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| From | Nicola Man <n.man@unsw.edu.au> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | st: Random slope model in xtmixed |
| Date | Wed, 4 Apr 2012 04:09:29 +0000 |
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?
Regards,
Nicola
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