Adjusting for Optimism in 'glmnet' Regression using Bootstrapping
Overview
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.
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- Help pages
- 9
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 90%
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Package metadata
- First published
- 2017-11-14
- Total releases
- 3 / 9 yrs
- License
- GPL (>= 2) OSI
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