Message-ID: <alpine.LMD.2.00.1309050628210.7949@orpheus.qimr.edu.au>
Date: 2013-09-04T20:35:51Z
From: David Duffy
Subject: Do “true” multi-level models require Bayesian methods?
In-Reply-To: <CAMHfot2XQizXrPPJ_6MY76bZ+UyC3JmFGtdAnoYAGgEfWYjD_w@mail.gmail.com>
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