I run a linear mixed model (shown below). I want to get a p-value for each of the margin comparisons. For example, how do I get a p-value for 1#Not Black vs. 1#Black in the margins table. Your help will be appreciated.
Code:
mixed sbp ib6.group c.NaK##i.black_or_not i.stratum age i.sex dbp || id2:
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: Log likelihood = -20033.822
Iteration 1: Log likelihood = -20033.822
Computing standard errors ...
Mixed-effects ML regression Number of obs = 5,542
Group variable: id2 Number of groups = 884
Obs per group:
min = 1
avg = 6.3
max = 7
Wald chi2(12) = 4419.31
Log likelihood = -20033.822 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------
sbp | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------------+----------------------------------------------------------------
group |
Acebutolol | -3.520989 .9389604 -3.75 0.000 -5.361318 -1.680661
Amlodipine | -4.247379 .9538252 -4.45 0.000 -6.116842 -2.377916
Chlorthalidone | -4.875394 .9388959 -5.19 0.000 -6.715596 -3.035192
Doxazosin | -2.144564 .9343436 -2.30 0.022 -3.975844 -.3132841
Enalapril | -2.51117 .9396513 -2.67 0.008 -4.352852 -.6694871
|
NaK | .5992256 .0808056 7.42 0.000 .4408496 .7576017
|
black_or_not |
Black | 2.374209 .8903708 2.67 0.008 .6291142 4.119304
|
black_or_not#c.NaK |
Black | -.3278207 .1302357 -2.52 0.012 -.5830779 -.0725635
|
2.stratum | .3558746 .5942132 0.60 0.549 -.8087618 1.520511
age | .5579565 .0451791 12.35 0.000 .4694071 .6465058
|
sex |
Female | 2.839032 .6170714 4.60 0.000 1.629594 4.04847
dbp | 1.118812 .0177193 63.14 0.000 1.084082 1.153541
_cons | 5.388162 3.020926 1.78 0.074 -.5327437 11.30907
------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
id2: Identity |
var(_cons) | 63.13963 3.48728 56.66163 70.35823
-----------------------------+------------------------------------------------
var(Residual) | 58.50251 1.212088 56.17445 60.92706
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 2268.80 Prob >= chibar2 = 0.0000
. testparm c.NaK#i.black_or_not
( 1) [sbp]2.black_or_not#c.NaK = 0
chi2( 1) = 6.34
Prob > chi2 = 0.0118
. margins black_or_not, at(NaK= (1.62 3.12 4.62 )) vsquish
Predictive margins Number of obs = 5,542
Expression: Linear prediction, fixed portion, predict()
1._at: NaK = 1.62
2._at: NaK = 3.12
3._at: NaK = 4.62
----------------------------------------------------------------------------------
| Delta-method
| Margin std. err. z P>|z| [95% conf. interval]
-----------------+----------------------------------------------------------------
_at#black_or_not |
1#Not Black | 124.0313 .3401181 364.67 0.000 123.3647 124.6979
1#Black | 125.8744 .7170319 175.55 0.000 124.4691 127.2798
2#Not Black | 124.9301 .3228599 386.95 0.000 124.2973 125.5629
2#Black | 126.2815 .6839433 184.64 0.000 124.941 127.622
3#Not Black | 125.8289 .349542 359.98 0.000 125.1439 126.514
3#Black | 126.6886 .6844899 185.08 0.000 125.3471 128.0302
----------------------------------------------------------------------------------
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