degreeCov {glmSparseNet} | R Documentation |
Calculate the degree of the covariance network based on xdata
degreeCov(xdata, cutoff = 0, consider.unweighted = FALSE, force.recalc.degree = FALSE, force.recalc.network = FALSE, n.cores = 1, ...)
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 |
a vector of the degrees
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)