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 <mspinol...@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 (< > carlosbautistal...@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 <formigare...@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 <carlosbautistal...@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@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@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 > mspin...@una.cr <mspin...@una.ac.cr> > mspinol...@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]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo