mirror of https://github.com/msberends/AMR.git
137 lines
6.1 KiB
R
137 lines
6.1 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/resistance_predict.R
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\name{resistance_predict}
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\alias{resistance_predict}
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\alias{rsi_predict}
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\alias{plot.resistance_predict}
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\alias{ggplot_rsi_predict}
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\title{Predict antimicrobial resistance}
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\usage{
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resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
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year_max = NULL, year_every = 1, minimum = 30,
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model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
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info = TRUE)
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rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
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year_max = NULL, year_every = 1, minimum = 30,
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model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
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info = TRUE)
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\method{plot}{resistance_predict}(x,
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main = paste("Resistance prediction of", attributes(x)$ab), ...)
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ggplot_rsi_predict(x, main = paste("Resistance prediction of",
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attributes(x)$ab), ribbon = TRUE, ...)
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}
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\arguments{
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\item{tbl}{a \code{data.frame} containing isolates.}
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\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
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\item{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}
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\item{year_min}{lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}}
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\item{year_max}{highest year to use in the prediction model, defaults to 10 years after today}
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\item{year_every}{unit of sequence between lowest year found in the data and \code{year_max}}
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\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
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\item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, 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 valid options.}
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\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
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\item{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 \code{NA}.}
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\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
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\item{x}{the coordinates of points in the plot. Alternatively, a
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single plotting structure, function or \emph{any \R object with a
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\code{plot} method} can be provided.}
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\item{main}{title of the plot}
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\item{...}{parameters passed on to the \code{first_isolate} function}
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\item{ribbon}{a logical to indicate whether a ribbon should be shown (default) or error bars}
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}
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\value{
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\code{data.frame} with extra class \code{"resistance_predict"} with columns:
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\itemize{
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\item{\code{year}}
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\item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
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\item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)}
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\item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)}
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\item{\code{observations}, the total number of available observations in that year, i.e. S + I + R}
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\item{\code{observed}, the original observed resistant percentages}
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\item{\code{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: \code{attributes(x)$model}, see Examples.
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}
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\description{
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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 \code{se_min} and \code{se_max}. See Examples for a real live example.
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}
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\details{
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Valid options for the statistical model are:
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\itemize{
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\item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
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\item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution}
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\item{\code{"lin"} or \code{"linear"}: a linear regression model}
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}
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}
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\section{Read more on our website!}{
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On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
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}
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\examples{
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x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
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plot(x)
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ggplot_rsi_predict(x)
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# use dplyr so you can actually read it:
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library(dplyr)
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x <- septic_patients \%>\%
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filter_first_isolate() \%>\%
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filter(mo_genus(mo) == "Staphylococcus") \%>\%
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resistance_predict("peni")
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plot(x)
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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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# create nice plots with ggplot2 yourself
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if (!require(ggplot2)) {
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data <- septic_patients \%>\%
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filter(mo == as.mo("E. coli")) \%>\%
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resistance_predict(col_ab = "amox",
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col_date = "date",
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info = FALSE,
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minimum = 15)
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ggplot(data,
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aes(x = year)) +
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geom_col(aes(y = value),
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fill = "grey75") +
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geom_errorbar(aes(ymin = se_min,
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ymax = se_max),
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colour = "grey50") +
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scale_y_continuous(limits = c(0, 1),
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breaks = seq(0, 1, 0.1),
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labels = paste0(seq(0, 100, 10), "\%")) +
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labs(title = expression(paste("Forecast of amoxicillin resistance in ",
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italic("E. coli"))),
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y = "\%IR",
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x = "Year") +
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theme_minimal(base_size = 13)
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}
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}
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\seealso{
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The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
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}
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