Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Package details |
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Author | Hui Xu and Raji Balasubramanian |
Maintainer | Hui Xu <huix@schoolph.umass.edu> |
License | GPL (>= 2) |
Version | 1.2 |
Package repository | View on CRAN |
Installation |
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