Xmat

<p>a 300 x 1000 covariate matrix that we simulated independently from binomial
             distribution with p=0.4.</p>

datasets

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

icRSF

A Modified Random Survival Forest Algorithm

Xmat function

<p>A matrix with 300 observations on 1000 variables.</p>

Xmat: A covariate matrix

Description

Usage

Arguments

Format