mirror of https://github.com/msberends/AMR.git
79 lines
2.9 KiB
R
Executable File
79 lines
2.9 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/rsi_analysis.R
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\name{rsi_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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rsi_predict(tbl, col_ab, col_date,
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year_max = as.integer(format(as.Date(Sys.Date()), "\%Y")) + 15,
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year_every = 1, 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}{table that contains columns \code{col_ab} and \code{col_date}}
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\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation}
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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, supports tidyverse-like quotation}
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\item{year_max}{highest year to use in the prediction model, deafults 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{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}{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}.}
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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 \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
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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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\examples{
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\dontrun{
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# use it directly:
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rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
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col_ab = "amcl", col_date = "date")
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# or with 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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rsi_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("bactid") \%>\%
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# calculate first isolates
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mutate(first_isolate =
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first_isolate(.,
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"date",
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"patient_id",
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"bactid",
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col_specimen = NA,
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col_icu = NA)) \%>\%
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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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rsi_predict(col_ab = "cfot",
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col_date = "date",
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year_max = 2025,
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preserve_measurements = FALSE)
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}
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\seealso{
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\code{\link{lm}} \cr \code{\link{glm}}
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}
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