% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/help.R
\name{ggRandomForests-package}
\alias{ggRandomForests-package}
\title{ggRandomForests: Visually Exploring Random Forests}
\description{
\code{ggRandomForests} is a utility package for
\code{randomForestSRC} (Ishwaran and Kogalur) for survival,
regression and classification forests and uses the \code{ggplot2}
(Wickham 2009) package for plotting results. \code{ggRandomForests} is
structured to extract data objects from the random forest and provides S3
functions for printing and plotting these objects.
Requires \code{randomForestSRC} >= 3.4.0.

The \code{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 \code{ggRandomForests} package are also
available directly from within the \code{randomForestSRC} package. However,
\code{ggRandomForests} offers the following advantages:
\itemize{
\item Separation of data and figures: \code{ggRandomForest} contains
functions that operate on either the \code{\link[randomForestSRC]{rfsrc}}
forest object directly, or on the output from \code{randomForestSRC} post
processing functions (i.e. \code{plot.variable}) to generate intermediate
\code{ggRandomForests}
data objects. S3 functions are provide to further process these objects and
plot results using the \code{ggplot2} graphics package. Alternatively,
users can use these data objects for additional custom plotting or
analysis operations.

\item Each data object/figure is a single, self contained object. This
allows simple modification and manipulation of the data or \code{ggplot2}
objects to meet users specific needs and requirements.

\item The use of \code{ggplot2} for plotting. We chose to use the
\code{ggplot2} package for our figures to allow users flexibility in
modifying the figures to their liking. Each S3 plot function returns either
a single \code{ggplot2} object, or a \code{list} of \code{ggplot2} objects,
allowing users to use additional \code{ggplot2} functions or themes to
modify and customize the figures to their liking.
}

The \code{ggRandomForests} package contains the following data functions:
\itemize{
\item \code{\link{gg_rfsrc}}: randomForestSRC predictions.
\item \code{\link{gg_error}}: randomForestSRC convergence rate based on
the OOB error rate.
\item \code{\link{gg_roc}}: ROC curves for randomForest classification
models.
\item \code{\link{gg_vimp}}: Variable Importance ranking for variable
selection.
(Ishwaran et.al. 2010).
\item \code{\link{gg_variable}}: Marginal variable dependence.

\item \code{\link{gg_survival}}: Kaplan-Meier/Nelson-Aalen hazard analysis.
}

Each of these data functions has an associated S3 plot function that
returns \code{ggplot2} objects, either individually or as a list, which can
be further customized using standard \code{ggplot2} commands.
}
\references{
Breiman, L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. randomForestSRC: Random Forests for Survival,
Regression and Classification. R package version >= 3.4.0.
\url{https://cran.r-project.org/package=randomForestSRC}

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.
}
