Dear all,

I am using the MuMIn package for multi-model inference (based upon sets on lmer models) for the first time and have a question about the interpretation of the relative importance values given for variables when using the model.avg function. If you use get.models to select only models with delta AIC<=4 or a 95% confidence set then it is likely that, in this model set, each predictor will appear in a different number of individual models. Do the calculations of relative importance allow for this so that the support for different predictors can be meaningfully compared, even if each appears in a different number of models? In the model.avg call I specify method="0" so that when predictors are absent from any single model they are assumed to actually be present with a parameter (slope) estimate of zero e.g.

TCB.mod.av.95CS<-model.avg(TCB.models2,method="0",rank="AIC",alpha=0.05)

I presume that this means that each predictor is treated as being present in all models in the top model set, so that the relative importance values can be compared across the predictors meaningfully. Do I understand this correctly?

All the best

Steve

Dr Stephen  Thackeray
Lake Ecosystem Group
Centre for Ecology and Hydrology
Lancaster Environment Centre
Library Avenue
Bailrigg
Lancaster
LA1 4AP
sjtr@ceh.ac.uk<mailto:sjtr@ceh.ac.uk>



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