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Re: st: RE: xtmixed variance functions
From
Leslie Roche <[email protected]>
To
[email protected]
Subject
Re: st: RE: xtmixed variance functions
Date
Tue, 8 Mar 2011 12:22:19 -0800
That is an interesting approach--I'll try that.
Thanks!
Leslie
On Tue, Mar 8, 2011 at 6:31 AM, Feiveson, Alan H. (JSC-SK311)
<[email protected]> wrote:
> 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: [email protected] [mailto:[email protected]] On Behalf Of Leslie Roche
> Sent: Monday, March 07, 2011 2:12 PM
> To: [email protected]
> 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
>
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