I have a simple data set to find out about the effect of cultivation period length on soil organisms. The main factor of interest is age_class, a categorical variable defining the age of a field under investigation. But, I also measured several environmental variables. I use R for the analysis.
My approach to select the best model would be to calculate all possible models using MuMIn::dredge(). I forced all models to include the main factor (age_class), since I want to make post-hoc pairwise comparisons to get compact letter display of differences between the age-classes.
Normally with this approach one would do model averaging and not simply define the best model, since other models might be as good as the best fit. However, the averaged model object of MuMIn can not be used in post-hoc tests and I really hate the cumbersome presentation of coefficients for the levels of a categorical variable. Whereas compact letter display is easy to understand for everybody. Besides, many journals want it that way and I read that post hoc comparisons based on confidence intervals of point estimates are not recommended
Furthermore the problem of including age_class as fixed variable gives rise to the problem that the relative importance of this parameter will be fixed at 1 Grueber et al 2011 (model selection and model averaging). In this paper the problem was stated, but no solution was given to it.
I was wondering if I could simply use multimodel calculation as tool to detect the best model and then give the strength of evidence (p. 26) for this particular model (which is given as weights in the dredge() Output?).???