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Information criteria may unfairly favor partition models over mixture models in phylogenetics. This study suggests these models are not suitable for information-theory based comparisons.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Information criteria (AIC, BIC) are widely used for phylogenetic model selection.
  • The complexity of phylogenetic models has increased, raising concerns about current selection practices.
  • Skepticism exists regarding the reliability of information theory in phylogenetics.

Purpose of the Study:

  • To analyze the suitability of information criteria for comparing partition and mixture models in phylogenetics.
  • To investigate the theoretical and simulation-based performance of information criteria in distinguishing between these model types.

Main Methods:

  • Theoretical analysis of information criteria's inherent biases.
  • Simulation studies to evaluate model selection outcomes.
  • Comparison of partition models versus mixture models using information criteria.

Main Results:

  • Information criteria demonstrate a theoretical bias favoring partition models over mixture models.
  • Simulation results confirm this bias, showing a higher likelihood of selecting partition models.
  • Partition and mixture models exhibit inherent differences making them unsuitable for direct information-criterion comparison.

Conclusions:

  • Information criteria are not appropriate for comparing partition and mixture phylogenetic models.
  • The inherent properties of these models lead to biased selection outcomes.
  • Re-evaluation of model selection methodologies in phylogenetics is warranted.