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Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

Shinichi Nakagawa1, Holger Schielzeth

  • 1Department of Zoology, University of Otago, 340 Great King Street, Dunedin, 9054, New Zealand. shinichi.nakagawa@otago.ac.nz

Biological Reviews of the Cambridge Philosophical Society
|June 24, 2010
PubMed
Summary
This summary is machine-generated.

This study reviews methods for calculating repeatability, a key measure of measurement accuracy and phenotype consistency. It provides guidelines for estimating repeatability in both Gaussian and non-Gaussian data, recommending linear mixed-effects models.

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Area of Science:

  • Biology
  • Genetics
  • Biostatistics

Background:

  • Repeatability quantifies measurement accuracy and phenotype constancy, crucial for biological research.
  • Existing methods for estimating repeatability are well-defined for Gaussian data but lack consensus for non-Gaussian data.
  • Accurate uncertainty estimates (standard errors, confidence intervals) and statistical significance are vital for repeatability estimates across all data types.

Purpose of the Study:

  • To review and recommend methods for calculating repeatability and associated statistics for both Gaussian and non-Gaussian data.
  • To provide guidelines for biologists on estimating repeatability and heritability.
  • To compare different approaches, including linear mixed-effects models (LMM) and generalized linear mixed-effects models (GLMM).

Main Methods:

  • Review of correlation-based, ANOVA-based, and LMM-based methods for Gaussian data.
  • Focus on GLMM for estimating repeatability in non-Gaussian data (binary, proportion, count).
  • Evaluation of parametric bootstrapping, randomization tests, and Bayesian approaches for uncertainty estimation and significance testing.

Main Results:

  • LMM and GLMM approaches are recommended for their ability to control for confounding variables.
  • GLMM allows repeatability estimation on original and latent scales for non-Gaussian data.
  • Comparison of ordinary and extrapolated repeatability in relation to narrow-sense heritability.

Conclusions:

  • Provides comprehensive guidelines for calculating repeatability and heritability from diverse data types.
  • Advocates for LMM/GLMM and robust statistical methods for accurate and reliable biological measurements.
  • Enhances the standardization and rigor of repeatability assessments in biological research.