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Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

  • Derek Beaton
  • Francesca Filbey
  • Hervé Abdi
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Abstract

We present an extension of pls—called partial least squares correspondence analysis (plsca)—tailored for the analysis of nominal data. As the name indicates, plsca combines features of pls (analyzing the information common to two tables) and correspondence analysis (ca, analyzing nominal data). We also present inferential techniques for plsca such as bootstrap, permutation, and \({\chi }^{2}\) omnibus tests. We illustrate plsca with two nominal data tables that store (respectively) behavioral and genetics information.

Key words

Partial least squares Correspondence analysis Multiple correspondence analysis Chi-square distance Genomics 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonUSA

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