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Re: st: Compute intraclass correlation coefficient after xtmelogit
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
>
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