ggm: icf – R documentation

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Documentation

ggm

icf

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.

icf