# Fitting an HLM model in lme4

I'm a fairly inexperienced statistician fighting a huge deadline and just need some peace of mind that I'm not making a massive error here. I'd be most grateful for pointers.

I've been playing around with different model combinations using the lmer() function, and have managed to get the AIC scores down from over 10,000 with the most basic combinations to -180 by factoring in various random effects. But I'm concerned I might be creating bias by using variables that already correlate. Is this a valid concern or does the model mitigate for this?

The ultimate goal is to compare HLM with other approaches (eg. OLS regression) in the context of crime spatial modelling. The concern here is if I am introducing biases into the model by including random effects variables (such as an area's deprivation score and crime rate) that are likely to have some correlation. I wonder if this distorts the results (meaning variables need to be relatively orthogonal), or if lmer is smart to this? My apologies if vague - I'm entirely self-taught and have found it pretty tough getting into the language, so just need some guidance. –  geotheory Sep 5 '12 at 9:46