icf: Iterative conditional fitting
In ggm: Graphical Markov Models with Mixed Graphs

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

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

1 2	icf(bi.graph, S, start = NULL, tol = 1e-06) icfmag(mag, S, tol = 1e-06)

`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.

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

`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.

Mathias Drton

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.

fitCovGraph, fitAncestralGraph

ggm documentation built on March 26, 2020, 7:49 p.m.