BootValidation: Adjusting for Optimism in 'glmnet' Regression using Bootstrapping

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, logistic, multinomial and cox 'glmnet' models as well as lm and glm models.

Getting started

Package details

AuthorAntonio Jose Canada Martinez
MaintainerAntonio Jose Canada Martinez <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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BootValidation documentation built on Aug. 14, 2018, 5:03 p.m.