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.

Author
Hui Xu and Raji Balasubramanian
Date of publication
2016-01-20 17:07:02
Maintainer
Hui Xu <huix@schoolph.umass.edu>
License
GPL (>= 2)
Version
1.0

View on CRAN

Man pages

icrsf
Permutation-based variable importance metric for high...
icRSF_1.0-package
Implements the icRSF algorithm to calculate variable...
pheno
A longitudinal data with diagnostic results for...
simout
Simulate error-prone test results for a user-defined vector...
Xmat
A covariate matrix

Files in this package

icRSF
icRSF/src
icRSF/src/loglikhood2.cpp
icRSF/src/dataproc.cpp
icRSF/src/RcppExports.cpp
icRSF/NAMESPACE
icRSF/data
icRSF/data/Xmat.rda
icRSF/data/pheno.rda
icRSF/R
icRSF/R/RcppExports.R
icRSF/R/simout.r
icRSF/R/icrsf.r
icRSF/MD5
icRSF/DESCRIPTION
icRSF/man
icRSF/man/icrsf.Rd
icRSF/man/pheno.Rd
icRSF/man/icRSF_1.0-package.Rd
icRSF/man/Xmat.Rd
icRSF/man/simout.Rd