In working with AICc model averaging and selection, I am not sure what the precedence is for choosing a subset of models to compare. We are investigating genetic loci under supposed adaptive selection pressure from environmental variables. We have no prior expectations of which specific variables may be influencing certain loci and can not a priori reduce models for comparison. Our data set includes a large number of plausible explanatory environmental variables. We initially compared a subset of models with delta AIC values ranging from 0-10. I have found information saying that delta AIC values from 0-2 indicate that a given model should be considered within the range of plausible models for the set under investigation. Coding in R for a given locus, when I change the input call for the subset of deltas to compare (I've tried 10, 5, and 3) the Deviance, AICc, and Delta of the various models do not change, however the weights given to the various models do change as do the values of Relative Variable Importance. For example, with input delta <10, the best model weight is 0.03, whereas it is 0.074 with input delta <3. Is there a precedence for how many models to compare, a subset Delta AICc cut-off line for model comparison? Any feedback is much appreciated!
Sincerely, Helen Bothwell
