qpAvgNrr {qpgraph}R Documentation

Average non-rejection rate estimation

Description

Estimates average non-rejection rates for every pair of variables.

Usage

## S4 method for signature 'ExpressionSet':
qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                                   pairup.i=NULL, pairup.j=NULL,
                                   long.dim.are.variables=TRUE,
                                   type=c("arith.mean"), verbose=TRUE,
                                   R.code.only=FALSE)
## S4 method for signature 'data.frame':
qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                                pairup.i=NULL, pairup.j=NULL,
                                long.dim.are.variables=TRUE,
                                type=c("arith.mean"), verbose=TRUE,
                                R.code.only=FALSE)
## S4 method for signature 'matrix':
qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                            pairup.i=NULL, pairup.j=NULL,
                            long.dim.are.variables=TRUE,
                            type=c("arith.mean"), verbose=TRUE,
                            R.code.only=FALSE)

Arguments

data data set from where to estimate the average non-rejection rates. It can be an ExpressionSet object, a data frame or a matrix.
qOrders either a number of partial-correlation orders or a vector of vector of particular orders to be employed in the calculation.
nTests number of tests to perform for each pair for variables.
alpha significance level of each test.
pairup.i subset of vertices to pair up with subset pairup.j
pairup.j subset of vertices to pair up with subset pairup.i
long.dim.are.variables logical; if TRUE it is assumed that when the data is a data frame or a matrix, the longer dimension is the one defining the random variables; if FALSE, then random variables are assumed to be at the columns of the data frame or matrix.
type type of average. By now only the arithmetic mean is available.
verbose show progress on the calculations.
R.code.only logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed.

Details

Note that when specifying a vector of particular orders q, these values should be in the range 1 to min(p,n-3), where p is the number of variables and n the number of observations. The computational cost increases linearly within each q value and quadratically in p.

Value

A symmetric matrix of estimated average non-rejection rates.

Author(s)

R. Castelo and A. Roverato

References

Castelo, R. and Roverato, A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comp. Biol., accepted, 2008.

See Also

qpNrr qpEdgeNrr qpHist qpGraphDensity 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)

avgnrr.estimates <- qpAvgNrr(X, verbose=FALSE)

summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & I])

summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & !I])


[Package qpgraph version 1.0.0 Index]