Regression by Parts: Fitting Visually Interpretable Models with GUIDE

Abstract

Regression modeling often requires many subjective decisions, such as choice of transformation for each variable and the type and number of terms to include in the model. The transformations may be as simple as powers and cross-products or as sophisticated as indicator functions and splines. Sometimes, the transformations are chosen to satisfy certain subjective criteria such as approximate normality of the marginal distributions of the predictor variables. Further, model building is almost always an iterative process, with the fit of the model evaluated each time terms are added or deleted.