R Package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification

• Activity Number: 312
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section for Statistical Programmers and Analysts
• Author(s): Jean-Eudes Dazard* and Michael Choe and Michael LeBlanc and J. Sunil Rao
• Companies: Case Western Reserve University and Case Western Reserve University and Fred Hutchinson Cancer Research Center and University of Miami
• Keywords: Bump Hunting ; Non-Parametric Methods ; Rule-Induction Methods ; Cross-Validation ; Parallel Programming ; R Package

Abstract

PRIMsrc is a novel implementation of a non-parametric bump hunting procedure, based on the Patient Rule Induction Method (PRIM), offering a unified treatment of outcome variables, including censored time-to-event (Survival), continuous (Regression) and discrete (Classification) responses. To fit the model, it uses a recursive peeling procedure with specific peeling criteria and stopping rules depending on the response. To validate the model, it provides an objective function based on prediction-error or other specific statistic, as well as two alternative cross-validation techniques, adapted to the task of decision-rule making and estimation in the three types of settings. PRIMsrc comes as an open source R package, including at this point: (i) a main function for fitting a Survival Bump Hunting model with various options allowing cross-validated model selection to control model size (#covariates) and model complexity (#peeling steps) and generation of cross-validated end-point estimates; (ii) parallel computing; (iii) various S3-generic and specific plotting functions for data visualization, diagnostic, prediction, summary and display of results. It is available on CRAN and GitHub.