ComparisonSEMean: Comparison of the resampling approaches based on the SE/MSE...

In FuzzyResampling: Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers

Comparison of the resampling approaches based on the SE/MSE for the mean.

Description

ComparisonSEMean estimates the standard error (SE) or the mean-squared error (MSE) for the mean for all resampling approaches.

Usage

ComparisonSEMean(
  generator,
  sampleSize = 10,
  numberOfSamples = 100,
  repetitions = 100,
  trueMean = NA,
  theta = 1/3,
  ...
)

Arguments

generator	Name of the generator for sampling initial samples. For the possible names check the values of samplingGenerators vector.
sampleSize	Size of the single initial sample.
numberOfSamples	Number of the initial samples.
repetitions	Number of the secondary samples which are created using the selected resampling method.
trueMean	If the value is given, then the mean-squared error (MSE) is calculated for this value and the means of the bootstrapped samples. Otherwise, the standard error (SE) is calculated based on the overall mean of the secondary samples.
theta	The weighting parameter for the mid/spread distance applied in the C-test.
...	Parameters which are passed to SEResamplingMean or the respective generator

Details

The function generates a sequence of initial samples (their number is given in initialSamples, the size is determined by sampleSize) for fuzzy numbers of the type specified by generator. Then the SE/MSE is calculated for each combination of the initial sample and resampling method using SEResamplingMean. The output values are the SE/MSE averaged by initialSamples.

Value

This function returns a vector of the averaged SE/MSE for the mean.

References

Bertoluzza, C., Corral, N., Salas, A. (1995) On a new class of distances between fuzzy numbers Mathware and Soft Computing, 2 (2), pp. 71-84

Grzegorzewski, P., Romaniuk, M. (2022) Bootstrap methods for fuzzy data Uncertainty and Imprecision in Decision Making and Decision Support: New Advances, Challenges, and Perspectives, pp. 28-47 Springer

See Also

ComparisonOneSampleCTest for the comparison of resampling methods based on power for the one-sample C-test for the mean, ComparePowerOneSampleCTest for the comparison of resampling methods based on power for the one-sample C-test for the mean.

Other comparison of resampling methods: ComparePowerOneSampleCTest(), ComparisonOneSampleCTest()

Examples

## Not run: 

# seed PRNG

set.seed(1234)

# calculate the SE of the mean for the synthetic data generated using GeneratorNU function

ComparisonSEMean(generator = "GeneratorNU",sampleSize = 10,
 numberOfSamples = 100, repetitions = 10,mu = 0, sigma = 1,a = 0.5, b = 1)
## End(Not run)

FuzzyResampling documentation built on Oct. 4, 2024, 5:11 p.m.