Extracts predicted survival probabilities from survival model fitted by glmnet, providing an interface as required by pmpec.
# S3 method for glmnet
predictProb(object, response, x, times, complexity, ...)Matrix with probabilities for each evaluation time point in times (columns) and each new observation (rows).
a fitted model of class glmnet.
response variable. Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions. For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions.
n*p matrix of covariates.
vector of evaluation time points.
lambda penalty value.
additional arguments, currently not used.
Thomas Hielscher \ t.hielscher@dkfz.de
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.
tools:::Rd_expr_doi("10.18637/jss.v062.i05")
predictProb.coxnet, peperr, glmnet