cmod: Graphical Gaussian model
In boennecd/gRim: Graphical Interaction Models

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

Specification of graphical Gaussian model. The 'c' in the name cmod refers to that it is a (graphical) model for 'c'ontinuous variables

1	cmod(formula, data, marginal = NULL, fit = TRUE, details = 0)

`formula`	Model specification in one of the following forms: 1) a right-hand sided formula, 2) as a list of generators, 3) an undirected graph (represented either as a graphNEL object or as an adjacency matrix). Notice that there are certain model specification shortcuts, see Section 'details' below
`data`	Data in one of the following forms: 1) A dataframe or 2) a list with elements `cov` and `n.obs` (such as returned by the `cov.wt()` function.)
`marginal`	Should only a subset of the variables be used in connection with the model specification shortcuts
`fit`	Should the model be fitted.
`details`	Control the amount of output; for debugging purposes.

The independence model can be specified as ~.^1 and the saturated model as ~.^.. The marginal argument can be used for specifying the independence or saturated models for only a subset of the variables.

An object of class cModel (a list)

Søren Højsgaard, sorenh@math.aau.dk

dmod, mmod, ggmfit

## Graphical Gaussian model
data(carcass)
cm1 <- cmod(~ .^., data=carcass)

## Stepwise selection based on BIC
cm2 <- backward(cm1, k=log(nrow(carcass)))

## Stepwise selection with fixed edges
cm3 <- backward(cm1, k=log(nrow(carcass)),
 fixin=matrix(c("LeanMeat", "Meat11", "Meat12", "Meat13", "LeanMeat", "Fat11", "Fat12", "Fat13"),
 ncol=2))