On interpretations of tests and effect sizes in regression models with a compositional predictor
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hdl:2117/362101
Tipus de documentArticle
Data publicació2020-06-26
EditorInstitut d'Estadística de Catalunya
Condicions d'accésAccés obert
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Abstract
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.
CitacióCoenders, G.; Pawlowsky-Glahn, V. On interpretations of tests and effect sizes in regression models with a compositional predictor. "SORT", 26 Juny 2020, vol. 44, núm. 1, p. 201-220.
ISSN1696-2281
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44.1.8.Coenders-Pawlowsky-Glahn.pdf | 216,0Kb | Visualitza/Obre |