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$simulCase1(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to
be generated.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each
one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$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.
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$simulCase3(n = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to
be generated.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers. Each
one is characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$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.
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$simulFRSTra(n = NA, w1 = NA, w2 = NA, w3 = NA, p = NA, q = NA)
Arguments
npositive integer. It is the number of trapezoidal fuzzy numbers to
be generated.
w1
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w2
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
w3
real number in [0,1]. It should be fulfilled that w1+w2+w3=1.
p
real number > 0. It is the first parameter of the beta distribution.
q
real number > 0. It is the second parameter of the beta distribution.
Returns
a TrapezoidalFuzzyNumberList with n TrapezoidalFuzzyNumbers with
values in the interval [0,1]. Each trapezoidal fuzzy rating response is
characterized by its four values inf0, inf1, sup1, sup0.
Examples
Simulation$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.