networkOptions {glmSparseNet} | R Documentation

## Setup network options

### Description

Setup network options, such as using weighted or unweighted degree, which centrality measure to use

### Usage

    networkOptions(method = "pearson", unweighted = TRUE, cutoff = 0,
      centrality = "degree", min.degree = 0, n.cores = 1,
      trans.fun = function(x) {     x })

### Arguments

`method` | in case of correlation and covariance, which method to use
`unweighted` | calculate degree using unweighted network
`cutoff` | cuttoff value in network edges to trim the network
`centrality` | centrality measure to use, currently only supports degree
`min.degree` | minimum value that individual penalty weight can take
`n.cores` | number of cores to use, default to 1 The trans.fun argument takes a function definition that will apply a transformation to the penalty vector calculated from the degree. This transformation allows to change how the penalty is applied.
`trans.fun` | see below

### Value

a list of options

### See Also

glmOrphan glmDegree

### Examples

    networkOptions(unweighted = FALSE)

[Package glmSparseNet version 1.6.0 Index]