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Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation

Author

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  • Laura Vicente-Gonzalez

    (Department of Statistics, Universidad de Salamanca, 37008 Salamanca, Spain)

  • Jose Luis Vicente-Villardon

    (Department of Statistics, Universidad de Salamanca, 37008 Salamanca, Spain)

Abstract

In this paper, we propose a generalization of Partial Least Squares Regression (PLS-R) for a matrix of several binary responses and a a set of numerical predictors. We call the method Partial Least Squares Binary Logistic Regression (PLS-BLR). That is equivalent to a PLS-2 model for binary responses. Biplot and even triplot graphical representations for visualizing PLS-BLR models are described, and an application to real data is presented. Software packages for the calculation of the main results are also provided. We conclude that the proposed method and its visualization using triplots are powerful tools for the interpretation of the relations among predictors and responses.

Suggested Citation

  • Laura Vicente-Gonzalez & Jose Luis Vicente-Villardon, 2022. "Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation," Mathematics, MDPI, vol. 10(15), pages 1-23, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2580-:d:871050
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    References listed on IDEAS

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    7. Jose Giovany Babativa-Márquez & José Luis Vicente-Villardón, 2021. "Logistic Biplot by Conjugate Gradient Algorithms and Iterated SVD," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
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