eval.subset: Performance evaluation derived from a subset of a data set

In survJamda: Survival Prediction by Joint Analysis of Microarray Gene Expression Data

Description

Select a subset of a single data set and split it into the training and testing sets. Generate a gene signature from the training set and evaluate its performance on the testing set.

Usage

eval.subset(x, y, censor, iter, method, gn.nb, train.nb)

Arguments

x	Matrix of gene expression data.
y	Vector of survival time.
censor	Vector of censoring status. In the censoring status vector, 1 = event occurred, 0 = censored.
iter	An integer specifying the current iteration.
method	A character string specifying the feature selection method: "none" for top-ranking or one of the adjusting methods specified by the p.adjust function.
gn.nb	An integer specifying the number of genes to select.
train.nb	An integer specifying the sample size of the training set.

Details

In top-ranking, genes are selected based on univariate Cox P-value ranking using the coxph function in the R survival package. In this feature selection method, the genes were ranked based on their likelihood ratio P-value and the top-gn.nb ranked genes with the smallest P-values were retained as the gene signature.

The p.adjust function in the R stats package is used and all adjusted p-values not greater than 0.05 are retained if method != "none".

Value

AUC and HR.

Warning

This function is not called by the user directly.

Author(s)

Haleh Yasrebi

survJamda documentation built on May 1, 2019, 8:50 p.m.