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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. Framework was built to handle genetic data, but works for any data.
| Version: | 1.2 |
| Depends: | R (≥ 3.0.0), glmnet, parallel, pROC |
| Published: | 2019-05-10 |
| DOI: | 10.32614/CRAN.package.roccv |
| Author: | Ben Sherwood [aut, cre] |
| Maintainer: | Ben Sherwood <ben.sherwood at ku.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | roccv results |
| Reference manual: | roccv.pdf |
| Package source: | roccv_1.2.tar.gz |
| Windows binaries: | r-devel: roccv_1.2.zip, r-release: roccv_1.2.zip, r-oldrel: roccv_1.2.zip |
| macOS binaries: | r-release (arm64): roccv_1.2.tgz, r-oldrel (arm64): roccv_1.2.tgz, r-release (x86_64): roccv_1.2.tgz, r-oldrel (x86_64): roccv_1.2.tgz |
| Old sources: | roccv archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.