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rocTree (version 1.1.1)

Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree

Description

Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) .

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install.packages('rocTree')

Monthly Downloads

25

Version

1.1.1

License

GPL (>= 3)

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Maintainer

Sy Chiou

Last Published

August 1st, 2020

Functions in rocTree (1.1.1)

rocTree

Roc-guided survival trees
print.rocTree

Printing an rocTree object
plot.rocTree

Plotting an rocTree object
simu

Function to generate simulated data used in the manuscript.
rocTree-package

rocTree:Receiver Operating Characteristic (ROC)-Guided Classification Survival Tree and Ensemble.
predict.rocTree

Predicting based on a rocTree model.
simDat

Simulated dataset for demonstration
export_Surv

Surv function imported from survival