A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R

Abstract

With recent increases in the size of datasets currently available in the behavioral and health sciences, the need for efficient and effective variable selection techniques has increased. A plethora of techniques exist, yet only a few are used within the psychological sciences (e.g., stepwise regression, which is most common, the LASSO, and Elastic Net). The purpose of this tutorial is to increase awareness of the various variable selection methods available in the popular statistical software R, and guide researchers through how each method can be used to select variables in the context of classification using a recent survey-based assessment of misophonia. Specifically, readers will learn about how to implement and interpret results from the LASSO, Elastic Net, a penalized SVM classifier, an implementation of random forest, and the genetic algorithm. The associated code and data implemented in this tutorial are available on OSF to allow for a more interactive experience. This paper is written with the assumption that individuals have at least a basic understanding of R.