[R-sig-ME] Do “true” multi-level models require Bayesian methods?

[David Duffy]

On Wed, 4 Sep 2013, Michael Wojnowicz wrote:

> 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?
>

Some structural equation models need a more flexible setup than lme4 
offers: see the sem, lavaan (and OpenMX) packages for gaussian and probit 
options. Bayesian packages like BUGS are by nature able to fit pretty 
arbitrary models.


| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v