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dc.contributor.authorMorrissey, Michael B.
dc.contributor.authorRuxton, Graeme D.
dc.date.accessioned2021-07-25T23:35:25Z
dc.date.available2021-07-25T23:35:25Z
dc.date.issued2020-07-26
dc.identifier.citationMorrissey , M B & Ruxton , G D 2020 , ' Revisiting advice on the analysis of count data ' , Methods in Ecology and Evolution , vol. Early View . https://doi.org/10.1111/2041-210X.13372en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 266484449
dc.identifier.otherPURE UUID: 2eedcc20-a59a-4a10-bf95-f4d393c5fcf5
dc.identifier.otherRIS: urn:A141FF465061C158918A5D3B69977FD1
dc.identifier.otherORCID: /0000-0001-8943-6609/work/78205024
dc.identifier.otherWOS: 000552502800001
dc.identifier.otherScopus: 85088424515
dc.identifier.urihttp://hdl.handle.net/10023/23622
dc.descriptionFunding: MBM is supported by a University Research Fellowship from the Royal Society (London).en
dc.description.abstract1. O’Hara and Kotze (2010; Methods in Ecology and Evolution 1: 118‐122) present simulation results that appear to show very poor behaviour (as judged by bias and overall accuracy) of linear models (LMs) applied to count data, especially in relation to generalised linear model (GLM) analysis. 2. We considered O’Hara and Kotze’s (2010) comparisons, and determined that the finding occurred primarily because the quantity that they estimated in their simulations of the LM analysis (the mean of a transformation of the count data) was not the same quantity that was simulated and to which the results were compared (the logarithm of the mean of the count data). We correct this discrepancy, re‐run O’Hara and Kotze’s simulations, and add additional simple analyses. 3. We found that the apparent superiority of the GLMs over LMs in O’Hara and Kotze’s (2010) simulations was primarily an artefact of divergence in the meanings of results from the two analyses. After converting results from LM analyses of transformed data to estimators of the same quantity as provided by the GLM, results from both analyses rarely differed substantially. Furthermore, under the circumstances considered by O’Hara and Kotze, we find that an even simpler implementation of LM analysis, inference of the mean of the raw data, performs even better, and gives identical results to the GLM. 4. While the analysis of count data with generalised linear models can certainly provide many benefits, we strongly caution against interpreting O’Hara and Kotze’s (2010) results as evidence that simpler approaches are severely flawed.
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsCopyright © 2020 British Ecological Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/2041-210X.13372en
dc.subjectBiasen
dc.subjectGeneralised linear modelen
dc.subjectLinear modelsen
dc.subjectOrdinary least squaresen
dc.subjectPrecisionen
dc.subjectStandard errorsen
dc.subjectStatisticsen
dc.subjectTransformationen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.titleRevisiting advice on the analysis of count dataen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1111/2041-210X.13372
dc.description.statusPeer revieweden
dc.date.embargoedUntil2021-07-26


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