At 07:30 AM 5/2/2007, Doxastic wrote: >Thanks. I used this and it gave me the same result as the "logLik" function. >The reason I ask is the SAS output gives me a loglik = 1089. R gives me >-298.09583. Both for my reduced model. For the saturated (or complex) >model, SAS gives me an loglik = 1143. R gives me -298.1993. The problem is >these give two very different pictures about whether I can drop the >interaction. However, I think the residual deviance in the R output is >equal to G^2. So, I can just take the difference between those two. If I >do this, I get a difference with an interpretation similar to that of what >comes from SAS. So I think I'll just go with that. But who knows if I'm >right (not me)?
Some comments: 1. Use summary() on your glm() object to get a fuller display of post-fit statistics, including the starting ("null") and residual deviances. 2. The "deviance" is - 2 L, where L = ln(likelihood). 3. To test two nested models for the difference in covariates, subtract the two residual deviances and two d.f. and perform a chi-square test. This can be done nicely by anova() on the two glm() objects. 4. Check the coefficients in your SAS and R models and make sure you are performing the same fit in both cases. ================================================================ Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED] Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239 Fax: 757-467-2947 "Vere scire est per causas scire" ______________________________________________ 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.