ggRandomForests: Survival with Random Forests

Random Forests (Breiman 2001) (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. Random Forests for survival (Ishwaran and Kogalur 2007; Ishwaran, Kogalur, Blackstone, and Lauer 2008) (RF-S) are an extension of Breiman’s RF techniques to survival settings, allowing efficient non-parametric analysis of time to event data. The randomForestSRC package (Ishwaran and Kogalur 2014) is a unified treatment of Breiman’s random forests for survival, regression and classification problems. Predictive accuracy make RF an attractive alternative to parametric models, though complexity and interpretability of the forest hinder wider application of the method. We introduce the ggRandomForests package, tools for creating and plotting data structures to visually understand random forest models grown in R with the randomForestSRC package. The ggRandomForests package is structured to extract intermediate data objects from randomForestSRC objects and generate figures using the ggplot2 (Wickham 2009) graphics package. This document is formatted as a tutorial for using the randomForestSRC for building random forests for survival and ggRandomForests package for investigating how the forest is constructed. This tutorial uses the Primary Biliary Cirrhosis (PBC) Data from the Mayo Clinic (Fleming and Harrington 1991) available in the randomForestSRC package. We use Variable Importance measure (VIMP) (Breiman 2001) as well as Minimal Depth (Ishwaran, Kogalur, Gorodeski, Minn, and Lauer 2010), a property derived from the construction of each tree within the forest, to assess the impact of variables on forest prediction. We will also demonstrate the use of variable dependence plots (Friedman 2000) to aid interpretation RF results in different response settings. We also will investigate interactions between covariates to demonstrate the strength of the Random Forest method in survival settings.