This package is called tfCox or trend filtering for Cox model, which is proposed in Jiacheng Wu & Daniela Witten (2019) Flexible and Interpretable Models for Survival Data, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2019.1592758. It provides an approach to fit additive Cox model in which each component function is estimated to be piecewise polynomial with adaptively-chosen knots.
Function tfCox fits the trend filtering Cox model for a range of tuning parameters. Function cv_tfCox returns the optimal tuning parameter selected by K-fold cross validation.
| Package: | tfCox |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2019-05-20 |
| License: | GPL (>= 2) |
The package includes the following functions:
tfCox, cv_tfCox, plot.tfCox, plot.cv_tfCox, predict.tfCox, summary.tfCox, summary.cv_tfCox, sim_dat, plot.sim_dat.
Jiacheng Wu & Daniela Witten (2019) Flexible and Interpretable Models for Survival Data, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2019.1592758