Dear friends, I would like to ask for some advice.
I am embarking in the analysis of 3,000 plant species occurrence data across biogeographic scales in South America. I am willing to try to jump from more traditional distance-based multivariate analysis (e.g., RDA on hellinger-transformed abundance data) to multivariate GLM as proposed by you (mvabund package) and also by Yee (VGAM package). However, distance-based methods have grown to incorporate spatial dependency through the development of MEM and AEM techniques, which model symmetric and asymmetric spatial relationships and can be included in the explanatory side of the analysis. Reading the multivariate GLM papers, however, I have not find exactly how to control or include spatial autocorrelation. I am thinking of including MEM and perhaps AEM variables simply as co-variables added to the explanatory environmental variables in the multivariate GLM. Is this a step I will regret later on? Is this ok? A second quick wondering: common GLM analyzes are carried out as a series of nested models in which we exclude variables from an initial full model based on anovas/AIC. I suppose this is also true for multivariate GLM. Is it? Can I compare successive models using the same approach used in common GLM? Thanks in advance for any thoughts, All the best, Alexandre
