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Comparative methods as a statistical fix: the dangers of ignoring an evolutionary model
Rob P Freckleton1, Natalie Cooper, Walter Jetz
1Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, United Kingdom. r.freckleton@sheffield.ac.uk
The American Naturalist
|June 15, 2011
Summary
Generalized least squares (GLS) methods outperform phylogenetic eigenvector regression (PVR) in comparative analyses. GLS offers more efficient parameter estimation and lower error rates, proving superior for evolutionary studies.
Area of Science:
- Ecology and evolutionary biology
- Comparative methods in biology
Background:
- Comparative methods are crucial in ecology and evolution, often relying on explicit evolutionary models.
- Recent popular comparative techniques lack an evolutionary basis or null model, raising concerns about their reliability.
Purpose of the Study:
- To highlight the limitations of non-evolutionary comparative methods.
- To compare the performance of generalized least squares (GLS) and phylogenetic eigenvector regression (PVR) using simulations.
Main Methods:
- Simulations were employed to compare two comparative methods: generalized least squares (GLS) and phylogenetic eigenvector regression (PVR).
- The study assessed parameter estimation efficiency, variance in estimates, phylogenetic signal in residuals, and Type I error rates.
Main Results:
- Generalized least squares (GLS) methods demonstrated greater efficiency in parameter estimation and lower variance compared to PVR.
- GLS methods exhibited lower phylogenetic signal in residuals and reduced Type I error rates.
- The findings suggest that GLS methods are more robust for comparative analyses.
Conclusions:
- Generalized least squares (GLS) methods are more efficient and reliable for comparative analyses than phylogenetic eigenvector regression (PVR).
- GLS methods offer flexibility for optimization and can be adapted to various datasets.
- The superiority of GLS is likely generalizable to other eigenvector methods controlling for space and phylogeny.
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