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70 lines
3.4 KiB
70 lines
3.4 KiB
# ==================================================================== # |
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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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