Hi Simon, Have a look at Chap. 11 of "An Introduction to R" (one of R's manuals), which explains the different ways of specifying models using formulae.
Briefly, y ~ x1 * x2 expands to y ~ x1 + x2 + x1:x2, where the last term (interaction term) amounts to a test of slope. Normally you would read its significance from F/chisq/p-value. Many practitioners consider the L.Ratio test to be a better option. For the fixed effects part in lmer() do: mod1 <- y ~ x1 + x2 == y ~ x1 + x2 mod2 <- y ~ x1 * x2 == y ~ x1 + x2 + x1:x2 anova(mod1, mod2) This will tell you if you need to worry about interaction or whether slopes are parallel. Regards, Mark. Simon Pickett-4 wrote: > > Cheers, > > Actually I was using quasipoisson for my models, but for the puposes of my > example, it doesnt really matter. > > I am trying to work out a way of quantifying whether the slopes (for > years) > are covary with habitat scores. > > The more I think about it, the more I am convinced that it isnt possible > do > to that using a glm approach. I think I have to run separate models for > each > site, calculate the gradient, then do a lm with gradient explained by > habitat score.... > > Thanks, Simon. > > > > >> On Tue, Mar 10, 2009 at 10:15 AM, Simon Pickett <simon.pick...@bto.org> >> wrote: >> >>> This is partly a statistical question as well as a question about R, but >>> I am stumped! >> >>> I have count data from various sites across years. (Not all of the sites >>> in the study appear in all years). Each site has its own habitat score >>> "habitat" that remains constant across all years. >> >>> I want to know if counts declined faster on sites with high "habitat" >>> scores. >> >>> I can construct a model that tests for the effect of habitat as a main >>> effect, controlling for year >> >>> model1<-lmer(count~habitat+yr+(1|site), family=quasibinomial,data=m) >>> model2<-lmer(count~yr+(1|site), family=quasibinomial,data=m) >>> anova(model1,model2) >> >> I'm curious as to why you use the quasibinomial family for count data. >> When you say "count data" do you mean just presence/absence or an >> actual count of the number present? Generally the binomial and >> quasibinomial families are used when you have a binary response, and >> the poisson or quasipoisson family are used for responses that are >> counts. >> > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/help-structuring-mixed-model-using-lmer%28%29-tp22436596p22441985.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.
