Calculates and plots the graph density as function of the nonrejection rate.
1 2 3 4  qpGraphDensity(nrrMatrix, threshold.lim=c(0,1), breaks=5,
plot=TRUE, qpGraphDensityOutput=NULL,
density.digits=0,
titlegd="graph density as function of threshold")

nrrMatrix 
matrix of nonrejection rates. 
threshold.lim 
range of threshold values on the nonrejection rate. 
breaks 
either a number of threshold bins or a vector of threshold breakpoints. 
plot 
logical; if TRUE makes a plot of the result; if FALSE it does not. 
qpGraphDensityOutput 
output from a previous call to

density.digits 
number of digits in the reported graph densities. 
titlegd 
main title to be shown in the plot. 
The estimate of the sparseness of the resulting qpgraphs is calculated as one minus the area enclosed under the curve of graph densities.
A list with the graph density as function of threshold and an estimate of the sparseness of the resulting qpgraphs across the thresholds.
R. Castelo and A. Roverato
Castelo, R. and Roverato, A. A robust procedure for Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res., 7:26212650, 2006.
qpNrr
qpAvgNrr
qpEdgeNrr
qpClique
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  require(mvtnorm)
nVar < 50 ## number of variables
maxCon < 5 ## maximum connectivity per variable
nObs < 30 ## number of observations to simulate
set.seed(123)
A < qpRndGraph(p=nVar, d=maxCon)
Sigma < qpG2Sigma(A, rho=0.5)
X < rmvnorm(nObs, sigma=as.matrix(Sigma))
## the higher the q the sparser the qpgraph
nrr.estimates < qpNrr(X, q=1, verbose=FALSE)
qpGraphDensity(nrr.estimates, plot=FALSE)$sparseness
nrr.estimates < qpNrr(X, q=5, verbose=FALSE)
qpGraphDensity(nrr.estimates, plot=FALSE)$sparseness

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