[R] help structuring mixed model using lmer()

[Mark Difford]

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.pickett at 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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