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 Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/362101 |
| Acceso en línea: | https://hdl.handle.net/2117/362101 https://dx.doi.org/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 Anàlisi multivariable Estadística matemàtica Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62H Multivariate analysis Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| 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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