qpGraph {qpgraph} | R Documentation |
Obtains a qp-graph from a matrix of non-rejection rates
qpGraph(nrrMatrix, threshold=NULL, topPairs=NULL, pairup.i=NULL, pairup.j=NULL, return.type=c("incidence.matrix", "edge.list", "graphNEL", "graphAM"))
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. |
This function requires the graph
package when return.type=graphNEL
or return.type=graphAM
.
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.
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
qpAnyGraph
qpGraphDensity
qpClique
qpPrecisionRecall
qpPRscoreThreshold
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)