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

In c060: Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

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 class 'glmnet'
predictProb(object, response, x, times, complexity, ...)

Arguments

object	a fitted model of class glmnet.
response	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.
x	n*p matrix of covariates.
times	vector of evaluation time points.
complexity	lambda penalty value.
...	additional arguments, currently not used.

Value

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

Author(s)

Thomas Hielscher \ [e-mail]

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v062.i05")}

See Also

predictProb.coxnet, peperr, glmnet

c060 documentation built on March 31, 2023, 5:15 p.m.