Roger Bivand wrote: > Ben Bolker <bolker <at> ufl.edu> writes: > >> Jean-Baptiste Ferdy <Jean-Baptiste.Ferdy <at> univ-montp2.fr> writes: >> >>> Dear R users, >>> >>> I want to explain binomial data by a serie of fixed effects. My >>> problem is that my binomial data are spatially correlated. Naively, >>> I thought I could found something similar to gls to analyze such >>> data. After some reading, I decided that lmer is probably to tool >>> I need. The model I want to fit would look like >>> > (...) >> You could *almost* use glmmPQL from the MASS package, >> which allows you to fit any lme model structure >> within a GLM 'wrapper', but as far as I know it wraps only lme ( >> which requires at least one random effect) and not gls. >> > > The trick used in: > > Dormann, C. F., McPherson, J. M., Araujo, M. B., Bivand, R., > Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., > Kissling, W. D., Kühn, I., Ohlemüller, R., Peres-Neto, P. R., > Reineking, B., Schröder, B., Schurr, F. M. & Wilson, R. J. (2007): > Methods to account for spatial autocorrelation in the analysis of > species distributional data: a review. Ecography 30: 609–628 > > (see online supplement), is to add a constant term "group", and set > random=~1|group. The specific use with a binomial family there is for > a (0,1) response, rather than a two-column matrix. > >> You could try gee or geoRglm -- neither trivially easy, I think ... > > The same paper includes a GEE adaptation, but for a specific spatial > configuration rather than a general one. > > Roger Bivand > >> Ben Bolker
I suggest you also check out the package geoRglm, where you can model binomial and Poisson spatially correlated data. I used it to model spatially correlated binomial data but without covariates, i.e. without your fixed effects (so my model was a logistic regression with the intercept only) (Reference below). But I understand that you can add covariates and use them to estimate the non-random set of predictors. Here is the geoRglm webpage: http://www.daimi.au.dk/~olefc/geoRglm/ This approach would be like tackling the problem from the point of view of geostatistics, rather than from mixed models. But I believe the likelihood-based geostatistical model is the same as a generalized linear mixed model where the distance is the random effect. In SAS you can do this using the macro glimmix but from the point of view of generalized linear mixed models because there they have implemented a correlation term, so that you can identify typical spatial correlation functions such as Gauss and exponential, particular cases of the Matern family. Rubén Roa-Ureta, R. and E.N. Niklitschek (2007) Biomass estimation from surveys with likelihood-based geostatistics. ICES Journal of Marine Science 64:1723-1734 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.