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# st: nlcom question

 From john metcalfe 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?