qpEdgeNrr: Non-rejection rate estimation for a pair of variables
In qpgraph: Estimation of genetic and molecular regulatory networks from high-throughput genomics data

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimates the non-rejection rate for one pair of variables.

## S4 method for signature 'ExpressionSet'
qpEdgeNrr(X, i=1, j=2, q=1, restrict.Q=NULL, fix.Q=NULL,
                                    nTests=100, alpha=0.05, exact.test=TRUE,
                                    use=c("complete.obs", "em"), tol=0.01,
                                    R.code.only=FALSE)
## S4 method for signature 'data.frame'
qpEdgeNrr(X, i=1, j=2, q=1, I=NULL, restrict.Q=NULL, fix.Q=NULL,
                                 nTests=100, alpha=0.05, long.dim.are.variables=TRUE,
                                 exact.test=TRUE, use=c("complete.obs", "em"), tol=0.01,
                                 R.code.only=FALSE)
## S4 method for signature 'matrix'
qpEdgeNrr(X, i=1, j=2, q=1, I=NULL, restrict.Q=NULL, fix.Q=NULL,
                             nTests=100, alpha=0.05, long.dim.are.variables=TRUE,
                             exact.test=TRUE, use=c("complete.obs", "em"), tol=0.01,
                             R.code.only=FALSE)
## S4 method for signature 'SsdMatrix'
qpEdgeNrr(X, i=1, j=2, q=1, restrict.Q=NULL, fix.Q=NULL,
                                nTests=100, alpha=0.05, R.code.only=FALSE)

`X`	data set from where the non-rejection rate should be estimated. It can be either an `ExpressionSet` object a data frame, a matrix or an `SsdMatrix-class` object. In the latter case, the input matrix should correspond to a sample covariance matrix of data from which we want to estimate the non-rejection rate for a pair of variables. The function `qpCov()` can be used to estimate such matrices.
`i`	index or name of one of the two variables in `X` to test.
`j`	index or name of the other variable in `X` to test.
`q`	order of the conditioning subsets employed in the calculation.
`I`	indexes or names of the variables in `X` that are discrete when `X` is a matrix or a data frame.
`restrict.Q`	indexes or names of the variables in `X` that restrict the sample space of conditioning subsets Q.
`fix.Q`	indexes or names of the variables in `X` that should be fixed within every conditioning conditioning subsets Q.
`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.
`exact.test`	logical; if `FALSE` an asymptotic conditional independence test is employed with mixed (i.e., continuous and discrete) data; if `TRUE` (default) then an exact conditional independence test with mixed data is employed.See details below regarding this argument.
`use`	a character string defining the way in which calculations are done in the presence of missing values. It can be either `"complete.obs"` (default) or `"em"`.
`tol`	maximum tolerance controlling the convergence of the EM algorithm employed when the argument `use="em"`.
`R.code.only`	logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed.

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 conditional 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.

When I is set different to NULL then mixed graphical model theory is employed and, concretely, it is assumed that the data comes from an homogeneous conditional Gaussian distribution. In this setting further restrictions to the maximum value of q apply, concretely, it cannot be smaller than p plus the number of levels of the discrete variables involved in the marginal distributions employed by the algorithm. By default, with exact.test=TRUE, an exact test for conditional independence is employed, otherwise an asymptotic one will be used. Full details on these features can be found in Tur, Roverato and Castelo (2014).

The argument I specifying what variables are discrete actually applies only when X is a matrix object since in the other cases data types are specified for each data columns or slot.

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

R. Castelo and A. Roverato

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.

Tur, I., Roverato, A. and Castelo, R. Mapping eQTL networks with mixed graphical Markov models. Genetics, 198:1377-1393, 2014.

qpNrr qpAvgNrr qpHist qpGraphDensity qpClique qpCov

require(mvtnorm)

nObs <- 100 ## number of observations to simulate

## the following adjacency matrix describes an undirected graph
## where vertex 3 is conditionally independent of 4 given 1 AND 2
A <- matrix(c(FALSE,  TRUE,  TRUE,  TRUE,
              TRUE,  FALSE,  TRUE,  TRUE,
              TRUE,   TRUE, FALSE, FALSE,
              TRUE,   TRUE, FALSE, FALSE), nrow=4, ncol=4, byrow=TRUE)
Sigma <- qpG2Sigma(A, rho=0.5)

X <- rmvnorm(nObs, sigma=as.matrix(Sigma))

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)