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A permutation test of differences between externally or internally defined groupings in compositional data sets.

Nikola Štefelová1, Javier Palarea-Albaladejo1, Josep Antoni Martín-Fernández1

  • 1Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Spain.

Statistical Methods in Medical Research
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

The PERLOG test identifies significant groupings in compositional data using pairwise logratios. This novel non-parametric permutation test works for both externally and internally defined groups, outperforming conventional methods.

Keywords:
Group comparisonscompositional datapairwise logratiospermutation testtime-use epidemiologyzeros

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Area of Science:

  • Statistics
  • Bioinformatics
  • Data Science

Background:

  • Compositional data analysis requires focusing on relative information between components.
  • Logratio coordinate systems are commonly used to analyze compositional data.
  • Groupings can be externally defined or emerge from internal data features like zero patterns.

Purpose of the Study:

  • Introduce the PERLOG test, a novel non-parametric permutation test.
  • Identify significant groupings in compositional data based on pairwise logratios.
  • Demonstrate suitability for both externally and internally defined groupings.

Main Methods:

  • Developed the PERLOG test, a non-parametric permutation test.
  • Utilized pairwise logratios as fundamental units of compositional information.
  • Applied to compositional data, including analysis of zero patterns for internal groupings.

Main Results:

  • The PERLOG test effectively identifies significant groupings in compositional data.
  • Demonstrated advantages over conventional multivariate tests through simulation.
  • Illustrated real-world applications in movement behaviours and time-use epidemiology.

Conclusions:

  • The PERLOG test is a powerful tool for analyzing compositional data.
  • It offers a robust method for identifying group structures, especially those defined by internal features.
  • The test shows promise for applications in various scientific fields.