Main algorithm for MLE fitting of Gaussian Covariance Graphs and Gaussian Ancestral models.
icf(bi.graph, S, start = NULL, tol = 1e-06)
icfmag(mag, S, tol = 1e-06)
the fitted covariance matrix.
matrix of the fitted regression coefficients associated to the directed edges.
matrix of the partial covariances of the residuals between regression equations.
the number of iterations.
a symmetric matrix with dimnames representing the adjacency matrix of an undirected graph.
a square matrix representing the adjacency matrix of an
ancestral graph (for example returned by makeAG).
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.
a symmetric matrix used as starting value
of the algorithm. If start=NULL the starting value
is a diagonal matrix.
A small positive number indicating the tolerance used in convergence tests.
Mathias Drton
These functions are not intended to be called directly by the user.
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