qpGraph {qpgraph}R Documentation

The qp-graph

Description

Obtains a qp-graph from a matrix of non-rejection rates

Usage

qpGraph(nrrMatrix, threshold=NULL, topPairs=NULL, pairup.i=NULL, pairup.j=NULL,
        return.type=c("incidence.matrix", "edge.list", "graphNEL", "graphAM"))

Arguments

nrrMatrix matrix of non-rejection rates.
threshold threshold on the non-rejection rate above which pairs of variables are assumed to be disconnected in the resulting qp-graph.
topPairs number of edges from the top of the ranking, defined by the non-rejection rates in nrrMatrix, to use to form the resulting qp-graph. This parameter is incompatible with a value different from NULL in threshold.
pairup.i subset of vertices to pair up with subset pairup.j
pairup.j subset of vertices to pair up with subset pairup.i
return.type type of data structure on which the resulting undirected graph should be returned. Either a logical incidence matrix with cells set to TRUE when the two indexing variables are connected in the qp-graph (default), or a list of edges in a matrix where each row corresponds to one edge and the two columns contain the two vertices defining each edge, or a graphNEL-class object, or a graphAM-class object.

Details

This function requires the graph package when return.type=graphNEL or return.type=graphAM.

Value

The resulting qp-graph as either an incidence matrix, a graphNEL object or a graphAM object, depending on the value of the return.type parameter. Note that when some gold-standard graph is available for comparison, a value for the parameter threshold can be found by calculating a precision-recall curve with qpPrecisionRecall with respect to this gold-standard, and then using qpPRscoreThreshold. Parameters threshold and topPairs are mutually exclusive, that is, when we specify with topPairs=n that we want a qp-graph with n edges then threshold cannot be used.

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 qpAnyGraph qpGraphDensity qpClique qpPrecisionRecall qpPRscoreThreshold

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)

nrr.estimates <- qpNrr(X, q=5, verbose=FALSE)

# the higher the threshold
g <- qpGraph(nrr.estimates, threshold=0.9)

# the denser the qp-graph
(sum(g)/2) / (nVar*(nVar-1)/2)

# the lower the threshold
g <- qpGraph(nrr.estimates, threshold=0.5)

# the sparser the qp-graph
(sum(g)/2) / (nVar*(nVar-1)/2)


[Package qpgraph version 1.0.0 Index]