degreeSparsebn: Calculate degree of correlation matrix

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

Description

Calculate degree of correlation matrix

Usage

degreeSparsebn(
  xdata,
  type = "continuous",
  levels = NULL,
  ivn = NULL,
  n = NULL,
  object = NULL,
  cutoff = 0,
  consider.unweighted = FALSE,
  n.cores = 1,
  show.message = FALSE,
  force.recalc.degree = FALSE,
  force.recalc.network = FALSE,
  ...
)

Arguments

xdata	calculate correlation matrix on each column
type	either "discrete" or "continuous", see sparsebnUtils::sparsebnData
levels	(optional) list of levels for each node. see sparsebnUtils::sparsebnData
ivn	(optional) list of interventions for each observation, see sparsebnUtils::sparsebnData
n	(optional) number of rows from data matrix to print, see sparsebnUtils::sparsebnData
object	(optional) an object of type sparsebnData, see sparsebnUtils::sparsebnData
cutoff	positive value that determines a cutoff value
consider.unweighted	consider all edges as 1 if they are greater than 0
n.cores	number of cores to be used
show.message	shows cache operation messages
force.recalc.degree	force recalculation, instead of going to cache
force.recalc.network	force recalculation of network and penalty weights, instead of going to cache
...	parameters for sparsebn::estimate.dag

Value

a vector of the degrees

Examples

# generate a random matrix of observations
xdata <- matrix(rnorm(1000), nrow = 20)
degreeSparsebn(xdata)

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