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

[Malcolm Fairbrother]

Hi Jan,

My understanding (and someone else can correct me if I'm wrong) is that nlme *can* take into account a *lowest*-level unit's location--even if some of those units occupy the same precise location, as long as any two units located in the same place are members of *different* higher-level units. In my case, all the lower-level units belonging to a given higher-level unit share the same location.

I'm not sure *why* nlme has these particular capacities and limits, but that's what I understand them to be.

Cheers,
Malcolm

Dr Malcolm Fairbrother
Lecturer
School of Geographical Sciences
University of Bristol



On 16 Jul 2010, at 11:14, Jan Hanspach wrote:

> 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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