When Relative and absolute information matter: compositional predictor with a total in generalized linear models

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, resear...

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Authors: Coenders, Germà, Martín Fernández, Josep Antoni, Ferrer Rosell, Berta
Format: article
Status:Versión aceptada para publicación
Publication Date:2017
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/22929
Online Access:http://hdl.handle.net/10256/22929
Access Level:Open access
Keyword:Anàlisi multivariable
Estadística matemàtica
Multivariate analysis
Mathematical statistics
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spelling When Relative and absolute information matter: compositional predictor with a total in generalized linear modelsCoenders, GermàMartín Fernández, Josep AntoniFerrer Rosell, BertaAnàlisi multivariableEstadística matemàticaMultivariate analysisMathematical statisticsThe 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 populationSAGE Publications2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionpeer-reviewed19 p.application/pdfhttp://hdl.handle.net/10256/22929http://hdl.handle.net/10256/22929© Statistical Modelling, 2017, vol. 17, núm. 6, p. 494-512Articles publicats (D-EC)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 512reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1177/1471082X17710398info:eu-repo/semantics/altIdentifier/issn/1471-082Xinfo:eu-repo/semantics/altIdentifier/eissn/1477-0342Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10256/229292026-05-29T05:05:01Z
dc.title.none.fl_str_mv When Relative and absolute information matter: compositional predictor with a total in generalized linear models
title When Relative and absolute information matter: compositional predictor with a total in generalized linear models
spellingShingle When Relative and absolute information matter: compositional predictor with a total in generalized linear models
Coenders, Germà
Anàlisi multivariable
Estadística matemàtica
Multivariate analysis
Mathematical statistics
title_short When Relative and absolute information matter: compositional predictor with a total in generalized linear models
title_full When Relative and absolute information matter: compositional predictor with a total in generalized linear models
title_fullStr When Relative and absolute information matter: compositional predictor with a total in generalized linear models
title_full_unstemmed When Relative and absolute information matter: compositional predictor with a total in generalized linear models
title_sort When Relative and absolute information matter: compositional predictor with a total in generalized linear models
dc.creator.none.fl_str_mv Coenders, Germà
Martín Fernández, Josep Antoni
Ferrer Rosell, Berta
author Coenders, Germà
author_facet Coenders, Germà
Martín Fernández, Josep Antoni
Ferrer Rosell, Berta
author_role author
author2 Martín Fernández, Josep Antoni
Ferrer Rosell, Berta
author2_role author
author
dc.subject.none.fl_str_mv Anàlisi multivariable
Estadística matemàtica
Multivariate analysis
Mathematical statistics
topic Anàlisi multivariable
Estadística matemàtica
Multivariate analysis
Mathematical statistics
description 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
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
peer-reviewed
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/22929
http://hdl.handle.net/10256/22929
url http://hdl.handle.net/10256/22929
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1177/1471082X17710398
info:eu-repo/semantics/altIdentifier/issn/1471-082X
info:eu-repo/semantics/altIdentifier/eissn/1477-0342
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19 p.
application/pdf
dc.publisher.none.fl_str_mv SAGE Publications
publisher.none.fl_str_mv SAGE Publications
dc.source.none.fl_str_mv © Statistical Modelling, 2017, vol. 17, núm. 6, p. 494-512
Articles publicats (D-EC)
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
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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