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dc.contributor.authorGraffelman, Jan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2013-04-04T07:23:09Z
dc.date.created2013-03-27
dc.date.issued2013-03-27
dc.identifier.citationGraffelman, J. Linear-Angle correlation plots: New graphs for revealing correlation structure. "Journal of computational and graphical statistics", 27 Març 2013, vol. 22, núm. 1, p. 92-106.
dc.identifier.issn1061-8600
dc.identifier.urihttp://hdl.handle.net/2117/18594
dc.description.abstractIn multivariate graphics, correlations between variables are often approximated by the cosines of the angles between vectors. In practice, it is difficult to reliably estimate correlations from such displays by eye. In this article, we therefore develop new graphs, called linear-angle correlation plots, that have a linear relationship between correlation and angle, and from which correlation coefficients are read off more easily. Several multivariate datasets are used to illustrate the proposed graphs. The goodness-of-fit properties of the new graphs are compared with standard multivariate methods such as principal component analysis and principal factor analysis. Cosine-based plots typically gave the poorest approximation to the correlation matrix. A linear interpretation rule for the angle often improved the fit. The best fit was generally obtained by principal factor analysis using scalar products to approximate correlations
dc.format.extent15 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses
dc.subject.lcshStatistics
dc.titleLinear-Angle correlation plots: New graphs for revealing correlation structure
dc.typeArticle
dc.subject.lemacEstadística
dc.contributor.groupUniversitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació
dc.identifier.doi10.1080/15533174.2012.707850
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://dx.doi.org/10.1080/15533174.2012.707850
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac11803929
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorGraffelman, J.
local.citation.publicationNameJournal of computational and graphical statistics
local.citation.volume22
local.citation.number1
local.citation.startingPage92
local.citation.endingPage106


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain