Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models.
| Version: | 1.0 |
| Depends: | R (≥ 3.0.0), glmnet, parallel, pROC |
| Published: | 2016-06-29 |
| Author: | Ben Sherwood [aut, cre] |
| Maintainer: | Ben Sherwood <bsherwo2 at jhu.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | roccv results |
| Reference manual: | roccv.pdf |
| Package source: | roccv_1.0.tar.gz |
| Windows binaries: | r-devel: roccv_1.0.zip, r-release: roccv_1.0.zip, r-oldrel: roccv_1.0.zip |
| OS X El Capitan binaries: | r-release: roccv_1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: roccv_1.0.tgz |
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