Data from: Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model

Phylogenetic comparative analysis is an approach to inferring evolutionary process from a combination of phylogenetic and phenotypic data. The last few years have seen increasingly sophisticated models employed in the evaluation of more and more detailed evolutionary hypotheses, including adaptive hypotheses with multiple selective optima and hypotheses with rate variation within and across lineages. The statistical performance of these sophisticated models has received relatively little systematic attention, however. We conducted an extensive simulation study to quantify the statistical properties of a class of models toward the simpler end of the spectrum that model phenotypic evolution using Ornstein-Uhlenbeck processes. We identify key determinants of statistical power in model selection. We find also that model parameter estimates are inherently difficult to estimate accurately, indicating a relative paucity of information in the data relative to these parameters. We therefore recommend that investigators explore the precision of their estimates through resampling methods such as the parametric bootstrap, before basing conclusions on parameter estimates. We argue that weak identifiability of parameter estimates need not forestall meaningful inference based on model selection. Inasmuch as more sophisticated methods include these models as special cases, our results have implications for these more parameter-rich methods. To unsubscribe from this group and stop receiving emails from it, send an email to [e-mail].