icrsf: Permutation-based variable importance metric for high...

In icRSF: A Modified Random Survival Forest Algorithm

Description

Let N and P denote the number of subjects and number of variables in the dataset, respectively. Let N** denote the total number of visits, summed over all subjects in the study [i.e. N** denotes the number of diagnostic test results available for all subjects in the study]. This algorithm builds a user-defined number of survival trees, using bootstrapped datasets. Using the out of bag (OOB) data in each tree, a permutation-based measure of variable importance for each of the P variables is obtained.

Usage

icrsf(data, subject, testtimes, result, sensitivity, specificity, Xmat,
  root.size, ntree, ns, node, pval = 1)

Arguments

data	name of the data frame that includes the variables subject, testtimes, result
subject	vector of subject IDs of length N**x1.
testtimes	vector of visit or test times of length N**x1.
result	vector of binary diagnostic test results (0 = negative for event of interest; 1 = positive for event of interest) of length N**x1.
sensitivity	the sensitivity of the diagnostic test.
specificity	the specificity of the diagnostic test.
Xmat	a N x P matrix of covariates.
root.size	minimum number of subjects in a terminal node.
ntree	number of survival trees.
ns	number of covariate selected at each node to split the tree.
node	For parallel computation, specify the number of nodes.
pval	P-value threshold of the Likelihood Ratio Test.

Value

a vector of the ensembled variable importance for modified random survival forest (icRSF).

Examples

library(parallel)
data(Xmat)
data(pheno)
vimp <- icrsf(data=pheno, subject=ID, testtimes=time, result=result, sensitivity=1,
             specificity=1, Xmat=Xmat, root.size=30, ntree=1, ns=sqrt(ncol(Xmat)), node=1, pval=1)

icRSF documentation built on May 2, 2019, 2:44 a.m.