dot-degreeGeneric: Generic function to calculate degree based on data

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

Description

The assumption to use this function is that the network represented by a matrix is symetric and without any connection the node and itself.

Usage

.degreeGeneric(
  fun = stats::cor,
  fun.prefix = "operator",
  xdata,
  cutoff = 0,
  consider.unweighted = FALSE,
  chunks = 1000,
  force.recalc.degree = FALSE,
  force.recalc.network = FALSE,
  n.cores = 1,
  ...
)

Arguments

fun	function that will calculate the edge weight between 2 nodes
fun.prefix	used to store low-level information on network as it can become to large to be stored in memory
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
chunks	calculate function at batches of this value (default is 1000)
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 fun

Value

a vector of the degrees

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