DOI: 10.5061/dryad.g4vp6jv
title: Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments -- part I

description: Nearly all current Bayesian phylogenetic applications rely on Markov chain
 Monte Carlo (MCMC) methods to approximate the posterior distribution for
 trees and other parameters of the model. These approximations are only
 reliable if Markov chains adequately converge and sample from the joint
 posterior distribution. While several studies of phylogenetic MCMC
 convergence exist, these have focused on simulated datasets or select
 empirical examples. Therefore, much that is considered common knowledge
 about MCMC in empirical systems derives from a relatively small family of
 analyses under ideal conditions. To address this, we present an overview
 of commonly applied phylogenetic MCMC diagnostics and an assessment of
 patterns of these diagnostics across more than 18,000 empirical analyses.
 Many analyses appeared to perform well and failures in convergence were
 most likely to be detected using the average standard deviation of split
 frequencies, a diagnostic that compares topologies among independent
 chains. Different diagnostics yielded different information about failed
 convergence, demonstrating that multiple diagnostics must be employed to
 reliably detect problems. The number of taxa and average branch lengths in
 analyses have clear impacts on MCMC performance, with more taxa and
 shorter branches leading to more difficult convergence. We show that the
 usage of models that include both Γ-distributed among-site rate variation
 and a proportion of invariable sites are not broadly problematic for MCMC
 convergence but are also unnecessary. Changes to heating and the usage of
 model-averaged substitution models can both offer improved convergence in
 some cases, but neither are a panacea.
description: These
 files make up larger tar files that have been split into &lt; 10 GB
 blocks in order to facilitate downloads. Once all blocks have been
 downloaded for one dataset (e.g., Amniotes.tar.block_[a-g]) they can be
 re-assembled using the unix command:<br>   cat DATASET.tar* | tar
 xzvf - This archive can then be unpacked using the unix
 command:<br>   tar -xvf DATASET.tar The resulting
 archives contain tar.gz files for the individual MrBayes analyses. They
 can be unpacked individually using the unix command:<br>   tar -xvf
 NAME.tar.gz or as a group using:<br>   tar -xvf
 *.tar.gz (This will require an extremely large amount of hard disk
 space.) each tar.gz contains several files:
 NAME.nex     --   (the data that was analyzed. Most of these
 files also contain analysis settings in a Bayes Block at the end of the
 file.)<br> NAME.bb      --   (some analyses may contain a separate
 bayes block with analysis settings when these were not included in the
 '.nex' file.)<br> NAME.run1.p  --   (continuous parameter
 samples from run 1)<br> NAME.run1.t  --   (tree samples from run
 1)<br> NAME.run2.p  --   (continuous parameter samples from run
 2)<br> NAME.run2.t  --   (tree samples from run 2)<br> some
 analyses will contain tree and parameter samples from additional runs,
 indicated by the suffix run3, run4, etc.<br> NAME.mcmc    --   (mcmc
 information from this analysis)<br> NAME_mb.log  --   (log file from
 this analysis)<br> NAME.pstat   --   (table of parameter
 estimates)<br> NAME.lstat   --   (table of marginal likelihood
 estimates)<br> NAME.ckp     --   (checkpoint file that can be used
 to restart the analysis)