The PCovR biplot: a graphical tool for principal covariates regression

[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...

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Detalles Bibliográficos
Autores: Frutos Bernal, Elisa, Vicente Villardón, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/160395
Acceso en línea:http://hdl.handle.net/10366/160395
Access Level:acceso embargado
Palabra clave:Biplots
Principal covariates regression
Regression analysis
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spelling The PCovR biplot: a graphical tool for principal covariates regressionFrutos Bernal, ElisaVicente Villardón, José LuisBiplotsPrincipal covariates regressionRegression analysis[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.Taylor and Francis Groupinfo202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/160395reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)Inglésinfo:eu-repo/semantics/embargoedAccessoai:gredos.usal.es:10366/1603952026-06-07T06:28:51Z
dc.title.none.fl_str_mv The PCovR biplot: a graphical tool for principal covariates regression
title The PCovR biplot: a graphical tool for principal covariates regression
spellingShingle The PCovR biplot: a graphical tool for principal covariates regression
Frutos Bernal, Elisa
Biplots
Principal covariates regression
Regression analysis
title_short The PCovR biplot: a graphical tool for principal covariates regression
title_full The PCovR biplot: a graphical tool for principal covariates regression
title_fullStr The PCovR biplot: a graphical tool for principal covariates regression
title_full_unstemmed The PCovR biplot: a graphical tool for principal covariates regression
title_sort The PCovR biplot: a graphical tool for principal covariates regression
dc.creator.none.fl_str_mv Frutos Bernal, Elisa
Vicente Villardón, José Luis
author Frutos Bernal, Elisa
author_facet Frutos Bernal, Elisa
Vicente Villardón, José Luis
author_role author
author2 Vicente Villardón, José Luis
author2_role author
dc.subject.none.fl_str_mv Biplots
Principal covariates regression
Regression analysis
topic Biplots
Principal covariates regression
Regression analysis
description [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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
info
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/160395
url http://hdl.handle.net/10366/160395
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.publisher.none.fl_str_mv Taylor and Francis Group
publisher.none.fl_str_mv Taylor and Francis Group
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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