Cargando…
A novel piecewise-linear method for detecting associations between variables
Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., co...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449123/ https://www.ncbi.nlm.nih.gov/pubmed/37616293 http://dx.doi.org/10.1371/journal.pone.0290280 |
_version_ | 1785094876334915584 |
---|---|
author | Wang, Panru Zhang, Junying |
author_facet | Wang, Panru Zhang, Junying |
author_sort | Wang, Panru |
collection | PubMed |
description | Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients’ default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future. |
format | Online Article Text |
id | pubmed-10449123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104491232023-08-25 A novel piecewise-linear method for detecting associations between variables Wang, Panru Zhang, Junying PLoS One Research Article Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients’ default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future. Public Library of Science 2023-08-24 /pmc/articles/PMC10449123/ /pubmed/37616293 http://dx.doi.org/10.1371/journal.pone.0290280 Text en © 2023 Wang, Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Panru Zhang, Junying A novel piecewise-linear method for detecting associations between variables |
title | A novel piecewise-linear method for detecting associations between variables |
title_full | A novel piecewise-linear method for detecting associations between variables |
title_fullStr | A novel piecewise-linear method for detecting associations between variables |
title_full_unstemmed | A novel piecewise-linear method for detecting associations between variables |
title_short | A novel piecewise-linear method for detecting associations between variables |
title_sort | novel piecewise-linear method for detecting associations between variables |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449123/ https://www.ncbi.nlm.nih.gov/pubmed/37616293 http://dx.doi.org/10.1371/journal.pone.0290280 |
work_keys_str_mv | AT wangpanru anovelpiecewiselinearmethodfordetectingassociationsbetweenvariables AT zhangjunying anovelpiecewiselinearmethodfordetectingassociationsbetweenvariables AT wangpanru novelpiecewiselinearmethodfordetectingassociationsbetweenvariables AT zhangjunying novelpiecewiselinearmethodfordetectingassociationsbetweenvariables |