cv.glmDegree {glmSparseNet} | R Documentation

## GLMNET cross-validation model penalizing nodes with small degree

### Description

This function overrides the 'trans.fun' options in 'network.options' with the inverse of a degree described in Veríssimo et al. (2015) that penalizes nodes with small degree.

### Usage

    cv.glmDegree(xdata, ydata, network, network.options = networkOptions(),
      ...)

### Arguments

`xdata` | input data, can be a matrix or MultiAssayExperiment
`ydata` | response data compatible with glmnet
`network` | type of network, see below
`network.options` | options to calculate network
`...` | parameters that glmnet accepts

### Value

see cv.glmSparseNet

### See Also

glmNetSparse

### Examples

    xdata <- matrix(rnorm(100), ncol = 5)
    cv.glmDegree(xdata, rnorm(nrow(xdata)), 'correlation',
                family = 'gaussian',
                nfolds = 5,
                network.options = networkOptions(min.degree = .2))

[Package glmSparseNet version 1.2.0 Index]