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# Re: st: Compute intraclass correlation coefficient after xtmelogit

 From Eric Booth To "" 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)
>
>
> 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
>
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```