Hi Anna,

Have you considered using glimmix in SAS? This allows you to specify 
e.g. ar(1) structured covmatrices and many more. You would have to 
specify "random _residual_" to obtain such structures. However, I think 
this can only be done for pseudo-likelihood methods, not for guass 
quadr. But this may not be a real drawback, as long as you're not going 
to use deviance differences.

Also, in Snijders there is a chapter on longitudinal data analysis. 
There models are discussed for continuous Y with different cov 
structures, and these can be translated to binary Y.

Ben.


On 7-1-2011 12:59, Anna Ekman wrote:
> Hi,
> I am a novice R user and do not know how to properly mail to this list. I apologies if I do it in the wrong way.
>
> I want to analyze my data using a random intercept (later extended also to random slope) logistic model for a binary outcome (later extended to a ordinal outcome).
> This I have been able to do in SAS if assuming the repeated measurements within an individual to be independent, but I want to be able to choose different covariance structures for the individual measurements. This I cannot do directly in either SAS or STATA, and therefore now turn to R.
> How can I do this in R?
>
> Anna
>
> ___________________________________
>
> Anna Ekman  (Grimby-Ekman)
> Ph Lic Statistics, Ph D Medicine
> Occupational and Environmental Medicine, Sahlgrenska Academy and University Hospital
> Dept of Public Health and Community Medicine, University of Gothenburg (UGOT)
> Box 414, SE - 405 30 Goteborg, Sweden
> Phone +46 (0)31 786 31 23
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> www.amm.se<http://www.amm.se/>
>
> Akademistatistik inom EpiStat
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