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From |
liliana <[email protected]> |

To |
[email protected] |

Subject |
st: Variance components model: why is the level two variance so high |

Date |
Fri, 15 Nov 2013 10:48:06 -0800 (PST) |

Hello, I am estimating a variance components model with xtmelogit because my independent variable is a dummy. However, I get no odds ratio for the constant and a really high (meaningless) level 2 variance. Do you know why? With the estimates table command I get the odds ratio for the intercept, which is tremendously high: 4456.12. These are the command and the table stata gives me back. I have added the laplace option because, otherwise, Stata would have been really slow. . xtmelogit y || cluster:, laplace variance or Refining starting values: Iteration 0: log likelihood = -1069.1034 Iteration 1: log likelihood = -1046.5944 Iteration 2: log likelihood = -1045.6258 Performing gradient-based optimization: Iteration 0: log likelihood = -1045.6258 Iteration 1: log likelihood = -1016.1863 Iteration 2: log likelihood = -993.04989 (not concave) Iteration 3: log likelihood = -989.44747 Iteration 4: log likelihood = -980.82198 Iteration 5: log likelihood = -980.77838 Iteration 6: log likelihood = -980.77835 Mixed-effects logistic regression Number of obs = 12451 Group variable: v001 Number of groups = 3252 Obs per group: min = 2 avg = 3.8 max = 17 Integration points = 1 Wald chi2(0) = . Log likelihood = -980.77835 Prob > chi2 = . ------------------------------------------------------------------------------ b5_60 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ v001: Identity | var(_cons) | 31.74218 5.709468 22.31162 45.1588 ------------------------------------------------------------------------------ LR test vs. logistic regression: chibar2(01) = 169.51 Prob>=chibar2 = 0.0000 Note: log-likelihood calculations are based on the Laplacian approximation. Any help would be really appreciated. Thanks, Liliana -- View this message in context: http://statalist.1588530.n2.nabble.com/Variance-components-model-why-is-the-level-two-variance-so-high-tp7580453.html Sent from the Statalist mailing list archive at Nabble.com. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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