mirror of
https://github.com/msberends/AMR.git
synced 2025-07-10 07:41:57 +02:00
(v2.1.1.9081) HUGE microorganisms update for fungi!
This commit is contained in:
@ -40,7 +40,7 @@
|
||||
#'
|
||||
#' The `AMR` package is available in `r vector_and(vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
|
||||
#' @section Reference Data Publicly Available:
|
||||
#' All data sets in this `AMR` package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' All data sets in this `AMR` package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @source
|
||||
#' To cite AMR in publications use:
|
||||
#'
|
||||
|
||||
@ -1563,6 +1563,8 @@ add_MO_lookup_to_AMR_env <- function() {
|
||||
# special for Salmonella - they have cities as subspecies but not the species (enterica) in the fullname:
|
||||
MO_lookup$fullname_lower[which(MO_lookup$subspecies %like_case% "^[A-Z]")] <- gsub(" enterica ", " ", MO_lookup$fullname_lower[which(MO_lookup$subspecies %like_case% "^[A-Z]")], fixed = TRUE)
|
||||
|
||||
MO_lookup$genus_lower <- tolower(MO_lookup$genus)
|
||||
|
||||
MO_lookup$full_first <- substr(MO_lookup$fullname_lower, 1, 1)
|
||||
MO_lookup$species_first <- tolower(substr(MO_lookup$species, 1, 1)) # tolower for groups (Streptococcus, Salmonella)
|
||||
MO_lookup$subspecies_first <- tolower(substr(MO_lookup$subspecies, 1, 1)) # tolower for Salmonella serovars
|
||||
|
||||
@ -42,7 +42,7 @@
|
||||
#' @param col_mo column name of the names or codes of the microorganisms (see [as.mo()]) - the default is the first column of class [`mo`]. Values will be coerced using [as.mo()].
|
||||
#' @param language language to translate text, which defaults to the system language (see [get_AMR_locale()])
|
||||
#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see *Source*.
|
||||
#' @param combine_SI a [logical] to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (default is `TRUE`)
|
||||
#' @param combine_SI a [logical] to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is `TRUE`)
|
||||
#' @param sep a separating character for antibiotic columns in combination antibiograms
|
||||
#' @param info a [logical] to indicate info should be printed - the default is `TRUE` only in interactive mode
|
||||
#' @param object an [antibiogram()] object
|
||||
|
||||
@ -31,7 +31,7 @@
|
||||
#'
|
||||
#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use [format()] on the result to prettify it to a publishable/printable format, see *Examples*.
|
||||
#' @inheritParams eucast_rules
|
||||
#' @param combine_SI a [logical] to indicate whether values S and I should be summed, so resistance will be based on only R - the default is `TRUE`
|
||||
#' @param combine_SI a [logical] to indicate whether values S, SDD, and I should be summed, so resistance will be based on only R - the default is `TRUE`
|
||||
#' @param add_ab_group a [logical] to indicate where the group of the antimicrobials must be included as a first column
|
||||
#' @param remove_intrinsic_resistant [logical] to indicate that rows and columns with 100% resistance for all tested antimicrobials must be removed from the table
|
||||
#' @param FUN the function to call on the `mo` column to transform the microorganism codes - the default is [mo_shortname()]
|
||||
|
||||
20
R/data.R
20
R/data.R
@ -64,7 +64,7 @@
|
||||
#' Synonyms (i.e. trade names) were derived from the PubChem Compound ID (column `cid`) and consequently only available where a CID is available.
|
||||
#'
|
||||
#' ### Direct download
|
||||
#' Like all data sets in this package, these data sets are publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' Like all data sets in this package, these data sets are publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @source
|
||||
#'
|
||||
#' * World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): <https://atcddd.fhi.no/atc_ddd_index/>
|
||||
@ -136,7 +136,7 @@
|
||||
#' The syntax used to transform the original data to a cleansed \R format, can be [found here](https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R).
