fit.glmnet: Interface function for fitting a penalized regression model...

Description Usage Arguments Details Value Author(s) References See Also

View source: R/peperr_glmnet.R


Interface for fitting penalized regression models for binary of survival endpoint using glmnet, conforming to the requirements for argument in peperr call.


fit.glmnet(response, x, cplx, ...)



a survival object (with Surv(time, status), or a binary vector with entries 0 and 1).


n*p matrix of covariates.


lambda penalty value.


additional arguments passed to glmnet call such as family.


Function is basically a wrapper for glmnet of package glmnet. Note that only penalized Cox PH (family="cox") and logistic regression models (family="binomial") are sensible for prediction error evaluation with package peperr.


glmnet object


Thomas Hielscher \ [email protected]


Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent,
Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13
Porzelius, C., Binder, H., and Schumacher, M. (2009) Parallelized prediction error estimation for evaluation of high-dimensional models, Bioinformatics, Vol. 25(6), 827-829.
Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22.

See Also

peperr, glmnet

c060 documentation built on May 30, 2017, 3:06 a.m.