degreeSparsebn {glmSparseNet} | R Documentation |
Calculate degree of correlation matrix
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, ...)
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 |
a vector of the degrees
# generate a random matrix of observations xdata <- matrix(rnorm(1000), nrow = 20) degreeSparsebn(xdata)