mirror of
https://github.com/msberends/AMR.git
synced 2024-12-26 06:46:11 +01:00
80 lines
2.9 KiB
R
80 lines
2.9 KiB
R
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/rsi_analysis.R
|
|
\name{rsi_predict}
|
|
\alias{rsi_predict}
|
|
\title{Predict antimicrobial resistance}
|
|
\usage{
|
|
rsi_predict(tbl, col_ab, col_date,
|
|
year_max = as.integer(format(as.Date(Sys.Date()), "\%Y")) + 15,
|
|
year_every = 1, model = "binomial", I_as_R = TRUE,
|
|
preserve_measurements = TRUE, info = TRUE)
|
|
}
|
|
\arguments{
|
|
\item{tbl}{table that contains columns \code{col_ab} and \code{col_date}}
|
|
|
|
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation}
|
|
|
|
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation}
|
|
|
|
\item{year_max}{highest year to use in the prediction model, deafults to 15 years after today}
|
|
|
|
\item{year_every}{unit of sequence between lowest year found in the data and \code{year_max}}
|
|
|
|
\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}{overwrite predictions of years that are actually available in the data, 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 \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
|
|
}
|
|
\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.
|
|
}
|
|
\examples{
|
|
\dontrun{
|
|
# use it directly:
|
|
rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
|
col_ab = "amcl", col_date = "date")
|
|
|
|
# or with dplyr so you can actually read it:
|
|
library(dplyr)
|
|
tbl \%>\%
|
|
filter(first_isolate == TRUE,
|
|
genus == "Haemophilus") \%>\%
|
|
rsi_predict(amcl, date)
|
|
}
|
|
|
|
|
|
# real live example:
|
|
library(dplyr)
|
|
septic_patients \%>\%
|
|
# get bacteria properties like genus and species
|
|
left_join_bactlist("bactid") \%>\%
|
|
# calculate first isolates
|
|
mutate(first_isolate =
|
|
first_isolate(.,
|
|
"date",
|
|
"patient_id",
|
|
"genus",
|
|
"species",
|
|
col_specimen = NA,
|
|
col_icu = NA)) \%>\%
|
|
# filter on first E. coli isolates
|
|
filter(genus == "Escherichia",
|
|
species == "coli",
|
|
first_isolate == TRUE) \%>\%
|
|
# predict resistance of cefotaxime for next years
|
|
rsi_predict(col_ab = cfot,
|
|
col_date = date,
|
|
year_max = 2025,
|
|
preserve_measurements = FALSE)
|
|
|
|
}
|
|
\seealso{
|
|
\code{\link{lm}} \cr \code{\link{glm}}
|
|
}
|