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From |
"Kieran McCaul" <kamccaul@meddent.uwa.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: convergence problems with zinb |

Date |
Thu, 14 Aug 2008 08:03:37 +0800 |

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 ______________________________________________ Kieran McCaul MPH PhD WA Centre for Health & Ageing (M573) University of Western Australia Level 6, Ainslie House 48 Murray St Perth 6000 Phone: (08) 9224-2140 Phone: -61-8-9224-2140 email: kamccaul@meddent.uwa.edu.au http://myprofile.cos.com/mccaul _______________________________________________ -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Holland, Margaret Sent: Thursday, 14 August 2008 5:15 AM To: statalist@hsphsun2.harvard.edu Subject: st: convergence problems with zinb I believe a zero-inflated negative binomial would be the best fit for a model I am trying to run, but I've had trouble with convergence. I was wondering if anyone else has had this problem and, if so, if there are any tricks to helping it converge or ways to try a different algorithm. I have tried running the same model in R and SAS with less success. I have done some imputation for this project and found a single imputation set that will converge on Stata, but will not converge on R or SAS. Based on this set, zinb is a better fit than zip or nbreg. I have found that a hurdle model (logit / zero-truncated negative binomial) converges more easily and has only slight worse fit than the zinb in the set that will converge. However, theoretically the zinb model makes more sense. Any suggestions? Thank you, Maggie * * 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/ * * 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/

**Follow-Ups**:**st: RE: RE: convergence problems with zinb***From:*"Holland, Margaret" <Margaret_Holland@URMC.Rochester.edu>

**Re: st: RE: convergence problems with zinb***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**References**:**st: RE: nl program conversion from Stata 8 to 9 or 10 including if statements***From:*"Holland, Margaret" <Margaret_Holland@URMC.Rochester.edu>

**st: convergence problems with zinb***From:*"Holland, Margaret" <Margaret_Holland@URMC.Rochester.edu>

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