Dear all,

I am running phylogenetic mixed models with multiple fixed effects using MCMCglmm. Several of the fixed effects are included in the final best model. I’d like to estimate the fixed effects' relative importance (i.e. like I would use sum of squares to estimate the proportion of variance explained by each fixed effect in a classical linear model). Some of the variables (but not all) being categorical, scaling them to get comparable posterior estimates is not an option. I also thought about comparing the DIC of the best model with the DIC of the best model excluding the variable which importance I want to test. For instance if the best model is (Y ~ a + b + c + d + random structure) and I want to estimate the importance of a, I would run the model (Y ~ b + c + d + random structure) and compare its DIC with the best model's DIC. I could then imagine ranking variables according to the deltaDIC, but I am not sure whether it makes sense or not. Model averaging methods could also be an option, but the time needed to run 1 model is too long to run all possible models. Do you have any advice on the best way to rank variables according to their relative importance with MCMCglmm?

Another quick point: I selected the best model using either the DIC, or by eliminating variables with pMCMC<0.05 step by step; even so in my case, both methods selected the same final model, which model selection approach do you think would be the most appropriate in general?

Thank you very much in advance for your help

Best regards

Simon

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