A new algorithm and a discussion about visualization for logistic reduced rank regression

Abstract

Logistic reduced rank regression is a useful data analysis tool when we have multiple binary response variables and a set of predictors. In this paper, we describe logistic reduced rank regression and present a new majorization minimization algorithm for the estimation of model parameters. Furthermore, we discuss Type I and Type D triplots for visualizing the results of a logistic reduced rank regression model, compare them, and then develop a hybrid triplot using elements of both types. Two empirical data sets are analyzed. This analysis is used to (1) compare the new algorithm to an existing one in terms of speed; and (2) to show the hybrid triplot and its interpretation.