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

33 Citations (Scopus)

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
StatePublished - Apr 1 2014
Externally publishedYes

Fingerprint

Correspondence Analysis
Multivariate Analysis
Factor analysis
Singular value decomposition
Principal component analysis
Centrobaric
Resampling
Factor Analysis
Discriminant Analysis
Discriminant
Bootstrap
Principal Component Analysis
Table
Permutation
Scaling
Metric
Graphics

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

Cite this

An ExPosition of multivariate analysis with the singular value decomposition in R. / Beaton, Derek; Chin Fatt, Cherise R.; Abdi, Hervé.

In: Computational Statistics and Data Analysis, Vol. 72, 01.04.2014, p. 176-189.

Research output: Contribution to journalArticle

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