[R-sig-Geo] spatial GLM using glmmPQL
Roger.Bivand at nhh.no
Fri Apr 11 09:38:03 CEST 2008
On Thu, 10 Apr 2008, Anne GOARANT wrote:
> Hi List,
> I have observations of insect counts and environmental variables. My
> first goal was to compute a GLM to explain the insect counts with the
> environment. The thing is that my insect counts are not spatially
> independant and show spatial autocorrelation (a spherical variogram
> model can be fit to the data).
> So I intend to compute the same GLM model and taking into account the
> spatial autocorrelation. The final objective would be to compare both
> model (spatial and non spatial and check which one is the best).
> I read that a way to do a spatial GLM is using the glmmPQL function of
> MASS and putting all the observations in the same group for the random
> effect (Dorman, Ecography 30, 2007). I was wondering if the "non
> computed" Log-Likelihood value (but it can be computed by changing a
> line code in the glmmPQL code function) is correct. I did some trials to
> compare the model outputs for the same dataset for glm and glmmPQL (with
> all the observations in the same group) and it gave me the same
> estimated parameters. It also gave me the same Log-Likelihood for both
> Do you have any idea if the Log-Likelihood from glmmPQL is correct when
> one considers all the data in the same group for the random effect?
> Is there any other methods to do what I want (comparison of spatial and
> non-spatial GLM)?
When this came up recently on R-help, Douglas Bates, whose views deserve
respect, commented that using a single group was not advisable:
Using one group for each observation may be another alternative. The
choice in the Dormann et al. paper was motivated by a desire to get the
same results as SAS GLIMMIX, not by any considered judgement - the authors
in fact call their "hack" an "abuse" of the method (see the electronic
Hope this helps,
> Thanks for your help.
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e-mail: Roger.Bivand at nhh.no
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