AMR/man/resistance_predict.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resistance_predict.R
\name{resistance_predict}
\alias{resistance_predict}
\alias{rsi_predict}
\title{Predict antimicrobial resistance}
\usage{
resistance_predict(tbl, col_ab, col_date, year_min = NULL,
  year_max = NULL, year_every = 1, minimum = 30,
  model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
  info = TRUE)

rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
  year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
  preserve_measurements = TRUE, info = TRUE)
}
\arguments{
\item{tbl}{a \code{data.frame} containing isolates.}

\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}

\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already}

\item{year_min}{lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}}

\item{year_max}{highest year to use in the prediction model, defaults to 15 years after today}

\item{year_every}{unit of sequence between lowest year found in the data and \code{year_max}}

\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}

\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"}).}

\item{I_as_R}{treat \code{I} as \code{R}}

\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}.}

\item{info}{print textual analysis with the name and \code{\link{summary}} of the model.}
}
\value{
\code{data.frame} with columns:
\itemize{
  \item{\code{year}}
  \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
  \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
  \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
  \item{\code{observations}, the total number of observations, i.e. S + I + R}
  \item{\code{observed}, the original observed values}
  \item{\code{estimated}, the estimated values, calculated by the model}
}
}
\description{
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.
}
\section{Read more on our website!}{

\if{html}{\figure{logo.png}{options: height=40px style=margin-bottom:5px} \cr}
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.
}

\examples{
\dontrun{
# use it with base R:
resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
                   col_ab = "amcl", col_date = "date")

# or use dplyr so you can actually read it:
library(dplyr)
tbl \%>\%
  filter(first_isolate == TRUE,
         genus == "Haemophilus") \%>\%
  resistance_predict(amcl, date)
}


# real live example:
library(dplyr)
septic_patients \%>\%
  # get bacteria properties like genus and species
  left_join_microorganisms("mo") \%>\%
  # calculate first isolates
  mutate(first_isolate = first_isolate(.)) \%>\%
  # filter on first E. coli isolates
  filter(genus == "Escherichia",
         species == "coli",
         first_isolate == TRUE) \%>\%
  # predict resistance of cefotaxime for next years
  resistance_predict(col_ab = "cfot",
                     col_date = "date",
                     year_max = 2025,
                     preserve_measurements = TRUE,
                     minimum = 0)

# create nice plots with ggplot
if (!require(ggplot2)) {

  data <- septic_patients \%>\%
    filter(mo == as.mo("E. coli")) \%>\%
    resistance_predict(col_ab = "amox",
                       col_date = "date",
                       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 = "\%IR",
         x = "Year") +
    theme_minimal(base_size = 13)
}
}
\seealso{
The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
}