[ADMB Users] Are parameters scaled for optimization.

[Mark Maunder]

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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