Description Usage Arguments Details Author(s) See Also Examples
graphical representation of the non-zero 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 non-zero coefficients for first population (second element) and only non-zero 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=1e-05)
#plot(wfrl1)
#plot(wfrl1, minn = 1, edgesThickness = TRUE)
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