ggmfit: Iterative proportional fitting of graphical Gaussian model
In boennecd/gRim: Graphical Interaction Models

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

Fit graphical Gaussian model by iterative proportional fitting.

1 2	ggmfit(S, n.obs, glist, start = NULL, eps = 1e-12, iter = 1000, details = 0, ...)

`S`	Empirical covariance matrix
`n.obs`	Number of observations
`glist`	Generating class for model (a list)
`start`	Initial value for concentration matrix
`eps`	Convergence criterion
`iter`	Maximum number of iterations
`details`	Controlling the amount of output.
`...`	Optional arguments; currently not used

ggmfit is based on a C implementation. ggmfitr is implemented purely in R (and is provided mainly as a benchmark for the C-version).

A list with

`lrt`	Likelihood ratio statistic (-2logL)
`df`	Degrees of freedom
`logL`	log likelihood
`K`	Estimated concentration matrix (inverse covariance matrix)

Søren Højsgaard, sorenh@math.aau.dk

cmod, loglin

## Fitting "butterfly model" to mathmark data
## Notice that the output from the two fitting functions is not
## entirely identical.
data(math)
ddd <- cov.wt(math, method="ML")
glist <- list(c("al","st","an"), c("me","ve","al"))
ggmfit (ddd$cov, ddd$n.obs, glist)
ggmfitr(ddd$cov, ddd$n.obs, glist)