A
variety of
options can significantly increase the speed of the
program. In the order in which we suggest you try them, these are,
If you have many observations, you can use a random subset in
the first stage (by setting _EselRnd to the fraction of
observations to include). Setting this global still results in all
observations still being included in the second stage, the result
being that estimates will still be available for all observations.
If a large is the issue, then this is the best way to speed
estimation without much cost.
You can use the asymptotic normal approximation, and eliminate
the importance sampling refinement, by setting
_EisFac or to go even faster use
_EisFac to exclude estimation uncertainty.
Choose good starting values (by setting _Estval). For
example, if you have many similar analyses to run, do a typical
analysis and use those numbers as starting values for the others.
If you have a guess as to the values of on the untruncated
scale, you can use the following command (documented only in
eirepar.src) to set the starting values: e.g.,
_Estval=eireparinv(0|0|-1|.4|.1).
Buying enough RAM so you do not need to take advantage of the
virtual memory feature of Gauss is helpful. Or, just buy a really
fast computer!
If you are willing to live with lower precision, you can draw
fewer simulations by setting _Esims to a number smaller
than the default (100).
You can set _EdirTol to a larger number. The default
is 0.0001; you can try 0.001 or larger. You could also reduce the
maximum number of iterations with _EmaxIter.
If you are running many analyses, run one analysis to make sure
that the globals are properly defined and then set
_Echeck=0 to turn off global checking. This will only
save a small bit of time and so is only helpful if you are running
many analyses. (If you turn off checking, and you define a global
incorrectly,
will not give a pretty error message.)