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

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

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,
  considerUnweighted = FALSE,
  forceRecalcDegree = FALSE,
  forceRecalcNetwork = FALSE,
  nCores = 1,
  ...,
  consider.unweighted = deprecated(),
  force.recalc.degree = deprecated(),
  force.recalc.network = deprecated(),
  n.cores = deprecated()
)

Arguments

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

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, considerUnweighted = TRUE)

sysbiomed/glmSparseNet documentation built on Feb. 17, 2024, 1:38 p.m.