Hamish and Joy Wilman
2010-Mar-19 02:14 UTC
[R] lmer: mixed effects models: predictors as random slopes but not found in the fixed effects?
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 _________________________________________________________________ The New Busy is not the old busy. Search, chat and e-mail from your inbox. N:WL:en-US:WM_HMP:032010_3 [[alternative HTML version deleted]]