Description

Exposition is defined as a comprehensive explanation of an idea. With ExPosition for R, a comprehensive explanation of your data will be provided with minimal effort.

The core of ExPosition is the singular value decomposition (SVD; see: svd). The point of ExPosition is simple: to provide the user with an overview of their data that only the SVD can provide. ExPosition includes several techniques that depend on the SVD (see below for examples and functions).

Details

Author(s)

Questions, comments, compliments, and complaints go to Derek Beaton exposition.software@gmail.com.

The following people are authors or contributors to ExPosition code, data, or examples:
Derek Beaton, Hervé Abdi, Cherise Chin-Fatt, Joseph Dunlop, Jenny Rieck, Rachel Williams, Anjali Krishnan, and Francesca M. Filbey.

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H. and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Abdi, H. (2007). Metric multidimensional scaling. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 598-605.
Greenacre, M. J. (2007). Correspondence Analysis in Practice. Chapman and Hall.
Benzécri, J. P. (1979). Sur le calcul des taux d'inertie dans l'analyse d'un questionnaire. Cahiers de l'Analyse des Données, 4, 377-378.

See Also

epPCA, epGPCA, epMDS, epCA, epMCA

Examples