icRSF: A Modified Random Survival Forest Algorithm
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.
- Hui Xu and Raji Balasubramanian
- Date of publication
- 2016-01-20 17:07:02
- Hui Xu <email@example.com>
- GPL (>= 2)
- Permutation-based variable importance metric for high...
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- Simulate error-prone test results for a user-defined vector...
- A covariate matrix
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