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Interface for determination of penalty lambda in penalized regression model via cross-validation

Source: R/peperr_glmnet.R
complexity.glmnet.Rd

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.

Usage

complexity.glmnet(response, x, full.data, ...)

Arguments

response

a survival object (with Surv(time, status), or a binary vector with entries 0 and 1).

x

n*p matrix of covariates.

full.data

data frame containing response and covariates of the full data set.

...

additional arguments passed to cv.glmnet call such as family.

Value

Scalar value giving the optimal lambda.

Details

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.

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.

See also

peperr, cv.glmnet

Author

Thomas Hielscher t.hielscher@dkfz.de