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) <arXiv:1809.05627>.
| Version: |
1.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
DiagrammeR (≥ 1.0.0), data.tree (≥ 0.7.5), graphics, stats, survival (≥ 2.38), ggplot2, MASS, flexsurv, Rcpp |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2020-08-01 |
| Author: |
Yifei Sun [aut],
Mei-Cheng Wang [aut],
Sy Han Chiou [aut, cre] |
| Maintainer: |
Sy Han Chiou <schiou at utdallas.edu> |
| BugReports: |
http://github.com/stc04003/rocTree/issues |
| License: |
GPL (≥ 3) |
| URL: |
http://github.com/stc04003/rocTree |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
rocTree results |
Documentation:
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