Abstract
Redundancy Analysis (RDA) is one of the many possible methods to extract and summarize the variation in a set of response variables that can be explained by a set of explanatory variables. The main idea is to use multivariate linear regression to explain the responses as a linear function of the explanatory and then use Principal Component Analysis (PCA) or a biplot to visualize the result. When response variables are categorical (binary, nominal, or ordinal), classical linear techniques are not adequate. Some alternatives such as Distance-Based RDA have been proposed in the literature. In this paper, we propose versions of RDA based on generalized linear models with logistic responses. The natural visualization methods for our techniques are the Logistic Biplots, recently proposed. The procedures are illustrated with an application to real data.
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Acknowledgements
This research was supported by grant RTI2018-093611-B-I00 from the Ministerio de Ciencia Innovación y Universidades of Spain.
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Vicente-Villardon, J.L., Vicente-Gonzalez, L. (2021). Redundancy Analysis for Binary Data Based on Logistic Responses. In: Chadjipadelis, T., Lausen, B., Markos, A., Lee, T.R., Montanari, A., Nugent, R. (eds) Data Analysis and Rationality in a Complex World. IFCS 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-60104-1_36
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DOI: https://doi.org/10.1007/978-3-030-60104-1_36
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