mirror of https://github.com/msberends/AMR.git
64 lines
2.5 KiB
R
Executable File
64 lines
2.5 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/mic.R
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\name{as.mic}
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\alias{as.mic}
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\alias{mic}
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\alias{is.mic}
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\title{Class 'mic'}
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\usage{
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as.mic(x, na.rm = FALSE)
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is.mic(x)
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}
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\arguments{
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\item{x}{vector}
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\item{na.rm}{a logical indicating whether missing values should be removed}
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}
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\value{
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Ordered \code{\link{factor}} with new class \code{\link{mic}}
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}
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\description{
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This transforms a vector to a new class \code{\link{mic}}, which is an ordered \code{\link{factor}} with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
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}
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\details{
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To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST and CLSI.
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}
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\section{Stable lifecycle}{
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\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
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The \link[AMR:lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, we are largely happy with the unlying code, and major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; we will avoid removing arguments or changing the meaning of existing arguments.
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If the unlying code needs breaking changes, they will occur gradually. To begin with, the function or argument will be deprecated; it will continue to work but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
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}
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\section{Read more on our website!}{
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On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
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}
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\examples{
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mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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is.mic(mic_data)
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# this can also coerce combined MIC/RSI values:
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as.mic("<=0.002; S") # will return <=0.002
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# interpret MIC values
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as.rsi(x = as.mic(2),
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mo = as.mo("S. pneumoniae"),
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ab = "AMX",
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guideline = "EUCAST")
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as.rsi(x = as.mic(4),
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mo = as.mo("S. pneumoniae"),
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ab = "AMX",
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guideline = "EUCAST")
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plot(mic_data)
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barplot(mic_data)
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freq(mic_data)
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}
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\seealso{
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\code{\link[=as.rsi]{as.rsi()}}
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}
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