qpGraphDensity {qpgraph} | R Documentation |
Calculates and plots the graph density as function of the non-rejection rate.
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 non-rejection rates. |
threshold.lim |
range of threshold values on the non-rejection 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
qpGraphDensity . This allows one to plot the result changing
some of the plotting parameters without having to do the calculation
again. |
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 qp-graphs 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 qp-graphs 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., 7:2621-2650, 2006.
qpNrr
qpAvgNrr
qpEdgeNrr
qpClique
nVar <- 50 # number of variables maxCon <- 5 # maximum connectivity per variable nObs <- 30 # number of observations to simulate I <- qpRndGraph(n.vtx=nVar, n.bd=maxCon) K <- qpI2K(I) X <- qpSampleMvnorm(K, nObs) # the higher the q the sparser the qp-graphs 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