IS YOUR PHYLOGENY INFORMATIVE? MEASURING THE POWER OF COMPARATIVE METHODS

  1. Carl Boettiger1,
  2. Graham Coop2,
  3. Peter Ralph2

DOI: 10.1111/j.1558-5646.2012.01574.x

Additional Information

Author Information

  1. 1

    Center for Population Biology, University of California, Davis, United States

  2. 2

    Department of Evolution and Ecology, University of California, Davis, United States

  1. This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1111/j.1558-5646.2012.01574.x

Publication History

  1. Accepted manuscript online: 18 JAN 2012 12:35AM EST
  2. Received June 14, 2011, Accepted December 13, 2011

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Keywords:

  • Comparative method;
  • sep phylogenetics;
  • sep model choice;
  • sep information criteria;
  • sep parametric bootstrap

ABSTRACT

Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the \texttt{pmc} (``Phylogenetic Monte Carlo'') package for the \texttt{R} programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.

2012 The Author(s). Journal compilation © 2012 The Society for the Study of Evolution

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