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(v1.3.0.9018) language corrections
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@ -16,6 +16,7 @@ as.mo(
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allow_uncertain = TRUE,
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reference_df = get_mo_source(),
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ignore_pattern = getOption("AMR_ignore_pattern"),
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language = get_locale(),
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...
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)
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@ -44,6 +45,8 @@ This excludes \emph{Enterococci} at default (who are in group D), use \code{Lanc
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\item{ignore_pattern}{a regular expression (case-insensitive) of which all matches in \code{x} must return \code{NA}. This can be convenient to exclude known non-relevant input and can also be set with the option \code{AMR_ignore_pattern}, e.g. \code{options(AMR_ignore_pattern = "(not reported|contaminated flora)")}.}
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\item{language}{language to translate text like "no growth", which defaults to the system language (see \code{\link[=get_locale]{get_locale()}})}
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\item{...}{other parameters passed on to functions}
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}
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\value{
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@ -106,7 +109,7 @@ With the default setting (\code{allow_uncertain = TRUE}, level 2), below example
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There are three helper functions that can be run after using the \code{\link[=as.mo]{as.mo()}} function:
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\itemize{
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\item Use \code{\link[=mo_uncertainties]{mo_uncertainties()}} to get a \code{\link{data.frame}} that prints in a pretty format with all taxonomic names that were guessed. The output contains a score that is based on the human pathogenic prevalence and the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} between the full taxonomic name and the user input.
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\item Use \code{\link[=mo_uncertainties]{mo_uncertainties()}} to get a \code{\link{data.frame}} that prints in a pretty format with all taxonomic names that were guessed. The output contains a score that is based on the human pathogenic prevalence and the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} between the user input and the full taxonomic name.
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\item Use \code{\link[=mo_failures]{mo_failures()}} to get a \code{\link{character}} \code{\link{vector}} with all values that could not be coerced to a valid value.
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\item Use \code{\link[=mo_renamed]{mo_renamed()}} to get a \code{\link{data.frame}} with all values that could be coerced based on old, previously accepted taxonomic names.
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}
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