Weighted distance based discriminant analysis: the R package WeDiBaDis

The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervised classification). WeDiBaDis is an easy to use package addressed to the biological and medical communities, and in general, to researchers interested in applied studies. It can be suitable when the...

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Detalles Bibliográficos
Autores: Irigoien, Itziar, Mestres i Naval, Francesc, Arenas Solà, Concepción
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/116041
Acceso en línea:https://hdl.handle.net/2445/116041
Access Level:acceso abierto
Palabra clave:R (Llenguatge de programació)
Anàlisi discriminant
Ciències de la salut
R (Computer program language)
Discriminant analysis
Medical sciences
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spelling Weighted distance based discriminant analysis: the R package WeDiBaDisIrigoien, ItziarMestres i Naval, FrancescArenas Solà, ConcepciónR (Llenguatge de programació)Anàlisi discriminantCiències de la salutR (Computer program language)Discriminant analysisMedical sciencesThe WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervised classification). WeDiBaDis is an easy to use package addressed to the biological and medical communities, and in general, to researchers interested in applied studies. It can be suitable when the user is interested in the problem of constructing a discriminant rule on the basis of distances between a relatively small number of instances or units of known unbalanced-class membership measured on many (possibly thousands) features of any type. This is a current situation when analyzing genetic biomedical data. This discriminant rule can then be used both, as a means of explaining differences among classes, but also in the important task of assigning the class membership for new unlabeled units. Our package implements two discriminant analysis procedures in an R environment: the well-known distance-based discriminant analysis (DB-discriminant) and a weighteddistance- based discriminant (WDB-discriminant), a novel classifier rule that we introduce. This new procedure is based on an improvement of the DB rule taking into account the statistical depth of the units. This article presents both classifying procedures and describes the implementation of each in detail. We illustrate the use of the package using an ecological and a genetic experimental example. Finally, we illustrate the effectiveness of the new proposed procedure (WDB), as compared with DB. This comparison is carried out using thirty-eight, high-dimensional, class-unbalanced, cancer data sets, three of which include clinical features.The R Foundation2017201720162017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion17 p.application/pdfhttps://hdl.handle.net/2445/116041Articles publicats en revistes (Genètica, Microbiologia i Estadística)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://journal.r-project.org/archive/2016/RJ-2016-057/index.htmlThe R Journal, 2016, vol. 8, num. 2, p. 434-450cc-by (c) Irigoien, Itziar et al., 2016http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1160412026-05-29T05:05:01Z
dc.title.none.fl_str_mv Weighted distance based discriminant analysis: the R package WeDiBaDis
title Weighted distance based discriminant analysis: the R package WeDiBaDis
spellingShingle Weighted distance based discriminant analysis: the R package WeDiBaDis
Irigoien, Itziar
R (Llenguatge de programació)
Anàlisi discriminant
Ciències de la salut
R (Computer program language)
Discriminant analysis
Medical sciences
title_short Weighted distance based discriminant analysis: the R package WeDiBaDis
title_full Weighted distance based discriminant analysis: the R package WeDiBaDis
title_fullStr Weighted distance based discriminant analysis: the R package WeDiBaDis
title_full_unstemmed Weighted distance based discriminant analysis: the R package WeDiBaDis
title_sort Weighted distance based discriminant analysis: the R package WeDiBaDis
dc.creator.none.fl_str_mv Irigoien, Itziar
Mestres i Naval, Francesc
Arenas Solà, Concepción
author Irigoien, Itziar
author_facet Irigoien, Itziar
Mestres i Naval, Francesc
Arenas Solà, Concepción
author_role author
author2 Mestres i Naval, Francesc
Arenas Solà, Concepción
author2_role author
author
dc.subject.none.fl_str_mv R (Llenguatge de programació)
Anàlisi discriminant
Ciències de la salut
R (Computer program language)
Discriminant analysis
Medical sciences
topic R (Llenguatge de programació)
Anàlisi discriminant
Ciències de la salut
R (Computer program language)
Discriminant analysis
Medical sciences
description The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervised classification). WeDiBaDis is an easy to use package addressed to the biological and medical communities, and in general, to researchers interested in applied studies. It can be suitable when the user is interested in the problem of constructing a discriminant rule on the basis of distances between a relatively small number of instances or units of known unbalanced-class membership measured on many (possibly thousands) features of any type. This is a current situation when analyzing genetic biomedical data. This discriminant rule can then be used both, as a means of explaining differences among classes, but also in the important task of assigning the class membership for new unlabeled units. Our package implements two discriminant analysis procedures in an R environment: the well-known distance-based discriminant analysis (DB-discriminant) and a weighteddistance- based discriminant (WDB-discriminant), a novel classifier rule that we introduce. This new procedure is based on an improvement of the DB rule taking into account the statistical depth of the units. This article presents both classifying procedures and describes the implementation of each in detail. We illustrate the use of the package using an ecological and a genetic experimental example. Finally, we illustrate the effectiveness of the new proposed procedure (WDB), as compared with DB. This comparison is carried out using thirty-eight, high-dimensional, class-unbalanced, cancer data sets, three of which include clinical features.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
2017
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 https://hdl.handle.net/2445/116041
url https://hdl.handle.net/2445/116041
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://journal.r-project.org/archive/2016/RJ-2016-057/index.html
The R Journal, 2016, vol. 8, num. 2, p. 434-450
dc.rights.none.fl_str_mv cc-by (c) Irigoien, Itziar et al., 2016
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Irigoien, Itziar et al., 2016
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17 p.
application/pdf
dc.publisher.none.fl_str_mv The R Foundation
publisher.none.fl_str_mv The R Foundation
dc.source.none.fl_str_mv Articles publicats en revistes (Genètica, Microbiologia i Estadística)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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