On the Use of Information Criteria for Model Selection in Phylogenetics. (5th November 2019)

• Record Type: Journal Article
• Authors: Susko, Edward; Roger, Andrew J
• Editors: Saitou, Naruya
• Is Part Of: Molecular biology and evolution. Volume 37:Number 2(2020)
• Page Start: 549
• Page End: 562
• Publication Date: 2019-11-05
• Subjects: Akaike information criteria -- Bayesian information criteria -- cross-validation -- model selection -- partition model -- mixture model -- phylogenetics; Molecular biology -- Periodicals; Molecular evolution -- Periodicals; Evolution, Molecular -- Periodicals; Molecular Biology -- Periodicals; 572.8
• DOI: 10.1093/molbev/msz228
• Languages: English
• ISSNs: 0737-4038

Abstract

Abstract: The information criteria Akaike information criterion (AIC), AICc, and Bayesian information criterion (BIC) are widely used for model selection in phylogenetics, however, their theoretical justification and performance have not been carefully examined in this setting. Here, we investigate these methods under simple and complex phylogenetic models. We show that AIC can give a biased estimate of its intended target, the expected predictive log likelihood (EPLnL) or, equivalently, expected Kullback–Leibler divergence between the estimated model and the true distribution for the data. Reasons for bias include commonly occurring issues such as small edge-lengths or, in mixture models, small weights. The use of partitioned models is another issue that can cause problems with information criteria. We show that for partitioned models, a different BIC correction is required for it to be a valid approximation to a Bayes factor. The commonly used AICc correction is not clearly defined in partitioned models and can actually create a substantial bias when the number of parameters gets large as is the case with larger trees and partitioned models. Bias-corrected cross-validation corrections are shown to provide better approximations to EPLnL than AIC. We also illustrate how EPLnL, the estimation target of AIC, can sometimes favor an incorrect model and give reasons for why selection of incorrectly under-partitioned models might be desirable in partitioned model settings.