#' resistance_predict(
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#' col_ab = "AMX",
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#' col_date = "date",
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#' model = "binomial",
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#' info = FALSE,
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#' minimum = 15
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#' )
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#' head(data)
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#' autoplot(data)
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#' }
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#' }
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resistance_predict <- function(x,
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col_ab,
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col_date = NULL,
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year_min = NULL,
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year_max = NULL,
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year_every = 1,
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minimum = 30,
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model = NULL,
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I_as_S = TRUE,
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preserve_measurements = TRUE,
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info = interactive(),
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...) {
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meet_criteria(x, allow_class = "data.frame")
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meet_criteria(col_ab, allow_class = "character", has_length = 1, is_in = colnames(x))
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meet_criteria(col_date, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
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meet_criteria(year_min, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
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meet_criteria(year_max, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
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meet_criteria(year_every, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
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meet_criteria(model, allow_class = c("character", "function"), has_length = 1, allow_NULL = TRUE)
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meet_criteria(I_as_S, allow_class = "logical", has_length = 1)
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meet_criteria(preserve_measurements, allow_class = "logical", has_length = 1)
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meet_criteria(info, allow_class = "logical", has_length = 1)
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stop_if(is.null(model), 'choose a regression model with the `model` argument, e.g. resistance_predict(..., model = "binomial")')
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x.bak <- x
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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# -- date
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if (is.null(col_date)) {
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col_date <- search_type_in_df(x = x, type = "date")
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stop_if(is.null(col_date), "`col_date` must be set")
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}
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stop_ifnot(
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col_date %in% colnames(x),
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"column '", col_date, "' not found"
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)
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year <- function(x) {
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# don't depend on lubridate or so, would be overkill for only this function
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if (all(grepl("^[0-9]{4}$", x))) {
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as.integer(x)
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} else {
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as.integer(format(as.Date(x), "%Y"))
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}
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}
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df <- x
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df[, col_ab] <- droplevels(as.sir(df[, col_ab, drop = TRUE]))
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if (I_as_S == TRUE) {
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# then I as S
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df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE], fixed = TRUE)
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} else {
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# then I as R
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df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE], fixed = TRUE)
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}
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df[, col_ab] <- ifelse(is.na(df[, col_ab, drop = TRUE]), 0, df[, col_ab, drop = TRUE])
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# remove rows with NAs
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df <- subset(df, !is.na(df[, col_ab, drop = TRUE]))
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df$year <- year(df[, col_date, drop = TRUE])
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df <- as.data.frame(rbind(table(df[, c("year", col_ab), drop = FALSE])),
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stringsAsFactors = FALSE
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)
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df$year <- as.integer(rownames(df))
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rownames(df) <- NULL
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df <- subset(df, sum(df$R + df$S, na.rm = TRUE) >= minimum)
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# nolint start
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df_matrix <- as.matrix(df[, c("R", "S"), drop = FALSE])
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# nolint end
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stop_if(NROW(df) == 0, "there are no observations")
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year_lowest <- min(df$year)
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if (is.null(year_min)) {
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year_min <- year_lowest
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} else {
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year_min <- max(year_min, year_lowest, na.rm = TRUE)
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}
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if (is.null(year_max)) {
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year_max <- year(Sys.Date()) + 10
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}
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years <- list(year = seq(from = year_min, to = year_max, by = year_every))
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if (model %in% c("binomial", "binom", "logit")) {
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model <- "binomial"
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model_lm <- with(df, glm(df_matrix ~ year, family = binomial))
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if (isTRUE(info)) {
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cat("\nLogistic regression model (logit) with binomial distribution")
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cat("\n------------------------------------------------------------\n")
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print(summary(model_lm))
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}
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predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else if (model %in% c("loglin", "poisson")) {
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model <- "poisson"
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model_lm <- with(df, glm(R ~ year, family = poisson))
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if (isTRUE(info)) {
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cat("\nLog-linear regression model (loglin) with poisson distribution")
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cat("\n--------------------------------------------------------------\n")
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print(summary(model_lm))
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}
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predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else if (model %in% c("lin", "linear")) {
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model <- "linear"
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model_lm <- with(df, lm((R / (R + S)) ~ year))
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if (isTRUE(info)) {
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cat("\nLinear regression model")
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cat("\n-----------------------\n")
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print(summary(model_lm))
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}
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predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else {
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stop("no valid model selected. See ?resistance_predict.")
