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From |
"Anderson, Bradley J" <[email protected]> |

To |
<[email protected]> |

Subject |
st: gllamm vs xtlogit results |

Date |
Mon, 4 Feb 2008 13:28:54 -0500 |

I'll start by pleading ignorant but I estimated what I think is the same random intercepts logistic regression model using gllamm and xtlogit (Stata version 9.0). There are 245 groups observed a total of 21,449 times. The number of observations per group ranged from 33 to 90. Here are the model commands: . gllamm sexday adrk age white cocfreq alsf06 sexwork if modaci==0, i(sid) link(logit) family(binomial) eform . xtlogit sexday adrk age white cocfreq alsf06 sexwork if modaci==0, i(sid) or Here are the results using gllamm: Iteration 0: log likelihood = -10681.222 Iteration 1: log likelihood = -10074.592 (not concave) Iteration 2: log likelihood = -10023.062 (not concave) Iteration 3: log likelihood = -10014.547 (not concave) Iteration 4: log likelihood = -10011.598 (not concave) Iteration 5: log likelihood = -10005.217 (not concave) Iteration 6: log likelihood = -9999.7371 (not concave) Iteration 7: log likelihood = -9987.7724 Iteration 8: log likelihood = -9983.0233 Iteration 9: log likelihood = -9981.5635 (not concave) Iteration 10: log likelihood = -9980.723 Iteration 11: log likelihood = -9980.2568 Iteration 12: log likelihood = -9979.7958 Iteration 13: log likelihood = -9979.7931 Iteration 14: log likelihood = -9979.7931 number of level 1 units = 21449 number of level 2 units = 245 Condition Number = 296.90711 gllamm model log likelihood = -9979.7931 ------------------------------------------------------------------------------ sexday | exp(b) Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- adrk | 2.990258 .1311356 24.98 0.000 2.743973 3.258648 age | .9625479 .0032265 -11.39 0.000 .9562447 .9688926 white | 2.32077 .1349106 14.48 0.000 2.070858 2.600842 cocfreq | .9992229 .0007336 -1.06 0.290 .9977861 1.000662 alsf06 | 1.633245 .1010145 7.93 0.000 1.44679 1.84373 sexwork | 1.726328 .1194638 7.89 0.000 1.507367 1.977094 ------------------------------------------------------------------------------ Variances and covariances of random effects ------------------------------------------------------------------------------ ***level 2 (sid) var(1): 1.8187606 (.08452159) ------------------------------------------------------------------------------ And here are the results using xtlogit: Fitting comparison model: Iteration 0: log likelihood = -14686.518 Iteration 1: log likelihood = -14140.695 Iteration 2: log likelihood = -14139.752 Iteration 3: log likelihood = -14139.752 Fitting full model: tau = 0.0 log likelihood = -14139.752 tau = 0.1 log likelihood = -11318.224 tau = 0.2 log likelihood = -10661.993 tau = 0.3 log likelihood = -10353.26 tau = 0.4 log likelihood = -10174.441 tau = 0.5 log likelihood = -10071.213 tau = 0.6 log likelihood = -10018.463 tau = 0.7 log likelihood = -10007.504 tau = 0.8 log likelihood = -10129.464 Iteration 0: log likelihood = -9928.7925 Iteration 1: log likelihood = -9861.779 Iteration 2: log likelihood = -9861.1508 Iteration 3: log likelihood = -9861.1499 Random-effects logistic regression Number of obs = 21449 Group variable (i): sid Number of groups = 245 Random effects u_i ~ Gaussian Obs per group: min = 33 avg = 87.5 max = 90 Wald chi2(6) = 568.07 Log likelihood = -9861.1499 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ sexday | OR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- adrk | 3.010925 .1407738 23.58 0.000 2.747278 3.299874 age | .9678424 .0152986 -2.07 0.039 .9383174 .9982965 white | 1.111466 .3423468 0.34 0.732 .6077351 2.032721 cocfreq | 1.002477 .0040672 0.61 0.542 .9945366 1.01048 alsf06 | 1.369673 .4218245 1.02 0.307 .7489773 2.504756 sexwork | 1.648899 .5901059 1.40 0.162 .817649 3.325225 -------------+---------------------------------------------------------------- /lnsig2u | 1.509917 .1031085 1.307828 1.712006 -------------+---------------------------------------------------------------- sigma_u | 2.127523 .1096829 1.923053 2.353734 rho | .5790974 .025132 .5292118 .6274186 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 8557.20 Prob >= chibar2 = 0.000 The magnitude of the estimated effects of adrk and age are similar though the standard error estimated for the age effect is much smaller when estimated by gllamm. The estimated coefficient for white is 2.32 when using gllamm but only 1.11 when estimated using xtlogit. Other coefficients are of relatively similar magnitude but standard errors and substantive conclusions would be quite different between the two models. Are these estimating the same model? And if so, why would some estimated coefficients and standard errors be so different? And finally, how do I figure out what results to trust? As an asside, I estimated the population averaged effects using both xtgee and logistic with standard errors adjusted for clustering. Substantive conclusions were similar to xtlogit. Thanks, * * 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/

**Follow-Ups**:**Re: st: gllamm vs xtlogit results***From:*"Austin Nichols" <[email protected]>

**References**:**st: gmm with a huge panel***From:*"jenny.montaldo" <[email protected]>

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