[ADMB Users] NLMM Model Selection

Tue Feb 22 15:35:51 PST 2011

Could this be calculated by modifying your code from

Random_effects_vector

To

Init_vector

And then running the model with the command line option –maxfn 0 and using the par file from the random effects model and the pin file

This would use the full joint likelihood without integrating over the random effects and evaluating it at the MLE of the fisxed effects and the empirical bayes estimates of the random effect realizations.

Not sure why you would do this though.

Mark

Mark Maunder

Head of the Stock Assessment Program
Inter-American  Tropical Tuna Commission

President
ADMB Foundation

8604 La Jolla Shores Drive
La Jolla, CA, 92037-1508, USA

Tel: (858) 546-7027
Fax: (858) 546-7133
mmaunder at iattc.org<mailto:mmaunder at iattc.org>
http://www.fisheriesstockassessment.com/TikiWiki/tiki-index.php?page=Mark+Maunder

Visit the AD Model Builder project at
 http://admb-project.org/

See the following website for information on fisheries stock assessment
http://www.fisheriesstockassessment.com/

From: Chris Gast [mailto:cmgast at gmail.com]
Sent: Tuesday, February 22, 2011 3:25 PM
To: Mark Maunder
Cc: users at admb-project.org
Subject: Re: [ADMB Users] NLMM Model Selection

Conditional on the values of the main effects. I'm working from a reference provided by Dr. Bolker:
Greven, Sonja, and Thomas Kneib. 2010. On the Behaviour of Marginal
and Conditional
Akaike Information Criteria in Linear Mixed
Models. Biometrika 97, no. 4: 773-789.
http://www.bepress.com/jhubiostat/paper202/.

where the marginal likelihood (for a LMM, not a GLMM or other nonlinear mixed model) is as discussed above, y~N(XB,V) (REs have been integrated over) and the conditional likelihood (at its optimum) is evaluated at the MLEs and EB estimates of REs, y | b ~ B*XB + Zb, I*sigma^2) where the b's are the REs.

So ADMB works with the marginal likelihood, and the optimum value is provided in the .par file.  However, it would be nice (at least at this, the exploratory model-selection phase of my work) to also have the conditional likelihood value, which would be obtained (I believe) by plugging the RE estimates and MLEs back into the likelihood function and evaluating. This isn't done automatically by ADMB, but (I think) could be done by changing the .par file to a .pin file, and re-evaluating the model and outputting the first objective function value.  I haven't tested this idea yet, though.  I'll probably play with it a bit with a GLMM for which I can compare AIC values with other software to try and understand what precisely each is doing.

Chris

-----------------------------
Chris Gast
cmgast at gmail.com<mailto:cmgast at gmail.com>

On Tue, Feb 22, 2011 at 9:30 AM, Mark Maunder <mmaunder at iattc.org<mailto:mmaunder at iattc.org>> wrote:
Conditional on what?

Mark Maunder

Head of the Stock Assessment Program
Inter-American  Tropical Tuna Commission

President
ADMB Foundation

8604 La Jolla Shores Drive
La Jolla, CA, 92037-1508, USA

Tel: (858) 546-7027
Fax: (858) 546-7133
mmaunder at iattc.org<mailto:mmaunder at iattc.org>
http://www.fisheriesstockassessment.com/TikiWiki/tiki-index.php?page=Mark+Maunder

Visit the AD Model Builder project at
 http://admb-project.org/

See the following website for information on fisheries stock assessment
http://www.fisheriesstockassessment.com/

-----Original Message-----
From: users-bounces at admb-project.org<mailto:users-bounces at admb-project.org> [mailto:users-bounces at admb-project.org<mailto:users-bounces at admb-project.org>] On Behalf Of H. Skaug
Sent: Tuesday, February 22, 2011 8:19 AM
To: Chris Gast
Cc: users at admb-project.org<mailto:users at admb-project.org>
Subject: Re: [ADMB Users] NLMM Model Selection

On Tue, Feb 22, 2011 at 5:10 PM, Chris Gast <cmgast at gmail.com<mailto:cmgast at gmail.com>> wrote:
> I'm sorry, I must be getting confused.  My message from yesterday (question
> 1) asked if the objective function value in the .par file was the
> post-integration marginal likelihood, to which you responded in the
> negative, but now it seems as if you're saying what I had originally
> postulated is true. Maybe it's just a terminology difference.  It is my
> understanding that the .par file contains the optimum loglikelihood value
> obtained, where the loglikelihood value is marginalized over the random
> effects.  Perhaps there is some miscommunication?  I think we're talking
> about the same thing.

Yes, we are. I misunderstood your original question.

> Since the marginal likelihood is available in the .par file, is there a way
> to output the conditional likelihood following optimization?

No, not any direct way that I am aware of. It is possible that this
is what you get before the random effects kick in (assuming those
active in phase 2 or later).

hans
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