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dr. M.S. (Matthijs) Berends
2023-01-21 11:07:55 +01:00
parent bee337dfe0
commit ee38689172
29 changed files with 18362 additions and 53 deletions
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2
man/as.sir.Rd
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@@ -11,7 +11,7 @@
\alias{as.sir.disk}
\alias{as.sir.data.frame}
\alias{sir_interpretation_history}
\title{Interpret MIC and Disk Values, or Clean Raw SIR Data}
\title{Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data}
\format{
An object of class \code{sir} (inherits from \code{ordered}, \code{factor}) of length 1.
}

8
man/mean_amr_distance.Rd
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@@ -16,7 +16,7 @@ mean_amr_distance(x, ...)
amr_distance_from_row(amr_distance, row)
}
\arguments{
\item{x}{a vector of class \link[=as.sir]{rsi}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes}
\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}
\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}}
@@ -46,9 +46,9 @@ Isolates with distances less than 0.01 difference from each other should be cons
}
\examples{
rsi <- random_sir(10)
rsi
mean_amr_distance(rsi)
sir <- random_sir(10)
sir
mean_amr_distance(sir)
mic <- random_mic(10)
mic

4
man/proportion.Rd
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@@ -71,7 +71,7 @@ sir_sf(
\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a \link{logical} to indicate that isolates must be tested for all antibiotics, see section \emph{Combination Therapy} below}
\item{ab_result}{antibiotic results to test against, must be one of more values of "R", "S", "I"}
\item{ab_result}{antibiotic results to test against, must be one or more values of "S", "I", or "R"}
\item{confidence_level}{the confidence level for the returned confidence interval. For the calculation, the number of S or SI isolates, and R isolates are compared with the total number of available isolates with R, S, or I by using \code{\link[=binom.test]{binom.test()}}, i.e., the Clopper-Pearson method.}
@@ -264,7 +264,7 @@ if (require("dplyr")) {
combination_n = count_all(CIP, GEN)
)
# Get proportions S/I/R immediately of all rsi columns
# Get proportions S/I/R immediately of all sir columns
example_isolates \%>\%
select(AMX, CIP) \%>\%
proportion_df(translate = FALSE)