How Many Bootstrap Replicates Are Necessary?
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations
Computational Statistics & Data Analysis
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Summary: A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations—convergence diagnostics—in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using trace-plots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation. Availability: The program is available as source under the GNU General Public License and as a web application at http://ceb.scs.fsu.edu/awty Contact: jwilgenb@scs.fsu.edu