Compositional data analysis — linear algebra, visualization and interpretation
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- dc.contributor.author Greenacre, Michael
- dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2024-11-14T10:09:41Z
- dc.date.available 2024-11-14T10:09:41Z
- dc.date.issued 2021-11-01
- dc.date.modified 2024-11-14T10:08:18Z
- dc.description.abstract Compositional data analysis is concerned with multivariate data that have a constant sum, usually 1 or 100%. These are data often found in biochemistry and geochemistry, but also in the social sciences, when relative values are of interest rather than the raw values. Recent applications are in the area of very high-dimensional "omics" data. Logratios are frequently used for this type of data, i.e. the logarithms of ratios of the components of the data vectors. These ratios raise interesting issues in matrix-vector representation, computation and interpretation, which will be dealt with in this chapter.
- dc.format.mimetype application/pdf*
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1805
- dc.identifier.citation
- dc.identifier.uri http://hdl.handle.net/10230/68579
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 1805
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword
- dc.subject.keyword Statistics, Econometrics and Quantitative Methods
- dc.title Compositional data analysis — linear algebra, visualization and interpretation
- dc.title.alternative
- dc.type info:eu-repo/semantics/workingPaper