EpistemicEstimator.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")
  EpistemicEstimator(testSample1$value)
Output
  $value
  [1] 1.142346

  $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")
  EpistemicEstimator(testSample1$value, estimator = "median", cutsNumber = 10)
Output
  $value
  [1] 0.015348

  $SE
  [1] 0.1825477

  $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")
  EpistemicEstimator(testSample1$value, estimator = "median", cutsNumber = 10,
  trueValue = 0.1)
Output
  $value
  [1] 0.015348

  $SE
  [1] 0.1825477

  $MSE
  [1] 0.03715726

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")
  EpistemicEstimator(testSample1$value, estimator = "var", cutsNumber = 5)
Output
  $value
  [1] 1.633499

  $SE
  [1] 0.2497924

  $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")
  EpistemicEstimator(testSample1$value, estimator = "sd", cutsNumber = 8,
  trueValue = 1)
Output
  $value
  [1] 1.116206

  $SE
  [1] 0.1742042

  $MSE
  [1] 0.04005767

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")
  EpistemicEstimator(testSample1$value[[1]], estimator = "mean", cutsNumber = 8,
  trueValue = 0)
Output
  $value
  [1] 0.6969027

  $SE
  [1] 0.1336097

  $MSE
  [1] 0.5012935

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  testSample1Epistemic <- EpistemicBootstrap(testSample1$value, cutsNumber = 8)
  EpistemicEstimator(testSample1Epistemic, estimator = "mean")
Output
  $value
  [1] -0.3019041

  $SE
  [1] 0.02403984

  $MSE
  [1] NA

Code
  set.seed(123456)
  testSample1 <- SimulateSample(n = 10, 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")
  testSample1Epistemic <- EpistemicBootstrap(testSample1$value, cutsNumber = 8)
  EpistemicEstimator(testSample1Epistemic, estimator = "mean", trueValue = 0)
Output
  $value
  [1] -0.3019041

  $SE
  [1] 0.02403984

  $MSE
  [1] 0.7332139