[ADMB Users] importance sampling

[dave fournier]

  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).

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.