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.
Value
Sigmahatthe fitted covariance matrix.
Bhatmatrix of the fitted regression coefficients
associated to the directed edges.
Omegahatmatrix of the partial covariances of the residuals
between regression equations.
iterationsthe number of iterations.
Details
These functions are not intended to be called directly by the user.
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.