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Re: st: Random slope model in xtmixed
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: st: Random slope model in xtmixed
Date
Wed, 4 Apr 2012 11:52:38 -0400
Dear Nicola,
No one else has answered yet, so let me ask a question.
In the -xtmixed- command that uses y2009 as a predictor, the
fixed-effects part of the model seems to contain a variable
(ib1.HIVprevG_UNGASS) that is not present in the -xtmixed- command
that uses y2007. The output, however, does not show a coefficient for
that variable. What results do you get when you remove it from the
fixed-effects part of the model? (If I'm missing something obvious, I
apologize.)
David Hoaglin
On Wed, Apr 4, 2012 at 12:09 AM, Nicola Man <[email protected]> wrote:
> Hi,
>
> I have a question about fitting random slopes in mixed models that I can't make sense of. The random slope variable is year which ranges from 2004 to 2009. As an illustrative example, I fitted two models which are exactly the same except:
>
> Model 1) year, y2009, is recalculated so that 2009 gets a value of 0 to 2004 which gets a value of -5 (i.e. y2009=2009-Year)
>
> Model 2) year, y2007, is recalculated so that 2009 gets a value of 2 to 2004 which gets a value of -3 (i.e. y2007=2007-Year)
>
> Below is the abbreviated output:
> . xtmixed lgt_P_ART4_nm2 y2009 ib1.HIVprevG_UNGASS || CtryN: y2009 , var nolog
>
> Mixed-effects ML regression Number of obs = 683
> Group variable: CtryN Number of groups = 128
>
> Obs per group: min = 1
> avg = 5.3
> max = 6
>
> Wald chi2(1) = 269.38
> Log likelihood = -920.36467 Prob > chi2 = 0.0000
>
> --------------------------------------------------------------------------------
> lgt_P_ART4_nm2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> ---------------+----------------------------------------------------------------
> y2009 | .4921368 .0299851 16.41 0.000 .4333672 .5509065
> _cons | .4236186 .1367187 3.10 0.002 .1556548 .6915824
> --------------------------------------------------------------------------------
>
> ------------------------------------------------------------------------------
> Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
> -----------------------------+------------------------------------------------
> CtryN: Independent |
> var(y2009) | .0856863 .0143532 .0617061 .1189858
> var(_cons) | 2.165006 .3010598 1.648517 2.843314
> -----------------------------+------------------------------------------------
> var(Residual) | .3364985 .0234767 .2934923 .3858065
> ------------------------------------------------------------------------------
> LR test vs. linear regression: chi2(2) = 670.34 Prob > chi2 = 0.0000
>
> . xtmixed lgt_P_ART4_nm2 y2007 || CtryN: y2007 , var nolog
>
> Mixed-effects ML regression Number of obs = 683
> Group variable: CtryN Number of groups = 128
>
> Obs per group: min = 1
> avg = 5.3
> max = 6
>
>
> Wald chi2(1) = 247.17
> Log likelihood = -914.25608 Prob > chi2 = 0.0000
>
> --------------------------------------------------------------------------------
> lgt_P_ART4_nm2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> ---------------+----------------------------------------------------------------
> y2007 | .4993922 .0317645 15.72 0.000 .4371349 .5616495
> _cons | -.5720239 .1271442 -4.50 0.000 -.8212219 -.3228259
> --------------------------------------------------------------------------------
>
> ------------------------------------------------------------------------------
> Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
> -----------------------------+------------------------------------------------
> CtryN: Independent |
> var(y2007) | .099806 .0169902 .0714916 .1393344
> var(_cons) | 1.960566 .2713017 1.494834 2.571403
> -----------------------------+------------------------------------------------
> var(Residual) | .3294286 .0225424 .288081 .3767106
> ------------------------------------------------------------------------------
> LR test vs. linear regression: chi2(2) = 682.56 Prob > chi2 = 0.0000
>
> I get completely different estimates including that for the LL for the model as a whole. Does anyone have an idea as to why that might be the case?
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