complexity.glmnet {c060}  R Documentation 
Interface for determination of penalty lambda in penalized regression model via crossvalidation
Description
Determines the amount of shrinkage for a penalized regression model fitted by glmnet via crossvalidation, conforming to the calling convention required by argument complexity
in peperr
call.
Usage
complexity.glmnet(response, x, full.data, ...)
Arguments
response 
a survival object (with 
x 

full.data 
data frame containing response and covariates of the full data set. 
... 
additional arguments passed to 
Details
Function is basically a wrapper for cv.glmnet
of package glmnet
. A nfold crossvalidation (default n=10) is performed to determine the optimal penalty lambda.
For Cox PH regression models the deviance based on penalized partial loglikelihood 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
.
Value
Scalar value giving the optimal lambda.
Author(s)
Thomas Hielscher \ t.hielscher@dkfz.de
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), 122 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)
113
https://www.jstatsoft.org/v39/i05/
Porzelius, C., Binder, H., and Schumacher, M. (2009)
Parallelized prediction error estimation for evaluation of highdimensional models,
Bioinformatics, Vol. 25(6), 827829.
Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and ElasticNet Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22.
doi:10.18637/jss.v062.i05