Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose.

Documentation

Manual: VSURF.pdf
Vignette: None available.

Maintainer: Robin Genuer <Robin.Genuer at isped.u-bordeaux2.fr>

Author(s): Robin Genuer*, Jean-Michel Poggi*, Christine Tuleau-Malot*

Install package and any missing dependencies by running this line in your R console:

install.packages("VSURF")

Depends
Imports doParallel, foreach, parallel, randomForest, rpart
Suggests testthat
Enhances
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Package VSURF
Materials
URL https://github.com/robingenuer/VSURF
Task Views
Version 1.0.3
Published 2016-04-26
License GPL (>= 2)
BugReports https://github.com/robingenuer/VSURF/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks VSURF check results
Package source VSURF_1.0.3.tar.gz