emgllmfitter: Generalized log-linear modelling by EM and iterative...

In gllm: Generalised log-Linear Model

Generalized log-linear modelling by EM and iterative proportional fitting

Description

Fits log-linear models for incomplete contingency tables, via an EM approach.

Usage

emgllmfitter(y,s,X,maxit,tol) 

Arguments

y	is the observed contingency table.
s	is a vector of indices, one for each cell of the full (unobserved) contingency table, representing the appropriate cell of y
X	is the design matrix.
maxit	is the number of EM iterations.
tol	is the convergence criterion for the LR criterion.

Details

The call to Andreas Borg's C code that fits the model by EM/IPF. The algorithm follows the approach in Haber (1984).

Value

A list with components:

y	the observed table
ji	s, the scatter vector
c	the design matrix
istop	maximum EM iterations
conv	the convergence tolerance
e	expected counts for the full (unobserved) table
ni	nrow(X)
nj	length(y)
nk	ncol(X)-1
f	expected counts

References

Haber M (1984). AS207: Fitting a general log-linear model. Appl Statist 33:358-362.

gllm documentation built on Oct. 18, 2022, 9:06 a.m.