cvRLassoCox {RLassoCox}R Documentation

Cross-validation for the RLasso-Cox model

Description

Does k-fold cross-validation for the RLasso-Cox model, produces a plot, and returns a value for lambda

Usage

cvRLassoCox(x, y, globalGraph = NULL, nfolds = 10, Gamma = 0.3, 
            DEBUG = TRUE, standardize = TRUE, ...)

Arguments

x

a n x p matrix of gene expression measurements with n samples and p genes.

y

a n x 2 matrix of survival data. The two columns represent disease status 'status' and survival time 'time' respectively.

globalGraph

An igraph R object containing the interaction network.

nfolds

number of folds - default is 10.

Gamma

A numeric value. The restart probability in directed random walk. Default is Gamma = 0.3.

DEBUG

Logical. Should debugging information be shown.

standardize

Logical flag for x standardization, prior to fitting the model. Default is TRUE.

...

Arguments to be passed to cv.glmnet in R package glmnet.

Value

glmnetRes

An object of class "cv.glmnet"

PT

The topological weights of genes

Author(s)

Wei Liu

References

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction. To be published.

Examples

library("survival")
library("igraph")
library("glmnet")
library("Matrix")

data(dGMMirGraph)
data(mRNA_matrix)
data(survData)

trainSmpl.Idx <- sample(1:dim(mRNA_matrix)[1], floor(2/3*dim(mRNA_matrix)[1]))
testSmpl.Idx <- setdiff(1:dim(mRNA_matrix)[1], trainSmpl.Idx)
trainSmpl <- mRNA_matrix[trainSmpl.Idx ,]
testSmpl <- mRNA_matrix[testSmpl.Idx ,]

cv.res <- cvRLassoCox(x=trainSmpl, y=survData[trainSmpl.Idx ,], 
                        globalGraph=dGMMirGraph, nfolds = 5)


[Package RLassoCox version 1.1.0 Index]