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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