R/EpistemicTest.R

In FuzzySimRes: Simulation and Resampling Methods for Epistemic Fuzzy Data

#' Apply epistemic test for one or two fuzzy samples.
#'
#' @description
#' `EpistemicTest` calculates the p-value for the given real-valued statistical test using one of the
#' epistemic bootstrap approaches.
#'
#' @details
#' This is a general procedure that can be used to invoke other epistemic bootstrap tests: \code{AverageStatisticEpistemicTest}
#' (if the \code{algorithm} is set to \code{"avs"}), \code{MultiStatisticEpistemicTest} (if \code{algorithm="ms"}), and
#' \code{ResamplingStatisticEpistemicTest} (for \code{algorithm="res"}).
#' For additional details about these procedures and their parameters, see the respective functions.
#'
#'
#'
#' @return
#' The output is given in the form of a real number (the p-value) for the selected statistical test.
#'
#'
#'
#'
#' @param sample1 Sample of fuzzy numbers given in the form of a list or as a single number.
#'
#' @param sample2 Sample of fuzzy numbers given in the form of a list or as a single number (two-sample test case) or
#'  \code{NULL} (one-sample test case).
#'
#' @param algorithm Type of the epistemic bootstrap
#' algorithm used to calculate the output p-value (possible values: \code{avs, ms, res}).
#'
#' @param ... Additional arguments passed to the epistemic test.
#'
#' @family epistemic bootstrap statistical test
#'
#' @seealso \code{\link{MultiStatisticEpistemicTest}} for the epistemic bootstrap test related to multi-statistic approach,
#' \code{\link{ResamplingStatisticEpistemicTest}} for the epistemic bootstrap test related to resampling statistics,
#' \code{\link{AverageStatisticEpistemicTest}} for the epistemic bootstrap test related to averaging statistics,
#'
#' @examples
#'
#' # seed PRNG
#'
#' set.seed(1234)
#'
#' # generate two independent initial fuzzy samples
#'
#' list1<-SimulateSample(20,originalPD="rnorm",parOriginalPD=list(mean=0,sd=1),
#' incrCorePD="rexp", parIncrCorePD=list(rate=2),
#' suppLeftPD="runif",parSuppLeftPD=list(min=0,max=0.6),
#' suppRightPD="runif", parSuppRightPD=list(min=0,max=0.6),
#' type="trapezoidal")
#'
#'
#' list2<-SimulateSample(20,originalPD="rnorm",parOriginalPD=list(mean=0,sd=1),
#' incrCorePD="rexp", parIncrCorePD=list(rate=2),
#' suppLeftPD="runif",parSuppLeftPD=list(min=0,max=0.6),
#' suppRightPD="runif", parSuppRightPD=list(min=0,max=0.6),
#' type="trapezoidal")
#'
#' # apply the Kolmogorov-Smirnov two sample test for two different samples
#' # with the average statistics approach
#'
#' EpistemicTest(list1$value,list2$value,cutsNumber = 30)
#'
#' # apply the Kolmogorov-Smirnov two sample test for two different samples
#' # with the multi-statistic and antithetic approach
#'
#' EpistemicTest(list1$value,list2$value,algorithm = "ms",bootstrapMethod = "anti")
#'
#'
#'@references
#'
#'
#' Grzegorzewski, P., Romaniuk, M. (2022)
#' Bootstrap Methods for Epistemic Fuzzy Data.
#' International Journal of Applied Mathematics and Computer Science, 32(2)
#'
#' Grzegorzewski, P., Romaniuk, M. (2022)
#' Bootstrapped Kolmogorov-Smirnov Test for Epistemic Fuzzy Data.
#' Communications in Computer and Information Science, CCIS 1602, pp. 494-507, Springer
#'
#' Gagolewski, M., Caha, J. (2021) FuzzyNumbers Package: Tools to deal with fuzzy numbers in R.
#' R package version 0.4-7, https://cran.r-project.org/web/packages=FuzzyNumbers
#'
#'
#'
#'
#' @export





EpistemicTest <- function(sample1,sample2,algorithm="avs",...)
{
  # check parameters

  if(length(algorithm) > 1)
  {
    stop("Parameter algorithm should be a single value")
  }

  if(!(algorithm %in% c("avs","ms","res")))
  {
    stop("Parameter algorithm should be a proper name of epistemic bootstrap test - avs, ms or res")
  }


  # check and use the respective epistemic bootstrap test

  if(algorithm=="avs") {

    output <- AverageStatisticEpistemicTest(sample1=sample1,sample2=sample2,...)

  }

  if(algorithm=="ms") {

    output <- MultiStatisticEpistemicTest(sample1=sample1,sample2=sample2,...)

  }

  if(algorithm=="res") {

    output <- ResamplingStatisticEpistemicTest(sample1=sample1,sample2=sample2,...)

  }

  return(output)

}