Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data
نویسندگان
چکیده
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 χ2 omnibus tests. We illustrate PLSCA with two nominal data tables that store (respectively) behavioral and genetics information.
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