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Circular data in biology: advice for effectively implementing statistical procedures
Circular data are common in biological studies. The most fundamental question that can be asked of a sample of circular data is whether it suggests that the underlying population is uniformly distributed around the circle, or whether it is concentrated around at least one preferred direction (e.g. a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060829/ https://www.ncbi.nlm.nih.gov/pubmed/30100666 http://dx.doi.org/10.1007/s00265-018-2538-y |
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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 | Circular data are common in biological studies. The most fundamental question that can be asked of a sample of circular data is whether it suggests that the underlying population is uniformly distributed around the circle, or whether it is concentrated around at least one preferred direction (e.g. a migratory goal or activity phase). We compared the statistical power of five commonly used tests (the Rayleigh test, the V-test, Watson’s test, Kuiper’s test and Rao’s spacing test) across a range of different unimodal scenarios. The V-test showed higher power for symmetrical distributions, Rao’s spacing performed worst for all explored unimodal distributions tested and the remaining three tests showed very similar performance. However, the V-test only applies if the hypothesis is restricted to one (pre-specified) direction of interest. In all other unimodal cases, we recommend using the Rayleigh test. Much less explored is the multimodal case with data concentrated around several directions. We performed power simulations for a variety of multimodal situations, testing the performance of the widely used Rayleigh, Rao’s, Watson, and Kuiper’s tests as well as the more recent Bogdan and Hermans-Rasson tests. Our analyses of alternative statistical methods show that the commonly used tests lack statistical power in many of multimodal cases. Transformation of the raw data (e.g. doubling the angles) can overcome some of the issues, but only in the case of perfect f-fold symmetry. However, the Hermans-Rasson method, which is not yet implemented in any software package, outcompetes the alternative tests (often by substantial margins) in most of the multimodal situations explored. We recommend the wider uptake of the powerful but hitherto neglected Hermans-Rasson method. In summary, we provide guidance for biologists helping them to make decisions when testing circular data for single or multiple departures from uniformity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00265-018-2538-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6060829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-60608292018-08-09 Circular data in biology: advice for effectively implementing statistical procedures Landler, Lukas Ruxton, Graeme D. Malkemper, E. Pascal Behav Ecol Sociobiol Methods Paper Circular data are common in biological studies. The most fundamental question that can be asked of a sample of circular data is whether it suggests that the underlying population is uniformly distributed around the circle, or whether it is concentrated around at least one preferred direction (e.g. a migratory goal or activity phase). We compared the statistical power of five commonly used tests (the Rayleigh test, the V-test, Watson’s test, Kuiper’s test and Rao’s spacing test) across a range of different unimodal scenarios. The V-test showed higher power for symmetrical distributions, Rao’s spacing performed worst for all explored unimodal distributions tested and the remaining three tests showed very similar performance. However, the V-test only applies if the hypothesis is restricted to one (pre-specified) direction of interest. In all other unimodal cases, we recommend using the Rayleigh test. Much less explored is the multimodal case with data concentrated around several directions. We performed power simulations for a variety of multimodal situations, testing the performance of the widely used Rayleigh, Rao’s, Watson, and Kuiper’s tests as well as the more recent Bogdan and Hermans-Rasson tests. Our analyses of alternative statistical methods show that the commonly used tests lack statistical power in many of multimodal cases. Transformation of the raw data (e.g. doubling the angles) can overcome some of the issues, but only in the case of perfect f-fold symmetry. However, the Hermans-Rasson method, which is not yet implemented in any software package, outcompetes the alternative tests (often by substantial margins) in most of the multimodal situations explored. We recommend the wider uptake of the powerful but hitherto neglected Hermans-Rasson method. In summary, we provide guidance for biologists helping them to make decisions when testing circular data for single or multiple departures from uniformity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00265-018-2538-y) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2018-07-11 2018 /pmc/articles/PMC6060829/ /pubmed/30100666 http://dx.doi.org/10.1007/s00265-018-2538-y Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Methods Paper Landler, Lukas Ruxton, Graeme D. Malkemper, E. Pascal Circular data in biology: advice for effectively implementing statistical procedures |
title | Circular data in biology: advice for effectively implementing statistical procedures |
title_full | Circular data in biology: advice for effectively implementing statistical procedures |
title_fullStr | Circular data in biology: advice for effectively implementing statistical procedures |
title_full_unstemmed | Circular data in biology: advice for effectively implementing statistical procedures |
title_short | Circular data in biology: advice for effectively implementing statistical procedures |
title_sort | circular data in biology: advice for effectively implementing statistical procedures |
topic | Methods Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060829/ https://www.ncbi.nlm.nih.gov/pubmed/30100666 http://dx.doi.org/10.1007/s00265-018-2538-y |
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