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dc.contributor.authorFrutos Bernal, Elisa 
dc.contributor.authorVicente Villardón, José Luis 
dc.date.accessioned2024-10-25T10:08:09Z
dc.date.available2024-10-25T10:08:09Z
dc.date.issued2024
dc.identifier.citationFrutos-Bernal, E., & Vicente-Villardón, J. L. (2024). The PCovR biplot: a graphical tool for principal covariates regression. Journal of Applied Statistics, 1–16. https://doi.org/10.1080/02664763.2024.2417978es_ES
dc.identifier.issn0266-4763
dc.identifier.urihttp://hdl.handle.net/10366/160395
dc.description.abstract[EN]Biplots are useful tools because they provide a visual representation of both individuals and variables simultaneously, making it easier to explore relationships and patterns within multidimensional datasets. This paper extends their use to examine the relationship between a set of predictors X and a set of response variables Y using Principal Covariates Regression analysis (PCovR). The PCovR biplot provides a simultaneous graphical representation of individuals, predictor variables and response variables. It also provides the ability to examine the relationship between both types of variables in the form of the regression coefficient matrix.es_ES
dc.language.isoenges_ES
dc.publisherTaylor and Francis Groupes_ES
dc.subjectBiplotses_ES
dc.subjectPrincipal covariates regressiones_ES
dc.subjectRegression analysises_ES
dc.titleThe PCovR biplot: a graphical tool for principal covariates regressiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://doi.org/10.1080/02664763.2024.2417978es_ES
dc.identifier.doi10.1080/02664763.2024.2417978
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.identifier.essn1360-0532
dc.journal.titleJournal of Applied Statisticses_ES
dc.page.initial1es_ES
dc.page.final16es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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