Fast OpenMP parallel computing for unified Breiman random forests (Breiman 2001) for regression, classification, survival analysis, competing risks, multivariate, unsupervised, quantile regression, and class imbalanced q-classification. Missing data imputation includes missForest and multivariate missForest. New fast subsampling random forests. Confidence intervals for variable importance. New holdout vimp.
This package contains many useful functions and users should read the help file in its entirety for details. However, we briefly mention several key functions that may make it easier to navigate and understand the layout of the package.
This is the main entry point to the package. It grows a random forest
using user supplied training data. We refer to the resulting object
as a RF-SRC grow object. Formally, the resulting object has class
(rfsrc, grow).
A fast implementation of rfsrc using subsampling.
Univariate and multivariate quantile regression forest for training and testing. Different methods available including the Greenwald-Khanna (2001) algorithm, which is especially suitable for big data due to its high memory efficiency.
predict.rfsrc, predict
Used for prediction. Predicted values are obtained by dropping the
user supplied test data down the grow forest. The resulting object
has class (rfsrc, predict).
vimp.rfsrc, subsample.rfsrc,
holdout.vimp.rfsrc
Used for variable selection:
vimp calculates variable imporance (VIMP) from a
RF-SRC grow/predict object by noising up the variable (for example
by permutation). Note that grow/predict calls can always directly
request VIMP.
subsample calculates VIMP confidence itervals via
subsampling.
holdout.vimp measures the importance of a variable
when it is removed from the model.
Fast imputation mode for RF-SRC. Both rfsrc and
predict.rfsrc are capable of imputing missing data.
However, for users whose only interest is imputing data, this function
provides an efficient and fast interface for doing so.
Used to extract the partial effects of a variable or variables on the ensembles.
Regular stable releases of this package are available on CRAN at https://cran.r-project.org/package=randomForestSRC
Interim unstable development builds with bug fixes and sometimes additional functionality are available at https://github.com/kogalur/randomForestSRC
Bugs may be reported via https://github.com/kogalur/randomForestSRC/issues/new. Please provide the accompanying information with any reports:
sessionInfo()
A minimal reproducible example consisting of the following items:
a minimal dataset, necessary to reproduce the error
the minimal runnable code necessary to reproduce the error, which can be run on the given dataset
the necessary information on the used packages, R version and system it is run on
in the case of random processes, a seed (set by set.seed()) for reproducibility
This package implements OpenMP shared-memory parallel programming if the target architecture and operating system support it. This is the default mode of execution.
Additional instructions for configuring OpenMP parallel processing are available at https://kogalur.github.io/randomForestSRC/building.html.
An understanding of resource utilization (CPU and RAM) is necessary when running the package using OpenMP and Open MPI parallel execution. Memory usage is greater when running with OpenMP enabled. Diligence should be used not to overtax the hardware available.
Breiman L. (2001). Random forests, Machine Learning, 45:5-32.
Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.
Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.
Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.
Ishwaran H., Kogalur U.B., Gorodeski E.Z, Minn A.J. and Lauer M.S. (2010). High-dimensional variable selection for survival data. J. Amer. Statist. Assoc., 105:205-217.
Ishwaran H., Kogalur U.B., Chen X. and Minn A.J. (2011). Random survival forests for high-dimensional data. Stat. Anal. Data Mining, 4:115-132
Ishwaran H., Gerds T.A., Kogalur U.B., Moore R.D., Gange S.J. and Lau B.M. (2014). Random survival forests for competing risks. Biostatistics, 15(4):757-773.
Ishwaran H. and Malley J.D. (2014). Synthetic learning machines. BioData Mining, 7:28.
Ishwaran H. (2015). The effect of splitting on random forests. Machine Learning, 99:75-118.
Lu M., Sadiq S., Feaster D.J. and Ishwaran H. (2018). Estimating individual treatment effect in observational data using random forest methods. J. Comp. Graph. Statist, 27(1), 209-219
Ishwaran H. and Lu M. (2019). Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival. Statistics in Medicine, 38, 558-582.
O'Brien R. and Ishwaran H. (2019). A random forests quantile classifier for class imbalanced data. Pattern Recognition, 90, 232-249
Tang F. and Ishwaran H. (2017). Random forest missing data algorithms. Statistical Analysis and Data Mining, 10, 363-377.
imbalanced.rfsrc,
impute.rfsrc,
partial.rfsrc,
plot.competing.risk.rfsrc,
plot.rfsrc,
plot.survival.rfsrc,
plot.variable.rfsrc,
predict.rfsrc,
print.rfsrc,
rfsrc,
rfsrc.cart,
rfsrc.fast,