Imputation Procedures and Quality Tests for Fuzzy Data
Statistical epistemic tests the imputed values.
Calculation of the fuzzy measures for the imputed values.
Calculation of the errors for the imputed values.
Fuzzyfing the crisp values.
Main method to impute fuzzy values.
Conversion of a list of fuzzy numbers into a matrix.
DIMP (d-imputation) method for fuzzy numbers.
Battery of test for the imputed fuzzy values.
Introducing NAs to the specified matrix.
Conversion of a matrix to a list of fuzzy numbers.
Function to calculate the AHD distance between two fuzzy numbers.
Function to calculate the Euclidean distance between two fuzzy numbers...
Function to calculate the HSD distance between two fuzzy numbers.
A vector containing names of the resampling methods.
Comparison of imputation methods for fuzzy values.
Removing values that are not fuzzy numbers.
Calculation of statistical measures for errors of the imputed data.
Print summary of the benchmark for the imputation method.
Print summary of the comparison of the imputation methods.
Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.