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

[Carlos Bautista]

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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