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

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://www.jstatsoft.org/v062/i05/

Author

Thomas Hielscher \ t.hielscher@dkfz.de

See also