|
||||
#'
|
||||
#' ### Direct download
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @source
|
||||
#' Taxonomic entries were imported in this order of importance:
|
||||
#' 1. `r TAXONOMY_VERSION$LPSN$name`:\cr\cr
|
||||
@ -171,7 +171,7 @@
|
||||
#' - `code`\cr Commonly used code of a microorganism. ***This is a unique identifier.***
|
||||
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @seealso [as.mo()] [microorganisms]
|
||||
#' @examples
|
||||
#' microorganisms.codes
|
||||
@ -195,7 +195,7 @@
|
||||
#' - `mo_group_name`\cr Name of the species group / microbiological complex, as retrieved with [mo_name()]
|
||||
#' - `mo_name`\cr Name of the microorganism belonging in the species group / microbiological complex, as retrieved with [mo_name()]
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @seealso [as.mo()] [microorganisms]
|
||||
#' @examples
|
||||
#' microorganisms.groups
|
||||
@ -216,7 +216,7 @@
|
||||
#' - `mo`\cr ID of microorganism created with [as.mo()], see also the [microorganisms] data set
|
||||
#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.sir))` different antibiotics with class [`sir`] (see [as.sir()]); these column names occur in the [antibiotics] data set and can be translated with [set_ab_names()] or [ab_name()]
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @examples
|
||||
#' example_isolates
|
||||
"example_isolates"
|
||||
@ -231,7 +231,7 @@
|
||||
#' - `bacteria`\cr info about microorganism that can be transformed with [as.mo()], see also [microorganisms]
|
||||
#' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.sir()]
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @examples
|
||||
#' example_isolates_unclean
|
||||
"example_isolates_unclean"
|
||||
@ -267,7 +267,7 @@
|
||||
#' - `Date of data entry`\cr [Date] this data was entered in WHONET
|
||||
#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.sir))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.sir()].
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @examples
|
||||
#' WHONET
|
||||
"WHONET"
|
||||
@ -310,7 +310,7 @@
|
||||
#' The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this `AMR` package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, WHO, developers of WHONET software, and developers of this `AMR` package.
|
||||
#'
|
||||
#' ### Download
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw). 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.
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw). 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.
|
||||
#'
|
||||
#' **NOTE:** this `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 [microorganisms.groups] data set). It is important that this is considered when using the breakpoints for own use.
|
||||
#' @seealso [intrinsic_resistant]
|
||||
@ -328,7 +328,7 @@
|
||||
#' This data set is based on `r format_eucast_version_nr(3.3)`.
|
||||
#'
|
||||
#' ### Direct download
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#'
|
||||
#' 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.
|
||||
#' @examples
|
||||
@ -349,7 +349,7 @@
|
||||
#' - `original_txt`\cr Original text in the PDF file of EUCAST
|
||||
#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply, either `r vector_or(dosage$eucast_version, quotes = FALSE, sort = TRUE, reverse = TRUE)`
|
||||
#' @details
|
||||
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
|
||||
#' @examples
|
||||
#' dosage
|
||||
"dosage"
|
||||
|
||||
@ -32,7 +32,7 @@
|
||||
#' Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
|
||||
#' @param x a vector of class [sir][as.sir()], [mic][as.mic()] or [disk][as.disk()], or a [data.frame] containing columns of any of these classes
|
||||
#' @param ... variables to select (supports [tidyselect language][tidyselect::language] such as `column1:column4` and `where(is.mic)`, and can thus also be [antibiotic selectors][ab_selector()]
|
||||
#' @param combine_SI a [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 `TRUE`
|
||||
#' @param combine_SI a [logical] to indicate whether all values of S, SDD, and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`
|
||||
#' @details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
|
||||
#'
|
||||
#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is thus calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
|
||||
@ -118,7 +118,7 @@ mean_amr_distance.disk <- function(x, ...) {
|
||||
mean_amr_distance.sir <- function(x, ..., combine_SI = TRUE) {
|
||||
meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
|
||||
if (isTRUE(combine_SI)) {
|
||||
x[x == "I"] <- "S"
|
||||
x[x %in% c("I", "SDD")] <- "S"
|
||||
}
|
||||
mean_amr_distance(as.double(x))
|
||||
}
|
||||
|
||||
15
R/mo.R
15
R/mo.R
@ -315,12 +315,15 @@ as.mo <- function(x,
|
||||
|
||||
