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]