RE: st: nlcom question

["Nick Cox" <n.j.cox@durham.ac.uk>]

My guess is that -margins- was months of work for the developers
concerned. 

But what about -mfx-? 

Nick 
n.j.cox@durham.ac.uk 

john metcalfe

Thanks, Stas. How could I replicate Stata 11's -margins- command using
Stata
10? (I went ahead and ordered the upgrade but it won't arrive until next
week.)

On Wed, Mar 3, 2010 at 7:46 PM, Stas Kolenikov <skolenik@gmail.com>
wrote:

> If the distributions of the dependent variable are the same for two
levels
> of the categorical factor, then they will be the same no matter
whether you
> transformed them or you did not. Hence it suffices to use -test-
rather
> than
> -nlcom-. The latter will be answering a more subtle question of
whether the
> means are the same (you implicitly put zeroes for all other variables,
> which
> may or may not be appropriate); you still may have differences in
variance,
> skewness, kurtosis, etc. between groups even if you find the means to
be
> the
> same.
>
> Stata 11 has new -margins- command; have you looked at it?
>
> On Wed, Mar 3, 2010 at 9:31 PM, john metcalfe <johnzmetcalfe@gmail.com
> >wrote:
>
> > 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?

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