effloglin: Fitting Log-Linear Models by Message Passing
In boennecd/gRim: Graphical Interaction Models

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

Fit log-linear models to multidimensional contingency tables by Iterative Proportional Fitting.

1 2	effloglin(table, margin, fit = FALSE, eps = 0.01, iter = 20, print = TRUE)

`table`	A contingency table
`margin`	A generating class for a hierarchical log–linear model
`fit`	If TRUE, the fitted values are returned.
`eps`	Convergence limit; see 'details' below.
`iter`	Maximum number of iterations allowed
`print`	If TRUE, iteration details are printed.

The function differs from loglin in that 1) data can be given in the form of a list of sufficient marginals and 2) the model is fitted only on the cliques of the triangulated interaction graph of the model. This means that the full table is not fitted, which means that effloglin is efficient (in terms of storage requirements). However effloglin is implemented entirely in R and is therefore slower than loglin.

A list.

Søren Højsgaard, sorenh@math.aau.dk

loglin

data(reinis)
glist <-list(c("smoke", "mental"), c("mental", "phys"), c("phys", "systol"
), c("systol", "smoke"))

stab <- lapply(glist, function(gg) tableMargin(reinis, gg))
fv3 <- effloglin(stab, glist, print=FALSE)