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From | "Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | st: RE: xtmixed variance functions |
Date | Tue, 8 Mar 2011 08:31:02 -0600 |
Leslie - I don't know if this is what you're asking, but you can model the lowest-level variance in -xtmixed- by introducing the observation number as an artificial "level" e.g. Suppose this is my original analysis: . xtmixed y5 post ||isub: ,nolog Mixed-effects REML regression Number of obs = 48 Group variable: isub Number of groups = 24 Obs per group: min = 2 avg = 2.0 max = 2 Wald chi2(1) = 26.09 Log restricted-likelihood = -206.45646 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ y5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- post | -20.22917 3.960537 -5.11 0.000 -27.99168 -12.46666 _cons | 102.9958 4.68471 21.99 0.000 93.81397 112.1777 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ isub: Identity | sd(_cons) | 18.39799 3.547973 12.60723 26.84856 -----------------------------+------------------------------------------------ sd(Residual) | 13.7197 2.022859 10.27642 18.31671 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 12.25 Prob >= chibar2 = 0.0002 But I want to model the residual variance as a function of a variable x - so now I introduce a new "level" that is just the observation number: . gen ord = _n // (my artificial new level) . xtmixed y5 post ||isub: ||ord: x,noc nolog Mixed-effects REML regression Number of obs = 48 ----------------------------------------------------------- | No. of Observations per Group Group Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ isub | 24 2 2.0 2 ord | 48 1 1.0 1 ----------------------------------------------------------- Wald chi2(1) = 29.89 Log restricted-likelihood = -205.91786 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ y5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- post | -21.0016 3.841315 -5.47 0.000 -28.53044 -13.47276 _cons | 102.7677 4.689839 21.91 0.000 93.57579 111.9596 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ isub: Identity | sd(_cons) | 18.14146 3.553234 12.35815 26.6312 -----------------------------+------------------------------------------------ ord: Identity | sd(x) | 1.092523 .1624449 .8163333 1.462157 -----------------------------+------------------------------------------------ sd(Residual) | .0291567 .0633542 .0004123 2.062069 ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(2) = 13.33 Prob > chi2 = 0.0013 Note: LR test is conservative and provided only for reference. Hope this helps Al Feiveson -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Leslie Roche Sent: Monday, March 07, 2011 2:12 PM To: statalist@hsphsun2.harvard.edu Subject: st: xtmixed variance functions Hi All, I have been trying to figure out how to specify a variance function in Stata for within-group heteroscedasticity. I have run into this problem a few times. Basically, my residuals by predicted plot show a classic increase in variance. Even though the various residual plots looked fine, I have tried residuals(independent, by(id)), and residuals(independent, by(x category)), but none of these worked. The other residuals options available require a time variable, which I do not have. In S-plus (and R), the function I generally use to model this type of heteroscedasticity is "weights=varPower())". Here, the default covariate is ~fitted. Is there a similar function in Stata that is available outside the base commands? I would prefer not to have to transform the response variable. Any suggestions much appreciated. Thanks, Leslie * * 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/