Dear Luca, Have a look at Blangiarde & Cameletti (2015) Spatial and Spatio-temporal Bayesian Models with R - INLA ISBN: 978-1-118-32655-8 They describe how you can tackle this problem with mixed models with correlated random effects.
Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2016-04-17 14:39 GMT+02:00 Luca Candeloro <luca.candel...@gmail.com>: > Starting from environmental and metereological data, I have defined an > annual count variable (number of favourable events, raster type object). > The purpose of the analysis is to predict the next year favourable events > raster, given the time series (last 15 years). > Working at the pixel level, it would be possible to make a Poisson > regression, but treating pixels independently, would loose spatial > effects... > Which is, in your opinion, the best approach? > Is there a spatio-temporal model for this kind of data that could usefully > combine spatial effect with time series analysis? > Thanks for any suggestions > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo