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From | Eric Booth <ebooth@ppri.tamu.edu> |
To | "<statalist@hsphsun2.harvard.edu>" <statalist@hsphsun2.harvard.edu> |
Subject | Re: st: Compute intraclass correlation coefficient after xtmelogit |
Date | Fri, 29 Jul 2011 03:17:00 +0000 |
<> See: -findit xtmrho- - Eric On Jul 28, 2011, at 9:54 PM, Raquel Rangel de Meireles Guimarães wrote: > Hi all, > > Could you please give me and advice on how to compute the intraclass correlation coefficient after xtmelogit varying intercept model? > > It seems that sd_resid is not reported... > > Below you may find the output: > > . xtmelogit excluido_leitura masculino branco pardo atrasado nse_transf c_nse_escola c_atraso_escola capitalcultural_ > > transf /// > > envolvimento_transf motivacao_transf || escola: , or laplace > > Refining starting values: > > Iteration 0: log likelihood = -1123816,5 > Iteration 1: log likelihood = -1119658,1 > Iteration 2: log likelihood = -1119658,1 (backed up) > > Performing gradient-based optimization: > > Iteration 0: log likelihood = -1119658,1 > Iteration 1: log likelihood = -1119378 > Iteration 2: log likelihood = -1119371,6 > Iteration 3: log likelihood = -1119371,6 > > Mixed-effects logistic regression Number of obs = 2102433 > Group variable: escola Number of groups = 37300 > > Obs per group: min = 1 > avg = 56,4 > max = 518 > > Integration points = 1 Wald chi2(10) = 98039,45 > Log likelihood = -1119371,6 Prob > chi2 = 0,0000 > > ------------------------------------------------------------------------------ > excluido_l~a | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > masculino | 1,486187 ,0050551 116,49 0,000 1,476312 1,496128 > branco | ,7596705 ,0040705 -51,30 0,000 ,7517342 ,7676907 > pardo | ,6731994 ,0034304 -77,66 0,000 ,6665094 ,6799565 > atrasado | 2,03682 ,0077668 186,56 0,000 2,021654 2,0521 > nse_transf | 1,053706 ,0015861 34,75 0,000 1,050602 1,05682 > c_nse_esco~f | ,5189814 ,003619 -94,06 0,000 ,5119365 ,5261232 > c_atraso_e~a | ,9864226 ,0229888 -0,59 0,557 ,9423789 1,032525 > capitalcul~f | ,9296524 ,0014114 -48,05 0,000 ,9268902 ,9324228 > envolvimen~f | ,8468481 ,0013563 -103,80 0,000 ,844194 ,8495105 > motivacao_~f | 1,008081 ,0013549 5,99 0,000 1,005428 1,01074 > ------------------------------------------------------------------------------ > > ------------------------------------------------------------------------------ > Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] > -----------------------------+------------------------------------------------ > escola: Identity | > sd(_cons) | ,6075827 ,0033232 ,6011041 ,6141311 > ------------------------------------------------------------------------------ > LR test vs. logistic regression: chibar2(01) = 70351,01 Prob>=chibar2 = 0,0000 > > Note: log-likelihood calculations are based on the Laplacian approximation. > > Thank you very much. > > Best, > > Raquel > > -- > Raquel Rangel de Meireles Guimarães > MA Student International& Comparative Education > School of Education, Stanford University > http://stanford.academia.edu/RaquelGuimaraes > > * > * 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/ * * 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/