Abstract

A novel variable influence on projection approach for O2PLS (R) models, named VIPO2PLS, is presented in this paper. VIPO2PLS is a model-based method for judging the importance of variables. Its cornerstone is the 2-way formalism of the O2PLS models; i.e. the use of both predictive and orthogonal normalized loadings of the two modelled data matrices, and also a new weighting system based on the sum of squares of both data blocks (X, Y). The VIPO2PLS algorithm has been tested in one synthetic data set and two real cases, and the outcomes have been compared to the PLS-VIP, VIPOPLS, and i-PLS methods. The purpose is to achieve a sharper and enhanced model interpretation of O2PLS models by using the new VIPO2PLS method for assessing the importance of both X- and Y-variables.

Keywords

Multi-block variable selection; O2PLS; VIP; Variable importance; Model interpretation; Multivariate calibration

Published in

Chemometrics and Intelligent Laboratory Systems
2017, volume: 160, pages: 110-124

Authors' information