predictProb.coxnet: Extract predicted survival probabilities from a glmnet fit

Description

Extracts predicted survival probabilities from survival model fitted by glmnet, providing an interface as required by pmpec.

Usage

# S3 method for coxnet
predictProb(object, response, x, times, complexity, ...)

Value

Matrix with probabilities for each evaluation time point in times (columns) and each new observation (rows).

Arguments

object: a fitted model of class glmnet
response: a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function Surv() in package survival produces such a matrix
x: n*p matrix of covariates.
times: vector of evaluation time points.
complexity: lambda penalty value.
...: additional arguments, currently not used.

Author

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), 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.