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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 #
# 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. #
# doi:10.18637/jss.v104.i03 #
# #
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# Developed at the University of Groningen, the Netherlands, in #
# 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 #
# 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. #
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# 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. #
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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
#' @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
#' @export
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#' @examples
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#' kurtosis(rnorm(10000))
#' 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 )
meet_criteria ( excess , allow_class = " logical" , has_length = 1 )
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UseMethod ( " kurtosis" )
}
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#' @method kurtosis default
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#' @rdname kurtosis
#' @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 )
meet_criteria ( excess , allow_class = " logical" , has_length = 1 )
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x <- as.vector ( x )
if ( na.rm == TRUE ) {
x <- x [ ! is.na ( x ) ]
}
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
#' @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 )
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
#' @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 )
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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}