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Louis,<br>
<br>
I agree with Steve Martell that if you can rescale, you should.<br>
<br>
What Steve didn't mention -- and forgive me if you know it already
-- is that 1.0e+307 is close to the maximum floating-point number
that typical computers can represent in double precision, which is
the precision used for computation by ADMB (I am pretty sure), R,
SAS, and most other programs. Increasing ADMB precision beyond that
is likely to be a Big Deal, even for the highly talented people who
maintain it. While some compilers do have quad precision, not all
do, and a program that needs quad precision is not likely to be
portable. <br>
<br>
HTH<br>
<br>
<br>
<div class="moz-cite-prefix">Steve Martell wrote on 5/16/2014 9:51
AM:<br>
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<blockquote cite="mid:BEF631CB-7AA3-48CC-B689-2A8DBF9E7C32@iphc.int"
type="cite">
<pre wrap="">Can you rescale the problem?
On May 16, 2014, at 1:14 AM, Louis du Buisson <a class="moz-txt-link-rfc2396E" href="mailto:louis.dubuisson@ird.fr"><louis.dubuisson@ird.fr></a> wrote:
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<pre wrap="">Dear ADMB users,
Does anybody know how to deal with values greater than 1.e+307 in ADMB?
This might seem strange, but let me explain briefly:
I am trying to fit a relatively complex model that consists of numerous equations before reaching the final objective function. One of these equations predicts a ‘biomass’ with depth profile at a specific location and time (This is not really biomass and is unitless - call it eqn1.). This is then standardised by dividing biomass at depth by the total biomass of the profile of the water column to give a relative profile (eqn2.). There are a few more steps after this, but the objective function compares this profile (eqn2.) vs the observed data (also standardised).
The problem is that eqn1. is an exponential equation that results in values greater than 1.e+307, which become Inf in ADMB, in some of the iterations. This has a knock on effect that throws the objective function out, and the final stats for the parameters estimated give -1.#IND and 1.#QNB. I get no warning message usually at the end of running the model and I get estimates for the parameters, but the stats around the parameter estimates are as mentioned.
I have tried numerous approaches to get around the problem:
1. I tested the problem in matlab. I identified the parameters that seem to be causing the trouble in eqn1. by pushing it over Inf. I estimate these parameters using a logit approach because they can only be between 0 and 1, and therefore I used the logit approach to soften the bounds. Before the hard 0 and 1 bounds or anywhere in between this caused problems. Therefore, I need to keep the bounds as open as possible and can’t constrain these further.
2. Adding penalties to prevent the values of eqn 1. going near Inf. This does not work.
3. Mathematically solving the problem: This does not work. I have tried numerous approaches here, but the shape of the profile from eqn2. is changed every time. This shape cannot be changed.
So, is there any way of telling ADMB to handle those large numbers without having to change anything else?
Many thanks,
Louis
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<pre wrap="">
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<br>
<div class="moz-signature">-- <br>
<b>Michael Prager, Ph.D.</b><br>
d/b/a Prager Consulting<br>
Portland, Oregon, USA<br>
<a class="moz-txt-link-freetext" href="http://www.mhprager.com/">http://www.mhprager.com/</a><br>
mobile (252) 269-7005<br>
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