Simulation {FuzzyStatTraEOO} | R Documentation |
Simulation contains 5 different methods that gives the user a 'TrapezoidalFuzzyNumberList'.
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).
Simulation$simulCase1(n = NA)
n
positive integer. It is the number of trapezoidal fuzzy numbers to be generated.
See examples.
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Simulation$new()$simulCase1(10L)
simulCase2()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a symmetric distribution and with dependent components (for a detailed explanation of the simulation see Sinova et al. (2012) [3], namely, the Case 2 for noncontaminated samples).
Simulation$simulCase2(n = NA)
n
positive integer. It is the number of trapezoidal fuzzy numbers to be generated.
See examples.
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Simulation$new()$simulCase2(10L)
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).
Simulation$simulCase3(n = NA)
n
positive integer. It is the number of trapezoidal fuzzy numbers to be generated.
See examples.
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Simulation$new()$simulCase3(10L)
simulCase4()
This method generates n 'TrapezoidalFuzzyNumbers' contained in a 'TrapezoidalFuzzyNumberList' from a asymmetric distribution and with dependent components (for a detailed explanation of the simulation see Sinova et al. (2012) [4], namely, the Case 4 for noncontaminated samples).
Simulation$simulCase4(n = NA)
n
positive integer. It is the number of trapezoidal fuzzy numbers to be generated.
See examples.
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each one is characterized by its four values inf0, inf1, sup1, sup0.
Simulation$new()$simulCase4(10L)
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].
Simulation$simulFRSTra(n = NA, w1 = NA, w2 = NA, w3 = NA, p = NA, q = NA)
n
positive integer. It is the number of trapezoidal fuzzy numbers to be generated.
w1
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w2
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w3
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
p
real number > 0. It is the first parameter of the beta distribution.
q
real number > 0. It is the second parameter of the beta distribution.
See examples.
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers with values in the interval [0,1]. Each trapezoidal fuzzy rating response is characterized by its four values inf0, inf1, sup1, sup0.
Simulation$new()$simulFRSTra(100L,0.05,0.35,0.6,2,1)
clone()
The objects of this class are cloneable with this method.
Simulation$clone(deep = FALSE)
deep
Whether to make a deep clone.
In case you find (almost surely existing) bugs or have recommendations for improving the method comments are welcome to the below mentioned mail addresses.
(s) Andrea Garcia Cernuda <uo270115@uniovi.es>, Asun Lubiano <lubiano@uniovi.es>, Sara de la Rosa de Saa
[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)
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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)