ClassicalBootstrap {FuzzyResampling}

Classical bootstrap procedure for triangular and trapezoidal fuzzy numbers

Description

ClassicalBootstrap returns the bootstrapped (secondary) sample based on the initial sample and uses the Efron's (i.e. classical) resampling scheme (see (Efron, 1994)).

Usage

ClassicalBootstrap(initialSample, b = n, increases = FALSE)

Arguments

initialSample

Initial sample of triangular or trapezoidal fuzzy numbers.

b

The number of fuzzy values in the resampled (secondary) sample. If this parameter is not specified, the number of values in the initial sample is used. The parameter should be integer value more than 0.

increases

If TRUE is used, then the initial sample should consist of the fuzzy numbers in the form: left increment of the support, left end of the core, right end of the core, right increment of the support. Otherwise, the default value FALSE is used and the fuzzy numbers should be given in the form: left end of the support, left end of the core, right end of the core, right end of the support.

Details

The initial sample should consist of triangular or trapezoidal fuzzy numbers, given as a single vector or a whole matrix. In each row, there should be a single fuzzy number in one of the forms:

1. left end of the support, left end of the core, right end of the core, right end of the support, or

2. left increment of the support, left end of the core, right end of the core, right increment of the support.

In this second case, the parameter increases=TRUE has to be used.

The resampling procedure produces b fuzzy values, which are randomly chosen (with repetition) from the initial sample (without any alternations). If the parameter b is not specified, it is equal to the length of the initial sample. The output is given in the same form as the initial sample.

Value

This function returns matrix with b rows of double values. In each row, there is a single resampled fuzzy number. These fuzzy numbers have the same form as the values from the initial sample depending on the provided parameter increases.

References

Efron, B. (1994). An Introduction to the Bootstrap. CRC Press

See Also

VAMethod for the VA method, EWMethod for the EW method, VAFMethod for the VAF method, VAAMethod for the VAA method, DMethod for the d method, WMethod for the w method

Other resampling functions: DMethod(), EWMethod(), VAAMethod(), VAFMethod(), VAMethod(), WMethod()

Examples

# prepare some fuzzy numbers (first type of the initial sample)

fuzzyValues <- matrix(c(0.25,0.5,1,1.25,0.75,1,1.5,2.2,-1,0,0,2),
ncol = 4,byrow = TRUE)

# generate bootstrap sample

set.seed(12345)

ClassicalBootstrap(fuzzyValues)

ClassicalBootstrap(fuzzyValues,b=4)

# prepare some fuzzy numbers (second type of the initial sample)

fuzzyValuesInc <- matrix(c(0.25,0.5,1,0.25,0.25,1,1.5,0.7,1,0,0,2),
ncol = 4,byrow = TRUE)

# generate bootstrap sample

ClassicalBootstrap(fuzzyValuesInc,increases = TRUE)

ClassicalBootstrap(fuzzyValuesInc,b=4,increases = TRUE)