DOI: 10.5061/dryad.b568p21
title: Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments --part II

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., Barcodes.tar.block_[a-g]) they can be
 re-assembled using the unix command: cat DATASET.tar* |
 tar xzvf -   This archive can then
 be unpacked using the unix command: 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: tar -xvf
 NAME.tar.gz   *in the case of the
 reanalyses, the resulting archive will contain directories for each set of
 reanalyses: IG_chains contains chains originally
 analyzed under a +IG model reanalyzed using a +G only model
 new_heat_chains contains chains reanalyzed using modified heating
 of Metropolis-coupled chains nstMixed_chains contains
 chains reanalyzed using model averaged substitution models
 ManualRunLonger: chains manually examined and reanalyzed by
 running chains for longer ManualHKY: chains manually
 examined and reanalyzed by reducing GTR substitution models top
 HKY ManualempBF: chains manually examined and
 reanalyzed by using estimated base frequencies
 ManualNstMixed: chains manually examined and reanalyzed by using
 model averaged substitution models  
 or as a group using: tar -xvf *.tar.gz (This
 will require an extremely large amount of hard disk space.)