[ADMB Users] Restricting magnitude of random effects estimates, achieving convergence of RE models
Chris Gast
cmgast at gmail.com
Thu Aug 5 11:05:59 PDT 2010
Thanks for the explanation, Hans. I still haven't experimented with the
effect of -noinit on convergence, as I've been toying with the
logistic-transformed "bounded" random effects you suggested earlier.
I'm not having much luck getting them to work, and I think there's a valid
reason for it.
I define
init_bounded_number logtau(-10,3,2);
random_effects_vector el(0,n-2,2);
vector e(0,n-2);
in the parameter section (among many other things, of coures), and then at
the top of the procedure section, I compute
tau=mfexp(logtau);
for(i=0;i<(n-1);i++){
e[i] = -5 + 10*exp(el[i])/(1+exp(el[i]));
}
and then proceed to use el[i] values within the model, including
totL += -(n-1)*log(tau)-.5*norm2(e/tau);
with totL the objective function value. These random effects occur within a
logistic function themselves [survival probability =
exp(s+e[i])/(1+exp(s+e[i]))], so an upper limit on tau of exp(3) should
quite easily cover the range of survival probability within 0 and 1 along
with e[i] ranging from about -5 to 5. Also, my simulated datasets do not
contain an inordinate amount of variation in this survival probability, but
enough such that detection should be relatively easy.
I believe the problem is that, in this case, an el[i] value of 6 is just as
good as an el[i] value of 60 (they lead to the same result, namely that
logit(el[i])=1). Thus, there are many, many el[i]'s to get the same e[i],
and I end up with random effects values steadily increasing in magnitude
into the thousands, until I finally cease optimization.
Just wanted to update anyone who might be following along with my progress.
I'll keep working on a solution, but still welcome any other suggestions
that might be out there.
Chris
-----------------------------
Chris Gast
cmgast at gmail.com
On Wed, Aug 4, 2010 at 10:16 PM, H. Skaug <hskaug at gmail.com> wrote:
> Chris and Mark,
>
> No, by default the RE are initialized to zero (or whatever is found in
> the .pin file)
> at the start of each inner optimization (part of the Laplace
> approximation).
> This has been found to be more robust in general than using the RE estimate
> from the previous Laplace appr.
>
> I hoped that it could help here, but it is a shot in the dark. (One
> could also hope that -noinit reduces the computational time somewhat)
>
> Hans
>
>
> > I suppose I'm a bit confused about the -noinit option. Shouldn't ADMB
> use
> > the previous RE estimates as the starting point for the next optimization
> by
> > default? Perhaps I'm misunderstanding something.
> > Thanks again,
> > Christ
> >
>
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