R help - May 2013 - How to calculate Hightest Posterior Density (HPD) of coeficients in a simple regression (lm) in R?

Hi!

I am trying to calculate HPD for the coeficients of regression models
fitted with lm or lmrob in R, pretty much in the same way that can be
accomplished by the association of mcmcsamp and HPDinterval functions for
multilevel models fitted with lmer. Can anyone point me in the right
direction on which packages/how to implement this?

Thanks for your time!

R.

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Richard Asturia <richard.asturia <at> gmail.com> writes:
> 
> Hi!
> 
> I am trying to calculate HPD for the coeficients of regression models
> fitted with lm or lmrob in R, pretty much in the same way that can be
> accomplished by the association of mcmcsamp and HPDinterval functions for
> multilevel models fitted with lmer. Can anyone point me in the right
> direction on which packages/how to implement this?
> 
> Thanks for your time!
> 
> R.
> 
 Hmmm. 
  At least for lm(), if the assumptions of the model are met
then the sampling distribution of the parameters should be
multivariate normal, so with a flat prior the posterior distributions
should be symmetric and equivalent to the sampling distributions of
the parameters -- so I think that the highest 95% posterior density
interval should be equivalent to classical frequentist confidence
intervals [see confint()].

  You might be interested in the bayeslm() function from the arm
package.