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resistance predict

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dr. M.S. (Matthijs) Berends
2019-01-15 12:45:24 +01:00
parent cda7087722
commit 046d195064
16 changed files with 306 additions and 209 deletions
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2
man/first_isolate.Rd
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@ -26,7 +26,7 @@ filter_first_weighted_isolate(tbl, col_date = NULL,
\arguments{
\item{tbl}{a \code{data.frame} containing isolates.}
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}}
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
\item{col_patient_id}{column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)}

70
man/resistance_predict.Rd
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@ -3,23 +3,32 @@
\name{resistance_predict}
\alias{resistance_predict}
\alias{rsi_predict}
\alias{plot.resistance_predict}
\alias{ggplot_rsi_predict}
\title{Predict antimicrobial resistance}
\usage{
resistance_predict(tbl, col_ab, col_date, year_min = NULL,
resistance_predict(tbl, col_ab, col_date = NULL, 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)
rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
year_max = NULL, year_every = 1, minimum = 30,
model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
info = TRUE)
\method{plot}{resistance_predict}(x,
main = paste("Resistance prediction of", attributes(x)$ab), ...)
ggplot_rsi_predict(x, main = paste("Resistance prediction of",
attributes(x)$ab), ...)
}
\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{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}
\item{year_min}{lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}}
@ -29,16 +38,22 @@ rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
\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{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) or \code{"linear"} (or \code{"lin"}).}
\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
\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}.}
\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
\item{x}{the coordinates of points in the plot. Alternatively, a
single plotting structure, function or \emph{any \R object with a
\code{plot} method} can be provided.}
\item{...}{parameters passed on to the \code{first_isolate} function}
}
\value{
\code{data.frame} with columns:
\code{data.frame} with extra class \code{"resistance_predict"} 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}
@ -59,39 +74,20 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
}
\examples{
\dontrun{
# use it with base R:
resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
col_ab = "amcl", col_date = "date")
x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
plot(x)
ggplot_rsi_predict(x)
# or use dplyr so you can actually read it:
# use dplyr so you can actually read it:
library(dplyr)
tbl \%>\%
filter(first_isolate == TRUE,
genus == "Haemophilus") \%>\%
resistance_predict(amcl, date)
}
x <- septic_patients \%>\%
filter_first_isolate() \%>\%
filter(mo_genus(mo) == "Staphylococcus") \%>\%
resistance_predict("peni")
plot(x)
# 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
# create nice plots with ggplot yourself
if (!require(ggplot2)) {
data <- septic_patients \%>\%
@ -99,7 +95,7 @@ if (!require(ggplot2)) {
resistance_predict(col_ab = "amox",
col_date = "date",
info = FALSE,
minimum = 15)
minimum = 15)
ggplot(data,
aes(x = year)) +