[R] p(H0|data) for lm/lmer-objects R
A.Robinson at ms.unimelb.edu.au
Thu Dec 25 23:03:54 CET 2008
> Dear R-List,
> I am interested in the Bayesian view on parameter estimation for multilevel
> models and ordinary regression models.
You might find Gelman & Hill's recent book to be good reading, and
there is a book in the Use-R series that focuses on using R to perform
> AFAIU traditional frequentist p-values they give information about
> p(data_or_extreme|H0). AFAIU it further, p-values in the Fisherian
> sense are also no alpha/type I errors and therefor give no
> information about future replications.
I don't think that the last comment is necessarily relevant nor is it
> However, p(data_or_extreme|H0) is not really interesting for social science
> research questions (psychology). Much more interesting is
That's fine, but first you have to believe that the statement has
> Is there a way or formula to calculate these probabilities of the H0
> (or another hypothesis) from lm-/lmer objects in R?
See the books above. Note that in order to do so, you will need to
nominate a prior distribution of some kind.
> Yes I know that multi-level modeling as well as regression can be done in a
> purely Bayesian way. However, I am not capable of Bayesian statistics,
> therefor I ask that question. I am starting to learn it a little bit.
No offense, but it sounds to me like you want to have the Bayesian
omelette without breaking the Bayesian eggs ;). Certain kinds of
multi-level models are mathematically identical to certain kinds of
Empirical Bayes models, but that does not make them Bayesian (despite
what some people say). I caution against your implied goal of
obtaining Bayesian statistics without performing a Bayesian analysis.
Department of Mathematics and Statistics Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
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