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Selecting the best-fit model of nucleotide substitution (2001)
| Venue: | Syst |
| Citations: | 133 - 2 self |
BibTeX
@ARTICLE{Posada01selectingthe,
author = {David Posada and Keith and A. Crandall},
title = {Selecting the best-fit model of nucleotide substitution},
journal = {Syst},
year = {2001}
}
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Abstract
Abstract.—Despite the relevant role of models of nucleotide substitution in phylogenetics, choosing among different models remains a problem. Several statistical methods for selecting the model that best ts the data at hand have been proposed, but their absolute and relative performance has not yet been characterized. In this study, we compare under various conditions the performance of different hierarchical and dynamic likelihood ratio tests, and of Akaike and Bayesian information methods, for selecting best-t models of nucleotide substitution. We specically examine the role of the topology used to estimate the likelihood of the different models and the importance of the order in which hypotheses are tested. We do this by simulating DNA sequences under a known model of nucleotide substitution andrecording howoften this truemodel is recovered by thedifferentmethods.Our results suggest thatmodel selection is reasonablyaccurateandindicate that some likelihood ratio testmethods perform overall better than the Akaike or Bayesian information criteria. The tree used to estimate the likelihood scores does not inuence model selection unless it is a randomly chosen tree. The order in which hypotheses are tested, and the complexity of the initial model in the sequence of tests, inuence model selection in some cases. Model tting in phylogenetics has been suggested for many years, yet many authors still arbitrarily choose their models, often using the default models implemented
Keyphrases
nucleotide substitution best-fit model different model inuence model selection likelihood ratio testmethods initial model thatmodel selection many year relevant role dna sequence many author likelihood score default model bayesian information criterion several statistical method best-t model randomly chosen tree model selection various condition dynamic likelihood ratio test relative performance bayesian information method known model
