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From |
Nick Cox <n.j.cox@durham.ac.uk> |

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

Subject |
RE: st: RE: retransformation of ln(Y) coefficient and CI in regression |

Date |
Mon, 6 Jun 2011 11:53:05 +0100 |

This got beheaded in its previous version. Nick n.j.cox@durham.ac.uk Nick Cox You will evidently need to exponentiate quantities of the form prediction +/- favoured multiplier * standard error. On Sun, Jun 5, 2011 at 5:55 PM, Steve Rothenberg <drlead@prodigy.net.mx> wrote: > Thank you for the glm suggestion, Nick. > > After > > . glm Y i.factor, vce(robust) family(Gaussian) link(log) > > followed by > > . predict xxx, mu > > the command does indeed return the factor predictions in the original Y > metric. > > However, the regression table with 95% CI is still in the original ln(Y) > units and I am still stuck not being able to calculate the 95% CI in the > original Y unit metric. The predict command for returning prediction SE > (stdp) also only returns the SE in the ln(Y) metric. > > I've read the manual on glm postestimation and can derive no hints on this > issue. > > I'd welcome further suggestions for deriving the 95% confidence interval in > the original Y metric after either > > . regress ln(Y) ..., vce(robust) > > or > > . glm Y ..., link(log) vce(robust) > > or any other estimation commands. > > Steve Rothenberg > **************** > > If you recast your model as > > glm Y i.factor ... , link(log) > > no post-estimation fudges are required. -predict- automatically supplies > stuff in terms of Y, not ln Y. > > Nick > n.j.cox@durham.ac.uk > > -----Mensaje original----- > De: Steve Rothenberg [mailto:drlead@prodigy.net.mx] > Enviado el: Sunday, June 05, 2011 10:27 AM > Para: 'statalist@hsphsun2.harvard.edu' > Asunto: retransformation of ln(Y) coefficient and CI in regression > > I have a simple model with a natural log dependent variable and a three > level factor predictor. I've used > > . regress lnY i.factor, vce(robust) > > to obtain estimates in the natural log metric. I want to be able to display > the results in a graph as means and 95% CI for each level of the factor with > retransformed units in the original Y metric. > > I've also calculated geometric means and 95% CI for each level of the factor > variable using > > . ameans Y if factor==x > > simply as a check, though the 95% CI is not adjusted for the vce(robust) > standard error as calculated by the -regress- model. > > Using naïve transformation (i.e. ignoring retransformation bias) with > > . display exp(coefficient) > > from the output of -regress- for each level of the predictor, with the > classic formulation: > > Level 0 = exp(constant) > Level 1 = exp(constant+coef(1)) > Level 2 = exp(constant+coef(2)) > > the series of retransformations from the -regress- command is the same as > the geometric means from the series of -ameans- commands. > > When I try to do the same with the lower and upper 95% CI (substituting the > limits of the 95% CI for the coefficients) from the -regress- command, > however, the retransformed IC is much larger than calculated from the- > ameans- command, much more so than the differences in standard errors from > regress with and without the vce(robust) option would indicate. > > I've discovered -levpredict- for unbiased retransformation of log dependent > variables in regression-type estimations by Christopher Baum in SSC but it > only outputs the bias-corrected means from the preceding -regress-. To be > sure there is some small bias in the first or second decimal place of the > mean factor levels compared to naïve retransformation. > > Am I doing something wrong by treating the 95% CI of each level of the > factor variable in the same way I treat the coefficients without correcting > for retransformation bias? Is there any way I can obtain either the > retransformed CI or the bias-corrected retransformed CI for the different > levels of the factor variable in the original metric of Y? > > I'd like to retain the robust SE from the above estimation as there is > considerable difference in variance in each level of the factor variable. > * * 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: RE: retransformation of ln(Y) coefficient and CI in regression***From:*"Steve Rothenberg" <drlead@prodigy.net.mx>

**Re: st: RE: retransformation of ln(Y) coefficient and CI in regression***From:*Nick Cox <njcoxstata@gmail.com>

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