[R-sig-Geo] unable to remove spatial autocorrelation from a binomial gam

[Manuel Spínola]

Hello Carlos,

May be you want to take a look on the GSIF and spm packages.

Manuel

El dom., 12 abr. 2020 a las 15:11, Carlos Bautista (<
carlosbautistaleon using gmail.com>) escribió:

> Hello Olga
>
> Thanks a lot for your response. It is very helpful.
>
> Yes, my data is presence/absence because I'm observing the occurrence of
> bear damaging apiaries in a particular region. Since there is a
> compensation system that is running for a long time we can assume that
> almost all damage is included in the database. So perhaps a few absences
> could be presences (a beekeeper not claiming the damage) but I'm
> pretty sure that it'd be marginal. I have also read what you say about
> environmental data not being always an issue that should be removed from a
> model. But in some books and articles, it is written that properly
> accounting for autocorrelation is necessary for obtaining reliable
> statistical inference (
>
> http://highstat.com/index.php/mixed-effects-models-and-extensions-in-ecology-with-r
>  see also here
> https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.1674 ). What
> should I follow? So far my approach is more conservative and I try to
> remove since I imagine reviewers asking me to do so.
>
> I knew about the possibility of subsampling to avoid autocorrelation but
> I've read that it's not the best solution. That's why I was trying to use
> correlation structures. I have got the advice to use the function gamm that
> allow such correlations and check if the model fit is more ore less similar
> to the one of a gam model. I am in the middle of that now and waiting for
> the gamm to finish as it is computationally costly (it may take a few
> days).
> I didn't know about the package that you recommended so I will take a
> look at it. Maybe the weightCases() function will be a good solution to my
> problem.
>
> Thank you so much once again for your help.
>
> All the best,
> Carlos
>
> On Fri, 10 Apr 2020 at 12:04, Olga Boet <formigareina using gmail.com> wrote:
>
> > Hi Carlos,
> >
> > Excuse me, I don't sure that I can help you, I know little about GAM. I
> > don’t understand your script and variogram, I work different. I hope
> > someone else gives you a better answer than mine. But if it can help,
> here
> > are some considerations.
> >
> > Spatial data is often correlated, but it must be evaluated if it is a
> > problem or not. For exemple, some species are distributed by stains as
> > frogs, fihes or some plants species (this correlation should not be
> > eliminated).
> >
> > I think the smooothing function in GAM is to smooth the curves, that is,
> > it softens (less abrupt) the effect of environmental variables (not the
> > coordinates, since the coordinates are not environmental variables in a
> > spatial model).
> >
> > However, in Dimo package, there are two interesting functions: balancing
> > weights function and thinning function.
> >
> > Balance function is weightCases(), and it is used when the background is
> > very large with respect to the number of presences. So that the values of
> > the variables in the presence points have more weight in the model
> despite
> > the lower number.
> >
> > Thinning function removes points that are too close to each other (or in
> a
> > space where variable data is not available). It is used when there are
> > points that are too clustered as a result of sampling (but it does not
> > correspond to the actual distribution). In this function you can
> determine
> > the minimum distance between the points.
> >
> > thinning() is from package spThin (URL:
> > https://cran.r-project.org/web/packages/spThin)
> >
> >
> > Finally, are your data really presence/absence data? did you go to at
> 3355
> > cells and detect presence/absence of the species? spatial models are
> > different if we have absences, pseudoabsences or backround. The type of
> > absence data is important for choosing a model.
> >
> >
> > I'm sorry I couldn't answer your questions
> >
> >
> >
> > Kind regards,
> >
> >
> > Olga Boet
> > Documentalista de la col·lecció de cordats. CMCNB
> > *Myrmex*
> >
> >
> > Missatge de Carlos Bautista <carlosbautistaleon using gmail.com> del dia dj.,
> 9
> > d’abr. 2020 a les 17:52:
> >
> >> Dear list members,
> >>
> >> I am using gam (from mgcv package in R) to model presence/absence data
> in
> >> 3355 cells of 1x1km (151 presences and 3204 absences). Even though I
> >> include a smooth with the spatial locations in the model to address the
> >> spatial dependence in my data, the results from a variogram show spatial
> >> autocorrelation in the residuals of my gam (range=6000 meters). Since I
> am
> >> modelling a binary response, using a gamm with a correlation structure
> is
> >> not advisable because it "performs poorly with binary data", neither
> gamm4
> >> because (although is supposed to be appropriate for binary data) it has
> >> "no
> >> facility for nlme style correlation structures".
> >>
> >> The alternative I have found is to fit my model using the function magic
> >> from the same mgcv package. Because I found no examples of how to use
> >> magic
> >> for spatially correlated data I have adapted the ?magic example for
> >> temporally correlated data. The results of the output change the
> >> coefficients of the model but do not remove the spatial autocorrelation
> >> and
> >> the smooth plots show the same effect.
> >> You can find find the output from my models and figures of the
> variograms
> >> and plots of the smooth effects in the following link
> >>
> >>
> https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r
> >>
> >>
> >> Could someone tell me if there is something wrong in my script? Does
> >> anyone
> >> know another alternative to remove the residuals' spatial
> autocorrelation
> >> from a binomial gam?
> >>
> >> Thank you very much.
> >> Kind regards,
> >> Carlos
> >> --
> >> Carlos Bautista
> >> Institute of Nature Conservation
> >> Polish Academy of Sciences
> >> Mickiewicza 33
> >> 31-120 Krakow, Poland
> >> www.carpathianbear.pl
> >> www.iop.krakow.pl
> >>
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> >>
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> >>
> >
>
> --
> Carlos Bautista
> Institute of Nature Conservation
> Polish Academy of Sciences
> Mickiewicza 33
> 31-120 Krakow, Poland
> www.carpathianbear.pl
> www.iop.krakow.pl
>
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>
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-- 
*Manuel Spínola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
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