EpistemicBootstrap {FuzzySimRes}

Generate a bootstrap sample with the epistemic bootstrap.

Description

'EpistemicBootstrap' generates the secondary (real-valued) sample from the fuzzy-valued initial sample using the epistemic bootstrap.

Usage

EpistemicBootstrap(fuzzySample, cutsNumber = 1, ...)

Arguments

fuzzySample

Sample of fuzzy numbers given in the form of a list or as a single number.

cutsNumber

Number of cuts used in the epistemic bootstrap.

...

Possible parameters passed to other functions.

Details

The procedure randomly generates the secondary, bootstrapped sample of real values from the initial sample which consists of fuzzy numbers. The procedure applies the so-called epistemic bootstrap. The initial sample should be given in the form of a list of numbers or a single number. These values have to be the fuzzy numbers defined in the FuzzyNumbers package.

Value

The output is given in the form of a real-valued matrix, where the number of rows is equal to the number of cuts, and the number of columns is equal to the length of the initial sample. Rows are denoted with the randomly drawn values of alpha for the consecutive alpha-cuts.

References

Grzegorzewski, P., Romaniuk, M. (2022) Bootstrap Methods for Epistemic Fuzzy Data. International Journal of Applied Mathematics and Computer Science, 32(2)

Grzegorzewski, P., Romaniuk, M. (2022) Bootstrapped Kolmogorov-Smirnov Test for Epistemic Fuzzy Data. Communications in Computer and Information Science, CCIS 1602, pp. 494-507, Springer

Gagolewski, M., Caha, J. (2021) FuzzyNumbers Package: Tools to deal with fuzzy numbers in R. R package version 0.4-7, https://cran.r-project.org/web/packages=FuzzyNumbers

See Also

AntitheticBootstrap for the antithetic epistemic bootstrap algorithm

Other epistemic bootstrap function: AntitheticBootstrap()

Examples

# seed PRNG

set.seed(1234)

# generate the initial sample consisting of 5 trapezoidal fuzzy numbers

sample1 <- SimulateSample(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),
type="trapezoidal")

# apply the epistemic bootstrap with 10 cuts

EpistemicBootstrap (sample1$value, cutsNumber=10)