GGMnetworkStats.fused: Gaussian graphical model network statistics

In CFWP/rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data

Gaussian graphical model network statistics

Description

Compute various network statistics from a list sparse precision matrices. The sparse precision matrix is taken to represent the conditional independence graph of a Gaussian graphical model. This function is a simple wrapper for GGMnetworkStats.

Usage

GGMnetworkStats.fused(Plist)

Arguments

Plist

A list of sparse precision/partial correlation matrix.

Details

For details on the columns see GGMnetworkStats.

Value

A data.frame of the various network statistics for each class. The names of Plist is prefixed to column-names.

Author(s)

Anders E. Bilgrau, Carel F.W. Peeters <[e-mail]>, Wessel N. van Wieringen

See Also

GGMnetworkStats

Examples

## Create some "high-dimensional" data
set.seed(1)
p <- 10
ns <- c(5, 6)
Slist <- createS(ns, p)

## Obtain sparsified partial correlation matrix
Plist    <- ridgeP.fused(Slist, ns, lambda = c(5.2, 1.3), verbose = FALSE)
PCsparse <- sparsify.fused(Plist , threshold = "absValue", absValueCut = 0.2)
SPlist <- lapply(PCsparse, "[[", "sparsePrecision") # Get sparse precisions

## Calculate GGM network statistics in each class
## Not run: GGMnetworkStats.fused(SPlist)

CFWP/rags2ridges documentation built on Oct. 21, 2023, 10:19 a.m.