degreeCov {glmSparseNet} | R Documentation

## Calculate the degree of the covariance network based on xdata

### Description

Calculate the degree of the covariance network based on xdata

### Usage

    degreeCov(xdata, cutoff = 0, consider.unweighted = FALSE,
      force.recalc.degree = FALSE, force.recalc.network = FALSE,
      n.cores = 1, ...)

### Arguments

`xdata` | calculate correlation matrix on each column
`cutoff` | positive value that determines a cutoff value
`consider.unweighted` | consider all edges as 1 if they are greater than 0
`force.recalc.degree` | force recalculation of penalty weights (but not the network), instead of going to cache
`force.recalc.network` | force recalculation of network and penalty weights, instead of going to cache
`n.cores` | number of cores to be used
`...` | extra parameters for cov function

### Value

a vector of the degrees

### Examples

    n.col <- 6
    xdata <- matrix(rnorm(n.col * 4), ncol = n.col)
    degreeCov(xdata)
    degreeCov(xdata, cutoff = .5)
    degreeCov(xdata, cutoff = .5, consider.unweighted = TRUE)

[Package glmSparseNet version 1.6.0 Index]