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dc.contributor.authorMorrissey, Michael B.
dc.identifier.citationMorrissey , M B 2014 , ' In search of the best methods for multivariate selection analysis ' , Methods in Ecology and Evolution , vol. 5 , no. 10 , pp. 1095-1109 .
dc.identifier.otherPURE: 158989340
dc.identifier.otherPURE UUID: eef2dd8c-856b-4435-981e-a15b2e0ccc1d
dc.identifier.otherWOS: 000344598300013
dc.identifier.otherScopus: 84939201424
dc.identifier.otherWOS: 000344598300013
dc.descriptionThe collection of the Soay sheep data is supported by the National Trust for Scotland and QinetQ, with funding from NERC, the Royal Society and the Leverhulme Trust.en
dc.description.abstractRegression is an important method for characterizing the form of natural selection from individual-based data. Many kinds of regression analysis exist, but few are regularly employed in studies of natural selection. I provide an overview of some of the main underused types of regression analysis by applying them to test analyses of viability selection for lamb traits in Soay sheep (Ovis aries). This exercise highlights known problems with existing methods, uncovers some new ones and also reveals ways to harness underused methods to get around these problems. I first estimate selection gradients using generalized linear models, combined with recently published methods for obtaining quantitatively interpretable selection gradient estimates from arbitrary regression models of trait-fitness relationships. I then also apply generalized ridge regression, the lasso and projection-pursuit regression, in each case also deriving selection gradients. I compare inferences of nonlinear selection by diagonalization of the matrix and by projection-pursuit regression. Selection gradient estimates generally correspond across different regression methods. Although there is little evidence for nonlinear selection in the test data sets, very problematic aspects of the behaviour of analysis based on diagonalization of the are apparent. In addition to better-known problems, (i) the direction and magnitude of estimated major axes of quadratic selection are biased towards directions of phenotype that have little variance, and (ii) the magnitudes of selection of major axes of variance-standardized are not themselves interpretable in any standardized way. While all regression-based methods for analysis of selection have useful properties, projection-pursuit regression seems to stand out. This method can (i) provide both dimensionality reduction, (ii) be the basis for inference of quantitatively interpretable selection gradients and (iii) by characterizing major axes of selection, rather than of linear or quadratic selection separately, provide biologically interpretable inference of nonlinear selection. 
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rights© 2014 The Author. Methods in Ecology and Evolution © 2014 British Ecological Society. This is the accepted version of the following article: In search of the best methods for multivariate selection analysis Morrissey, M. B. Oct 2014 In : Methods in Ecology and Evolution. 5, 10, p. 1095-1109, which has been published in final form at
dc.subjectNatural selectionen
dc.subjectSelection gradientsen
dc.subjectTables of statisticsen
dc.subjectRegularized regressionen
dc.subjectProjection-pursuit regressionen
dc.subjectInformative priorsen
dc.subjectSoay sheepen
dc.subjectFluctuating selectionen
dc.subjectDirectional selectionen
dc.subjectPhenotypic selectionen
dc.subjectSexual selectionen
dc.subjectQH301 Biologyen
dc.titleIn search of the best methods for multivariate selection analysisen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.description.statusPeer revieweden

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