mirror of https://github.com/msberends/AMR.git
81 lines
4.0 KiB
R
Executable File
81 lines
4.0 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# CITE AS #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# doi:10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Kurtosis of the Sample
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#'
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#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. A normal distribution has a kurtosis of 3 and a excess kurtosis of 0.
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#' @param x a vector of values, a [matrix] or a [data.frame]
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#' @param na.rm a [logical] to indicate whether `NA` values should be stripped before the computation proceeds
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#' @param excess a [logical] to indicate whether the *excess kurtosis* should be returned, defined as the kurtosis minus 3.
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#' @seealso [skewness()]
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#' @rdname kurtosis
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#' @export
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#' @examples
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#' kurtosis(rnorm(10000))
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#' kurtosis(rnorm(10000), excess = TRUE)
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kurtosis <- function(x, na.rm = FALSE, excess = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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meet_criteria(excess, allow_class = "logical", has_length = 1)
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UseMethod("kurtosis")
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}
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#' @method kurtosis default
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#' @rdname kurtosis
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#' @export
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kurtosis.default <- function(x, na.rm = FALSE, excess = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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meet_criteria(excess, allow_class = "logical", has_length = 1)
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x <- as.vector(x)
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if (na.rm == TRUE) {
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x <- x[!is.na(x)]
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}
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n <- length(x)
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k <- n * sum((x - mean(x, na.rm = na.rm))^4, na.rm = na.rm) /
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(sum((x - mean(x, na.rm = na.rm))^2, na.rm = na.rm)^2)
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k - ifelse(excess, 3, 0)
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}
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#' @method kurtosis matrix
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#' @rdname kurtosis
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#' @export
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kurtosis.matrix <- function(x, na.rm = FALSE, excess = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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meet_criteria(excess, allow_class = "logical", has_length = 1)
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apply(x, 2, kurtosis.default, na.rm = na.rm, excess = excess)
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}
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#' @method kurtosis data.frame
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#' @rdname kurtosis
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#' @export
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kurtosis.data.frame <- function(x, na.rm = FALSE, excess = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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meet_criteria(excess, allow_class = "logical", has_length = 1)
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vapply(FUN.VALUE = double(1), x, kurtosis.default, na.rm = na.rm, excess = excess)
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}
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