[R-sig-ME] mixed modelling with random effects with different correlation structures? GLMM/GEE mix
Ben Bolker
bbolker at gmail.com
Tue Oct 1 16:28:07 CEST 2013
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On 13-10-01 10:04 AM, R.Klink, van wrote:
> Dear Prof. Bolker
>
> I write you on behalf of my colleagues working in the same
> project. We have a complicated study design, and as far as I have
> been able to tell, there are no published statistical packages that
> can deal with this design correctly.
>
> We have a blocked design, of 3 blocks, with each 5 plots with each
> a different treatment. Each of these 15 plots has been sampled for
> plant, insect and bird species richness during 4 consecutive years.
> We would like to analyze all this in a mixed model where we
> include plot nested in block, and also the year effect as random
> factors. But this seems not to be possible, since the nested design
> has an "exchangeable correlation structure" (in GEE terms), or
> compound symmetry, whereas the time series is auto-regressive.
> Hence, we would like to construct a model that combines different
> correlation structures for the different random factors. My
> question to you is of course whether something like this is
> currently possible, if yes how? and if no, what would your advise
> be to analyze these data.
>
> Feel free to post this question on one of the GLMM-fora
>
> We hope you can give us some advice.
>
> On behalf of me and my colleagues`
Short answer: not that I know of (maybe AS-REML?) You will probably
have to 'roll your own' with WinBUGS/JAGS, or AD Model Builder. I
would also be quite concerned about fitting a model of this complexity
to a data set of this size -- it's going to be hard to estimate a year
effect with n=4, let alone an autoregressive correlation term. I
would be inclined to fit a simpler model: year as a fixed effect --
and then test/examine the residuals for autocorrelation within plots.
Hopefully others will come forward with different ideas.
[cc'ing to r-sig-mixed-models]
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