Densities of resulting qp-graphs

Description

Calculates and plots the graph density as function of the non-rejection rate.

Usage

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qpGraphDensity(nrrMatrix, threshold.lim=c(0,1), breaks=5,
               plot=TRUE, qpGraphDensityOutput=NULL,
               density.digits=0,
               titlegd="graph density as function of threshold")

Arguments

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.

Details

The estimate of the sparseness of the resulting qp-graphs is calculated as one minus the area enclosed under the curve of graph densities.

Value

A list with the graph density as function of threshold and an estimate of the sparseness of the resulting qp-graphs across the thresholds.

Author(s)

R. Castelo and A. Roverato

References

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.

See Also

qpNrr qpAvgNrr qpEdgeNrr qpClique

Examples

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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 qp-graph

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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