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new SDD and N for as.sir()

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dr. M.S. (Matthijs) Berends
2024-05-20 15:27:04 +02:00
parent b68f47d985
commit 08a27922a8
28 changed files with 225 additions and 172 deletions
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23
man/as.sir.Rd
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@ -7,6 +7,7 @@
\alias{NA_sir_}
\alias{is.sir}
\alias{is_sir_eligible}
\alias{as.sir.default}
\alias{as.sir.mic}
\alias{as.sir.disk}
\alias{as.sir.data.frame}
@ -30,6 +31,16 @@ is.sir(x)
is_sir_eligible(x, threshold = 0.05)
\method{as.sir}{default}(
x,
S = "^(S|U)+$",
I = "^(I|H)+$",
R = "^(R)+$",
N = "^(N|V)+$",
SDD = "^(SDD|D)+$",
...
)
\method{as.sir}{mic}(
x,
mo = NULL,
@ -85,6 +96,8 @@ sir_interpretation_history(clean = FALSE)
\item{threshold}{maximum fraction of invalid antimicrobial interpretations of \code{x}, see \emph{Examples}}
\item{S, I, R, N, SDD}{a case-indepdendent \link[base:regex]{regular expression} to translate input to this result. This regular expression will be run \emph{after} all non-letters are removed from the input.}
\item{mo}{any (vector of) text that can be coerced to valid microorganism codes with \code{\link[=as.mo]{as.mo()}}, can be left empty to determine it automatically}
\item{ab}{any (vector of) text that can be coerced to a valid antimicrobial drug code with \code{\link[=as.ab]{as.ab()}}}
@ -117,14 +130,14 @@ Ordered \link{factor} with new class \code{sir}
\description{
Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. \code{\link[=as.sir]{as.sir()}} transforms the input to a new class \code{\link{sir}}, which is an ordered \link{factor}.
Currently breakpoints are available:
These breakpoints are currently available:
\itemize{
\item For \strong{clinical microbiology} from EUCAST 2011-2023 and CLSI 2011-2023;
\item For \strong{veterinary microbiology} from EUCAST 2021-2023 and CLSI 2019-2023;
\item ECOFFs (Epidemiological cut-off values) from EUCAST 2020-2023 and CLSI 2022-2023.
}
All breakpoints used for interpretation are publicly available in the \link{clinical_breakpoints} data set.
All breakpoints used for interpretation are available in our \link{clinical_breakpoints} data set.
}
\details{
\emph{Note: The clinical breakpoints in this package were validated through, and imported from, \href{https://whonet.org}{WHONET}. The public use of this \code{AMR} package has been endorsed by both CLSI and EUCAST. See \link{clinical_breakpoints} for more information.}
@ -132,7 +145,7 @@ All breakpoints used for interpretation are publicly available in the \link{clin
The \code{\link[=as.sir]{as.sir()}} function can work in four ways:
\enumerate{
\item For \strong{cleaning raw / untransformed data}. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with SIR interpretations and MIC values such as \code{"<0.25; S"} will be coerced to \code{"S"}. Combined interpretations for multiple test methods (as seen in laboratory records) such as \code{"S; S"} will be coerced to \code{"S"}, but a value like \code{"S; I"} will return \code{NA} with a warning that the input is unclear.
\item For \strong{cleaning raw / untransformed data}. The data will be cleaned to only contain valid values, namely: \strong{S} for susceptible, \strong{I} for intermediate or 'susceptible, increased exposure', \strong{R} for resistant, \strong{N} for non-interpretable, and \strong{SDD} for susceptible dose-dependent. Each of these can be set using a \link[base:regex]{regular expression}. Furthermore, \code{\link[=as.sir]{as.sir()}} will try its best to clean with some intelligence. For example, mixed values with SIR interpretations and MIC values such as \code{"<0.25; S"} will be coerced to \code{"S"}. Combined interpretations for multiple test methods (as seen in laboratory records) such as \code{"S; S"} will be coerced to \code{"S"}, but a value like \code{"S; I"} will return \code{NA} with a warning that the input is invalid.
\item For \strong{interpreting minimum inhibitory concentration (MIC) values} according to EUCAST or CLSI. You must clean your MIC values first using \code{\link[=as.mic]{as.mic()}}, that also gives your columns the new data class \code{\link{mic}}. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the \code{mo} argument.
\itemize{
\item Using \code{dplyr}, SIR interpretation can be done very easily with either:
@ -198,7 +211,7 @@ The repository of this package \href{https://github.com/msberends/AMR/blob/main/
The function \code{\link[=is.sir]{is.sir()}} detects if the input contains class \code{sir}. If the input is a \link{data.frame}, it iterates over all columns and returns a \link{logical} vector.
The function \code{\link[=is_sir_eligible]{is_sir_eligible()}} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} argument. If the input is a \link{data.frame}, it iterates over all columns and returns a \link{logical} vector.
The function \code{\link[=is_sir_eligible]{is_sir_eligible()}} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R and/or N and/or SDD), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} argument. If the input is a \link{data.frame}, it iterates over all columns and returns a \link{logical} vector.
}
\code{NA_sir_} is a missing value of the new \code{sir} class, analogous to e.g. base \R's \code{\link[base:NA]{NA_character_}}.
@ -294,7 +307,7 @@ if (require("dplyr")) {
# For CLEANING existing SIR values ------------------------------------
as.sir(c("S", "I", "R", "A", "B", "C"))
as.sir(c("S", "SDD", "I", "R", "N", "A", "B", "C"))
as.sir("<= 0.002; S") # will return "S"
sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
is.sir(sir_data)

