View in EDS

Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to comparative analyses.

Saved in:

Bibliographic Details
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

Description
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]
ISSN:	00143820
DOI:	10.1093/evolut/qpag050