When Relative and absolute information matter: compositional predictor with a total in generalized linear models
dc.contributor.author
dc.date.accessioned
2023-04-21T12:32:32Z
dc.date.available
2023-04-21T12:32:32Z
dc.date.issued
2017-12-01
dc.identifier.issn
1471-082X
dc.identifier.uri
dc.description.abstract
The analysis of Compositional Data (CoDa) consists in the study of the relative importance of parts of a whole rather than the size of the whole, because absolute information is either unavailable or not of interest. On the other hand, when absolute and relative information are both relevant, research hypotheses concern both. This article introduces a model including both the logratios used in CoDa and a total variable carrying absolute information, as predictors in an otherwise standard statistical model. It shows how logratios can be tailored to the researchers' hypotheses and alternative ways of computing the total. The interpretational advantages with respect to traditional approaches are presented and the equivalence and invariance properties are proven. A sequence of nested models is presented to test the relevance of relative and absolute information. The approach can be applied to dependent metric, binary, ordinal or count variables. Two illustrations are provided, the first on tourist expenditure and satisfaction and the second on solid waste management and floating population
dc.format.extent
19 p.
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application/pdf
dc.language.iso
eng
dc.publisher
SAGE Publications
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Versió postprint del document publicat a: https://doi.org/10.1177/1471082X17710398
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© Statistical Modelling, 2017, vol. 17, núm. 6, p. 494-512
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Articles publicats (D-EC)
dc.rights
Tots els drets reservats
dc.source
Coenders, Germà Martín Fernández, Josep Antoni Ferrer Rosell, Berta 2017 When Relative and absolute information matter: compositional predictor with a total in generalized linear models Statistical Modelling 17 6 494 512
dc.subject
dc.title
When Relative and absolute information matter: compositional predictor with a total in generalized linear models
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
027204
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1477-0342