qpNrr {qpgraph}R Documentation

Non-rejection rate estimation

Description

Estimates non-rejection rates for every pair of variables.

Usage

## S4 method for signature 'ExpressionSet':
qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                                pairup.j=NULL, long.dim.are.variables=TRUE,
                                verbose=TRUE, R.code.only=FALSE)
## S4 method for signature 'data.frame':
qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                             pairup.j=NULL, long.dim.are.variables=TRUE,
                             verbose=TRUE, R.code.only=FALSE)
## S4 method for signature 'matrix':
qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                         pairup.j=NULL, long.dim.are.variables=TRUE,
                         verbose=TRUE, R.code.only=FALSE)

Arguments

data data set from where to estimate the non-rejection rates. It can be an ExpressionSet object, a data frame or a matrix.
q partial-correlation order to be employed.
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 data are in a data frame or in a matrix, the longer dimension is the one defining the random variables (default); if FALSE, then random variables are assumed to be at the columns of the data frame or matrix.
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 the possible values of q 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 with q and quadratically in p.

Value

A symmetric matrix of estimated non-rejection rates.

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

qpAvgNrr 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)

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

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

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


[Package qpgraph version 1.0.0 Index]