mirror of
https://github.com/msberends/AMR.git
synced 2024-12-27 23:26:13 +01:00
75 lines
3.6 KiB
R
Executable File
75 lines
3.6 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE: #
|
|
# AMR: An R Package for Working with Antimicrobial Resistance Data #
|
|
# #
|
|
# SOURCE CODE: #
|
|
# https://github.com/msberends/AMR #
|
|
# #
|
|
# PLEASE CITE THIS SOFTWARE AS: #
|
|
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
|
|
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
|
|
# Data. Journal of Statistical Software, 104(3), 1-31. #
|
|
# https://doi.org/10.18637/jss.v104.i03 #
|
|
# #
|
|
# Developed at the University of Groningen and the University Medical #
|
|
# Center Groningen in The Netherlands, in collaboration with many #
|
|
# colleagues from around the world, see our website. #
|
|
# #
|
|
# This R package is free software; you can freely use and distribute #
|
|
# it for both personal and commercial purposes under the terms of the #
|
|
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
|
# the Free Software Foundation. #
|
|
# We created this package for both routine data analysis and academic #
|
|
# research and it was publicly released in the hope that it will be #
|
|
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
|
|
# #
|
|
# Visit our website for the full manual and a complete tutorial about #
|
|
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
|
|
# ==================================================================== #
|
|
|
|
#' Skewness of the Sample
|
|
#'
|
|
#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
|
|
#'
|
|
#' 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.
|
|
#' @param x a vector of values, a [matrix] or a [data.frame]
|
|
#' @param na.rm a [logical] value indicating whether `NA` values should be stripped before the computation proceeds
|
|
#' @seealso [kurtosis()]
|
|
#' @rdname skewness
|
|
#' @export
|
|
#' @examples
|
|
#' skewness(runif(1000))
|
|
skewness <- function(x, na.rm = FALSE) {
|
|
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
|
UseMethod("skewness")
|
|
}
|
|
|
|
#' @method skewness default
|
|
#' @rdname skewness
|
|
#' @export
|
|
skewness.default <- function(x, na.rm = FALSE) {
|
|
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
|
x <- as.vector(x)
|
|
if (isTRUE(na.rm)) {
|
|
x <- x[!is.na(x)]
|
|
}
|
|
n <- length(x)
|
|
(sum((x - mean(x))^3) / n) / (sum((x - mean(x))^2) / n)^(3 / 2)
|
|
}
|
|
|
|
#' @method skewness matrix
|
|
#' @rdname skewness
|
|
#' @export
|
|
skewness.matrix <- function(x, na.rm = FALSE) {
|
|
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
|
apply(x, 2, skewness.default, na.rm = na.rm)
|
|
}
|
|
|
|
#' @method skewness data.frame
|
|
#' @rdname skewness
|
|
#' @export
|
|
skewness.data.frame <- function(x, na.rm = FALSE) {
|
|
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
|
vapply(FUN.VALUE = double(1), x, skewness.default, na.rm = na.rm)
|
|
}
|