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| From | "Nella Vidal" <nvidal@sbs.gob.pe> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | st: stata ado with odbc connection |
| Date | Tue, 16 Nov 2010 18:34:53 -0500 |
Hi everyone: This is my first time programming in STATA. I have been trying to program an ado to connect an oracle database (odbc load, exec command) but I cant make of it. I would appreciate If someone can send me a simple example of an ado to load and execute an sql command from an oracle database (DATAMART). Greetings to all! -----Mensaje original----- De: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Trey Causey Enviado el: Martes, 16 de Noviembre de 2010 05:27 p.m. Para: statalist@hsphsun2.harvard.edu Asunto: st: gllamm, xtmixed, and level-2 standard errors Greetings all. I am estimating a two-level, random-effects linear model. I know that gllamm is not the most computationally efficient option for this, but I am running into some very weird problems. I have ~21,000 individuals nested in 16 countries. I have 9 individual-level predictors (listed as ind1-9) and 2 country-level predictors (listed as c1 and c2). When I estimate the model using gllamm, here are my results: . gllamm DV ind1 ind2 ind3 ind4 ind5 ind6 ind7 ind8 ind9 c1 c2,i(id) adapt nip(16) Running adaptive quadrature Iteration 0: log likelihood = -22865.024 Iteration 1: log likelihood = -22841.735 Iteration 2: log likelihood = -22807.82 Iteration 3: log likelihood = -22797.118 Iteration 4: log likelihood = -22794.274 Iteration 5: log likelihood = -22792.672 Iteration 6: log likelihood = -22791.582 Iteration 7: log likelihood = -22791.557 Iteration 8: log likelihood = -22791.428 Iteration 9: log likelihood = -22791.426 Adaptive quadrature has converged, running Newton-Raphson Iteration 0: log likelihood = -22791.426 (not concave) Iteration 1: log likelihood = -22791.426 (not concave) Iteration 2: log likelihood = -22789.86 Iteration 3: log likelihood = -22789.371 Iteration 4: log likelihood = -22788.767 Iteration 5: log likelihood = -22788.613 Iteration 6: log likelihood = -22788.604 Iteration 7: log likelihood = -22788.604 number of level 1 units = 21360 number of level 2 units = 16 Condition Number = 433.81863 gllamm model log likelihood = -22788.604 ------------------------------------------------------------------------------ DV | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind1 | -.0020515 .000392 -5.23 0.000 -.0028198 -.0012833 ind2 | -.3839988 .010841 -35.42 0.000 -.4052468 -.3627508 ind3 | -.079134 .0113476 -6.97 0.000 -.1013749 -.0568931 ind4 | .0800358 .0109386 7.32 0.000 .0585966 .101475 ind5 | .0468417 .0048978 9.56 0.000 .0372423 .0564411 ind6 | .1685022 .0149735 11.25 0.000 .1391546 .1978497 ind7 | -.2057474 .0171485 -12.00 0.000 -.2393579 -.1721368 ind8 | -.093775 .0094251 -9.95 0.000 -.1122479 -.0753021 ind9 | -.0080367 .0021554 -3.73 0.000 -.0122613 -.0038122 c1 | .762577 .0802034 9.51 0.000 .6053813 .9197727 c2 | .1763846 .0664327 2.66 0.008 .0461789 .3065903 _cons | 1.265279 .1023452 12.36 0.000 1.064686 1.465872 ------------------------------------------------------------------------------ Variance at level 1 ------------------------------------------------------------------------------ .49269203 (.00476915) Variances and covariances of random effects ------------------------------------------------------------------------------ ***level 2 (id) var(1): .09866295 (.01101541) ------------------------------------------------------------------------------ When I estimate the model using xtmixed or xtreg, the output is essentially the same until I get to the country-level predictors; the coefficients are slightly different and the standard errors are approximately *ten* times smaller: . xtmixed DV ind1 ind2 ind3 ind4 ind5 ind6 ind7 ind8 ind9 c1 c2 || id:,mle Performing EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -22785.965 Iteration 1: log likelihood = -22785.965 Computing standard errors: Mixed-effects ML regression Number of obs = 21360 Group variable: id Number of groups = 16 Obs per group: min = 730 avg = 1335.0 max = 2875 Wald chi2(11) = 2296.06 Log likelihood = -22785.965 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ DV | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind1 | -.0020472 .0003917 -5.23 0.000 -.002815 -.0012794 ind2 | -.3840113 .0108422 -35.42 0.000 -.4052615 -.362761 ind3 | -.0790874 .0113578 -6.96 0.000 -.1013483 -.0568264 ind4 | .0799408 .0109411 7.31 0.000 .0584966 .101385 ind5 | .0468955 .0048961 9.58 0.000 .0372994 .0564916 ind6 | .1686695 .0149734 11.26 0.000 .1393222 .1980167 ind7 | -.2054921 .0172501 -11.91 0.000 -.2393018 -.1716824 ind8 | -.0941011 .0093698 -10.04 0.000 -.1124655 -.0757367 ind9 | -.0079976 .0021584 -3.71 0.000 -.0122279 -.0037672 c1 | .6718781 .2659761 2.53 0.012 .1505744 1.193182 c2 | .1812668 .1083347 1.67 0.094 -.0310652 .3935988 _cons | 1.306302 .2079643 6.28 0.000 .8986998 1.713905 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | sd(_cons) | .2033876 .0363049 .1433454 .2885792 -----------------------------+------------------------------------------------ sd(Residual) | .7019342 .0033974 .695307 .7086246 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 1684.50 Prob >= chibar2 = 0.0000 This is obviously a big problem for establishing significance. I have read previous threads about this problem with xtlogit but have not seen it mentioned for linear models nor I have a seen a solution. It is not immediately clear to me why the estimates or standard errors should differ at all -- as Rabe-Hesketh and Skrondal say in their book, gllamm is not as computationally efficient for linear models but the results should be essentially the same. I have replicated this in Stata 10 and Stata 11. Thank you very much. Trey ----- Trey Causey Department of Sociology University of Washington * * 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/ Aviso Legal. Este correo electronico y cualquier archivo adjunto al mismo son confidenciales y de uso exclusivo del individuo o empresa al cual han sido dirigidos. Si usted no es el destinatario, no debe difundir, distribuir o copiar este correo electronico. En caso de que haya recibido este correo electronico por error, sirvase notificar inmediatamente al remitente y elimine el mensaje de su sistema. Por favor, tenga en cuenta que cualquier opinion emitida en este correo electronico es propia del autor o remitente y no representa necesariamente la opinion de la SBS. 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