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On distance-based permutation tests for between-group comparisons.

Philip T Reiss1, M Henry H Stevens, Zarrar Shehzad

  • 1Department of Child and Adolescent Psychiatry, New York University, New York 10016, USA. phil.reiss@nyumc.org

Biometrics
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Summary
This summary is machine-generated.

This study reveals when two distance-based permutation testing frameworks are equivalent for analyzing biological data. These findings unify multiresponse permutation procedures and pseudo-F tests for ecological and neuroimaging applications.

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Area of Science:

  • Multivariate statistics
  • Biological sciences
  • Bioinformatics

Background:

  • Permutation tests using distances among multivariate observations are widely used in biological sciences.
  • Key frameworks include multiresponse permutation procedures (MRPP) and pseudo-F tests derived from distance-based multivariate analysis of variance (MANOVA).

Purpose of the Study:

  • To derive the conditions under which MRPP and pseudo-F tests are equivalent.
  • To provide a unified theoretical framework for distance-based permutation testing in biology.

Main Methods:

  • Derivation of equivalence conditions between MRPP and pseudo-F tests.
  • Application of the derived equivalence to an ecological dataset.
  • Novel application to functional magnetic resonance imaging (fMRI) data.

Main Results:

  • Specific conditions for the equivalence of MRPP and pseudo-F tests were established.
  • The reanalysis of ecological data demonstrated the practical implications of the equivalence.
  • The study successfully applied the unified framework to fMRI data, showcasing its versatility.

Conclusions:

  • The equivalence between MRPP and pseudo-F tests provides a more unified approach to distance-based permutation testing.
  • This unification simplifies the interpretation and application of these methods in biological research.
  • The findings have broad implications for analyzing complex biological data, including ecological and neuroimaging datasets.