[R-sig-ME] Modelling random effects with SITE, YEAR and SPECIES

[CL Pressland]

How can you work out how A, B or C affect SPECIES? By this I mean, could 
you find out how species n is affected by A, B and C in the correlation 
output? Or would you need to adjust the response to look at individual 
species separately?

--On 29 April 2009 17:58 -0400 Ben Bolker <bolker at ufl.edu> wrote:

> David R. wrote:
>> Hello all,
>>
>> First, sorry for the english and the basic questions. I'm using mixed
>> models (lme4 package) to analyse variability in 13 SPECIES of birds
>> observed during 15 YEARS across 5 SITES. All the SPECIES were
>> observed in all the sites in most years.
>>
>> My fixed effects are A, B, C and Year. I'm interested in the
>> stochastic effect of A, B and C on the dependent variable, but also
>> in a possible linear trend of the dependent variable over time.
>>
>> My random effects are SPECIES, YEAR and SITE, to control for the
>> effects of nonindependence.
>>
>> I have a model with SITE, YEAR and SPECIES as crossed random effects
>> like A + B + C + Year + (1|SITE) + (1|YEAR) + (1|SPECIES).
>>
>> My questions are:
>>
>> 1) Is this model correct? It is correct to model YEAR both as random
>> effect and fixed effect? Is there the possibility that the variance
>> accounted for by the random effect could robbing year as a fixed
>> effect of explanatory power?
>
>   Seems OK and sensible to me.
>   I would guess that the linear trend and the random variation are
> sufficiently different patterns that they would not conflict too badly,
> but you could try the different nested models and see what happens ...
>
>>
>> 2) It is meaningful, instead,  to model YEAR as repeated measure, if
>> the experimental unit were species within sites?
>
>   "Modeling YEAR as a random effect" and "Modeling YEAR as a repeated
> measure" are, in my opinion, almost the same thing (but I'm ready to be
> corrected, as always).  The only aspect of "repeated measures" that
> would be different would be if you wanted to fit an autoregressive model
> so that samples closer together in time were more correlated (which you
> can't do with lmer at this
> point).
>
>   Ben Bolker
>
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