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 in peperr call.
complexity.glmnet(response, x, full.data, ...)Scalar value giving the optimal lambda.
a survival object (with Surv(time, status), or a
binary vector with entries 0 and 1).
n*p matrix of covariates.
data frame containing response and covariates of the full data set.
additional arguments passed to cv.glmnet call such as
family.
Thomas Hielscher t.hielscher@dkfz.de
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 args.complexity. Note that only
penalized Cox PH (family="cox") and logistic regression models
(family="binomial") are sensible for prediction error evaluation with
package peperr.
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.
peperr, cv.glmnet