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124 lines
5.0 KiB
R
124 lines
5.0 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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\title{Predict antimicrobial resistance}
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\usage{
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resistance_predict(tbl, col_ab, col_date, 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, year_min = NULL, year_max = NULL,
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year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
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preserve_measurements = TRUE, info = 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}
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\item{year_min}{lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}}
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\item{year_max}{highest year to use in the prediction model, defaults to 15 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. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).}
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\item{I_as_R}{treat \code{I} as \code{R}}
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\item{preserve_measurements}{logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be \code{NA}.}
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\item{info}{print textual analysis with the name and \code{\link{summary}} of the model.}
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}
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\value{
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\code{data.frame} 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}}
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\item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
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\item{\code{observations}, the total number of observations, i.e. S + I + R}
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\item{\code{observed}, the original observed values}
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\item{\code{estimated}, the estimated values, calculated by the model}
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}
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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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\section{Read more on our website!}{
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\if{html}{\figure{logo.png}{options: height=40px style=margin-bottom:5px} \cr}
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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 omprehensive tutorial} about how to conduct AMR analysis and find \href{https://msberends.gitlab.io/AMR/reference}{the complete documentation of all functions}, which reads a lot easier than in R.
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}
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\examples{
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\dontrun{
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# use it with base R:
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resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
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col_ab = "amcl", col_date = "date")
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# or use dplyr so you can actually read it:
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library(dplyr)
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tbl \%>\%
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filter(first_isolate == TRUE,
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genus == "Haemophilus") \%>\%
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resistance_predict(amcl, date)
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}
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# real live example:
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library(dplyr)
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septic_patients \%>\%
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# get bacteria properties like genus and species
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left_join_microorganisms("mo") \%>\%
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# calculate first isolates
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mutate(first_isolate = first_isolate(.)) \%>\%
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# filter on first E. coli isolates
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filter(genus == "Escherichia",
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species == "coli",
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first_isolate == TRUE) \%>\%
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# predict resistance of cefotaxime for next years
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resistance_predict(col_ab = "cfot",
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col_date = "date",
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year_max = 2025,
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preserve_measurements = TRUE,
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minimum = 0)
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# create nice plots with ggplot
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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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