Modelling random effects with SITE, YEAR and SPECIES

Dear Kate,

Adding SPECIES as a random effect indicates that you want to take the
effect of SPECIES into account but not need to know the effect of the
individual SPECIES. If you do want to know that effect then you have to
add species to fixed effects. Examining the effect of A, B and C on
species (as a fixed effect) requires interactions between them. The
model then looks like (A + B + C) * SPECIES + Year + (1|SITE) + (1|YEAR)
This will only work if you have sufficiend data.

Another option is to keep species as a random effect and add random
slopes according to A, B and C. This will allow a different effect of A,
B anc C for each species. The model would look like A + B + C + Year +
(1|SITE) + (1|YEAR) + (A + B + C|SPECIES)

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
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tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens CL Pressland
Verzonden: woensdag 6 mei 2009 20:18
Aan: R Mixed Models
Onderwerp: Re: [R-sig-ME] Modelling random effects with SITE, YEAR and
SPECIES

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?

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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----------------------
Kate Pressland
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School of Biological Sciences
University of Bristol
Woodland Road
Bristol, BS8 1UG
Tel: 0117 9288918 (Internal 88918)
Kate.Pressland at bristol.ac.uk
www.bio.bris.ac.uk/people/staff.cfm?key=1137

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