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Missing data estimation in morphometrics: how much is too much?

Julien Clavel1, Gildas Merceron, Gilles Escarguel

  • 1Laboratoire de Géologie de Lyon, UMR 5276, CNRS, UCB Lyon 1, ENS Lyon, Campus de la Doua, 2 rue Raphaël Dubois, 69622 Villeurbanne Cedex, France; and IPHEP, UMR 7262, CNRS & Université de Poitiers, Bat. B35, 6 rue M. Brunet, 86022 Poitiers Cedex, France.

Systematic Biology
|December 17, 2013
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) techniques effectively handle missing fossil data, outperforming simple thresholds. These methods, particularly Fully Conditional Specification and Expectation-Maximization, offer robust solutions for paleontological morphometric analyses.

Keywords:
Missing dataProcrustes superimpositionR functionmorphometricsmultiple imputationordinationsimulation

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Area of Science:

  • Paleontology
  • Evolutionary Biology
  • Computational Biology

Background:

  • Fossil diversity and evolutionary dynamics are often estimated using morphological variation.
  • Post-mortem taphonomic processes frequently alter fossil remains, leading to information loss and hindering quantitative morphometric analyses.
  • Missing data in morphometric datasets pose significant challenges for statistical comparisons of extinct species.

Purpose of the Study:

  • To evaluate the performance of seven multiple imputation (MI) techniques for handling missing data in paleontological morphometrics.
  • To compare the effectiveness of MI methods against proposed thresholds for acceptable missing data proportions.
  • To develop a novel approach combining MI with visualization techniques for assessing imputation effects.

Main Methods:

  • A simulation-based analysis was conducted to assess seven distinct multiple imputation (MI) techniques.
  • Three different patterns of missing data distribution were simulated to test method robustness.
  • The performance of MI techniques was evaluated based on imputation accuracy and coverage probability.
  • A new approach combining MI with Procrustean superimposition of Principal Component Analysis (PCA) results was developed for visualization.

Main Results:

  • Fully Conditional Specification and Expectation-Maximization algorithms demonstrated superior performance in balancing imputation accuracy and coverage probability.
  • MI techniques exhibited remarkable robustness against biased missing data patterns (taxonomic or anatomical).
  • The chosen MI method had a greater impact on results than the distribution pattern of missing data.

Conclusions:

  • Multiple imputation (MI) methods are highly effective and robust for addressing missing data in morphometric analyses of fossils.
  • The study recommends against fixed thresholds for missing data, advocating for MI-based approaches.
  • A practical R function is provided to integrate MI with PCA visualization for improved paleontological data analysis.