plotwfgl: Joint partial correlation matrix estimator plot
In ldstatsHD: Linear Dependence Statistics for High-Dimensional Data

Description Usage Arguments Author(s) See Also Examples

graphical representation of the non-zero joint partial correlation structure.

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## S3 method for class 'wfgl'
plot(x, minn = 0, col = c("blue","red","green"), vertex.size = 3,  
      edgesThickness = FALSE, zoomThick = 10, ...)

`x`	object of class `wfgl`.
`minn`	used for visualization purposes in very dense networks. It only plots nodes that have degree larger than `minn`.
`col`	vector defining estimated edge colors: common edges (first element), only non-zero coefficients for first population (second element) and only non-zero coefficients for second population (third element).
`vertex.size`	`plot.igraph` parameter: vertex sizes.
`edgesThickness`	if `TRUE`, an edge thickness is proportional to the magnitude of its underlying estimated partial correlation coefficient.
`zoomThick`	it increases the thickness of all edges by `zoomThick` times (used for visualization purposes).
`...`	arguments passed to or from other methods to the low level.

Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.

wfgl for joint estimation of multiple partial correlation matrices.

EX2   <- pcorSimulatorJoint(nobs =50, nclusters = 3, nnodesxcluster = c(30, 30,30), 
                            pattern = "pow", diffType = "cluster", dataDepend = "diag", 
                            diagCCtype = "dicot", diagNZ.strength = 0.6)
## not run
#wfgl1 <- wfgl(EX2$D1, EX2$D2, lambda1 = 0.01, lambda2 = 0.1, paired = TRUE, 
#              automLambdas = TRUE, sigmaEstimate = "CRmad", 
#              pairedEst = "Reg-based-sim", maxiter = 30)
#plot(wfgl1)
#plot(wfgl1, minn = 1, edgesThickness = TRUE)