qpGraphDensity {qpgraph} | R Documentation

## Densities of resulting qp-graphs

### Description

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

### Usage

    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

    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

[Package qpgraph version 1.0.0 Index]