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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: nlcom question |

Date |
Wed, 3 Mar 2010 21:46:31 -0600 |

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? > Thanks in advance, > John > * > * 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/ > -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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/

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

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

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