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Algorithm 1017: fuzzyreg: An R Package for Fitting Fuzzy Regression Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU141150" target="_blank" >RIV/00216305:26210/21:PU141150 - isvavai.cz</a>

  • Alternative codes found

    RIV/68081766:_____/21:00544030 RIV/00216224:14310/21:00124079

  • Result on the web

    <a href="https://dl.acm.org/doi/10.1145/3451389" target="_blank" >https://dl.acm.org/doi/10.1145/3451389</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3451389" target="_blank" >10.1145/3451389</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Algorithm 1017: fuzzyreg: An R Package for Fitting Fuzzy Regression Models

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE

  • ISSN

    0098-3500

  • e-ISSN

    1557-7295

  • Volume of the periodical

    47

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    18

  • Pages from-to

    1-18

  • UT code for WoS article

    000668366600010

  • EID of the result in the Scopus database

    2-s2.0-85103601641