Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Klaus Pforr <kpforr@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: SAS vs STATA : why is xtlogit SO slow ? |

Date |
Fri, 03 Feb 2012 18:14:21 +0100 |

<>

Am 03.02.2012 17:34, schrieb Clyde B Schechter:

I don't really know much about how xtlogit (or any of the other xt estimators) work "under the hood" [that's "under the bonnet" to Nick Cox] but I have used these estimators a fair amount and have some pragmatic tips for dealing with non-convergence of random effects models that have served me well.

1. Check all of your categorical predictors. If any of them have any level that is only instantiated in a small number of cases in the estimation sample, the coefficient for that level can be very difficult to estimate. Try combining some levels in that variable (or, if it is a dichotomous variable drop it from the model.) 2. Similarly check your continuous variables to be sure the have some reasonable amount of variability in the estimation sample. 3. Check the scales of your continuous variables to see that they are all in the same "ballpark." If two variables differ by several orders of magnitude, Stata will often thrash around trying to fit coefficients and ultimately fail. 4. Try providing Stata with starting values of your own using the from() option. Other responders have already suggested this. I have a couple of specific suggestions for selecting starting values: a. Try the non-xt version of the same model, in this case logit. See if those values will get Stata over the hump. b. Try the population averaged version of the same model. The population averaged estimator is calcualted using a different approach that seems to be more robust to quirks in the data, and those estimates often work well as starting values for the random effects model. [Which surprises me, because the population averaged parameters are actually different conceptually and often distant numerically from the corresponding parameters of a random effects model. But my experience is that they almost always work as a starting point nonetheless.]

Hope this helps. Clyde Schechter Department of Family& Social Medicine Albert Einstein College of Medicine Bronx, New York, USA * * 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/

best Klaus -- __________________________________ Klaus Pforr MZES AB - A Universität Mannheim D - 68131 Mannheim Tel: +49-621-181 2797 fax: +49-621-181 2803 URL: http://www.mzes.uni-mannheim.de Besucheranschrift: A5, Raum A309 __________________________________ * * 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**:**Re: st: SAS vs STATA : why is xtlogit SO slow ?***From:*k7br@gmx.fr

**References**:**Re: st: SAS vs STATA : why is xtlogit SO slow ?***From:*Clyde B Schechter <clyde.schechter@einstein.yu.edu>

- Prev by Date:
**Re: st: re: nearstat version** - Next by Date:
**RE: st: repeated measures ANOVA to MANOVA - revisit [on behalf of Rob Ploutz-Snyder]** - Previous by thread:
**Re: st: SAS vs STATA : why is xtlogit SO slow ?** - Next by thread:
**Re: st: SAS vs STATA : why is xtlogit SO slow ?** - Index(es):