Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Nick Cox Gesendet: Freitag, 5. März 2010 00:07 An: statalist@hsphsun2.harvard.edu Betreff: RE: st: nlcom question 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? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: nlcom question***From:*john metcalfe <johnzmetcalfe@gmail.com>

**Re: st: nlcom question***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: nlcom question***From:*john metcalfe <johnzmetcalfe@gmail.com>

**RE: st: nlcom question***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

- Prev by Date:
**st: Marginal effects with natural log of independent variable** - Next by Date:
**st: clogit with multiple cases** - Previous by thread:
**RE: st: nlcom question** - Next by thread:
**Re: st: nlcom question** - Index(es):