graphical representation of the nonzero joint regression coefficients structure
1 2 3 4 
x 
object of class 
minn 
used visualization purposes in very dense networks. It only plots nodes that have degree larger than 
col 
vector defining estimated edge colors: common edges (first element), only nonzero coefficients for first population (second element) and only nonzero coefficients for second population (third element). 
vertex.size 

vertex.color 
vector defining the vertex colors for directed graph: first element describes the color of explanatory variables and second element describes the color for response variables. 
edgesThickness 
if 
zoomThick 
it increases the thickness of all edges by 
... 
arguments passed to or from other methods to the low level. 
It produces a directed graph structure that connects explanatory variables to response variables.
Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.
wfrl
for joint estimation of regression coefficients.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  N < 200
EX2 < pcorSimulatorJoint(nobs = N, nclusters = 3, nnodesxcluster = c(60,40,50),
pattern = "pow", diffType = "cluster", dataDepend = "diag",
low.strength = 0.5, sup.strength = 0.9, pdiff = 0.5, nhubs = 5,
degree.hubs = 20, nOtherEdges = 30, alpha = 2.3, plus = 0,
prob = 0.05, perturb.clust = 0.2, mu = 0, diagCCtype = "dicot",
diagNZ.strength = 0.6, mixProb = 0.5, probSign = 0.7,
exactZeroTh = 0.05)
P < EX2$P
q < 50
BETA1 < array(0,dim=c(P,q))
diag(BETA1) < rep(0.35,q)
BETA2 < BETA1
diag(BETA2)[c(1:floor(q/2))]<0
sigma2 < 1.3
Q < scale(EX2$D1)
W < scale(EX2$D2)
X < Q%*%BETA1 + mvrnorm(N,rep(0,q),diag(rep(sigma2,q)))
Y < W%*%BETA2 + mvrnorm(N,rep(0,q),diag(rep(sigma2,q)))
D1 < list(scale(X),scale(Y))
D2 < list(scale(Q),scale(W))
## not run
#wfrl1 < wfrl(D1, D2, lambda1 = 0.01, lambda2 = 0.05, automLambdas = TRUE, paired = FALSE,
# sigmaEstimate = "CRmad", maxiter=30, tol=1e05)
#plot(wfrl1)
#plot(wfrl1, minn = 1, edgesThickness = TRUE)

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