qpG2Sigma {qpgraph} | R Documentation

## Random covariance matrix

### Description

Builds a positive definite matrix from an undirected graph G that can be used as a covariance matrix for a Gaussian graphical model with graph G. The inverse of the resulting matrix contains zeroes at the missing edges of the given undirected graph G.

### Usage

    qpG2Sigma(g, rho=0, matrix.completion=c("HTF", "IPF"), tol=0.001,
              verbose=FALSE, R.code.only=FALSE)

### Arguments

`g` | undirected graph specified either as a `graphNEL` object or as an adjacency matrix.
`rho` | real number between -1/(n.var-1) and 1 corresponding to the mean marginal correlation
`matrix.completion` | algorithm to employ in the matrix completion operations employed to construct a positive definite matrix with the zero pattern specified in `g`
`tol` | tolerance under which the matrix completion algorithm stops.
`verbose` | show progress on the calculations.
`R.code.only` | logical; if FALSE then the faster C implementation is used in the internal call to the HTF, or IPF, algorithm (default); if TRUE then only R code is executed.

### Details

The random covariance matrix is built by first generating a random matrix with the function `qpRndWishart` from a Wishart distribution whose expected value is a matrix with unit diagonal and constant off-diagonal entries equal to `rho`.

### Value

A random positive definite matrix that can be used as a covariance matrix for a Gaussian graphical model with graph `G`.

### Author(s)

A. Roverato

### References

Tur, I., Roverato, A. and Castelo, R. Mapping eQTL networks with mixed graphical Markov models. Genetics, 198(4):1377-1393, 2014.

### See Also

`qpRndGraph` `qpGetCliques` `qpIPF` `qpRndWishart` `rmvnorm`

### Examples

    set.seed(123)
    G <- qpRndGraph(p=5, d=2)

    Sigma <- qpG2Sigma(G, rho=0.5)

    round(solve(Sigma), digits=2)

    as(G, "matrix")

[Package qpgraph version 2.16.0 Index]