R: Iterative conditional fitting

icf {ggm} | R Documentation

## Iterative conditional fitting

### Description

Main algorithm for MLE fitting of Gaussian Covariance Graphs and Gaussian Ancestral models.

### Usage

    icf(bi.graph, S, start = NULL, tol = 1e-06)
    icfmag(mag, S, tol = 1e-06)

### Arguments

`bi.graph` | a symmetric matrix with dimnames representing the adjacency matrix of an undirected graph.
`mag` | a square matrix representing the adjacency matrix of an ancestral graph (for example returned by `makeAG`).
`S` | a symmetric positive definite matrix, the sample covariance matrix. The order of the variables must be the same of the order of vertices in the adjacency matrix.
`start` | a symmetric matrix used as starting value of the algorithm. If `start=NULL` the starting value is a diagonal matrix.
`tol` | A small positive number indicating the tolerance used in convergence tests.

### Details

These functions are not intended to be called directly by the user.

### Value

`Sigmahat` | the fitted covariance matrix.
`Bhat` | matrix of the fitted regression coefficients associated to the directed edges.
`Omegahat` | matrix of the partial covariances of the residuals between regression equations.
`iterations` | the number of iterations.

### Author(s)

Mathias Drton

### References

Drton, M. \& Richardson, T. S. (2003). A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence. Proceedings of the Ninetheen Conference on Uncertainty in Artificial Intelligence, 184–191.

Drton, M. \& Richardson, T. S. (2004). Iterative Conditional Fitting for Gaussian Ancestral Graph Models. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, Department of Statistics, 130–137.

### See Also

`fitCovGraph`, `fitAncestralGraph`

[Package ggm version 2.3 Index]