[R-sig-ME] spatial correlation structures in multilevel models?

[Jan Hanspach]

Hi Malcolm,
I just missed the argument why defining a correlation structure in nlme 
would not be an option? Could you expand on this?
Thanks
Jan

Malcolm Fairbrother schrieb:
> Thanks to both Ben Bolker and Steven Pierce for responses to this question--their general consensus seems to be that WinBUGS would be an option, but lme4 and nlme won't work.
>
> I also subsequently discovered that MLwiN can fit multilevel models taking into account the location of the higher-level units. It can do that because it can fit "multiple membership" multilevel models (e.g., where a given student is nested within more than one school for a single observation period, and the memberships are weighted in some way which sums to 1). The trick for spatial multilevel models is to treat each lower-level unit as a member of both the higher-level unit in which it is located (first, standard random effect), and of all of its weighted neighbouring units (second random effect).
>
> - Malcolm
>
>
> Dr Malcolm Fairbrother
> Lecturer
> School of Geographical Sciences
> University of Bristol
>
>
>
> On 12 Jul 2010, at 20:19, Steven J. Pierce wrote:
>
>   
>> You might also try doing that model with WinBUGS. There are packages that
>> will help you move the data out to WinBUGS from R and then bring the results
>> back into R for post processing. 
>>
>> Steven J. Pierce, Ph.D. 
>> Associate Director 
>> Center for Statistical Training & Consulting (CSTAT) 
>> Michigan State University 
>> E-mail: pierces1 at msu.edu 
>> Web: http://www.cstat.msu.edu 
>>
>> -----Original Message-----
>> From: Malcolm Fairbrother [mailto:m.fairbrother at bristol.ac.uk] 
>> Sent: Monday, July 12, 2010 12:00 PM
>> To: r-sig-mixed-models at r-project.org
>> Subject: [R-sig-ME] spatial correlation structures in multilevel models?
>>
>> Dear all,
>>
>> I'm interested in fitting a three-level model where the 1st level units are
>> individuals, and the 2nd and 3rd levels are (nested) geographical units,
>> whose locations (centroids) are known. (The precise location of each
>> individual is not known--just the unit to which he/she belongs.) I'd like to
>> exploit the fact that the locations are known, since people in
>> neighbouring/nearby units should be more similar than people in units that
>> are distant from each other. To be specific, I'd like a given unit's random
>> intercept to be adjusted according to the data from nearby/neighbouring
>> units--especially for instances where I have few observations for a unit but
>> lots of observations for neighbouring units.
>>
>> My understanding is that lme4 and MCMCglmm cannot do this, in the sense that
>> they cannot specify spatial correlation structures. Using these packages, at
>> most, some characteristic of a unit's location (e.g., latitude, distance
>> from X point) and/or some (weighted) characteristic of a unit's neighbour(s)
>> could be included as a fixed effect.
>>
>> However, as I understand it, nlme can do this, using the "correlation"
>> argument (e.g., "correlation = corExp(form = ~ ...").
>>
>> Is this correct? Will nlme adjust the random intercepts in such a way? And
>> would it be a problem that it's the higher-level units, not the lowest-level
>> units, for which I know the locations?
>>
>> If I'm being over-ambitious/demanding here, no worries at all--I'm just
>> curious whether this is possible. I don't have the data yet.
>>
>> Many thanks,
>> Malcolm
>>
>>
>> Dr Malcolm Fairbrother
>> Lecturer
>> School of Geographical Sciences
>> University of Bristol
>>
>>
>>
>>     
>
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