P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-21 10:53:18 +02:00
Code Releases Activity

documentation for 'data.table' AB selectors

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2023-03-11 16:54:02 +01:00
parent 45e840c02f
commit 7ad8635994
9 changed files with 174 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

51
R/proportion.R
View File

@ -43,6 +43,7 @@
#' @param ab_result antibiotic results to test against, must be one or more values of "S", "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
#' @inheritSection as.sir Interpretation of SIR
#' @details
#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()].
@ -112,6 +113,10 @@
#' sir_confidence_interval(example_isolates$AMX,
#' confidence_level = 0.975
#' )
#' sir_confidence_interval(example_isolates$AMX,
#' confidence_level = 0.975,
#' collapse = ", "
#' )
#'
#' # determines %S+I:
#' susceptibility(example_isolates$AMX)
@ -260,10 +265,16 @@ sir_confidence_interval <- function(...,
as_percent = FALSE,
only_all_tested = FALSE,
confidence_level = 0.95,
side = "both") {
side = "both",
collapse = FALSE) {
meet_criteria(ab_result, allow_class = c("character", "sir"), has_length = c(1, 2, 3), is_in = c("S", "I", "R"))
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
meet_criteria(confidence_level, allow_class = "numeric", is_positive = TRUE, has_length = 1)
meet_criteria(side, allow_class = "character", has_length = 1, is_in = c("both", "b", "left", "l", "lower", "lowest", "less", "min", "right", "r", "higher", "highest", "greater", "g", "max"))
meet_criteria(collapse, allow_class = c("logical", "character"), has_length = 1)
x <- tryCatch(
sir_calc(...,
ab_result = ab_result,
@ -281,19 +292,7 @@ sir_confidence_interval <- function(...,
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
if (n < minimum) {
warning_("Introducing NA: ",
ifelse(n == 0, "no", paste("only", n)),
" results available for `sir_confidence_interval()` (`minimum` = ", minimum, ").",
call = FALSE
)
if (as_percent == TRUE) {
return(NA_character_)
} else {
return(NA_real_)
}
}
# this applies the Clopper-Pearson method
out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int
out <- set_clean_class(out, "double")
@ -302,11 +301,29 @@ sir_confidence_interval <- function(...,
} else if (side %in% c("right", "r", "higher", "highest", "greater", "g", "max")) {
out <- out[2]
}
if (as_percent == TRUE) {
percentage(out, digits = 1)
if (isTRUE(as_percent)) {
out <- percentage(out, digits = 1)
} else {
out
out <- round(out, digits = 3)
}
if (!isFALSE(collapse) && length(out) > 1) {
out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse))
}
if (n < minimum) {
warning_("Introducing NA: ",
ifelse(n == 0, "no", paste("only", n)),
" results available for `sir_confidence_interval()` (`minimum` = ", minimum, ").",
call = FALSE
)
if (is.character(out)) {
return(NA_character_)
} else {
return(NA_real_)
}
}
out
}
#' @rdname proportion