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From |
"Schmidt Alexander" <alexander.schmidt@wiso.uni-koeln.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: xtmixed: standard error calculation failed - Log Likelihood valid? |

Date |
Thu, 3 May 2012 14:14:03 +0200 |

Hello users, I am trying to fit a hierarchical mixed model with xtmixed. I have only 20 groups, so my df for the second level are quite limited. However, when I include more than two random slopes I get the following output: Performing EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -30145.49 Iteration 1: log likelihood = -30145.082 Iteration 2: log likelihood = -30145.08 Computing standard errors: standard-error calculation failed Mixed-effects ML regression Number of obs = 22697 Group variable: Country Number of groups = 20 Obs per group: min = 481 avg = 1134.8 max = 1822 Wald chi2(21) = 2289.74 Log likelihood = -30145.08 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------- redincd_z | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- imbadeco_z | .0422334 .0119448 3.54 0.000 .018822 .0656448 imbadclt_z | -.028792 .0102362 -2.81 0.005 -.0488546 -.0087294 lrscale_z | -.2005485 .0062901 -31.88 0.000 -.2128768 -.1882201 male | -.1047215 .0123947 -8.45 0.000 -.1290147 -.0804282 agea_z | .0390616 .008855 4.41 0.000 .0217061 .056417 agesq | -.0195111 .0069855 -2.79 0.005 -.0332024 -.0058197 hinctnta_z | -.1177657 .0077734 -15.15 0.000 -.1330012 -.1025301 educ1 | .1012111 .0436147 2.32 0.020 .0157278 .1866945 educ2 | .1237265 .0402994 3.07 0.002 .0447411 .202712 educ3 | .0838198 .0387172 2.16 0.030 .0079355 .159704 educ5 | -.1082862 .0391648 -2.76 0.006 -.1850479 -.0315246 bhealth | .0672738 .0145339 4.63 0.000 .0387879 .0957596 married | .0223739 .0139982 1.60 0.110 -.0050621 .0498099 emplstat1 | -.0165358 .0183434 -0.90 0.367 -.0524883 .0194167 emplstat2 | -.1097799 .0342492 -3.21 0.001 -.1769071 -.0426528 emplstat3 | .0902096 .0334965 2.69 0.007 .0245577 .1558615 con_mig_z | -.040176 .0470907 -0.85 0.394 -.1324721 .0521201 pro_wel_z | .1222106 .0503462 2.43 0.015 .0235339 .2208874 social_esping | -.1646089 .180994 -0.91 0.363 -.5193507 .1901329 cons_esping | .2178327 .1544646 1.41 0.158 -.0849124 .5205778 eastern | .2879099 .1531898 1.88 0.060 -.0123366 .5881563 _cons | -.0693932 .145979 -0.48 0.635 -.3555068 .2167205 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Country: Unstructured | var(imbade~z) | .0015712 . . . var(imbadc~z) | .0007214 . . . var(_cons) | .0361669 . . . cov(imbade~z,imbadc~z) | .0009588 . . . cov(imbade~z,_cons) | -.0023431 . . . cov(imbadc~z,_cons) | .0006795 . . . -----------------------------+------------------------------------------------ var(Residual) | .8299551 . . . ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(6) = 800.77 Prob > chi2 = 0.0000 Obviously, Stata cannot determine the standard errors of the random components. So, my first question is: Can I trust the point estimates or do I have a really series problem with my model? Please note that the problem does not occur during the optimization but after the Log Likelihood has converged. Second, if I use an independent covariance structure (all covariance restricted to be zero), I get standard errors for the random effects and xtmixed reports no problems. So, my question is: Can I use a LR Test to compare the Log-Likelihood of the model with free covariances to the model with restricted covariances? The idea is to show that the model with restricted covariances performs as good as the model with free covariances. Then, I could simply select the model with restricted covariances and I would have a model that can be estimated including the standard errors. However, when I use stata's lrtest command to compare the two models, stata says "mixed models are not nested". This error occurs only if the standard errors cannot be estimated. Usually, lrtest can, of course, be used to compare models with different random effect specifications. So, what do you think? Does stata refuse to perform the test for any substantial reasons? If not, I would perform the test by hand. In other words, do you think the Log Likelihood of the model which fails to give standard errors is valid and can be used to compare it with other models? Thanks in advance, Alexander * * 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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