[R] metafor mixed-effect models: testing different predictor combinations for lowest AIC
emhanlon at go.olemiss.edu
Sat Jun 1 23:12:21 CEST 2013
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 =
#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,
View this message in context: http://r.789695.n4.nabble.com/metafor-mixed-effect-models-testing-different-predictor-combinations-for-lowest-AIC-tp4668486.html
Sent from the R help mailing list archive at Nabble.com.
More information about the R-help