cvRLassoCox {RLassoCox} | R Documentation |
Does k-fold cross-validation for the RLasso-Cox model, produces a plot, and returns a value for lambda
cvRLassoCox(x, y, globalGraph = NULL, nfolds = 10, Gamma = 0.3, DEBUG = TRUE, standardize = TRUE, ...)
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 |
nfolds |
number of folds - default is 10. |
Gamma |
A numeric value. The restart probability in directed random walk. Default
is |
DEBUG |
Logical. Should debugging information be shown. |
standardize |
Logical flag for |
... |
Arguments to be passed to |
glmnetRes |
An object of class "cv.glmnet" |
PT |
The topological weights of genes |
Wei Liu
Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction. To be published.
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)