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.
|Author||Hui Xu and Raji Balasubramanian|
|Date of publication||2018-02-27 05:25:02 UTC|
|Maintainer||Hui Xu <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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