EpistemicMean.md
In FuzzySimRes: Simulation and Resampling Methods for Epistemic Fuzzy Data

Function returns correct values

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  EpistemicMean(testSample1$value)
Output
  $value
  [1] -0.07957496

  $SE
  [1] NA

  $MSE
  [1] NA

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  EpistemicMean(testSample1$value, cutsNumber = 10)
Output
  $value
  [1] -0.2325832

  $SE
  [1] 0.1211593

  $MSE
  [1] NA

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  EpistemicMean(testSample1$value, cutsNumber = 10, trueValue = 0.1)
Output
  $value
  [1] -0.2325832

  $SE
  [1] 0.1211593

  $MSE
  [1] 0.1238232

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  EpistemicMean(testSample1$value, cutsNumber = 12, trueValue = 0.1)
Output
  $value
  [1] -0.1716568

  $SE
  [1] 0.1254708

  $MSE
  [1] 0.08822842

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 1, originalPD = "rnorm", parOriginalPD = list(
    mean = 0, sd = 1), suppLeftPD = "runif", parSuppLeftPD = list(min = 0, max = 0.6),
  suppRightPD = "runif", parSuppRightPD = list(min = 0, max = 0.6), type = "triangular")
  EpistemicMean(testSample1$value[[1]], cutsNumber = 12)
Output
  $value
  [1] 0.6149814

  $SE
  [1] 0.1314047

  $MSE
  [1] NA

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 3, 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), knotNumbers = 10, type = "PLFN")
  EpistemicMean(testSample1$value, cutsNumber = 8, trueValue = -0.1)
Output
  $value
  [1] -0.5295721

  $SE
  [1] 0.2914375

  $MSE
  [1] 0.2588511