Determines the amount of shrinkage for a penalized regression model fitted by glmnet via cross-validation, conforming to the calling convention required by argument
complexity.glmnet(response, x, full.data, ...)
a survival object (with
data frame containing response and covariates of the full data set.
additional arguments passed to
Scalar value giving the optimal lambda.
Function is basically a wrapper for
cv.glmnet of package
glmnet. A n-fold cross-validation (default n=10) is performed to determine the optimal penalty lambda.
For Cox PH regression models the deviance based on penalized partial log-likelihood is used as loss function. For binary endpoints other loss functions are available as well (see
type.measure). Deviance is default. Calling
peperr, the default arguments of
cv.glmnet can be changed by passing a named list containing these as argument
Note that only penalized Cox PH (
family="cox") and logistic regression models (
family="binomial") are sensible for prediction error
evaluation with package
Friedman, J., Hastie, T. and Tibshirani, R. (2008)
Regularization Paths for Generalized Linear Models via Coordinate
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. https://www.jstatsoft.org/v062/i05/
Thomas Hielscher \ email@example.com