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From |
"Clyde Schechter" <clyde.schechter@einstein.yu.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: xtmelogit--any quick way to identify variance components near zero? |

Date |
Mon, 19 Jul 2010 08:16:35 -0700 |

I'm running a series of analyses using xtmelogit. The data set has a little over 5,000 observations, and there are three levels of random effects (including a random slope at the one level). Even using the laplace option, each of these analyses takes about 10 hours to run. Fair enough -- it is what it is. But what's particularly frustrating is that for some of the analyses, one or more of the variance components apparently is zero or very close to zero. So the analysis gets hung up as the estimate of the log-sigma tries to march down to negative infinity and goes on "forever." (I cut my losses at 25 iterations.) I tried picking a random sample of about 500 observations from the data set to test the model--that runs in a tolerable amount of time, but it didn't pick up the problem: in the subset the variance component was pretty small, but not small enough to prevent convergence in a reasonable number of iterations. (FWIW, from a theoretical perspective it is very surprising that these variance components are turning out so small--the best I can think of to explain it is that the fixed effects in the model are accounting for much more of the anticipated higher-level variation than I expected them to--when I omit some of those fixed effects the model is much better behaved.) So, does anybody know of a reasonably quick way to diagnose very small variable coefficients in xtmelogit in advance of trying to run it? Thanks in advance for any help. Clyde Schechter, MA MD Associate Professor of Family & Social Medicine Albert Einstein College of Medicine, Bronx, NY, 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/

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