ggRandomForests is a utility package for
randomForestSRC (Ishwaran et.al. 2014, 2008, 2007) for survival,
regression and classification forests and uses the ggplot2
(Wickham 2009) package for plotting results. ggRandomForests is
structured to extract data objects from the random forest and provides S3
functions for printing and plotting these objects.
The randomForestSRC package provides a unified treatment of
Breiman's (2001) random forests for a variety of data settings. Regression
and classification forests are grown when the response is numeric or
categorical (factor) while survival and competing risk forests
(Ishwaran et al. 2008, 2012) are grown for right-censored survival data.
Many of the figures created by the ggRandomForests package are also
available directly from within the randomForestSRC package. However,
ggRandomForests offers the following advantages:
Separation of data and figures: ggRandomForest contains
functions that operate on either the rfsrc
forest object directly, or on the output from randomForestSRC post
processing functions (i.e. plot.variable, var.select,
find.interaction) to generate intermediate ggRandomForests
data objects. S3 functions are provide to further process these objects and
plot results using the ggplot2 graphics package. Alternatively,
users can use these data objects for additional custom plotting or
analysis operations.
Each data object/figure is a single, self contained object. This
allows simple modification and manipulation of the data or ggplot2
objects to meet users specific needs and requirements.
The use of ggplot2 for plotting. We chose to use the
ggplot2 package for our figures to allow users flexibility in
modifying the figures to their liking. Each S3 plot function returns either
a single ggplot2 object, or a list of ggplot2 objects,
allowing users to use additional ggplot2 functions or themes to
modify and customize the figures to their liking.
The ggRandomForests package contains the following data functions:
gg_rfsrc: randomForest[SRC] predictions.
gg_error: randomForest[SRC] convergence rate based on
the OOB error rate.
gg_roc: ROC curves for randomForest classification
models.
gg_vimp: Variable Importance ranking for variable
selection.
gg_minimal_depth: Minimal Depth ranking for variable
selection
(Ishwaran et.al. 2010).
gg_minimal_vimp: Comparing Minimal Depth and VIMP
rankings for variable selection.
gg_interaction: Minimal Depth interaction detection
(Ishwaran et.al. 2010)
gg_variable: Marginal variable dependence.
gg_partial: Partial (risk adjusted) variable
dependence.
gg_partial_coplot: Partial variable conditional
dependence (computationally expensive).
gg_survival: Kaplan-Meier/Nelson-Aalen hazard analysis.
Each of these data functions has an associated S3 plot function that
returns ggplot2 objects, either individually or as a list, which can
be further customized using standard ggplot2 commands.
Breiman, L. (2001). Random forests, Machine Learning, 45:5-32.
Ishwaran H. and Kogalur U.B. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.5.12.
Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25--31.
Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841--860.
Ishwaran, H., U. B. Kogalur, E. Z. Gorodeski, A. J. Minn, and M. S. Lauer (2010). High-dimensional variable selection for survival data. J. Amer. Statist. Assoc. 105, 205-217.
Ishwaran, H. (2007). Variable importance in binary regression trees and forests. Electronic J. Statist., 1, 519-537.
Wickham, H. ggplot2: elegant graphics for data analysis. Springer New York, 2009.