On Aug 17, 2011; 5:43pm Luke Duncan wrote: Hi Luke,
The differences you are seeing are almost certainly due to different contrast codings: Statistica probably uses sum-to-zero contrasts whereas R uses treatment (Dunnett) contrasts by default. You would be well advised to consult a local statistician for a deeper understanding. For some immediate insight do the following: ## Fits your model with different contrasts + a few other things. ## library(car) ?contrast ?contr.treatment model1 <- glm((cbind(spec,total)) ~ behav * loc, family=binomial, data=behdata, contrasts=list(behav="contr.treatment", loc="contr.treatment")) model2 <- glm((cbind(spec,total)) ~ behav * loc, family=binomial, data=behdata, contrasts=list(behav="contr.sum", loc="contr.sum")) summary(model1) summary(model2) anova(model1, model2) ## see: models seem different but are identical ## Type I SS anova(model1) anova(model2) ## Type II SS library(car) Anova(model1, type="II") Anova(model2, type="II") Regards, Mark. ----- Mark Difford (Ph.D.) Research Associate Botany Department Nelson Mandela Metropolitan University Port Elizabeth, South Africa -- View this message in context: http://r.789695.n4.nabble.com/Getting-vastly-different-results-when-running-GLMs-tp3750496p3751115.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
