Description
Logistic Biplot Estimation Using Machine Learning Algorithms.
Description
Implements methods for fitting logistic biplot models to multivariate binary data. The logistic biplot represents individuals as points and binary variables as directed vectors in a low-dimensional subspace; the orthogonal projection of each individual onto a variable vector approximates the expected probability that the corresponding characteristic is present. Available fitting methods include conjugate gradient algorithms, a coordinate descent Majorization-Minimization (MM) algorithm, and a block coordinate descent algorithm based on data projection that supports matrices with missing values and allows new individuals to be projected as supplementary rows without refitting the model. A cross-validation procedure is provided to select the number of latent dimensions k. References: Babativa-Marquez and Vicente-Villardon (2021) <doi:10.3390/math9162015>; Vicente-Villardon and Galindo (2006, ISBN:9780470973196).