fitCovGraph | R Documentation |
Fits a Gaussian covariance graph model by maximum likelihood.
fitCovGraph(amat, S,n ,alg = "icf", dual.alg = 2, start.icf = NULL, tol = 1e-06)
amat |
A symmetric Booloean matrix with dimnames representing the adjacency matrix of an UG. |
S |
A symmetric positive definite matrix with dimnames, the sample covariance matrix. |
n |
A positive integer, the sample size. |
alg |
A character string, the algorithm used.
If |
dual.alg |
And integer equal to 1 or 2. It is used if
|
start.icf |
A symmetric matrix used as starting value
of the algorithm. If |
tol |
A small positive number indicating the tolerance used in convergence tests. |
A covariance graph is an undirected graph in which the variables associated to two non-adjacent nodes are marginally independent. The edges of these models are represented by bi-directed edges (Drton and Richardson, 2003) or by dashed lines (Cox and Wermuth, 1996).
By default, this function gives the ML estimates in the covariance graph model, by iterative conditional fitting (Drton and Richardson, 2003). Otherwise, the estimates from a “dual likelihood” estimator can be obtained (Kauermann, 1996; Edwards, 2000, section 7.4).
Shat |
the fitted covariance matrix. |
dev |
the ‘deviance’ of the model. |
df |
the degrees of freedom. |
it |
the iterations. |
Mathias Drton
Cox, D. R. & Wermuth, N. (1996). Multivariate dependencies. London: Chapman & Hall.
Drton, M. and Richardson, T. S. (2003). A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 184–191.
Kauermann, G. (1996). On a dualization of graphical Gaussian models. Scandinavian Journal of Statistics. 23, 105–116.
fitConGraph
, icf
## Correlations among four strategies to cope with stress for
## 72 students. Cox & Wermuth (1996), p. 73.
data(stress)
## A chordless 4-cycle covariance graph
G <- UG(~ Y*X + X*U + U*V + V*Y)
fitCovGraph(G, S = stress, n=72)
fitCovGraph(G, S = stress, n=72, alg="dual")
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