degreeCov: Calculate the degree of the covariance network based on xdata

In glmSparseNet: Network Centrality Metrics for Elastic-Net Regularized Models

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)

glmSparseNet documentation built on April 14, 2021, 6 p.m.