rocTree
Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree
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)
- Version1.1.1
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release08/01/2020
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Team
Sy Han Chiou
Yifei Sun
Show author detailsRolesAuthorMei-Cheng Wang
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- Depends1 package
- Imports8 packages
- Linking To2 packages