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]