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dc.creatorKaradžić, B
dc.date.accessioned2017-11-23T11:15:44Z
dc.date.available2015-11-17T10:26:51Z
dc.date.issued1999sr
dc.identifier.issn1195-6860sr
dc.identifier.otherRad_konverzija_3845sr
dc.identifier.urihttps://ibiss-r.rcub.bg.ac.rs/handle/123456789/1850
dc.description.abstractBoth correspondence analysis (CA) and principal components analysis (PCA) may generate either 'arch' or 'horseshoe' effect. In this paper I review the methods that attenuate these undesirable effects, and consequently improve CA and PCA. Detrending methods (detrending by segments and detrending by polynomials) reduce the arch, but not the horseshoe effect. In contrast, the generalized standardization procedure (GSP) is able to unfold involuted ends of a coenospace and thus eliminate the horseshoe effect. This method significantly improves PCA but not CA, since CA is insensitive to GSP.en
dc.description.sponsorshipnullsr
dc.language.isoEnglishsr
dc.rightsrestrictedAccess
dc.sourceEcosciencesr
dc.titleOn detrending in correspondence analysis and principal component analysisen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractКараджић, Б;
dc.citation.issue1sr
dc.citation.volume6sr
dc.citation.epage116sr
dc.type.versionpublishedVersionen


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