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]