Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to comparative analyses.
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| Title: | Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to comparative analyses. |
|---|---|
| Authors: | Chen, Zheng-Lin1 (AUTHOR), Niu, Deng-Ke1 (AUTHOR) |
| Source: | Evolution. May2026, Vol. 80 Issue 5, p912-925. 14p. |
| Subjects: | Eigenvectors, Phylogeny, Phylogenetic models, Robust statistics, Evolutionary theories, Comparative studies, Statistical models |
| Abstract: | Phylogenetic eigenvector regression (PVR) is widely used in ecology and evolution by representing phylogenetic structure through separable eigenvectors (EVs). Despite this flexibility, its implementation faces three key challenges: (1) the selection of EVs, (2) the reduced robustness of ordinary least-squares (OLS) regression under shift-like evolutionary heterogeneity, and (3) the applicability of conventional model complexity rules such as the "samples-per-variable (SPV) ≥ 10" guideline. Here, we propose an optimized PVR framework that addresses these limitations. First, we show that trait-specific selections of EVs often diverge, sometimes producing inconsistent results, and that using their union offers stronger control of phylogenetic nonindependence. Second, we evaluate robust regression estimators within PVR, demonstrating that PVR-MM—and in most cases PVR-L2, the standard OLS estimator—maintains high accuracy under nonstationary evolutionary shifts, where other nonrobust methods fail. Third, through simulation, we reassess the SPV ≥ 10 rule, showing that PVR tolerates EV counts well beyond this threshold, offering greater flexibility while requiring attention to potential overfitting. Extensive simulations across diverse trees and evolutionary scenarios confirm that the optimized framework improves accuracy and robustness. By addressing key aspects of EV selection, regression, and model complexity, our findings strengthen the reliability and applicability of PVR. [ABSTRACT FROM AUTHOR] |
| Copyright of Evolution is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194211874 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to comparative analyses. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Zheng-Lin%22">Chen, Zheng-Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Niu%2C+Deng-Ke%22">Niu, Deng-Ke</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Evolution%22">Evolution</searchLink>. May2026, Vol. 80 Issue 5, p912-925. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Eigenvectors%22">Eigenvectors</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogeny%22">Phylogeny</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogenetic+models%22">Phylogenetic models</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+theories%22">Evolutionary theories</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Phylogenetic eigenvector regression (PVR) is widely used in ecology and evolution by representing phylogenetic structure through separable eigenvectors (EVs). Despite this flexibility, its implementation faces three key challenges: (1) the selection of EVs, (2) the reduced robustness of ordinary least-squares (OLS) regression under shift-like evolutionary heterogeneity, and (3) the applicability of conventional model complexity rules such as the "samples-per-variable (SPV) ≥ 10" guideline. Here, we propose an optimized PVR framework that addresses these limitations. First, we show that trait-specific selections of EVs often diverge, sometimes producing inconsistent results, and that using their union offers stronger control of phylogenetic nonindependence. Second, we evaluate robust regression estimators within PVR, demonstrating that PVR-MM—and in most cases PVR-L2, the standard OLS estimator—maintains high accuracy under nonstationary evolutionary shifts, where other nonrobust methods fail. Third, through simulation, we reassess the SPV ≥ 10 rule, showing that PVR tolerates EV counts well beyond this threshold, offering greater flexibility while requiring attention to potential overfitting. Extensive simulations across diverse trees and evolutionary scenarios confirm that the optimized framework improves accuracy and robustness. By addressing key aspects of EV selection, regression, and model complexity, our findings strengthen the reliability and applicability of PVR. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Evolution is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194211874 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/evolut/qpag050 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 912 Subjects: – SubjectFull: Eigenvectors Type: general – SubjectFull: Phylogeny Type: general – SubjectFull: Phylogenetic models Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: Evolutionary theories Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Statistical models Type: general Titles: – TitleFull: Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to comparative analyses. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Zheng-Lin – PersonEntity: Name: NameFull: Niu, Deng-Ke IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00143820 Numbering: – Type: volume Value: 80 – Type: issue Value: 5 Titles: – TitleFull: Evolution Type: main |
| ResultId | 1 |
