James Milks <james.milks <at> wright.edu> writes: > > My problem is deciding which model to use. I have created several, > one without interaction terms (Total.vines~Site+Species+DBH), one > with an interaction term between Site and Species > (Total.vines~Site*Species+DBH), and one with interactions between all > variables (Total.vines~Site*Species*DBH). Here is my output from R > for the first two models (the last model has the same number (and > identity) of significant variables as the second model, even though > the last model had more interaction terms overall): >
A few comments: - the narrow answer to your question is to use the interaction model: this would be the answer in several different statistical frameworks. In information-criteria-land, the AIC is 10 points lower which constitutes a much better expected predictive accuracy. In classical likelihood-ratio-testing land (try anova(model1,model2,test="Chisq")), you can probably also reject the null hypothesis that adding the interaction terms doesn't improve the model (sorry about the convoluted language, but that's what you get in LRT-land). Also, the presence of *any* statistically significant interaction suggests that you probably can't neglect interactions. The number of significant terms in each model is largely irrelevant. - You should probably consider whether there is overdispersion in your data (e.g. try fitting a quasipoisson or negative binomial model, although you can't use AIC or LRT (=anova()) with quasipoisson models), since there usually is in ecological data. - your question suggests you might want to read up a bit more on generalized linear models -- Agresti's intro to categorical data analysis or Crawley's intro to data analysis with S-PLUS would work. (If you're familiar with logistic regression, Poisson regression should follow almost exactly the same rules and conventions.) good luck, Ben Bolker ______________________________________________ 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.