R/ks_test.R

In nim-ach/seasonalSensitivity: Data and analysis from the seasonal sensitivity study

#' Prueba de normalidad de Lilliefors (Kolmogorov-Smirnov)
#'
#' @description
#' `r lifecycle::badge("experimental")`
#'
#' Realiza la prueba de Lilliefors (Kolmogorov-Smirnov) para la hipótesis de normalidad.
#' Para más detalles ver `nortest::lillie.test()`.
#'
#' @param x A numeric vector to be tested.
#'
#' @export

ks_test <- function(x)  {

  DNAME <- deparse(substitute(x))
  x <- sort(x[!is.na(x)])
  n <- length(x)

  if (n < 5) stop("sample size must be greater than 4")

  p <- stats::pnorm((x - mean(x)) / stats::sd(x))
  Dplus <- max(seq(1:n) / n - p)
  Dminus <- max(p - (seq(1:n) - 1) / n)
  K <- max(Dplus, Dminus)

  if (n <= 100) {
    Kd <- K
    nd <- n
  } else {
    Kd <- K * ((n / 100) ^ 0.49)
    nd <- 100
  }

  pvalue <- exp(-7.01256 * Kd^2 * (nd + 2.78019) + 2.99587 *
    Kd * sqrt(nd + 2.78019) - 0.122119 + 0.974598 / sqrt(nd) +
    1.67997 / nd)

  if (pvalue > 0.1) {
    KK <- (sqrt(n) - 0.01 + 0.85 / sqrt(n)) * K
    if (KK <= 0.302) {
        pvalue <- 1
    } else if (KK <= 0.5) {
        pvalue <- 2.76773 - 19.828315 * KK + 80.709644 *
            KK ^ 2 - 138.55152 * KK ^ 3 + 81.218052 * KK ^ 4
    } else if (KK <= 0.9) {
        pvalue <- -4.901232 + 40.662806 * KK - 97.490286 *
            KK ^ 2 + 94.029866 * KK ^ 3 - 32.355711 * KK ^ 4
    } else if (KK <= 1.31) {
        pvalue <- 6.198765 - 19.558097 * KK + 23.186922 *
            KK ^ 2 - 12.234627 * KK ^ 3 + 2.423045 * KK ^ 4
    } else {
        pvalue <- 0
    }
  }

  RVAL <- list(statistic = c(D = K),
               p.value = pvalue,
               method = "Lilliefors (Kolmogorov-Smirnov) normality test",
               data.name = DNAME)

  class(RVAL) <- "htest"

  return(RVAL)
}