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.
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Beaton, D., Filbey, F., Abdi, H. (2013). Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data. In: Abdi, H., Chin, W., Esposito Vinzi, V., Russolillo, G., Trinchera, L. (eds) New Perspectives in Partial Least Squares and Related Methods. Springer Proceedings in Mathematics & Statistics, vol 56. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8283-3_4
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