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From |
Jared Saletin <[email protected]> |

To |
[email protected] |

Subject |
st: Stumped...xtmixed and ANOVA F-stats not agreeing for balanced design |

Date |
Thu, 5 May 2011 00:49:53 -0700 |

Dear all, Thanks for all the help in previous messages. This thoughtful user community makes learning all the more enjoyable, so thank you. Hope some of the experts out there can help me get passed a stumping issue, comparing xtmixed with ANOVA. I'm running a Within-Subject model with 2 factors (a, b). The F-stats produced by the two methods are not agreeing, despite having balanced data. Following the suggestions made in this message: http://www.stata.com/statalist/archive/2010-03/msg01340.html I'm parameterizing my ANOVA as: anova y s a/s#a b/s#b a#b/ which yields Number of obs = 66 R-squared = 0.9746 Root MSE = .052335 Adj R-squared = 0.9175 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 2.10365629 45 .046747918 17.07 0.0000 | s | .202683096 10 .02026831 1.58 0.1840 a | .358787807 2 .179393903 13.99 0.0002 s#a | .256516955 20 .012825848 -----------+---------------------------------------------------- b | 1.1109981 1 1.1109981 76.02 0.0000 s#b | .146140374 10 .014614037 -----------+---------------------------------------------------- a#b | .028529959 2 .014264979 5.21 0.0151 | Residual | .054779806 20 .00273899 -----------+---------------------------------------------------- Total | 2.1584361 65 .033206709 and then estimating the same model with xtmixed, followed by anovalator for the fixed effects: xtmixed y a##b || s: || s: R.a || s: R.b anovalator a b, main 2way fratio yielding: Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = 50.636505 Iteration 1: log restricted-likelihood = 50.790265 Iteration 2: log restricted-likelihood = 50.792902 Iteration 3: log restricted-likelihood = 50.792903 Computing standard errors: Mixed-effects REML regression Number of obs = 66 Group variable: s Number of groups = 11 Obs per group: min = 6 avg = 6.0 max = 6 Wald chi2(5) = 120.97 Log restricted-likelihood = 50.792903 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- a | 2 | -.1066975 .0371271 -2.87 0.004 -.1794653 -.0339297 3 | .0104766 .0371271 0.28 0.778 -.0622911 .0832444 | 2.b | -.2348552 .0340381 -6.90 0.000 -.3015687 -.1681417 | a#b | 2 2 | -.083192 .0316799 -2.63 0.009 -.1452834 -.0211005 3 2 | .0092982 .0316799 0.29 0.769 -.0527932 .0713897 | _cons | .8529769 .0318999 26.74 0.000 .7904542 .9154996 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ s: Identity | sd(_cons) | 2.70e-08 .0000919 0 . -----------------------------+------------------------------------------------ s: Identity | sd(R.a) | .0694361 .0134607 .047487 .1015305 -----------------------------+------------------------------------------------ s: Identity | sd(R.b) | .0601026 .014489 .0374708 .0964037 -----------------------------+------------------------------------------------ sd(Residual) | .0525352 .0083535 .0384683 .071746 ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(3) = 15.67 Prob > chi2 = 0.0013 Note: LR test is conservative and provided only for reference. . anovalator a b, main 2way fratio anovalator main-effect for a chi2(2) = 28.928213 p-value = 5.228e-07 scaled as F-ratio = 14.464106 anovalator main-effect for b chi2(1) = 81.709563 p-value = 1.576e-19 scaled as F-ratio = 81.709563 anovalator two-way interaction for a#b chi2(2) = 10.337154 p-value = .00569266 scaled as F-ratio = 5.1685769 --- Despite the data being balanced, the F-statistics generated from xtmixed and ANOVA still don't match. I presume it has something to do with the standard error and therefore CI for the constant in the random-effects model not being estimated. I'm not sure why this is the case. I've run larger models, even unbalanced models where the random effects are estimated fine. Is there something inherently in the data that would cause that SE estimate to fail? Thanks for any suggestions you all may have. Cheers, Jared * * 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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