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

Description Usage Arguments Value Examples

View source: R/centrality.R

Description

Calculate the degree of the covariance network based on xdata

Usage

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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

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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.