> I got the following error 3 times in the log window when n=25:
ERROR: NEWRAP Optimization cannot be completed.
ERROR: NEWRAP needs more than 200 iterations or 500 function calls.
How can I fix this?
This error message appears for 3 out of 1000 simulation trials. Errors like this appear in simulation studies when a random sample contains extreme values or is otherwise not representative. For those samples, the MLE step does not converge. This does not indicate a problem in your code, but rather a hazard when you are simulating small data sets (in your example, n=25). For details and a discussion, see Monitor convergence during simulation studies in SAS - The DO Loop
There are two ways to address your concern:
Unless you are specifically interested in Newton-Raphson optimization, you can switch optimization schemes. For example, the quasi-Newton optimization in NLPQN might be faster and more robust. Since quasi-Newton methods do not require Hessian computations, you can replace your call to NLPNRA with call nlpqn(rc, res, "SUMLOGLHF", initial_param,opt) grd="driv"; /*Use quasi-Newton algorithm that minimizes negative log likelihood function */ This is what I would do.
If, for some reason, Newton iteration is important, you can keep track of the return code (rc) and remove the results from samples for which the MLE does not converge. This is the suggestion in the blog post, as applied to your IML program. This requires allocating a vector for the return codes, then using the LOC function to keep only the results from MLE that converged:
rc_status = j(replicate,1);
do k=1 to replicate;
...
call nlpnra(rc, res, "SUMLOGLHF", initial_param, opt) grd="driv" hes="HessLogLik";
Est[k,]=res;
rc_status[k] = rc;
end;
/* find trials that converged */
good_idx = loc(rc_status > 0);
Est = Est[good_idx, ];
nSim = nrow(Est);
colMean=t(mean(Est));
colStanddev=t(std(Est));
print nSim colMean colStanddev;
... View more
