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From |
Tim Wade <[email protected]> |

To |
[email protected] |

Subject |
st: completely determined panels in xtlogit |

Date |
Thu, 24 Feb 2005 12:18:33 -0500 |

Hello statalisters: I have a dataset with a binary outcome variable (hcgi) and a "group" cluster I would like to model with random effects. The main predictor, x1 is continuous. There are four clusters and they range in size from about 2,000 to over 10,000 observations. When I fit an xtlogit model, I get the response that "2 completely determined panels" or occasionally "4 completely determined panels" when I fit slightly differnt models. I read the FAQ about this which says it means the same as logistic regression when observations are completely determined. While I understand the message in a regular logistic model, I don't understand how a panel could be completely determined, or what I can do to avoid this. Any clarifications would be greatly appreciated! . xi: xtlogit hcgi x1 gicontact_any i.agecat if anycontact==1, i(group) i.agecat1 _Iagecat1_1-5 (naturally coded; _Iagecat1_1 omitted) Fitting comparison model: Iteration 0: log likelihood = -3937.4203 Iteration 1: log likelihood = -3863.6323 Iteration 2: log likelihood = -3846.2755 Iteration 3: log likelihood = -3846.1091 Iteration 4: log likelihood = -3846.1091 Fitting full model: tau = 0.0 log likelihood = -2522.9427 tau = 0.1 log likelihood = -2516.1209 tau = 0.2 log likelihood = -2517.9593 Iteration 0: log likelihood = -2516.1209 Iteration 1: log likelihood = -2512.8231 Iteration 2: log likelihood = -2512.8038 Iteration 3: log likelihood = -2512.8033 Iteration 4: log likelihood = -2512.8033 Random-effects logistic regression Number of obs = 13535 Group variable (i): beachnum Number of groups = 4 Random effects u_i ~ Gaussian Obs per group: min = 1294 avg = 3383.8 max = 7239 Wald chi2(6) = 80.85 Log likelihood = -2512.8033 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ hcgi | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .1349034 .0622243 2.17 0.030 .012946 .2568608 gicontact_~y | 1.342348 .1607566 8.35 0.000 1.027271 1.657426 _Iagecat1_2 | .0855625 .224333 0.38 0.703 -.3541221 .5252471 _Iagecat1_3 | -.2844456 .2464818 -1.15 0.248 -.767541 .1986498 _Iagecat1_4 | .0568205 .2148344 0.26 0.791 -.3642472 .4778882 _Iagecat1_5 | -.2423311 .2175506 -1.11 0.265 -.6687224 .1840601 _cons | -2.683717 .2941536 -9.12 0.000 -3.260248 -2.107187 -------------+---------------------------------------------------------------- /lnsig2u | -2.458037 1.163423 -4.738303 -.1777706 -------------+---------------------------------------------------------------- sigma_u | .2925796 .1701969 .0935601 .9149505 rho | .0253603 .0287565 .0026537 .2028432 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 2666.61 Prob >= chibar2 = 0.000 Note: 2 completely determined panels Tim Wade * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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