predict.cvRLassoCox {RLassoCox} | R Documentation

## Make predictions from a cross-validated RLasso-Cox model

### Description

This function makes predictions from a cross-validated RLasso-Cox model, using the optimal value chosen for lambda.

### Usage

    ## S3 method for class 'cvRLassoCox'
    predict(object, newx, ...)

### Arguments

`object` | cross-validated RLasso-Cox model
`newx` | A matrix with new samples to predict.
`...` | Arguments to be passed to `predict.cv.glmnet` in R package `glmnet`.

### Value

Predicted results of new patients in `newx`.

### 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)
    lp <- predict(object = cv.res, newx = testSmpl,
                                s = "lambda.min")

[Package RLassoCox version 1.2.0 Index]