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st: suest after regress


From   Ricardo Ovaldia <ovaldia@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: suest after regress
Date   Sat, 1 Oct 2005 19:50:45 -0700 (PDT)

Dear _all,

I am fitting the same model to cases and controls and
would like to test if the estimated coefficients in
the two models are the same. I think I can use suest
for this but the test results appear to be to
conservative. This is what I am doing:

xi:regress  homocum i.alle  vitb1p1 drkb1p1_1
smkb1p1_1 i.m_race mage if case==0
noi est store Controls
xi:regress  homocum i.alle   vitb1p1 drkb1p1_1
smkb1p1_1 i.m_race mage if case==1
noi est store Cases
matrix A=e(b)
local x: colnames A
suest Controls Cases
local y=substr("`x'",1,length("`x'")-5)
foreach var in `x'  {
 noi test [Controls_mean]`var'=[Cases_mean]`var'
}

Results are found after my signature. I particularly
question the results obtained for the -alle- variable.
Is this correct or am I using suest incorrectly?

Regards,
Ricardo.


i.alle            _Ialle_0-1          (naturally
coded; _Ialle_0 omitted)
i.m_race          _Im_race_0-3        (naturally
coded; _Im_race_0 omitted)

      Source |       SS       df       MS             
Number of obs =     153
-------------+------------------------------          
F(  8,   144) =    0.96
       Model |  23.0809151     8  2.88511438          
Prob > F      =  0.4699
    Residual |  432.835163   144  3.00579974          
R-squared     =  0.0506
-------------+------------------------------          
Adj R-squared = -0.0021
       Total |  455.916078   152  2.99944788          
Root MSE      =  1.7337

------------------------------------------------------------------------------
     homocum |      Coef.   Std. Err.      t    P>|t| 
   [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _Ialle_1 |   .1580478   .4602291     0.34   0.732 
  -.7516295    1.067725
     vitb1p1 |  -.0142011   .3059768    -0.05   0.963 
  -.6189871    .5905849
   drkb1p1_1 |    -.61993   .3360724    -1.84   0.067 
  -1.284202    .0443424
   smkb1p1_1 |   .3558431   .3637287     0.98   0.330 
   -.363094     1.07478
  _Im_race_1 |   .1907797    .390875     0.49   0.626 
  -.5818141    .9633736
  _Im_race_2 |  -.5546023   .6097384    -0.91   0.365 
  -1.759796    .6505914
  _Im_race_3 |   .9383422   1.780466     0.53   0.599 
  -2.580882    4.457566
        mage |   .0561531    .029505     1.90   0.059 
  -.0021656    .1144719
       _cons |   6.118908   .8357678     7.32   0.000 
   4.466951    7.770866
------------------------------------------------------------------------------
i.alle            _Ialle_0-1          (naturally
coded; _Ialle_0 omitted)
i.m_race          _Im_race_0-3        (naturally
coded; _Im_race_0 omitted)

      Source |       SS       df       MS             
Number of obs =     337
-------------+------------------------------          
F(  8,   328) =    1.62
       Model |  72.7850755     8  9.09813444          
Prob > F      =  0.1171
    Residual |  1838.43973   328  5.60499916          
R-squared     =  0.0381
-------------+------------------------------          
Adj R-squared =  0.0146
       Total |   1911.2248   336  5.68816905          
Root MSE      =  2.3675

------------------------------------------------------------------------------
     homocum |      Coef.   Std. Err.      t    P>|t| 
   [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _Ialle_1 |   .9186363   .3813954     2.41   0.017 
   .1683466    1.668926
     vitb1p1 |  -.2829661    .265222    -1.07   0.287 
  -.8047168    .2387847
   drkb1p1_1 |   .1332106   .2952119     0.45   0.652 
   -.447537    .7139583
   smkb1p1_1 |   .4791066   .3093139     1.55   0.122 
  -.1293827    1.087596
  _Im_race_1 |   .0411555    .384826     0.11   0.915 
   -.715883    .7981939
  _Im_race_2 |  -.1755515   .4548408    -0.39   0.700 
  -1.070325    .7192217
  _Im_race_3 |   .9152593    2.38543     0.38   0.701 
  -3.777413    5.607932
        mage |   .0470688   .0236712     1.99   0.048 
   .0005024    .0936353
       _cons |    7.77337   .7035925    11.05   0.000 
   6.389247    9.157494
------------------------------------------------------------------------------

