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

Mon Apr 13 11:08:41 CEST 2020

Hello Manuel.

Thanks a lot. I'll take a look at them.

All the best
Carlos

On Mon, 13 Apr 2020 at 00:07, Manuel Spínola <mspinola10 using gmail.com> wrote:

> 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
>> >>
>> >>         [[alternative HTML version deleted]]
>> >>
>> >> _______________________________________________
>> >> R-sig-Geo mailing list
>> >> R-sig-Geo using r-project.org
>> >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>> >>
>> >
>>
>> --
>> Carlos Bautista
>> Institute of Nature Conservation
>> Polish Academy of Sciences
>> Mickiewicza 33
>> 31-120 Krakow, Poland
>> www.carpathianbear.pl
>> www.iop.krakow.pl
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo using r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
>
> --
> *Manuel Spínola, Ph.D.*
> Instituto Internacional en Conservación y Manejo de Vida Silvestre
> Universidad Nacional
> Apartado 1350-3000
> Heredia
> COSTA RICA
> mspinola using una.cr <mspinola using una.ac.cr>
> mspinola10 using gmail.com
> Teléfono: (506) 8706 - 4662
> Personal website: Lobito de río
> <https://sites.google.com/site/lobitoderio/>
> Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>
>

-- 
Carlos Bautista
Institute of Nature Conservation
Polish Academy of Sciences
Mickiewicza 33
31-120 Krakow, Poland
www.carpathianbear.pl
www.iop.krakow.pl

	[[alternative HTML version deleted]]