mirror of https://github.com/msberends/AMR.git
71 lines
3.4 KiB
R
Executable File
71 lines
3.4 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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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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# LICENCE #
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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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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#' Skewness of the Sample
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#'
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#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
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#'
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#' When negative ('left-skewed'): the left tail is longer; the mass of the distribution is concentrated on the right of a histogram. When positive ('right-skewed'): the right tail is longer; the mass of the distribution is concentrated on the left of a histogram. A normal distribution has a skewness of 0.
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#' @inheritSection lifecycle Stable Lifecycle
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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] value indicating whether `NA` values should be stripped before the computation proceeds
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#' @seealso [kurtosis()]
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#' @rdname skewness
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#' @inheritSection AMR Read more on Our Website!
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#' @export
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skewness <- function(x, na.rm = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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UseMethod("skewness")
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}
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#' @method skewness default
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#' @rdname skewness
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#' @export
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skewness.default <- function(x, na.rm = FALSE) {
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meet_criteria(na.rm, 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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(sum((x - mean(x))^3) / n) / (sum((x - mean(x)) ^ 2) / n) ^ (3 / 2)
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}
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#' @method skewness matrix
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#' @rdname skewness
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#' @export
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skewness.matrix <- function(x, na.rm = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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apply(x, 2, skewness.default, na.rm = na.rm)
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}
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#' @method skewness data.frame
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#' @rdname skewness
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#' @export
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skewness.data.frame <- function(x, na.rm = FALSE) {
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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vapply(FUN.VALUE = double(1), x, skewness.default, na.rm = na.rm)
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}
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