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%2F68081766%3A_____%2F21%3A00544030" target="_blank" >RIV/68081766:_____/21:00544030 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26210/21:PU141150 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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
29
UT code for WoS article
000668366600010
EID of the result in the Scopus database
2-s2.0-85103601641