predictProb.coxnet.RdExtracts predicted survival probabilities from survival model fitted by glmnet, providing an interface as required by pmpec.
# S3 method for coxnet predictProb(object, response, x, times, complexity, ...)
| object | a fitted model of class |
|---|---|
| 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 |
| x |
|
| times | vector of evaluation time points. |
| complexity | lambda penalty value. |
| ... | additional arguments, currently not used. |
Matrix with probabilities for each evaluation time point in times (columns) and each new observation (rows).
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.
doi: 10.18637/jss.v062.i05
Thomas Hielscher \ t.hielscher@dkfz.de