[R] nlme, MASS and geoRglm for spatial autocorrelation?

From: Beale, Colin <Colin.Beale_at_rspb.org.uk>
Date: Wed 13 Jul 2005 - 19:41:45 EST


I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS) with form = ~ x+y (and a nugget if appropriate). However, my data are binomial, so I need a different approach. Using various packages I could define a mixed model (eg using glmmPQL() in MASS) with similar correlation structure, but I seem to need to define a random effect to use glmmPQL(), and I don't have any. Could this requirement be switched off and still use the mixed model approach? Alternatively, it may be possible to define the variance appropriately in gls and use logits directly, but I'm not quite sure how and suspect there's a more straight-forward alternative. Looking at geoRglm suggests there may be solutions here, but it seems like it might be overkill for what is, at first appearance at least, not such a difficult problem. Maybe I'm just being statistically naive, but I think I'm looking for a function somewhere between gls() and glmmPQL() and would be grateful for any pointers.

Thanks very much,

Colin Beale


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