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Assessing the Goodness of Fit of Phylogenetic Comparative Methods: A Meta-Analysis and Simulation Study

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  • Dwueng-Chwuan Jhwueng

Abstract

Background: Phylogenetic comparative methods (PCMs) have been applied widely in analyzing data from related species but their fit to data is rarely assessed. Question: Can one determine whether any particular comparative method is typically more appropriate than others by examining comparative data sets? Data: I conducted a meta-analysis of 122 phylogenetic data sets found by searching all papers in JEB, Blackwell Synergy and JSTOR published in 2002–2005 for the purpose of assessing the fit of PCMs. The number of species in these data sets ranged from 9 to 117. Analysis Method: I used the Akaike information criterion to compare PCMs, and then fit PCMs to bivariate data sets through REML analysis. Correlation estimates between two traits and bootstrapped confidence intervals of correlations from each model were also compared. Conclusions: For phylogenies of less than one hundred taxa, the Independent Contrast method and the independent, non-phylogenetic models provide the best fit.For bivariate analysis, correlations from different PCMs are qualitatively similar so that actual correlations from real data seem to be robust to the PCM chosen for the analysis. Therefore, researchers might apply the PCM they believe best describes the evolutionary mechanisms underlying their data.

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  • Dwueng-Chwuan Jhwueng, 2013. "Assessing the Goodness of Fit of Phylogenetic Comparative Methods: A Meta-Analysis and Simulation Study," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    • Handle: RePEc:plo:pone00:0067001
    DOI: 10.1371/journal.pone.0067001
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    Cited by:

    1. Dwueng-Chwuan Jhwueng, 2021. "Two Gaussian Bridge Processes for Mapping Continuous Trait Evolution along Phylogenetic Trees," Mathematics, MDPI, vol. 9(16), pages 1-14, August.
    2. D.-C. Jhwueng & V. Maroulas, 2016. "Adaptive trait evolution in random environment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2310-2324, September.
    3. Jhwueng, Dwueng-Chwuan, 2020. "Modeling rate of adaptive trait evolution using Cox–Ingersoll–Ross process: An Approximate Bayesian Computation approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).

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