[ADMB Users] importance sampling

[Ben Bolker]

On 13-03-15 11:04 AM, dave fournier wrote:
> 
> A while back  it became clear that the Laplace approximation is not 
> good enough for some simple RE models. With no crossed effects one
> can use Gauss-Hermite quadrature (This can be done for more than one
> RE per group in ADMB although I have seen reports on the notoriously
> inaccurate R list claiming that ADMB can only do GH for one RE per
> block).

  Dave: those reports were from me, following the published documentation.

  When I go to

  http://admb-project.org/documentation/manuals/admb-user-manuals

and open

http://admb-project.googlecode.com/files/admbre-10.0-rev1.pdf

and search for "Gauss-Hermite quadrature", I find this:

> In the situation where the model is separable of type “Block diagonal Hessian,” with only a
> single random effect in each block (see Section 4), Gauss-Hermite quadrature is available as
> an option to the Laplace approximation and to the -is (importance sampling) option. It is
> invoked with command line option -gh N, where N is the number of quadrature points.

  I also checked in the LaTeX code of the latest version from the SVN
repository -- this text is current.

  Perhaps the documentation can be corrected?  If you tell me what it
should say, I can submit a documentation patch ...

  Ben



> 
> However for models with crossed effects in general GH would be 
> impractical.  The alternative is importance sampling.  We should
> encourage users to routinely do importance sampling to test the 
> stability of the estimates.  We could make this simple with a command
> like
> 
> -is 25  121  50
> 
> this would do 50 fits with 25 random points and starting with the
> random number seed 121. The seed would get changed for each fit.  A
> report would get generated showing the variability of the LL and
> parameter estimates.
> 
> One problem is that since lme? in R can not do this the idea will 
> probably get surpressed on the R list.  Why do I care?  Well users of
> glmmadmb should be encouraged to take advantage of importance
> sampling.  I worry that the kind of sloppy practice encouraged in the
> R community  extends to glmmadmb which uses my software.

   You'll also notice that I *suggested* using ADMB with importance
sampling on the R mailing list in the first place (in the same message
that you're referring to obliquely above) ...

  Ben