[ADMB Users] -lprof

[John Sibert]

I'll give it a try.

John Sibert
Emeritus Researcher, SOEST
University of Hawaii at Manoa
(Sent from the N7; please excuse the typos.)
On Jul 20, 2014 11:25 AM, "dave fournier" <davef at otter-rsch.com> wrote:

> On 07/20/2014 02:03 PM, John Sibert wrote:
>
> Did you use  something like
>
>
>   feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW );
>
> with that you shoud be able to find out where the problem occurs suing the
> debugger.
>
>  Ugh.
>>
>> The variable I'm trying to profile has a pretty low estimated standard
>> deviation. From the .std file:
>>
>>>     87   natural_mortality 1.5239e-01 7.8301e-03
>>>
>> By fiddling with the model structure and the lprof control variables, it
>> will sometimes work, but the resulting profile plot is pretty strange. See
>> attached.
>>
>> I'm working to track down the problem, but even simplifying the model it
>> is slow going because of the need to compute the Hessian every time.
>>
>> The contents of diags is:
>>
>>> f$ cat diags
>>> tempint1
>>>        inf       inf       inf       inf       inf inf inf       inf
>>>   inf
>>> m(2)
>>>        inf       inf       inf       inf       inf inf inf       inf
>>>   inf
>>> m(3)
>>>      0.882     0.956     0.989     0.999         1     0.999 0.989
>>> 0.956     0.882
>>> xdistance
>>>  1 1 1 1 1 1 1 1 1
>>>
>>
>> Changing exp to mfexp for the computation of tempint1 in newmodmn.cpp
>> seem to help a bit, but does not solve the problem. I think the cause
>> occurs earlier, but I'm having trouble tracking it down.
>>
>> Does anyone really use this option? It seems like a good option to
>> diagnose an ill-defined parameter when MCMC would take too long.
>>
>> Would it discomfort other users to change the way the procedure composes
>> the file name from the variable name?
>>
>> John
>>
>> John Sibert
>> Emeritus Researcher, SOEST
>> University of Hawaii at Manoa
>> Honolulu HI (GMT-10)
>> 808-294-3842 (mobile)
>>
>> Visit the ADMB projecthttp://admb-project.org/
>>
>> On 07/17/2014 06:03 PM, dave fournier wrote:
>>
>>>
>>> Almost anything might go wrong with lprof.  It works by maximizing the
>>> log-likelihood subject to a constraint
>>> on the value of the variable being profiled.  This process drags the
>>> values into what might be areas where the
>>> function becomes unstable in some way.
>>>
>>> Hard to say more about a particular case without seeing it
>>>
>>>      Dave
>>>
>>>
>>
>
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