
Hello all,
I using lmer to develop a mixed effects model.  I start with an overly parameterized model (as suggested in Zuur et al. Mixed Effects Models and Extension in Ecology with R) that looks something like this:
m1 <- lmer( Y ~ aS + bS + c + d + e + (c|SpeciesId) + (d|SpeciesId) + (e|SpeciesId))
aS and bS are species level predictors an so do not vary within a SpeciesId. However, c, d, and e are population level predictors and can all potentially vary significantly within species.
I am trying to arrive at the best model describing Y.
I have been beginning with selection on the random effects, subtracting one term and performing a likelihood ratio test, then another term and another LRT, etc. I then delete the term with the lowest non-significant test statistic and do the whole procedure again on the reduced model.
After getting the "optimal" random effects structure, I move on and perform a similar procedure on the fixed effects.
First of all, does this sound at all sensible?
Secondly, when I do this with my data the resulting "optimal" or best fitting model looks like this:
mFinal <- lmer( Y ~ aS + bS + c + (d|SpeciesId) + (e|SpeciesId))
d and e show up as random slopes but not in the fixed effects.Is this ok, and if so, I'm not sure what the interpretation is...

Thanks so much,
HamishUCSD EBEhamishv8@hotmail.com   		 	   		  
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