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

[Malcolm Fairbrother]

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