Interface function for fitting a penalized regression model with glmnet

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

Usage

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

Arguments

response: a survival object (with Surv(time, status), or a binary vector with entries 0 and 1).
x: n*p matrix of covariates.
cplx: lambda penalty value.
...: additional arguments passed to glmnet call such as family.

Value

glmnet object

Details

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.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, https://web.stanford.edu/~hastie/Papers/glmnet.pdf
Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
https://www.jstatsoft.org/v33/i01/
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
https://www.jstatsoft.org/v39/i05/
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. https://doi.org/10.18637/jss.v062.i05.

Author

Thomas Hielscher t.hielscher@dkfz.de

Interface function for fitting a penalized regression model with `glmnet`