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Model selection versus traditional hypothesis testing in circular statistics: a simulation study

Many studies in biology involve data measured on a circular scale. Such data require different statistical treatment from those measured on linear scales. The most common statistical exploration of circular data involves testing the null hypothesis that the data show no aggregation and are instead u...

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Autores principales: Landler, Lukas, Ruxton, Graeme D., Malkemper, E. Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Company of Biologists Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327993/
https://www.ncbi.nlm.nih.gov/pubmed/32554482
http://dx.doi.org/10.1242/bio.049866
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author Landler, Lukas
Ruxton, Graeme D.
Malkemper, E. Pascal
author_facet Landler, Lukas
Ruxton, Graeme D.
Malkemper, E. Pascal
author_sort Landler, Lukas
collection PubMed
description Many studies in biology involve data measured on a circular scale. Such data require different statistical treatment from those measured on linear scales. The most common statistical exploration of circular data involves testing the null hypothesis that the data show no aggregation and are instead uniformly distributed over the whole circle. The most common means of performing this type of investigation is with a Rayleigh test. An alternative might be to compare the fit of the uniform distribution model to alternative models. Such model-fitting approaches have become a standard technique with linear data, and their greater application to circular data has been recently advocated. Here we present simulation data that demonstrate that such model-based inference can offer very similar performance to the best traditional tests, but only if adjustment is made in order to control type I error rate.
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spelling pubmed-73279932020-07-01 Model selection versus traditional hypothesis testing in circular statistics: a simulation study Landler, Lukas Ruxton, Graeme D. Malkemper, E. Pascal Biol Open Methods & Techniques Many studies in biology involve data measured on a circular scale. Such data require different statistical treatment from those measured on linear scales. The most common statistical exploration of circular data involves testing the null hypothesis that the data show no aggregation and are instead uniformly distributed over the whole circle. The most common means of performing this type of investigation is with a Rayleigh test. An alternative might be to compare the fit of the uniform distribution model to alternative models. Such model-fitting approaches have become a standard technique with linear data, and their greater application to circular data has been recently advocated. Here we present simulation data that demonstrate that such model-based inference can offer very similar performance to the best traditional tests, but only if adjustment is made in order to control type I error rate. The Company of Biologists Ltd 2020-06-23 /pmc/articles/PMC7327993/ /pubmed/32554482 http://dx.doi.org/10.1242/bio.049866 Text en © 2020. Published by The Company of Biologists Ltd http://creativecommons.org/licenses/by/4.0This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Methods & Techniques
Landler, Lukas
Ruxton, Graeme D.
Malkemper, E. Pascal
Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title_full Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title_fullStr Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title_full_unstemmed Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title_short Model selection versus traditional hypothesis testing in circular statistics: a simulation study
title_sort model selection versus traditional hypothesis testing in circular statistics: a simulation study
topic Methods & Techniques
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327993/
https://www.ncbi.nlm.nih.gov/pubmed/32554482
http://dx.doi.org/10.1242/bio.049866
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