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Empirical problems of the hierarchical likelihood ratio test for model selection
1Division of Paleontology, American Museum of Natural History, New York, NY 10024, USA. dpol@amnh.org
Systematic Biology
|March 15, 2005
Summary
Maximum likelihood (ML) model selection in phylogenetics can be sensitive to starting points and parameter sequences. Different protocols may yield different evolutionary models and tree topologies, impacting phylogenetic inference.
Area of Science:
- Evolutionary Biology
- Computational Biology
- Bioinformatics
Background:
- Maximum likelihood (ML) methods are widely used in phylogenetics for their objective statistical criteria.
- The likelihood ratio test (LRT) is the predominant model-selection criterion in phylogenetic analyses.
- Current LRT implementations require arbitrary choices for starting points and parameter addition/removal sequences.
Purpose of the Study:
- To assess the influence of different starting points and parameter sequences on ML model selection and tree topology.
- To investigate the impact of varying model-selection protocols on phylogenetic inference.
- To explore potential solutions and alternative criteria for robust model selection in phylogenetics.
Main Methods:
- Testing several alternative starting points and parameter sequences.
- Applying these protocols to empirical phylogenetic data sets.
- Comparing the resulting selected models and ML tree topologies.
Main Results:
- Varying model-selection protocols led to the selection of different evolutionary models across the studied data sets.
- In some cases, different selected models resulted in different optimal ML tree topologies.
- The sensitivity of the LRT to protocol choices was confirmed.
Conclusions:
- The choice of starting point and parameter sequence significantly impacts ML model selection and phylogenetic tree inference.
- The current LRT approach in phylogenetics exhibits sensitivity that can affect outcomes.
- Further exploration of alternative model-selection criteria is warranted to address these sensitivities and improve phylogenetic analyses.
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