Two steps variable selection procedure in a context of high-dimensional dependent data but few observations. First step is dedicated to eliminate dependence between variables (clustering of variables, followed by factor analysis inside each cluster). Second step is a variable selection using by aggregation of adapted methods. Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment. 2018. <https://hal.archives-ouvertes.fr/hal-01939694>.
| Version: | 0.1.0 |
| Imports: | stats, mvtnorm, ClustOfVar, FAMT, graphics, VSURF, glmnet, anapuce, qvalue, parallel, doParallel, impute, ComplexHeatmap, circlize |
| Published: | 2019-04-04 |
| Author: | Aurelie Gueudin [aut, cre], Anne Gegout-Petit [aut] |
| Maintainer: | Aurelie Gueudin <aurelie.gueudin at univ-lorraine.fr> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | armada results |
| Reference manual: | armada.pdf |
| Package source: | armada_0.1.0.tar.gz |
| Windows binaries: | r-devel: armada_0.1.0.zip, r-release: armada_0.1.0.zip, r-oldrel: armada_0.1.0.zip |
| macOS binaries: | r-release: armada_0.1.0.tgz, r-oldrel: armada_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=armada to link to this page.