selectQE {GGMselect} | R Documentation |
Select a graph within the family of graphs QE
selectQE(X, dmax=min(3,nrow(X)-3,ncol(X)-1), K=2.5, min.ev=10**(-8), max.iter=10**6, max.nG=10**8, max.size=10**8, verbose=FALSE)
X |
n x p matrix where n is the sample size and
p the number of variables. n should be greater than
3 and p greater than 1 . |
dmax |
integer or p -dimensional vector of integers
smaller or equal to
min(n-3, p-1) . When dmax is a scalar, it gives the maximum
degree of the estimated graph. When dmax is a vector,
dmax[a] gives the maximum degree of the node a . |
K |
scalar or vector with values greater than 1. Tuning parameter in the penalty function. |
min.ev |
minimum eigenvalue for matrix inversion. |
max.iter |
integer. Maximum number of stepwise iterations. |
max.nG |
integer. Maximum number of graphs considered in the exhaustive search. Stepwise procedure beyond. |
max.size |
integer. Maximum number of calculations of the residuals sums of squares. Execution stopped beyond. |
verbose |
logical. If TRUE a trace of the current process is displayed in real time. |
More details are available on ../doc/Notice.pdf
Neighb |
array of dimension p x max(dmax) x length(K) or, when
length(K) equals 1, matrix of dimension p x max(dmax) . Neighb[a, , k ] contains the indices of the nodes connected
to node a for K[k] . |
crit.min |
vector of dimension length(K) .
The minimal values of the selection criterion
for each value of K . |
G |
array of dimension p x p x length(K) or, when
length(K) equals 1, matrix of dimension p x p .
G[,,k] gives the adjacency matrix for K[k] . |
Bouvier A, Giraud C, Huet S, Verzelen N.
Please use citation("GGMselect")
.
selectFast
, selectMyFam
,
simulateGraph
, penalty
,
convertGraph
p=30 n=30 # simulate graph eta=0.11 Gr <- simulateGraph(p,eta) # simulate data X <- rmvnorm(n, mean=rep(0,p), sigma=Gr$C) # estimate graph GQE <- selectQE(X) # plot the result library(network) par(mfrow=c(1,2)) gV <- network(Gr$G) plot(gV,jitter=TRUE, usearrows = FALSE, label=1:p,displaylabels=TRUE) gQE <- network(GQE$G) plot(gQE, jitter=TRUE, usearrows = FALSE, label=1:p,displaylabels=TRUE)