[R] metafor mixed-effect models: testing different predictor combinations for lowest AIC

[emhanlon]

Good afternoon,


I am trying to use a mixed-effect model to find the best predictors of
wildlife response (fledgling success, offspring growth rate, etc...) to
human disturbance. I am using a Log Response Ratio as my measure of effect
size along with study variance, and testing to see how: habitat, diet, mass,
generation time, sociality, etc... influence how a species responds to
disturbance. I have 9 total predictors, but a sample size that would only
allow for 3, at most, in a model. Does anyone know of any code that could
allow me to automatically test all combinations of 1,2, or 3 predictors to
get the model with the lowest overall AIC?

#Code I had been using in different combinations manually:
> res <- rma(LRR, est_var, mods = cbind(Gentime, Sociality, Season), data =
> data)

#Also, if the best model has more than one predictor, how can you get
specific mean effect sizes for different levels of the #variable, especially
if it is categorical (non-ranked)?


Thank you so much for your help,

Edward  



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