| Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. | |
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MedLine Citation:
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PMID: 20569253 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data. |
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Authors:
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Shinichi Nakagawa; Holger Schielzeth |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Biological reviews of the Cambridge Philosophical Society Volume: 85 ISSN: 1469-185X ISO Abbreviation: Biol Rev Camb Philos Soc Publication Date: 2010 Nov |
Date Detail:
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Created Date: 2010-11-02 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0414576 Medline TA: Biol Rev Camb Philos Soc Country: England |
Other Details:
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Languages: eng Pagination: 935-56 Citation Subset: IM |
Copyright Information:
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© 2010 The Authors. Biological Reviews © 2010 Cambridge Philosophical Society. |
Affiliation:
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Department of Zoology, University of Otago, 340 Great King Street, Dunedin, 9054, New Zealand. shinichi.nakagawa@otago.ac.nz |
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