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st: nlcom question


From   john metcalfe <johnzmetcalfe@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: nlcom question
Date   Wed, 3 Mar 2010 19:31:24 -0800

Dear Statalist,
I have a simple question I hope someone can help me with.
I am using OLS with robust variance estimators to model a continuous,
log-transformed DV ranging from 0 to 10 in increments of 0.01 (this is
an immunologic test in common use in the U.S.). My goal is to
determine whether or not there are differences in this test
performance according to a categorical independent variable (rax, 4
levels) with an interaction term (nt, 3 levels) and other categorical
covariates, as below:

Linear regression                              Number of obs =    2734
                                                       F( 17,  1716) =  133.04
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.4007
                                                       Root MSE      =  1.6254

------------------------------------------------------------------------------
             |               Robust
       ln_ag |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     _Irax_1 |  -.3175203   .1598442    -1.99   0.047    -.6310302   -.0040104
     _Irax_2 |  -.5266611     .22524    -2.34   0.019     -.968435   -.0848873
     _Irax_3 |  -.0842108   .1791368    -0.47   0.638    -.4355602    .2671386
      _Int_1 |   3.201428   .1158472    27.63   0.000     2.974212    3.428645
      _Int_2 |   2.228758   .1441987    15.46   0.000     1.945934    2.511582
_IraxXnt~1_1 |     .05004   .2237556     0.22   0.823    -.3888224    .4889025
_IraxXnt~1_2 |   1.110956   .4651867     2.39   0.017     .1985636    2.023349
_IraxXnt~2_1 |   1.094455   .2752091     3.98   0.000     .5546741    1.634236
_IraxXnt~2_2 |   1.225901   .4853438     2.53   0.012      .273973    2.177829
_IraxXnt~3_1 |   .5545474   .2237545     2.48   0.013     .1156871    .9934077
_IraxXnt~3_2 |   1.250381   .3833228     3.26   0.001     .4985518     2.00221
    age_cntr |   .0047114   .0028156     1.67   0.094    -.0008109    .0102338
      female |  -.1865305   .0811696    -2.30   0.022    -.3457323   -.0273287
        jka1 |  -.3056762   .0994589    -3.07   0.002    -.5007496   -.1106027
        jka2 |  -.3657116   .1416161    -2.58   0.010      -.64347   -.0879533
       prevt |   .3526168   .1933732     1.82   0.068    -.0266552    .7318889
          dm |   .3199483   .1509238     2.12   0.034     .0239343    .6159623
       _cons |   -3.02986   .1154191   -26.25   0.000    -3.256237   -2.803483
------------------------------------------------------------------------------

To estimate the difference in the backtransformed DV between rax3 and
rax0, I am using:

scalar rmse = e(rmse)
nlcom exp(_b[_cons]+_b[_Irax_3] + _b[_Int_2] + _b[_IraxXnt_3_2]
+rmse^2/2)-exp(_b[_cons] + _b[_Int_2] + rmse^2/2), but I think I need
to also add in the coefficients for the other predictors, multiplied
by the average value of the covariate in both sides of the nlcom
statement. Is there an easy way to go about doing this?
Thanks in advance,
John
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