# take out the parts, split by space
|
||||
x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]]
|
||||
|
||||
# do a pre-match on first character (and if it contains a space, first chars of first two terms)
|
||||
if (length(x_parts) %in% c(2, 3)) {
|
||||
# for genus + species + subspecies
|
||||
if (paste(x_parts[1:2], collapse = " ") %in% MO_lookup_current$fullname_lower) {
|
||||
filtr <- which(MO_lookup_current$fullname_lower %like% paste(x_parts[1:2], collapse = " "))
|
||||
} else if (x_parts[1] %in% MO_lookup_current$genus_lower && !paste(x_parts[1:2], collapse = " ") %in% MO_lookup_current$fullname_lower) {
|
||||
# for a known genus, but unknown (sub)species
|
||||
filtr <- which(MO_lookup_current$genus_lower == x_parts[1])
|
||||
minimum_matching_score <- 0.05
|
||||
} else if (nchar(gsub("[^a-z]", "", x_parts[1], perl = TRUE)) <= 3) {
|
||||
filtr <- which(MO_lookup_current$full_first == substr(x_parts[1], 1, 1) &
|
||||
(MO_lookup_current$species_first == substr(x_parts[2], 1, 1) |
|
||||
@ -953,11 +956,15 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
|
||||
),
|
||||
collapse = "\n"
|
||||
),
|
||||
# Add note if result was coerced to accepted taxonomic name
|
||||
ifelse(x[i, ]$keep_synonyms == FALSE & x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")],
|
||||
ifelse(x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")],
|
||||
paste0(
|
||||
strrep(" ", nchar(x[i, ]$original_input) + 6),
|
||||
font_red(paste0("This outdated taxonomic name was converted to ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", synonym_mo_to_accepted_mo(x[i, ]$mo), ")."), collapse = NULL)
|
||||
ifelse(x[i, ]$keep_synonyms == FALSE,
|
||||
# Add note if result was coerced to accepted taxonomic name
|
||||
font_red(paste0("This outdated taxonomic name was converted to ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", synonym_mo_to_accepted_mo(x[i, ]$mo), ")."), collapse = NULL),
|
||||
# Or add note if result is currently another taxonomic name
|
||||
font_red(paste0(font_bold("Note: "), "The current name is ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", AMR_env$MO_lookup$ref[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], ")."), collapse = NULL)
|
||||
)
|
||||
),
|
||||
""
|
||||
),
|
||||
|
||||
@ -62,7 +62,7 @@
|
||||
#' - Any genus present in the **established** list also has `prevalence = 1.0` in the [microorganisms] data set;
|
||||
#' - Any other genus present in the **putative** list has `prevalence = 1.25` in the [microorganisms] data set;
|
||||
#' - Any other species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 1.5` in the [microorganisms] data set;
|
||||
#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the [microorganisms] data set: `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`;
|
||||
#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the [microorganisms] data set: `r vector_or(MO_RELEVANT_GENERA, quotes = "*")`;
|
||||
#' - All other records have `prevalence = 2.0` in the [microorganisms] data set.
|
||||
#'
|
||||
#' When calculating the matching score, all characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
|
||||
|
||||
@ -872,7 +872,8 @@ mo_info <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
|
||||
|
||||
info <- lapply(x, function(y) {
|
||||
c(
|
||||
list(mo = as.character(x)),
|
||||
list(mo = as.character(y),
|
||||
rank = mo_rank(y, language = language, keep_synonyms = keep_synonyms)),
|
||||
mo_taxonomy(y, language = language, keep_synonyms = keep_synonyms),
|
||||
list(
|
||||
status = mo_status(y, language = language, keep_synonyms = keep_synonyms),
|
||||
|
||||
@ -39,8 +39,8 @@
|
||||
#' @param data a [data.frame] containing columns with class [`sir`] (see [as.sir()])
|
||||
#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]
|
||||
#' @inheritParams ab_property
|
||||
#' @param combine_SI a [logical] to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`
|
||||
#' @param ab_result antibiotic results to test against, must be one or more values of "S", "I", or "R"
|
||||
#' @param combine_SI a [logical] to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is `TRUE`
|
||||
#' @param ab_result antibiotic results to test against, must be one or more values of "S", "SDD", "I", or "R"
|
||||
#' @param 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 [binom.test()], i.e., the Clopper-Pearson method.
|
||||
#' @param side the side of the confidence interval to return. The default is `"both"` for a length 2 vector, but can also be (abbreviated as) `"min"`/`"left"`/`"lower"`/`"less"` or `"max"`/`"right"`/`"higher"`/`"greater"`.
|
||||
#' @param collapse a [logical] to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing
|
||||
|
||||
BIN
R/sysdata.rda
BIN
R/sysdata.rda
Binary file not shown.
Reference in New Issue
Block a user