[ADMB Users] Are parameters scaled for optimization.

[Mark Maunder]

Thanks Dave.  

> Extending this so that std dev terms  can be scaled in multivariate
> random effects models sounds useful. However,  I will let others comment on whether this is a useful project. 

Regards,

Mark
IATTC

> On Jan 1, 2014, at 6:35 PM, "dave fournier" <davef at otter-rsch.com> wrote:
> 
> On 14-01-01 01:43 PM, Mark Maunder wrote:
> 
> for an unbounded parameter  (an init_number)
> the scalefactor is implmented OK.
> 
> 238 void df1b2_init_number::set_value(const init_df1b2vector& _x,const int& _ii,
> 239     const df1b2variable& pen)
> 240 {
> 241   ADUNCONST(init_df1b2vector,x)
> 242   ADUNCONST(int,ii)
> 243   if (scalefactor==0.0)
> 244     operator = (x(ii++));
> 245   else
> 246     operator = (x(ii++)/scalefactor);
> 247 }
> 248
> 
> So this is OK.
> 
> However the code for an init_bounded_vector never wrote itself.
> 
> 569 void df1b2_init_bounded_vector::set_value(const init_df1b2vector& _x,
> 570   const int& ii,const df1b2variable& pen)
> 571 {
> 572   init_df1b2vector& x=(init_df1b2vector&) _x;
> 573   if (initial_params::mc_phase)
> 574   {
> 575     ::set_value_mc(*this,x,ii,minb,maxb);
> 576   }
> 577   else
> 578   {
> 579     ::set_value(*this,x,ii,minb,maxb,pen);
> 580   }
> 
> I was hoping that someone would take up the job after the init_bounded_number was done,
> but there seems to be more interest in doxygen and unbelievably complicated script files.
> 
> If there is some interest I can extend this a bit.  One application would be to multivariate
> random effects models.  It appears that scaling the std dev terms greatly improves the stability of
> glmmadmb and I expect this is a fairly general result.
> 
>> I assume it is the same for standard parameters and re parameters.
> 
> 
> I'm not sure what is meant here.
> 
> 
>      Dave
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>> Thanks Dave. What if it is unbounded?  I assume it is the same for standard parameters and re parameters.
>> 
>> Regards,
>> 
>> Mark
>> IATTC
>> 
>>> On Dec 31, 2013, at 1:03 PM, "dave fournier" <davef at otter-rsch.com> wrote:
>>> 
>>> 
>>> For a random effects model
>>> The relevant code appears to be
>>> 
>>> if the scalefactor has not been set (i.e. =0)
>>> 
>>> 409 df1b2variable boundp(const df1b2variable& x, double fmin, double fmax,
>>> 410   const df1b2variable& _fpen)
>>> 411 {
>>> 412   ADUNCONST(df1b2variable,fpen)
>>> 413   df1b2variable t;
>>> 414   //df1b2variable y;
>>> 415   //y=x;
>>> 416   double diff=fmax-fmin;
>>> 417   const double l4=log(4.0);
>>> 418   df1b2variable ss=0.49999999999999999*sin(x*1.57079632679489661)+0.50;
>>> 419   t=fmin + diff*ss;
>>> 420
>>> 
>>> So the parameter is effectively scaled by diff.
>>> Better would be
>>> 
>>> 418   df1b2variable ss=0.49999999999999999*sin(x/diff*1.57079632679489661)+0.50;
>>> 
>>> The only difficulty would be that the function minimizer report does not deal with large numbers well.
>>> 
>>> For parameters whose scalefactor has been set the code is
>>> 
>>> 
>>> 460 df1b2variable boundp(const df1b2variable& _x, double fmin, double fmax,
>>> 461   const df1b2variable& _fpen,double s)
>>> 462 {
>>> 463   ADUNCONST(df1b2variable,fpen)
>>> 464   df1b2variable t;
>>> 465   df1b2variable x=_x/s;
>>> 466   //df1b2variable y;
>>> 467   //y=x;
>>> 468   double diff=fmax-fmin;
>>> 469   const double l4=log(4.0);
>>> 470
>>> 471   // ss is underlying varialbe on [0,1] and t lives in [fmin,fmax]
>>> 472   df1b2variable ss=0.49999999999999999*sin(x*1.57079632679489661)+0.50;
>>> 473   t=fmin + diff*ss;
>>> 474
>>> 
>>> again
>>> 
>>> 472   df1b2variable ss=0.49999999999999999*sin(x/diff*1.57079632679489661)+0.50;
>>> 
>>> would be better.
>>> 
>>> I wrote a note about this earlier, but it did not generate much interest.
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
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