splitMerged.auc.plot: Determine the indices of the training and testing sets.

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

Description

Determine the indices of the training and testing sets prior to the plot of ROC curves.

Usage

splitMerged.auc.plot(geno.files, lst, i, j, col, method, time.dep)

Arguments

geno.files	A vector of character strings containing the names of the expression files.
lst	A list of two objects, (i) gene expression data and (ii) list of two vectors, survival time and censoring status. In the censoring status vector, 1 = event occurred, 0 = censored.
i	A vector of character strings containing the names of the expression files used for the training set.
j	A character string specifying the name of the expression file used as the testing set.
col	Color of ROC curve.
method	A character string specifying the feature selection method: "none" for top-100 ranking or one of the adjusting methods specified by the p.adjust function
time.dep	An integer 0 or 1, 1 to plot time-dependent ROC curves for different time points and 0 for no plot

Details

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

None.

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.