Hello,
I got several code 6 when running simulations. Some of those cases have a near singular estimated hessian, though the calculated hessian is fine. Is this the source of non-convergence? Would it work to re- run the data with the parameter estimates as initial values? Is it possible to have the optimizer use the calculated hessian all the time?
Thanks
- Hao
Yes, code 6 is more likely to occur if the estimated Hessian is inaccurate, especially if it is nearly singular.
Indeed it is a good idea to rerun such models from their solutions, which is extremely easy to do in OpenMx.
fittedModel <- mxRun(originalModel)
refittedModel <- mxRun(fittedModel)
This procedure may be repeated if refittedModel fits better than originalModel. If it doesn't then a third run is unlikely to help.
Also, it is a good idea to try other starting values in the originalModel
sounds like good information (in the "what to do now" section ) to add to the code 6 error message