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AW: st: nlcom question
From
"Martin Weiss" <[email protected]>
To
<[email protected]>
Subject
AW: st: nlcom question
Date
Fri, 5 Mar 2010 09:03:44 +0100
<>
" My guess is that -margins- was months of work for the developers
concerned."
I am putting all my money on "years"...
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Nick Cox
Gesendet: Freitag, 5. März 2010 00:07
An: [email protected]
Betreff: RE: st: nlcom question
My guess is that -margins- was months of work for the developers
concerned.
But what about -mfx-?
Nick
[email protected]
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 <[email protected]>
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 <[email protected]
> >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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