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# st: Random slope model in xtmixed

 From Nicola Man To "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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```