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

Function returns correct values

Code
  set.seed(1234567)
  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")
Output
  $original
  [1] 0.6022571

  $value
  $value$X1
  Trapezoidal fuzzy number with:
     support=[0.264901,0.620074],
        core=[0.602257,0.602257].

Code
  set.seed(1234567)
  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")
Output
  $original
   [1]  0.6022571  1.0515808  1.1707096 -0.2010067  1.9470200 -0.4471801
   [7] -1.3579031 -1.0830953  1.0460586  1.4999121

  $value
  $value$X1
  Trapezoidal fuzzy number with:
     support=[-1.63434,2.36678],
        core=[-1.29698,1.84901].

  $value$X2
  Trapezoidal fuzzy number with:
     support=[0.0474095,1.66711],
        core=[0.345371,1.37004].

  $value$X3
  Trapezoidal fuzzy number with:
     support=[0.837073,1.53013],
        core=[0.944438,1.27437].

  $value$X4
  Trapezoidal fuzzy number with:
     support=[-1.52015,0.71556],
        core=[-1.30857,0.23387].

  $value$X5
  Trapezoidal fuzzy number with:
     support=[1.4571,2.46841],
        core=[1.88074,1.96471].

  $value$X6
  Trapezoidal fuzzy number with:
     support=[-0.90152,0.915065],
        core=[-0.773345,0.387909].

  $value$X7
  Trapezoidal fuzzy number with:
     support=[-2.8275,-0.564381],
        core=[-2.3653,-0.783202].

  $value$X8
  Trapezoidal fuzzy number with:
     support=[-1.45215,-0.689634],
        core=[-1.09701,-0.883424].

  $value$X9
  Trapezoidal fuzzy number with:
     support=[0.650088,1.92015],
        core=[0.995504,1.44282].

  $value$X10
  Trapezoidal fuzzy number with:
     support=[0.688557,3.68201],
        core=[1.09081,3.32955].

Code
  set.seed(1234567)
  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")
Output
  $original
  [1] 0.6022571 0.0443457 0.7074159

  $value
  $value$X1
  Piecewise linear fuzzy number with 10 knot(s),
     support=[-1.63434,2.36678],
        core=[-1.29698,1.84901].

  $value$X2
  Piecewise linear fuzzy number with 10 knot(s),
     support=[-1.15848,0.466801],
        core=[-0.76226,0.37051].

  $value$X3
  Piecewise linear fuzzy number with 10 knot(s),
     support=[-1.19169,1.96022],
        core=[-0.944723,1.77184].

Code
  set.seed(1234567)
  SimulateSample(n = 5, 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 = 0, type = "PLFN")
Output
  $original
  [1]  0.6022571  1.0515808  1.1707096 -0.2010067  1.9470200

  $value
  $value$X1
  Trapezoidal fuzzy number with:
     support=[-1.63434,2.36678],
        core=[-1.29698,1.84901].

  $value$X2
  Trapezoidal fuzzy number with:
     support=[0.0474095,1.66711],
        core=[0.345371,1.37004].

  $value$X3
  Trapezoidal fuzzy number with:
     support=[0.837073,1.53013],
        core=[0.944438,1.27437].

  $value$X4
  Trapezoidal fuzzy number with:
     support=[-1.52015,0.71556],
        core=[-1.30857,0.23387].

  $value$X5
  Trapezoidal fuzzy number with:
     support=[1.4571,2.46841],
        core=[1.88074,1.96471].