gRim (version 0.1.2)

ggmfit: Iterative proportional fitting of graphical Gaussian model

Description

Fit graphical Gaussian model by iterative proportional fitting.

Usage

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

Arguments

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

Value

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

Details

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

See Also

cmod, loglin

Examples

## 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)