qpEdgeNrr {qpgraph} | R Documentation

## Non-rejection rate estimation for a pair of variables

### Description

Estimates non-rejection rate for one pair of variables.

### Usage

    ## S4 method for signature 'ExpressionSet':
    qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100,
                                        alpha=0.05, long.dim.are.variables=TRUE,
                                        R.code.only=FALSE)
    ## S4 method for signature 'data.frame':
    qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100,
                                     alpha=0.05, long.dim.are.variables=TRUE,
                                     R.code.only=FALSE)
    ## S4 method for signature 'matrix':
    qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100,
                                 alpha=0.05, long.dim.are.variables=TRUE,
                                 R.code.only=FALSE)

### Arguments

`data` | data set from where the non-rejection rate should be estimated. It can be either an `ExpressionSet` object, a data frame, or a matrix. If it is a matrix and the matrix is squared then this function assumes the matrix is the sample covariance matrix of the data and the sample size parameter `N` should be provided.
`N` | number of observations in the data set. Only necessary when the sample covariance matrix is provided through the `data` parameter.
`i` | index or name of one of the two variables.
`j` | index or name of the other variable.
`q` | partial-correlation order.
`nTests` | number of tests to perform for each pair for variables.
`alpha` | significance level of each test.
`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.
`R.code.only` | logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed.

### Details

The estimation of the non-rejection rate for a pair of variables is calculated as the fraction of tests that accept the null hypothesis of independence given a set of randomly sampled q-order conditionals.

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`.

### Value

An estimate of the non-rejection rate for the particular given pair of variables.

### 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` `qpHist` `qpGraphDensity` `qpClique`

### Examples

    # in this graph 3 is conditional independent of 4 given 1 AND 2

    I <- matrix(c(FALSE,  TRUE,  TRUE,  TRUE,
                  TRUE,  FALSE,  TRUE,  TRUE,
                  TRUE,   TRUE, FALSE, FALSE,
                  TRUE,   TRUE, FALSE, FALSE), nrow=4, ncol=4, byrow=TRUE)
    K <- qpI2K(I)

    X <- qpSampleMvnorm(K, N=100)

    qpEdgeNrr(X, i=3, j=4, q=1, long.dim.are.variables=FALSE)

    qpEdgeNrr(X, i=3, j=4, q=2, long.dim.are.variables=FALSE)

[Package qpgraph version 1.0.0 Index]