| Simulation {FuzzyStatTraEOO} | R Documentation |
'Simulation' contains several methods to simulate 'TrapezoidalFuzzyNumberLists'.
Description
Simulation contains 5 different methods that gives the user a 'TrapezoidalFuzzyNumberList'.
Methods
Public methods
Method simulCase1()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a symmetric distribution and with independent components (for a detailed explanation of the simulation see Sinova et al. (2012) [3], namely, the Case 1 for noncontaminated samples).
Usage
Simulation$simulCase1(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to be generated.
Details
See examples.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$new()$simulCase1(10L)
Method simulCase2()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a symmetric distribution and with dependent components (for a detailed explanation of the simulation see Sinova et al. (2012) [3], namely, the Case 2 for noncontaminated samples).
Usage
Simulation$simulCase2(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to be generated.
Details
See examples.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$new()$simulCase2(10L)
Method simulCase3()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a asymmetric distribution and with independent components (for a detailed explanation of the simulation see Sinova et al. (2012) [4], namely, the Case 3 for noncontaminated samples).
Usage
Simulation$simulCase3(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to be generated.
Details
See examples.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$new()$simulCase3(10L)
Method simulCase4()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a asymmetric distribution and with dependent components (for a detailed explanation of the simulation see Sinova et al. (2012) [4], namely, the Case 4 for noncontaminated samples).
Usage
Simulation$simulCase4(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to be generated.
Details
See examples.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$new()$simulCase4(10L)
Method simulFRSTra()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' based on the fuzzy rating scale. They are simulated mimicking the human behavior, considering for it a finite mixture of three different procedures (for a detailed explanation of the simulation see De la Rosa de Saa et al. (2012) [1]), and generated in the interval [0,1].
Usage
Simulation$simulFRSTra(n = NA, w1 = NA, w2 = NA, w3 = NA, p = NA, q = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to be generated.
w1real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w2real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w3real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
preal number > 0. It is the first parameter of the beta distribution.
qreal number > 0. It is the second parameter of the beta distribution.
Details
See examples.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers with values in the interval [0,1]. Each trapezoidal fuzzy rating response is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$new()$simulFRSTra(100L,0.05,0.35,0.6,2,1)
Method clone()
The objects of this class are cloneable with this method.
Usage
Simulation$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Note
In case you find (almost surely existing) bugs or have recommendations for improving the method comments are welcome to the below mentioned mail addresses.
Author(s)
(s) Andrea Garcia Cernuda <uo270115@uniovi.es>, Asun Lubiano <lubiano@uniovi.es>, Sara de la Rosa de Saa
References
[1] De la Rosa de Saa, S.; Gil, M.A.; Gonzalez-Rodriguez, G.; Lopez, M.T.; Lubiano M.A.: Fuzzy rating scale-based questionnaires and their statistical analysis, IEEE Transactions on Fuzzy Systems 23(1), 111-126 (2015)
[2] Lubiano, M.A.; Salas, A.; Carleos, C.; De la Rosa de Sáa, S.; Gil, M.Á.: Hypothesis testing-based comparative analysis between rating scales for intrinsically imprecise data, International Journal of Approximate Reasoning 88, 128-147 (2017)
[3] Sinova, B.; Gil, M.A.; Colubi, A.; Van Aelst, S.: The median of a random fuzzy number. The 1-norm distance approach, Fuzzy Sets and Systems 200, 99-115 (2012)
[4] Sinova, B.; Gil, M.A.; Van Aelst, S.: M-estimates of location for the robust central tendency of fuzzy data, IEEE Transactions on Fuzzy Systems 24(4), 945-956 (2016)
Examples
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## Method `Simulation$simulCase1`
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Simulation$new()$simulCase1(10L)
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## Method `Simulation$simulCase2`
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Simulation$new()$simulCase2(10L)
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## Method `Simulation$simulCase3`
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Simulation$new()$simulCase3(10L)
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## Method `Simulation$simulCase4`
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Simulation$new()$simulCase4(10L)
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## Method `Simulation$simulFRSTra`
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Simulation$new()$simulFRSTra(100L,0.05,0.35,0.6,2,1)