On interpretations of tests and effect sizes in regression models with a compositional predictor

Germà Coenders Gallart; Vera Pawlowsky Glahn

Ayuda

On interpretations of tests and effect sizes in regression models with a compositional predictor

Germà Coenders ^[1] ; Vera Pawlowsky-Glahn ^[1]
1. [1] Universitat de Girona
  
  Universitat de Girona
  
  Gerona, España
Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 44, Nº. 1, 2020, págs. 201-220
Idioma: inglés
DOI: 10.2436/20.8080.02.100
Enlaces
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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.
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