Do “true” multi-level models require Bayesian methods?
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