[R] analyzing binomial data with spatially correlated errors

[Roger Bivand]

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
>