Using relative weight analysis with residualization to detect relevant nonlinear interaction effects in ordinary and logistic regressions

The relative weight analysis is a classic tool to detect if one variable or interaction in a model is relevant or not. In this paper, we will focus on the construction of relative weights for non-linear interactions using restricted cubic splines. Our aim is to provide an accessible method to analyze a multivariate model and identify one subset with the most representative set of variables. Furthermore, we developed a procedure treating control, fixed, free and interactions terms at the same time in the residual weight analysis. The interactions are residualized properly against their main effects to keep their true effect in the model. We test this method with two simulated examples.