Sparse N-way partial least squares with R package sNPLS

Abstract

We introduce the R package sNPLS that performs N-way partial least squares (N-PLS) regression and Sparse (L1-penalized) N-PLS regression in three-way arrays. N-PLS regression is superior to other methods for three-way data based in unfolding, thanks to a better stabilization of the decomposition. This provides better interpretability and improves predictions. The sparse version also adds variable selection through L1 penalization. The sparse version of N-PLS is able to provide lower prediction errors and to further improve interpretability and usability of the N-PLS results. After a short introduction to both methods, the different functions of the package are presented by displaying their use in simulated and a real dataset.