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

[ONKELINX, Thierry]

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
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----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?

--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
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models



----------------------
Kate Pressland
Office D95
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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