mirror of https://github.com/msberends/AMR.git
93 lines
3.6 KiB
R
93 lines
3.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/mean_amr_distance.R
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\name{mean_amr_distance}
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\alias{mean_amr_distance}
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\alias{mean_amr_distance.sir}
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\alias{mean_amr_distance.data.frame}
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\alias{amr_distance_from_row}
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\title{Calculate the Mean AMR Distance}
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\usage{
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mean_amr_distance(x, ...)
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\method{mean_amr_distance}{sir}(x, ..., combine_SI = TRUE)
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\method{mean_amr_distance}{data.frame}(x, ..., combine_SI = TRUE)
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amr_distance_from_row(amr_distance, row)
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}
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\arguments{
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\item{x}{a vector of class \link[=as.sir]{sir}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes}
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\item{...}{variables to select (supports \link[tidyselect:language]{tidyselect language} such as \code{column1:column4} and \code{where(is.mic)}, and can thus also be \link[=ab_selector]{antibiotic selectors}}
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\item{combine_SI}{a \link{logical} to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}}
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\item{amr_distance}{the outcome of \code{\link[=mean_amr_distance]{mean_amr_distance()}}}
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\item{row}{an index, such as a row number}
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}
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\description{
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Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
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}
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\details{
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The mean AMR distance is effectively \href{https://en.wikipedia.org/wiki/Standard_score}{the Z-score}; a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
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MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is thus calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
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SIR values (see \code{\link[=as.sir]{as.sir()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
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For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see \emph{Examples}.
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Use \code{\link[=amr_distance_from_row]{amr_distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
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}
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\section{Interpretation}{
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Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
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}
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\examples{
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sir <- random_sir(10)
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sir
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mean_amr_distance(sir)
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mic <- random_mic(10)
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mic
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mean_amr_distance(mic)
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# equal to the Z-score of their log2:
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(log2(mic) - mean(log2(mic))) / sd(log2(mic))
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disk <- random_disk(10)
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disk
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mean_amr_distance(disk)
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y <- data.frame(
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id = LETTERS[1:10],
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amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),
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cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
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gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
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tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
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)
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y
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mean_amr_distance(y)
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y$amr_distance <- mean_amr_distance(y, where(is.mic))
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y[order(y$amr_distance), ]
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if (require("dplyr")) {
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y \%>\%
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mutate(
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amr_distance = mean_amr_distance(y),
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check_id_C = amr_distance_from_row(amr_distance, id == "C")
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) \%>\%
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arrange(check_id_C)
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}
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if (require("dplyr")) {
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# support for groups
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example_isolates \%>\%
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filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
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select(mo, TCY, carbapenems()) \%>\%
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group_by(mo) \%>\%
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mutate(dist = mean_amr_distance(.)) \%>\%
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arrange(mo, dist)
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}
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}
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