Skip to main content
Log in

S-PLUS code for simple and multiple correspondence analysis

  • Published:
Computational Statistics Aims and scope Submit manuscript

Abstract

An exploration of the application of S-PLUS code designed to perform classical correspondence analysis is made in this paper. This code allows for the “classical” analysis to be performed on categorical data consisting of two or more variables. For multi-way contingency tables, correspondence analysis can be performed by considering either the indicator matrix or the Burt matrix. The code also allows the user to incorporate into the analysis confidence circles (to identify categorical responses that deviate from the hypothesis of independence) and for an asymmetrical analysis to be performed. The function includes various warnings and stoppages to help the user properly analyse their data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  • Beh, E. J. (2003), CORR: S-PLUS code for simple and multiple correspondence analysis (Version I), Research Report QMMS2003-04, School of Quantitative Methods and Mathematical Sciences, University of Western Sydney, Australia. Internet address:http://www.uws.edu.au/qmms/research/reports

    Google Scholar 

  • Beh, E. J. (2004a), Simple correspondence analysis: A bibliographic review,International Statistical Review,72, 257–284.

    Article  Google Scholar 

  • Beh, E. J. (2005), S-PLUS code for ordinal correspondence analysis, Computational Statistics, (to appear).

  • Beh, E. J. and Davy, P. J. (1998), Partitioning Pearson’s chi-squared statistic for a completely ordered three-way contingency table,Australian and New Zealand Journal of Statistics,40, 465–477.

    Article  MathSciNet  Google Scholar 

  • Beh, E. J. and Davy, P. J. (1999), Partitioning Pearson’s chi-squared statistic for a partially ordered three-way contingency table,Australian and New Zealand Journal of Statistics,41, 233–246.

    Article  MathSciNet  Google Scholar 

  • Burt, C. (1950), The factorial analysis of qualitative data,Journal of Statistical Psychology,3, 166–185.

    Article  Google Scholar 

  • Carroll, J. D., Kumbasar, E. and Romney, K. (1997), An equivalence relation between correspondence analysis and classical metric multidimensional scaling for the recovery of Euclidean distances,British Journal of Mathematical and Statistical Psychology,50, 81–92.

    Article  MathSciNet  Google Scholar 

  • Clogg, C. C. (1982), Some models for the analysis of association in multi-way cross-classifications having ordered categories,Journal of the American Statistical Association,77, 803–815.

    Article  MathSciNet  Google Scholar 

  • Davis, J. A. (1977), Codebook for the 1977 General Social Survey, National Opinion Research Center, Chicago.

    Google Scholar 

  • Everitt, B. S. (1994), A Handbook of Statistical Analysis using S-PLUS, Chapman and Hall, London.

    MATH  Google Scholar 

  • Goodman, L. A. (1986), Some useful extensions of the usual correspondence analysis approach and the usual log-linear models approach in the analysis of contingency tables,International Statistical Review,54, 243–309.

    Article  MathSciNet  Google Scholar 

  • Greenacre, M. J. (1984), Theory and Application of Correspondence Analysis, Academic Press, London.

    MATH  Google Scholar 

  • Lebart, L., Morineau, A. and Warwick, L. (1984), Multivariate Descriptive Statistical Analysis, Wiley, New-York.

    MATH  Google Scholar 

  • Srole, L., Langner, T. S., Michael, S. T., Opler M. K. and Rennie, T. A. C. (1962), Mental Health in the Metropolis: The Midtown Manhattan Study, McGraw-Hill, New-York.

    Book  Google Scholar 

  • Venables, W. N. and Ripley, B. D. (1999), Modern Applied Statistics with S-PLUS (Third Edition), Springer, New-York.

    Book  Google Scholar 

  • Weiler, S. C. and Romney, A. K. (1990), Metric Scaling: Correspondence Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, 07–075. Newbury Park, CA: Sage.

    Book  Google Scholar 

  • Young, F. W. and Hamer, R. M. (1987), Multidimensional Scaling: History, Theory, and Applications, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Beh, E.J. S-PLUS code for simple and multiple correspondence analysis. Computational Statistics 20, 415–438 (2005). https://doi.org/10.1007/BF02741306

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02741306

Keywords

Navigation