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From |
Martin Weiss <martin.weiss1@gmx.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: AW: disagreement between xtreg and xtmixed outputs |

Date |
Mon, 29 Jun 2009 20:40:17 +0200 |

<> They seem to be in perfect agreement in this example. Does she say anything about -loneway- in the pages cited by you? ************* webuse nlswork, clear gen age2 = age*age gen ttl_exp2 = ttl_exp*ttl_exp gen tenure2 = tenure*tenure gen byte black = race==2 xtreg ln_w, mle xtmixed ln_w || idcode:, mle /* have to take the square root of the results for variance to see equivalence */ gllamm ln_w, i(idcode)/* */ nip(12) adapt ************* HTH Martin -----UrsprÃ¼ngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von JosÃ© Maria Pacheco de Souza Gesendet: Montag, 29. Juni 2009 17:57 An: statalist@hsphsun2.harvard.edu Betreff: st: disagreement between xtreg and xtmixed outputs Dear statalisters: I ran xtreg and xtmixed (and 1oneway) with the same dataset but got different results, as I show bellow: xtreg mensuraÃ§Ã£o , i(id) mle Iteration 0: log likelihood = -38.39068 Random-effects ML regression Number of obs = 60 Group variable: id Number of groups = 30 Random effects u_i ~ Gaussian Obs per group: min = 2 avg = 2.0 max = 2 Wald chi2(0) = 0.00 Log likelihood = -38.39068 Prob > chi2 = . mensuraÃ§Ã£o Coef. Std. Err. z P>z [95% Conf. Interval] _cons 2.132833 .1942075 10.98 0.000 1.752194 2.513473 /sigma_u 1.052955 .1387291 .8133212 1.363193 /sigma_e .2134579 0 .2134579 .2134579 rho .9605257 . . . Likelihood-ratio test of sigma_u=0: chibar2(01)= 101.09 Prob>=chibar2 = 0.000 xtmixed mensuraÃ§Ã£o || id:, mle Performing EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -23.147063 Iteration 1: log likelihood = -23.147063 Computing standard errors: Mixed-effects ML regression Number of obs = 60 Group variable: id Number of groups = 30 Obs per group: min = 2 avg = 2.0 max = 2 Wald chi2(0) = . Log likelihood = -23.147063 Prob > chi2 = . mensuraÃ§Ã£o Coef. Std. Err. z P>z [95% Conf. Interval] _cons .132833 .1942075 10.98 0.000 1.752194 2.513473 Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] id: Identity sd(_cons) 1.062051 .1375417 .823967 1.368929 sd(Residual) .0841923 .0108692 .0653706 .1084331 LR test vs. linear regression: chibar2(01) = 131.58 Prob >= chibar2 = 0.0000 . loneway mensuraÃ§Ã£o id One-way Analysis of Variance for mensuraÃ§Ã£o: Number of obs = 60 R-squared = 0.9969 Source SS df MS F Prob > F Between id 67.889766 29 2.3410264 330.26 0.0000 Within id .21265011 30 .00708834 Total 68.102416 59 1.1542782 Intraclass Asy. correlation S.E. [95% Conf. Interval] ------------------------------------------------ 0.99396 0.00222 0.98962 0.99831 Estimated SD of id effect 1.080263 Estimated SD within id .0841923 Est. reliability of a id mean 0.99697 (evaluated at n=2.00) . dis sqrt(2.3410264) 1.5300413 . dis sqrt(.00708834) .08419228 . dis 1.062051^2/((1.062051^2)+(.0841923^2)) .99375499 . dis 1.052955^2/(( 1.052955^2)+(.2134579^2)) .96052575 I am using version 10 updated, intercooled. The data is in the long format. I followed the models presented in pages 64 and 65 of Sophia Rabe-HeskethÂ´s Multilevel and Longitudinal Modeling Using Stata, second edition and really am puzlled with the no concordance of results. Could it be some problem with my data set (which is not that presented by Sophia)? Thank you for any advice. JosÃ© Maria Jose Maria Pacheco de Souza, Professor Titular (aposentado) Departamento de Epidemiologia/Faculdade de Saude Publica, USP Av. Dr. Arnaldo, 715 01246-904 - S. Paulo/SP - Brasil fones (11)3061-7747; (11)3768-8612;(11)3714-2403 www.fsp.usp.br/~jmpsouza * * 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/

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