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 > > ... > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > -- Brian D. Ripley, [EMAIL PROTECTED] 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 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html