Independent contrasts and PGLS regression estimators are equivalent
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We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
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Modeling the errors using fat-tailed distributions may mitigate the impact of singular evolutionary events on the estimation of the slope in PGLS and other linear modeling approaches.
“For PGLS and other linear modeling approaches, modeling the errors using fat-tailed distributions (Blomberg et al. 2012;Landis et al. 2012;Elliot and Mooers 2014;Duchen et al. 2017) may mitigate the impact of singular evolutionary events on the estimation of the slope (also see Slater and Pennell 2013, for an alternative approach using robust regression).”
From Rethinking phylogenetic comparative methods (2018) · cites this paper as Blomberg et al. 2012
Cites
Phylogenies and the Comparative Method
Felsenstein, J. (1985) · The American Naturalist · doi:10.1086/284325 · Cited by 9,793 · see citing works
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050 · cited 2× · see citing works
“A uniform prior can be used for σ (Gelman 2006).”
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Rethinking phylogenetic comparative methods
Uyeda, J. C., Zenil-Ferguson, R., & Pennell, M. W. (2018) · Systematic Biology · doi:10.1093/sysbio/syy031 · Cited by 313 · see citing works
- Authors
- Blomberg, S. P., Lefevre, J. G., Wells, J. A., & Waterhouse, M.
- Affiliations
- The University of Queensland (AU)
- Queensland University of Technology (AU)
- Corresponding author
- Blomberg, Simon P.
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-03
- Volume
- 61
- Issue
- 3
- Pages
- 382-391
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W1964959183
- Cited by
- 158
- References
- 2
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 7 · 2023: 28 · 2022: 8 · 2021: 11 · 2020: 7 · 2019: 11 · 2018: 9 · 2017: 4 · 2016: 2 · 2015: 26 · 2014: 16 · 2013: 11 · 2012: 2
- FWCI
- 13.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Systematic Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Physical Sciences › Mathematics › Geometry and Topology › Morphological variations and asymmetry
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- phylogenetically independent contrasts, generalized least squares, Brownian motion model, ordinary least squares, best linear unbiased estimator, phylogenetic regression, phylogenetic uncertainty, comparative analysis
- MeSH terms
- Algorithms, Bayes Theorem, Classification, Computer Simulation, Data Interpretation, Statistical, Phylogeny, Least-Squares Analysis
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