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tfCox (version 0.1.0)

Fits Piecewise Polynomial with Data-Adaptive Knots in Cox Model

Description

In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, . The fitted functions are piecewise polynomial with adaptively chosen knots.

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Version

Install

install.packages('tfCox')

Monthly Downloads

162

Version

0.1.0

License

GPL (>= 2)

Maintainer

Jiacheng Wu

Last Published

August 1st, 2019

Functions in tfCox (0.1.0)

tfCox-package

Fit the Additive Trend Filtering Cox Model
tfCox

Fit the additive trend filtering Cox model with a range of tuning parameters
predict.tfCox

plot.tfCox

Plot Fitted Functions from Class "tfCox"
negloglik

Calculate the negative log likelihood from Cox model.
cv_tfCox

Fit Trend Filtering Cox model and Choose Tuning Parameter via K-Fold Cross-Validation
summary.tfCox

Summarize tfCox object
summary.cv_tfCox

Summarize cv_tfCox object
tfCox_choose_lambda

Choose the tuning parameter lambda using training and testing dataset
predict_best_lambda

Predict from the optimal lambda from tfCox_choose_lambda
sim_dat

Simulate Data from a Variety of Functional Scenarios
plot.cv_tfCox

Plots Cross-Validation Curve for Object of Class "cv_tfCox"
plot.sim_dat

Plot the true covariate effects