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.
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