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A Practical Guide to Phylogenetics for Nonexperts

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Assessing parameter identifiability in phylogenetic models using data cloning.

José Miguel Ponciano¹, J Gordon Burleigh, Edward L Braun

¹Department of Biology, University of Florida, Gainesville, FL 32611, USA. josemi@ufl.edu

Systematic Biology

|June 1, 2012

Summary

This summary is machine-generated.

Data Cloning (DC) offers a practical method to assess parameter identifiability in phylogenetic models. This approach helps distinguish nonidentifiability from other estimation issues, improving model validation in computational phylogenetics.

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Area of Science:

Computational Phylogenetics
Statistical Modeling
Bioinformatics

Background:

Model-based phylogenetics requires robust validation methods for complex, computer-intensive models.
Practical guidance on reliable parameter identification in phylogenetic models is limited.
Distinguishing structural parameter nonidentifiability (NI) from other estimability problems is crucial.

Purpose of the Study:

To illustrate how Data Cloning (DC) can diagnose structural parameter nonidentifiability (NI).
To differentiate NI from parameters that are not estimable (INE) or weakly estimable (WE).
To provide a tool for assessing parameter identifiability in complex phylogenetic models.

Main Methods:

Utilized Data Cloning (DC), a methodology for computing maximum likelihood estimates and asymptotic variance.
Applied DC theorem using established Bayesian computational techniques.
Leveraged Bayesian phylogenetics software for diagnosing nonidentifiability.

Main Results:

DC effectively diagnoses structural parameter nonidentifiability (NI).
DC distinguishes NI from INE and WE parameter issues.
DC can examine and mitigate the influence of priors in model analysis.

Conclusions:

Data Cloning provides a powerful and simple tool for detecting nonidentifiability in complex phylogenetic models.
DC aids in assessing the identifiability of discrete parameters, such as tree topology.
DC can inform the development of efficient sampling methods for computationally intensive posterior densities.