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From |
"Lukas Bösch" <L.Boesch@gmx.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: xtmixed with nonrtolerance. What happens? |

Date |
Wed, 22 Jun 2011 22:33:14 +0200 |

Dear Statalist community. I am using Stata 10.0 and doing a mixed model analysis of export data. After trying different options and always having trouble to get a propper output i finally found a way to get to my results. I however could not find any information about why it works and if it is allright. But let us first start with the problem: 1) This is the command i enter and the output stata creates: xtmixed quantity year centforestarea2 centgdp2 centlandarea2 centpopulation2 || _all: R.country || _all: R.genus Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -77051.164 Iteration 1: log restricted-likelihood = -77046.704 Iteration 2: log restricted-likelihood = -77046.565 Iteration 3: log restricted-likelihood = -77046.5 Iteration 4: log restricted-likelihood = -77046.468 (backed up) Iteration 5: log restricted-likelihood = -77046.46 (backed up) Iteration 6: log restricted-likelihood = -77046.456 (backed up) Iteration 7: log restricted-likelihood = -77046.454 (backed up) numerical derivatives are approximate nearby values are missing Iteration 8: log restricted-likelihood = -77046.453 (backed up) numerical derivatives are approximate nearby values are missing Hessian has become unstable or asymmetric Mixed-effects REML regression Number of obs = 6192 Group variable: _all Number of groups = 1 Obs per group: min = 6192 avg = 6192.0 max = 6192 Wald chi2(5) = 9.26 Log restricted-likelihood = -77051.164 Prob > chi2 = 0.0991 quantity | Coef. Std. Err. z P>|z| [95% Conf. Interval] year | -429.7599 215.8898 -1.99 0.047 -852.8961 -6.623654 centfores~a2 | -9875.264 6631.861 -1.49 0.136 -22873.47 3122.945 centgdp2 | -2024.629 4138.469 -0.49 0.625 -10135.88 6086.621 centlandar~2 | -52889.76 63817.96 -0.83 0.407 -177970.7 72191.13 centpopul~n2 | 22296.98 10234.72 2.18 0.029 2237.304 42356.66 _cons | 895402.2 433369.4 2.07 0.039 46013.74 1744791 Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] _all: Identity | sd(R.country) | 313329.2 . _all: Identity | sd(R.genus) | 6757.304 . sd(Residual) | 60169.26 . LR test vs. linear regression: chi2(2) = 7810.42 Prob > chi2 = 0.0000 Note: LR test is conservative and provided only for reference. Warning: convergence not achieved; estimates are based on iterated EM Obviously Stata has a problem and can't calculate the standard errors of the random factors. 2) With the option nonrtolerance it works however: xtmixed quantity year centforestarea2 centgdp2 centlandarea2 centpopulation2 || _all: R.country || _all: R.genus, nonrtolerance Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -77051.164 Iteration 1: log restricted-likelihood = -77046.704 Iteration 2: log restricted-likelihood = -77046.565 Iteration 3: log restricted-likelihood = -77046.5 Iteration 4: log restricted-likelihood = -77046.468 (backed up) Iteration 5: log restricted-likelihood = -77046.46 (backed up) Iteration 6: log restricted-likelihood = -77046.456 (backed up) Computing standard errors: Mixed-effects REML regression Number of obs = 6192 Group variable: _all Number of groups = 1 Obs per group: min = 6192 avg = 6192.0 max = 6192 Wald chi2(5) = 9.22 Log restricted-likelihood = -77046.456 Prob > chi2 = 0.1008 quantity | Coef. Std. Err. z P>|z| [95% Conf. Interval] year | -429.7645 216.4073 -1.99 0.047 -853.915 -5.614053 centfores~a2 | -9885.307 6647.52 -1.49 0.137 -22914.21 3143.592 centgdp2 | -2021.312 4148.464 -0.49 0.626 -10152.15 6109.527 centlandar~2 | -52859.75 63778.66 -0.83 0.407 -177863.6 72144.12 centpopul~n2 | 22276.96 10257.46 2.17 0.030 2172.715 42381.2 _cons | 895338.1 434389.3 2.06 0.039 43950.68 1746726 Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] _all: Identity | sd(R.country) | 313133.2 36075.6 249840.9 392459.4 _all: Identity | sd(R.genus) | 3440.288 1355.694 1589.157 7447.712 sd(Residual) | 60315.87 545.9681 59255.23 61395.5 LR test vs. linear regression: chi2(2) = 7819.83 Prob > chi2 = 0.0000 Note: LR test is conservative and provided only for reference. Can someone explain to me why it works with nonrtolerance and tell me if these outputs are as reliable as if they were created without nonrtolerance. I searched in the stata help and on stata.com but could not find more information about this. Kind regards Lukas -- NEU: FreePhone - kostenlos mobil telefonieren! Jetzt informieren: http://www.gmx.net/de/go/freephone * * 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/

**Follow-Ups**:**Re: st: xtmixed with nonrtolerance. What happens?***From:*Stas Kolenikov <skolenik@gmail.com>

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