# loglin-effloglin: Fitting Log-Linear Models by Message Passing In gRim: Graphical Interaction Models

 loglin-effloglin R Documentation

## Fitting Log-Linear Models by Message Passing

### Description

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

### Usage

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

### Arguments

 `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.

### Details

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`. Argument names are chosen so as to match those of loglin()

A list.

### Author(s)

Søren Højsgaard, sorenh@math.aau.dk

### References

Radim Jirousek and Stanislav Preucil (1995). On the effective implementation of the iterative proportional fitting procedure. Computational Statistics & Data Analysis Volume 19, Issue 2, February 1995, Pages 177-189

`loglin`

### Examples

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

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

```

gRim documentation built on Oct. 16, 2022, 1:10 a.m.