[R] How to calculate Hightest Posterior Density (HPD) of coeficients in a simple regression (lm) in R?
Ben Bolker
bbolker at gmail.com
Wed May 8 15:11:07 CEST 2013
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.
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