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]