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}
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# prepare the output dataframe
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df_prediction <- data.frame(
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year = unlist(years),
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value = prediction,
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se_min = prediction - se,
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se_max = prediction + se,
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stringsAsFactors = FALSE
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)
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if (model == "poisson") {
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df_prediction$value <- as.integer(format(df_prediction$value, scientific = FALSE))
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df_prediction$se_min <- as.integer(df_prediction$se_min)
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df_prediction$se_max <- as.integer(df_prediction$se_max)
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} else {
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# se_max not above 1
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df_prediction$se_max <- pmin(df_prediction$se_max, 1)
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}
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# se_min not below 0
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df_prediction$se_min <- pmax(df_prediction$se_min, 0)
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df_observations <- data.frame(
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year = df$year,
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observations = df$R + df$S,
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observed = df$R / (df$R + df$S),
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stringsAsFactors = FALSE
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)
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df_prediction <- df_prediction %pm>%
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pm_left_join(df_observations, by = "year")
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df_prediction$estimated <- df_prediction$value
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if (preserve_measurements == TRUE) {
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# replace estimated data by observed data
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df_prediction$value <- ifelse(!is.na(df_prediction$observed), df_prediction$observed, df_prediction$value)
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df_prediction$se_min <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_min)
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df_prediction$se_max <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_max)
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}
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df_prediction$value <- ifelse(df_prediction$value > 1, 1, pmax(df_prediction$value, 0))
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df_prediction <- df_prediction[order(df_prediction$year), , drop = FALSE]
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out <- as_original_data_class(df_prediction, class(x.bak)) # will remove tibble groups
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structure(out,
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class = c("resistance_predict", class(out)),
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I_as_S = I_as_S,
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model_title = model,
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model = model_lm,
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ab = col_ab
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)
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}
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#' @rdname resistance_predict
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#' @export
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sir_predict <- resistance_predict
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#' @method plot resistance_predict
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#' @export
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#' @importFrom graphics plot axis arrows points
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#' @rdname resistance_predict
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plot.resistance_predict <- function(x, main = paste("Resistance Prediction of", x_name), ...) {
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x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
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meet_criteria(main, allow_class = "character", has_length = 1)
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if (attributes(x)$I_as_S == TRUE) {
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ylab <- "%R"
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} else {
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ylab <- "%IR"
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}
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plot(
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x = x$year,
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y = x$value,
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ylim = c(0, 1),
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yaxt = "n", # no y labels
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pch = 19, # closed dots
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ylab = paste0("Percentage (", ylab, ")"),
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xlab = "Year",
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main = main,
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sub = paste0(
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"(n = ", sum(x$observations, na.rm = TRUE),
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", model: ", attributes(x)$model_title, ")"
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),
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cex.sub = 0.75
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)
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axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
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# hack for error bars: https://stackoverflow.com/a/22037078/4575331
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arrows(
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x0 = x$year,
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y0 = x$se_min,
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x1 = x$year,
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y1 = x$se_max,
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length = 0.05, angle = 90, code = 3, lwd = 1.5
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)
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# overlay grey points for prediction
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points(
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x = subset(x, is.na(observations))$year,
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y = subset(x, is.na(observations))$value,
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pch = 19,
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col = "grey40"
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)
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}
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#' @rdname resistance_predict
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#' @export
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ggplot_sir_predict <- function(x,
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main = paste("Resistance Prediction of", x_name),
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ribbon = TRUE,
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...) {
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x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
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meet_criteria(main, allow_class = "character", has_length = 1)
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meet_criteria(ribbon, allow_class = "logical", has_length = 1)
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stop_ifnot_installed("ggplot2")
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stop_ifnot(inherits(x, "resistance_predict"), "`x` must be a resistance prediction model created with resistance_predict()")
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if (attributes(x)$I_as_S == TRUE) {
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ylab <- "%R"
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} else {
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ylab <- "%IR"
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}
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p <- ggplot2::ggplot(
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as.data.frame(x, stringsAsFactors = FALSE),
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ggplot2::aes(x = year, y = value)
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) +
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ggplot2::geom_point(
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data = subset(x, !is.na(observations)),
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size = 2
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) +
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scale_y_percent(limits = c(0, 1)) +
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ggplot2::labs(
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title = main,
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y = paste0("Percentage (", ylab, ")"),
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x = "Year",
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caption = paste0(
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"(n = ", sum(x$observations, na.rm = TRUE),
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", model: ", attributes(x)$model_title, ")"
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)
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)
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if (ribbon == TRUE) {
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p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25)
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} else {
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p <- p + ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5)
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}
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p <- p +
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# overlay grey points for prediction
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ggplot2::geom_point(
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data = subset(x, is.na(observations)),
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size = 2,
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colour = "grey40"
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)
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p
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}
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#' @method autoplot resistance_predict
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#' @rdname resistance_predict
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# will be exported using s3_register() in R/zzz.R
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autoplot.resistance_predict <- function(object,
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main = paste("Resistance Prediction of", x_name),
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ribbon = TRUE,
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...) {
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x_name <- paste0(ab_name(attributes(object)$ab), " (", attributes(object)$ab, ")")
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meet_criteria(main, allow_class = "character", has_length = 1)
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meet_criteria(ribbon, allow_class = "logical", has_length = 1)
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ggplot_sir_predict(x = object, main = main, ribbon = ribbon, ...)
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}
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#' @method fortify resistance_predict
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#' @noRd
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# will be exported using s3_register() in R/zzz.R
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fortify.resistance_predict <- function(model, data, ...) {
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as.data.frame(model)
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}
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