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

    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

[Package qpgraph version 2.16.0 Index]