On interpretations of tests and effect sizes in regression models with a compositional predictor
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 regre...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:225692 |
| Acceso en línea: | https://ddd.uab.cat/record/225692 https://dx.doi.org/urn:doi:10.2436/20.8080.02.100 |
| Access Level: | acceso abierto |
| Palabra clave: | Compositional regression models Coda Composition as explanatory Centred log-ratios Pivot coordinates Pairwise log-ratios Additive log-ratios Effect size |
| Sumario: | 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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