Determine support of multiple partial correlation/precision matrices
A simple wrapper for sparsify which determines the support of
a list of partial correlation/precision matrix by various methods and
returns the sparsified matrices.
sparsify.fused(Plist, ...)
Plist |
A |
... |
Arguments passed to |
A list of the same length as Plist with the output from
sparsify.
Anders Ellern Bilgrau, Wessel N. van Wierigen, Carel F.W. Peeters <cf.peeters@vumc.nl>
ns <- c(10, 11)
Ylist <- createS(ns, p = 16, dataset = TRUE)
Slist <- lapply(Ylist, covML)
Tlist <- default.target.fused(Slist, ns)
# Obtain regularized precision under optimal penalty
opt <- optPenalty.fused.auto(Ylist, Tlist, cv.method = "aLOOCV",
maxit.ridgeP.fused = 1500)
# Use the optimal penalties
Plist <- ridgeP.fused(Slist, ns, lambda = opt$lambda, maxit = 1000)
# Determine support regularized (standardized) precision under optimal penalty
res <- sparsify.fused(Plist, threshold = "top", verbose = FALSE)
round(res[[1]]$sparsePrecision, 1)
round(res[[2]]$sparsePrecision, 1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.