Main objective of a predictive model is to provide accurated predictions of a new observations. Unfortunately we don't know how well the model performs. In addition, at the current era of omic data where p >> n, is not reasonable applying internal validation using data-splitting. Under this background a good method to assessing model performance is applying internal bootstrap validation (Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation for the linear and logistic 'glmnet' models.
| Version: | 0.1.3 |
| Imports: | glmnet, pbapply, pROC, parallel |
| Published: | 2017-11-14 |
| Author: | Antonio Jose Canada Martinez |
| Maintainer: | Antonio Jose Canada Martinez <ancamar2 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | BootValidation results |
| Reference manual: | BootValidation.pdf |
| Package source: | BootValidation_0.1.3.tar.gz |
| Windows binaries: | r-devel: BootValidation_0.1.3.zip, r-release: BootValidation_0.1.3.zip, r-oldrel: BootValidation_0.1.3.zip |
| OS X binaries: | r-release: BootValidation_0.1.3.tgz, r-oldrel: BootValidation_0.1.3.tgz |
Please use the canonical form https://CRAN.R-project.org/package=BootValidation to link to this page.