Re: [R] analyzing binomial data with spatially correlated errors

From: Roger Bivand <Roger.Bivand_at_nhh.no>
Date: Thu, 20 Mar 2008 12:36:49 +0000 (UTC)

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
>



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