DOI: 10.5061/dryad.rr6400b4
title: Data from: Assessing parameter identifiability in phylogenetic models using Data Cloning

description: The success of model-based methods in phylogenetics has motivated much
 research aimed at generating new, biologically informative models. This
 new computer-intensive approaches to phylogenetics demands validation
 studies and sound measures of performance. To date such work has consisted
 only of simulation studies, estimation of known phylogenies and difficult
 mathematical analyses assessing the estimability of parameters. Little
 practical guidance has been available to practitioners and theoreticians
 alike as to when and why the parameters in a particular model can be
 identified reliably. Here, we illustrate how Data Cloning (DC), a recently
 developed methodology to compute the Maximum Likelihood estimates along
 with their asymptotic variance, can be used to diagnose structural
 parameter non-identifiability (NI) and distinguish it from other parameter
 estimability problems including the case where parameters are structurally
 identifiable, but are not estimable in given data set (INE), and the case
 where parameters are identifiable, and estimable, but only weakly so (WE).
 The application of the DC theorem uses well-known and widely used Bayesian
 computational techniques. With the DC approach, practitioners can use any
 Bayesian phylogenetics software to be able to diagnose
 non-identifiability. Theoreticians and practitioners alike now have a
 powerful tool to detect non-identifiability while investigating complex
 modeling scenarios, where getting closed-form expressions in a
 probabilistic study is complicated. Furthermore, here we also show how DC
 can be used as a tool to examine and eliminate the influence of the
 priors, in particular if the process of prior elicitation is not
 straightforward. Finally, when applied to phylogenetic inference, DC can
 be used to study at least two important statistical questions: assessing
 identifiability of discrete parameters, like the tree topology, and
 developing efficient sampling methods for computationally expensive
 posterior densities.
description: atpBSee details in: Moore,
 M., P. Soltis, C. Bell, J. Burleigh, and D. Soltis. 2010. Phylogenetic
 analysis of 83 plastid genes further resolves the early diversi cation of
 eudicots. Proc. Natl. Acad. Sci. 107:4623-4628.chloroplast.86taxaSee details in: Moore, M., P. Soltis, C. Bell, J. Burleigh, and D. Soltis. 2010. Phylogenetic analysis of 83 plastid genes further resolves the early diversi cation of eudicots. Proc. Natl. Acad. Sci. 107:4623-4628.matKSee details in: Moore, M., P. Soltis, C. Bell, J. Burleigh, and D. Soltis. 2010. Phylogenetic analysis of 83 plastid genes further resolves the early diversi cation of eudicots. Proc. Natl. Acad. Sci. 107:4623-4628.primatesThis is the Primates data set from: Hayasaka, K., T. Gojobori, and S. Horai. 1988. Molecular phylogeny and evolution of primate mitochondrial dna. Mol. Biol. Evol. 5:626-644.rbcLThis is the rbcL sequence of a gene from multiple taxa, see details in Moore, M., P. Soltis, C. Bell, J. Burleigh, and D. Soltis. 2010. Phylogenetic analysis of 83 plastid genes further resolves the early diversi cation of eudicots. Proc. Natl. Acad. Sci. 107:4623-4628.