An ExPosition of multivariate analysis with the singular value decomposition in R

Derek Beaton, Cherise R. Chin Fatt, Hervé Abdi

Research output: Contribution to journalArticle

35 Scopus citations

Abstract

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.

Original languageEnglish (US)
Pages (from-to)176-189
Number of pages14
JournalComputational Statistics and Data Analysis
Volume72
DOIs
Publication statusPublished - Apr 1 2014
Externally publishedYes

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Keywords

  • Bootstrap
  • Correspondence analysis
  • Partial least squares
  • Principal components analysis
  • R
  • Singular value decomposition

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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