[ADMB Users] ADMB and hierarchical (multi-level) models
shyunuw at gmail.com
Wed Aug 18 13:51:17 PDT 2010
Thank you very much for your kind and thoughtful suggestion. I have
not yet heeded the random effect option in ADMB. I will try it soon.
On Aug 18, 5:37 am, "H. Skaug" <hsk... at gmail.com> wrote:
> Hi Saang-Yoon,
> Let me add one point that maybe is of use to you. If you
> have your prediction problem with
> Ynew = beta0 + beta1*X + error, where error ~ N(0, sigma2)
> Since "error" is contiunous here, you can deal properly with this in ADMB.
> You simply define error to be a random effect (see RE manual).
> There is no likelihood contribution associated with Ynew, except
> from that comming from "error". Hence your concern about
> Ynew affecting the beta estimates is solved.
> This may slow down you program severly, but uncertainty in Ynew will
> be dealt with properly. That is: both the parameter uncertainty
> and randomness in error is accounted for. If error is discrete
> this approach does not work, however.
> On Mon, Aug 16, 2010 at 11:59 PM, Saang-Yoon <shyu... at gmail.com> wrote:
> > Hello, Hans.
> > Thank you very much for your comments. By the way, in my example,
> > newY is continuous. In the multinomial component, newY is a
> > continuous parameter. In the regression component (expressed as
> > Prior), newY is a continuous random variable.
> > Saang-Yoon
> > On Aug 16, 9:17 am, "H. Skaug" <hsk... at gmail.com> wrote:
> >> Saang-Yoon,
> >> Because your newY is a discrete random variable, I do not think
> >> the prediction problem fits well into ADMB. I would fit the model
> >> in ADMB, and write a small back-end that does Monte Carlo simulation.
> >> >> Y = beta0 + beta1*X + error, where error ~ N(0, sigma2)
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