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

[Prof Brian Ripley]

You seem to want to model spatially correlated bernoulli variables.
That's a difficult task, especially as these are bernoulli and not 
binomial(n>1).  With a much fuller description of the problem we may be 
able to help, but I at least have no idea of the aims of the analysis.

glmmPQL is designed for independent observations conditional on the 
random effects.

On Wed, 13 Jul 2005, Beale, Colin wrote:

> Hi.
>
> 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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>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595