Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications). You can use rpvm instead of Rmpi if you want but I've only tested with Rmpi.
| Version: | 0.6-5 |
| Depends: | R (≥ 2.0.0), randomForest |
| Suggests: | snow, Rmpi |
| Date: | 2008-04-17 |
| Author: | Ramon Diaz-Uriarte |
| Maintainer: | Ramon Diaz-Uriarte <rdiaz at ligarto.org> |
| License: | GPL version 2 or newer |
| URL: | http://ligarto.org/rdiaz/Software/Software.html, |
| In views: | MachineLearning |
| CRAN checks: | varSelRF results |
Downloads:
| Package source: | varSelRF_0.6-5.tar.gz |
| MacOS X binary: | varSelRF_0.6-5.tgz |
| Windows binary: | varSelRF_0.6-5.zip |
| Reference manual: | varSelRF.pdf |
| Old sources: | varSelRF archive |