2
man/bug_drug_combinations.Rd
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@ -49,7 +49,7 @@ bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
decimal point.}
}
\value{
The function \code{\link[=bug_drug_combinations]{bug_drug_combinations()}} returns a \link{data.frame} with columns "mo", "ab", "S", "I", "R" and "total".
The function \code{\link[=bug_drug_combinations]{bug_drug_combinations()}} returns a \link{data.frame} with columns "mo", "ab", "S", "SDD", "I", "R", and "total".
}
\description{
Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{\link[=format]{format()}} on the result to prettify it to a publishable/printable format, see \emph{Examples}.

2
man/clinical_breakpoints.Rd
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@ -56,7 +56,7 @@ The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}. They allow for machine reading EUCAST and CLSI guidelines, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI, though initiatives have started to overcome these burdens.
\strong{NOTE:} this \code{AMR} package (and the WHONET software as well) contains internal methods to apply the guidelines, which is rather complex. For example, some breakpoints must be applied on certain species groups (which are in case of this package available through the \link{microorganisms.groups} data set). It is important that this is considered when using the breakpoints for own use.
\strong{NOTE:} this \code{AMR} package (and the WHONET software as well) contains rather complex internal methods to apply the guidelines. For example, some breakpoints must be applied on certain species groups (which are in case of this package available through the \link{microorganisms.groups} data set). It is important that this is considered when using the breakpoints for own use.
}
}
\examples{

20
man/count.Rd
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@ -4,11 +4,11 @@
\alias{count}
\alias{count_resistant}
\alias{count_susceptible}
\alias{count_R}
\alias{count_IR}
\alias{count_I}
\alias{count_SI}
\alias{count_S}
\alias{count_SI}
\alias{count_I}
\alias{count_IR}
\alias{count_R}
\alias{count_all}
\alias{n_sir}
\alias{count_df}
@ -18,15 +18,15 @@ count_resistant(..., only_all_tested = FALSE)
count_susceptible(..., only_all_tested = FALSE)
count_R(..., only_all_tested = FALSE)
count_IR(..., only_all_tested = FALSE)
count_I(..., only_all_tested = FALSE)
count_S(..., only_all_tested = FALSE)
count_SI(..., only_all_tested = FALSE)
count_S(..., only_all_tested = FALSE)
count_I(..., only_all_tested = FALSE)
count_IR(..., only_all_tested = FALSE)
count_R(..., only_all_tested = FALSE)
count_all(..., only_all_tested = FALSE)

5
man/g.test.Rd
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@ -45,8 +45,9 @@ A list with class \code{"htest"} containing the following
\item{residuals}{the Pearson residuals,
\code{(observed - expected) / sqrt(expected)}.}
\item{stdres}{standardized residuals,
\code{(observed - expected) / sqrt(V)}, where \code{V} is the residual cell variance (Agresti, 2007,
section 2.4.5 for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
\code{(observed - expected) / sqrt(V)}, where \code{V} is the
residual cell variance (Agresti, 2007, section 2.4.5
for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
}
\description{
\code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable (1). A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).

2
man/pca.Rd
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@ -39,7 +39,7 @@ pca(
standard deviations are less than or equal to \code{tol} times the
standard deviation of the first component.) With the default null
setting, no components are omitted (unless \code{rank.} is specified
less than \code{min(dim(x))}.). Other settings for tol could be
less than \code{min(dim(x))}.). Other settings for \code{tol} could be
\code{tol = 0} or \code{tol = sqrt(.Machine$double.eps)}, which
would omit essentially constant components.}

2
man/proportion.Rd
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@ -99,7 +99,7 @@ These functions can be used to calculate the (co-)resistance or susceptibility o
\details{
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}.
The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}. Since AMR v3.0, \code{\link[=proportion_SI]{proportion_SI()}} and \code{\link[=proportion_I]{proportion_I()}} include dose-dependent susceptibility ('SDD').
Use \code{\link[=sir_confidence_interval]{sir_confidence_interval()}} to calculate the confidence interval, which relies on \code{\link[=binom.test]{binom.test()}}, i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial \emph{resistance}. Change the \code{side} argument to "left"/"min" or "right"/"max" to return a single value, and change the \code{ab_result} argument to e.g. \code{c("S", "I")} to test for antimicrobial \emph{susceptibility}, see Examples.