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From |
Partha Deb <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: RE: convergence problems with zinb |

Date |
Thu, 14 Aug 2008 21:53:07 -0400 |

Hi Margaret,

Given this output, you may want to consider another source of your issues. The output suggests that collinearities or near collinearities between your regressors, or "perfect" predictors in the inflate part might be the source of your convergence problems. Do you have indicators for rare conditions? Could there be other collinearities between conditions? Someone already suggested starting with a pared down model - I would definitely try that. In addition, it might be well worth seeing what happens if you remove chronic and/or asthbronc from your specifications.

Finally, I've observed convergence problems using nb-based models if there isn't evidence of overdispersion. What is the value of alpha?

Hope these help.

Partha

Holland, Margaret wrote:

Thanks for the helpful suggestions.

I've tried a few of the different algorithms, with and without the

"difficult" option and I'm getting closer (I think). I don't get any

error messages, it looks like it converges, and I get coefficient

estimates, but some of the standard errors are missing (or huge):

-------------+------------------------------------------------------

inflate |

chronic | -11.51164 . . . . .

asthmbronc | -26.67018 193706.5 -0.00 1.000 -379684.4 379631.1

I'm interpreting this as there was a problem in the estimation, but for

some reason it didn't trigger an error message. Is this an appropriate?

I'll continue working to refine the model based on that assumption, but

was curious if there was anything else I should know.

I have not yet taken the time to explore potential problems with my

imputed data, but I suspect the problem is more in the model itself

because I have seen the same problem in my raw (not imputed) data.

However, I will take a look as suggested, just in case there is

something unusual. I should probably have done that by now anyway...

Thanks again for the help on this,

Maggie

-----Original Message-----

Sent: Wednesday, August 13, 2008 8:04 PM

Subject: st: RE: convergence problems with zinb

Hi Margaret,

You might like to look at the various maximize options that you can

tinker with.

Type - help maximize

There are four different algorithms used by ml and Stata follows a rule

for stepping though these when fitting a model. There is an option

called - difficult - which tells the ml program to use a different

stepping rule. You could try this.

Any algorithm used to maximize the log-likelihood has to start with some

initial coefficient values and sometimes these can be near a flat or

concave region of the likelihood function. Consequently, the fitting

algorithm will just wander around this region, unable to get out.

Changing the initial values might therefore improve the performance of

the algorithm.

The option - trace - will give you the current coefficient estimates at

each iteration. This might give you a clue as to what's going on.

I don't know how many variables you have in your model, but you could

try fitting a separate model for each variable thus obtaining a

coefficient estimate for each variable and then use these as your

initial estimates in the full model.

Kieran

*

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-- Partha Deb Department of Economics Hunter College ph: (212) 772-5435 fax: (212) 772-5398 http://urban.hunter.cuny.edu/~deb/ Emancipate yourselves from mental slavery None but ourselves can free our minds. - Bob Marley * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: nl program conversion from Stata 8 to 9 or 10 including if statements***From:*"Holland, Margaret" <[email protected]>

**st: convergence problems with zinb***From:*"Holland, Margaret" <[email protected]>

**st: RE: convergence problems with zinb***From:*"Kieran McCaul" <[email protected]>

**st: RE: RE: convergence problems with zinb***From:*"Holland, Margaret" <[email protected]>

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