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 > ______________________________________________ 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.
