

	Dear all, 

	As Ben suggests it is possible to fit models with compound symmetry
between plots within blocks and autocorrelation between times in
asreml-r (and GenStat) However, these are commercial packages
(http://www.vsni.co.uk/ [1]).  

	 I also agree with Ben that regarding Years as fixed is likely to be
more successful than Years as random. 

	 Further discussion of the analysis of such experiments using models
like those being proposed is available in a paper on which I am an
author: 

	 Brien, C. J. and C. G. B. Demetrio (2009). "Formulating Mixed
Models for Experiments, Including Longitudinal Experiments." Journal
of Agricultural, Biological and Environmental Statistics 14(3):
253-280. 

	 Cheers,  

	   Chris Brien 

	 Adjunct Senior Lecturer in Statistics 

	----- 

	Phenomics and Bioinformatics Research Centre University of South
Australia GPO Box 2471 ADELAIDE  5001  South Australia 

	Phone:  +61 8 8302 5535   Fax:  +61 8 8302 5785 

	Email:   Chris.Brien@unisa.edu.au [2] 

	WEB page:   

	CRICOS No 00121B   

----- Original Message -----
From: "Ben Bolker" 
To:"R.Klink van" 
Cc:"steffi nolte" , "freek mandema" ,
"r-sig-mixed-models@r-project.org" 
Sent:Tue, 01 Oct 2013 10:28:07 -0400
Subject:Re: [R-sig-ME] mixed modelling with random effects with
different correlation structures? GLMM/GEE mix

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 Hash: SHA1

 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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Links:
------
[1] http://www.vsni.co.uk/
[2] mailto:Chris.Brien@unisa.edu.au
[3] http://people.unisa.edu.au/Chris.Brien


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