ggRandomForests: Exploring Random Forest Survival

Random forest (Breiman 2001a) (RF) is a non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF is a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. Random survival forests (RSF) (Ishwaran and Kogalur 2007; Ishwaran, Kogalur, Blackstone, and Lauer 2008) are an extension of Breiman’s RF techniques 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 forest for survival, regression and classification problems. Predictive accuracy makes 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 visually understand random forest models grown in R (R Core Team 2014) 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 structured as a tutorial for building random forest for survival with the randomForestSRC package and using the ggRandomForests package for investigating how the forest is constructed. We analyse the Primary Biliary Cirrhosis (PBC) of the liver data from a clinical trial at the Mayo Clinic (Fleming and Harrington 1991). We demonstrate random forest variable selection using Variable Importance (VIMP) (Breiman 2001a) and Minimal Depth (Ishwaran, Kogalur, Gorodeski, Minn, and Lauer 2010), a property derived from the construction of each tree within the forest. We will also demonstrate the use of variable dependence and partial dependence plots (Friedman 2000) to aid in the interpretation of RSF results. We then examine variable interactions between covariates using conditional variable dependence plots. Our aim is to demonstrate the strength of using Random Forest methods for both prediction and information retrieval, specifically in time to event data settings.