I think you need to learn about deviances, which R does print. Log-likelihoods are only defined up to additive constants. In this case the conventional constant differs if you view this as a Poisson or as a product-multinomial log-linear model, and R gives you the log-likelihood for a Poisson log-linear model (assuming you specified family=poisson). However, deviances and differences in log-likelihoods do not depend on which.
More details and worked examples can be found in MASS (the book, see the FAQ), including other ways to fit log-linear models in R. On Tue, 1 May 2007, someone ashamed of his real name wrote: > I've computed a loglinear model on a categorical dataset. I would like to > test whether an interaction can be dropped by comparing the log-likelihoods > from two models(the model with the interaction vs. the model without). > Since R does not immediately print the log-likelihood when I use the "glm" > function, I used SAS initially. After searching for an extracting function, > I found one in R. But, the log-likelihood given by SAS is different from > the one given by R. I'm not sure if the "logLik" function in R is giving me > something I don't want. Or if I'm misinterpreting the SAS output. Can > anyone help? > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
