Hi Michael,

This is certainly possible in, e.g., lme4 or nlme packages. My perception is actually that these kind of models are discussed pretty routinely in the traditional multilevel literature under the term "cross-level interactions." Taking a quick look at my bookshelf, I find discussions of cross-level interactions in Snijders & Bosker (2011), Hox (2010), and Goldstein (2010).

Jake

> Date: Tue, 3 Sep 2013 14:53:23 -0700
> From: mwojnowi@uci.edu
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Do “true” multi-level models require Bayesian methods?
> 
> I've been recently learning about mixed effects models (e.g. via
> Fitzmaurice, Laird, and Ware 's book *Applied Longitudinal Analysis*) as
> well as Bayesian hierarchical models (e.g. via Gelman and Hill's book *Data
> Analysis Using Regression and Multilevel/Hierarchical Models*)
> 
> One curious thing I've noticed: The Bayesian literature tends to emphasize
> that their models can handle covariates at multiple level of analysis. For
> example, if the clustering is by person, and each person is measured in
> multiple "trials," then the Bayesian hierarchical models can investigate
> the main effects of covariates both at the subject and trial level, as well
> as interactions across "levels."
> 
> However, I have not seen these kinds of models in the textbooks introducing
> frequentist methods.
> 
> I'm not sure if this is a coincidence, or an example of where Bayesian
> methods can do "more complicated things." Is it possible to use mixed
> effects models (e.g. the lme4 or nlme packages in the R statistical
> software) to investigate interactions of fixed effect covariates across
> "levels" of analysis?
> 
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