[R-sig-Geo] CODE for spatial logistic regression
Henk.Sierdsema at sovon.nl
Thu Oct 9 16:09:55 CEST 2008
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...
SOVON Vogelonderzoek Nederland / SOVON Dutch Centre for Field Ornithology
6573 DG Beek-Ubbergen
tel: +31 (0)24 6848145
fax: +31 (0)24 6848122
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 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
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.
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