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On interpretations of tests and effect sizes in regression models with a compositional predictor - RP:4903

Autor según el artículo:Pawlowsky-Glahn, Vera
Coenders, Germà
Año de publicación de la revista:2020
Tipo de publicación:##rt.metadata.pkp.peerReviewed##
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
Resumen:Compositional data analysis is concerned with the relative importance of positive variables, expressed through their log-ratios. The literature has proposed a range of manners to compute log-ratios, some of whose interrelationships have never been reported when used as explanatory variables in regression models. This article shows their similarities and differences in interpretation based on the notion that one log-ratio has to be interpreted keeping all others constant. The article shows that centred, additive, pivot, balance and pairwise log-ratios lead to simple reparametrizations of the same model which can be combined to provide useful tests and comparable effect size estimates.
Palabras clave:compositional regression models
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