Simultaneous results for Controls, Cases

                                                 
Number of obs   =        490

------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z| 
   [95% Conf. Interval]
-------------+----------------------------------------------------------------
Controls_m~n |
    _Ialle_1 |   .1580478   .3431422     0.46   0.645 
  -.5144986    .8305942
     vitb1p1 |  -.0142011   .3015033    -0.05   0.962 
  -.6051367    .5767345
   drkb1p1_1 |    -.61993   .3593731    -1.73   0.085 
  -1.324288    .0844284
   smkb1p1_1 |   .3558431   .4023459     0.88   0.376 
  -.4327404    1.144427
  _Im_race_1 |   .1907797    .374775     0.51   0.611 
  -.5437659    .9253253
  _Im_race_2 |  -.5546023   .4616476    -1.20   0.230 
  -1.459415    .3502104
  _Im_race_3 |   .9383422   .4126832     2.27   0.023 
   .1294979    1.747186
        mage |   .0561531    .031217     1.80   0.072 
  -.0050311    .1173374
       _cons |   6.118908   .7973963     7.67   0.000 
    4.55604    7.681776
-------------+----------------------------------------------------------------
Controls_l~r |
       _cons |   1.100544   .1145661     9.61   0.000 
   .8759982    1.325089
-------------+----------------------------------------------------------------
Cases_mean   |
    _Ialle_1 |   .9186363   .4902185     1.87   0.061 
  -.0421743    1.879447
     vitb1p1 |  -.2829661   .2579176    -1.10   0.273 
  -.7884753    .2225432
   drkb1p1_1 |   .1332106   .3097102     0.43   0.667 
  -.4738102    .7402315
   smkb1p1_1 |   .4791066   .2928241     1.64   0.102 
  -.0948182    1.053031
  _Im_race_1 |   .0411555   .3700614     0.11   0.911 
  -.6841516    .7664625
  _Im_race_2 |  -.1755515    .444215    -0.40   0.693 
  -1.046197    .6950939
  _Im_race_3 |   .9152593   .2957542     3.09   0.002 
   .3355917    1.494927
        mage |   .0470688   .0227471     2.07   0.039 
   .0024853    .0916523
       _cons |    7.77337   .6455117    12.04   0.000 
   6.508191     9.03855
-------------+----------------------------------------------------------------
Cases_lnvar  |
       _cons |   1.723659    .079775    21.61   0.000 
   1.567303    1.880015
------------------------------------------------------------------------------

 ( 1)  [Controls_mean]_Ialle_1 - [Cases_mean]_Ialle_1
= 0

           chi2(  1) =    1.62
         Prob > chi2 =    0.2037

 ( 1)  [Controls_mean]vitb1p1 - [Cases_mean]vitb1p1 =
0

           chi2(  1) =    0.46
         Prob > chi2 =    0.4982

 ( 1)  [Controls_mean]drkb1p1_1 -
[Cases_mean]drkb1p1_1 = 0

           chi2(  1) =    2.52
         Prob > chi2 =    0.1124

 ( 1)  [Controls_mean]smkb1p1_1 -
[Cases_mean]smkb1p1_1 = 0

           chi2(  1) =    0.06
         Prob > chi2 =    0.8044

 ( 1)  [Controls_mean]_Im_race_1 -
[Cases_mean]_Im_race_1 = 0

           chi2(  1) =    0.08
         Prob > chi2 =    0.7763

 ( 1)  [Controls_mean]_Im_race_2 -
[Cases_mean]_Im_race_2 = 0

           chi2(  1) =    0.35
         Prob > chi2 =    0.5541

 ( 1)  [Controls_mean]_Im_race_3 -
[Cases_mean]_Im_race_3 = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9637

 ( 1)  [Controls_mean]mage - [Cases_mean]mage = 0

           chi2(  1) =    0.06
         Prob > chi2 =    0.8141

 ( 1)  [Controls_mean]_cons - [Cases_mean]_cons = 0

           chi2(  1) =    2.60
         Prob > chi2 =    0.1068


Ricardo Ovaldia, MS
Statistician 
Oklahoma City, OK


	
		
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