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Circular data in biology: advice for effectively implementing statistical procedures

Behavioral ecology and sociobiology, 2018-08, Vol.72 (8), p.1-10, Article 128 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2018 ;The Author(s) 2018 ;Behavioral Ecology and Sociobiology is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0340-5443 ;EISSN: 1432-0762 ;DOI: 10.1007/s00265-018-2538-y ;PMID: 30100666

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  • Title:
    Circular data in biology: advice for effectively implementing statistical procedures
  • Author: Landler, Lukas ; Ruxton, Graeme D. ; Malkemper, E. Pascal
  • Subjects: Animal Ecology ; Behavioral Sciences ; Biological effects ; Biology ; Biomedical and Life Sciences ; Circularity ; Life Sciences ; METHODS PAPER ; Population (statistical) ; Statistical analysis ; Statistical methods ; Transformation ; Zoology
  • Is Part Of: Behavioral ecology and sociobiology, 2018-08, Vol.72 (8), p.1-10, Article 128
  • 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.
  • Publisher: Berlin/Heidelberg: Springer
  • Language: English
  • Identifier: ISSN: 0340-5443
    EISSN: 1432-0762
    DOI: 10.1007/s00265-018-2538-y
    PMID: 30100666
  • Source: ProQuest One Psychology
    Springer Nature OA/Free Journals
    ProQuest Central
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