I recently moved from distance-based techniques to model-based techniques and I am trying to analyse a dataset I collected during my PhD using the Bayesian method described in Hui 2016 (boral R package). I collected 50 macroinvertebrate samples in a river stretch (approximatively 10x10 m, so in a very small area) according to a two axes grid (x-axis parallel to the shoreline, y-axis transversal to the river stretch). For each point I have several environmental variables, relative coordinates inside the grid and the community matrix (site x species) with abundance data. With these data I would create a correlated response model (e.i. including both environmental covariates and latent variables) using the boral R package (this will allow me to quantify the effect of environmental variable as well as latent variables for each taxon). According to the boral manual there are two different ways to implement site correlation in the model: via random row-effect or by assuming a non-independence correlation structure for the latent variables across sites (in this case the distance matrix for sites has to be added to the model). As specified at page 6, the latter should be used whether one a-priori believes that the spatial correlation cannot be sufficiently well accounted for by row effect. However, moving away from an independence correlation structure for the latent variables massively increases computation time for MCMC sampling. So, my questions are: which is the best solution accounting for spatial correlation? How can be interpreted the random row-effect? Can it be seen as a proxy for spatial correlation?
Any suggestion would be really appreciated
Thank you
Gemma
https://cran.r-project.org/web/packages/boral/boral.pdf
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12514
Usually both tests are employed, first a random or fixed effect model using the F statistic, then post hoc tests and correlations. This can all be preceded with ordination if you want
See: Article Testing for Main Random Effects in Two-Way Random and Mixed ...
Thank you very much, I will read carefully the paper you suggest
Gemma
Hello Mrs. GEMME,
While working on the boral model, I realized that you had the same problem. So, I would like to know if you have been able to find a solution as to the best way to take into account the spatial correlation in a boral model?
cordially
Romuald HOUNYEME
Hounyeme Romuald I did not find exactly a solution, I found an alternative. While working with boral I came across another R package, the HMSC package, which allowed me to model my communities explicitly including also spatial correlation.
I am quite satisfied with using this package, so I would recommend also to you to try it. You can find the link below.
Best regards
Gemma
https://github.com/hmsc-r/HMSC
https://www.helsinki.fi/en/researchgroups/statistical-ecology/hmsc
Article How to make more out of community data? A conceptual framewo...
Thank you very much for your return. Thank you for this exchange, for links and documents too.
cordially
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