# Implements the icRSF algorithm to calculate variable importance

### Description

Implements a modification to the Random Survival Forests algorithm [1] 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 [2]. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic [3].

### Details

Package: | icRSF |

Type: | Package |

Version: | 1.0 |

Date: | 2016-01-01 |

License: | GPL (>= 2) |

### Author(s)

Hui Xu and Raji Balasubramanian

Maintainer: Hui Xu <huix@schoolph.umass.edu> and Raji Balasubramanian <rbalasub@schoolph.umass.edu>

### References

[1] Ishwaran, H. and Kogalur, U. B. and Blackstone, E. H. and Lauer, M. S. (2008). Random Survival Forests, Annals of Applied Statistics, Vol.2, Number 3, pp. 841-860. <DOI: 10.1214/08-AOAS169>.

[2] Gu, X., Ma, Y., Balasubramanian, R. (2015). Semi-parametric time to event models in the presence of error-prone, self-reported outcomes - with application to the Women's Health Initiative, Annals of Applied Statistics, 9 (2), 714-730. <DOI: 10.1214/15-AOAS810>

[3] Xu, H., Gu, X., Tadesse, M. G., Balasubramanian, R. (2016). A modified Random Survival Forests algorithm for variable selection in the presence of imperfect self-reports or laboratory based diagnostic tests, Submitted to Journal of Computational and Graphical Statistics.

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.