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

[Olga Boet]

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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