[R-sig-Geo] CODE for spatial logistic regression

[Henk Sierdsema]

Hi Rubén,

Thanks for defining 'small' better. For me a dataset up to a thousand points is a small dataset, but for most people this is quite a big data set...

Henk




Henk Sierdsema

SOVON Vogelonderzoek Nederland / SOVON Dutch Centre for Field Ornithology

Rijksstraatweg 178
6573 DG  Beek-Ubbergen
The Netherlands
tel: +31 (0)24 6848145
fax: +31 (0)24 6848122



-----Oorspronkelijk bericht-----
Van: Rubén Roa-Ureta [mailto:rroa at udec.cl]
Verzonden: donderdag 9 oktober 2008 14:23
CC: r-sig-geo at stat.math.ethz.ch
Onderwerp: Re: [R-sig-Geo] CODE for spatial logistic regression


Henk Sierdsema wrote:
> Hi Ivan,
>
> Can you tell me what the purpose is of your modelling? Is it simply producing spatial predictions based on a logistic model or do you want to incorporate spatial autocorrelation in the models? Given your last mail it seems you want to incorporate spatial autocorrelation despite the fact that you deny this in your second mail. So please extend more on the type of data you have and your aim. Next to geoRglm, which is only suitable for small datasets, you might also try regression-kriging.
>
> Is there by the way anyone who has experience with autoregressive models?
>
> Henk
>
>
>
> Henk Sierdsema
>
> SOVON Vogelonderzoek Nederland / SOVON Dutch Centre for Field Ornithology
>
> Rijksstraatweg 178
> 6573 DG  Beek-Ubbergen
> The Netherlands
> tel: +31 (0)24 6848145
> fax: +31 (0)24 6848122
>   
What do you mean by small datasets?
I have used geoRglm to fit spatial binomial models with hundreds of 
point observations.
One approach that would allow the fit of models with even thousand of 
point observations with geoRglm is to create spatial cells (i.e. 
logistic regression with grouped data) and count the number of trials 
and successes in each cell. I implemented that approach in Roa-Ureta and 
Niklitschek, 2007, Biomass estimation from surveys with liklihood-based 
geostatistics, ICES Journal of Marine Science 64:1723-1734.
Rubén

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