Algorithm 1017: fuzzyreg: an R package for fitting fuzzy regression models

0544030 - ÚBO 2022 RIV US eng J - Journal Article
Škrabánek, P. - Martínková, Natália
Algorithm 1017: fuzzyreg: an R package for fitting fuzzy regression models.
ACM Transactions on Mathematical Software. Roč. 47, č. 3 (2021), č. článku 29. ISSN 0098-3500. E-ISSN 1557-7295
R&D Projects: GA ČR(CZ) GA17-20286S
Institutional support: RVO:68081766
Keywords : Fuzzy regression * fuzzy set * possibilistic-based fuzzy regression * statistics-based fuzzy regression * R
OECD category: Statistics and probability
Impact factor: 2.464, year: 2021
Method of publishing: Limited access
https://dl.acm.org/doi/10.1145/3451389

Fuzzy regression provides an alternative to statistical regression when the model is indefinite, the relationships between model parameters are vague, the sample size is low, or the data are hierarchically structured. Such cases allow to consider the choice of a regression model based on the fuzzy set theory. In fuzzyreg, we implement fuzzy linear regression methods that differ in the expectations of observational data types, outlier handling, and parameter estimation method. We provide a wrapper function that prepares data for fitting fuzzy linear models with the respective methods from a syntax established in R for fitting regression models. The function fuzzylm thus provides a novel functionality for R through standardized operations with fuzzy numbers. Additional functions allow for conversion of real-value variables to be fuzzy numbers, printing, summarizing, model plotting, and calculation of model predictions from new data using supporting functions that perform arithmetic operations with triangular fuzzy numbers. Goodness of fit and total error of the fit measures allow model comparisons. The package contains a dataset named bats with measurements of temperatures of hibernating bats and the mean annual surface temperature reflecting the climate at the sampling sites. The predictions from fuzzy linear models fitted to this dataset correspond well to the observed biological phenomenon. Fuzzy linear regression has great potential in predictive modeling where the data structure prevents statistical analysis and the modeled process exhibits inherent fuzziness.
Permanent Link: http://hdl.handle.net/11104/0321092