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From |
"Steve Rothenberg" <drlead@prodigy.net.mx> |

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

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

Date |
Mon, 6 Jun 2011 06:31:48 -0500 |

Solved! Once again Nick (and through the magic of Statalist archives, Martin Weiss) provide the key information. My problem was using -predict- instead of -predictnl-, since the "exp" operator is non-linear. The syntax, after Martin's earlier post (http://www.stata.com/statalist/archive/2009-04/msg00006.html) should be, after estimation: . predictnl expphat= exp(xb()), ci(lbexp ubexp) Thanks again to Nick and Martin. Steve Rothenberg Date: Mon, 6 Jun 2011 07:14:00 +0100 From: Nick Cox <njcoxstata@gmail.com> Subject: Re: st: RE: retransformation of ln(Y) coefficient and CI in regression prediction +/- favoured multiplier * standard error. Nick -----Mensaje original----- De: Steve Rothenberg [mailto:drlead@prodigy.net.mx] Enviado el: Sunday, June 05, 2011 11:55 AM Para: 'statalist@hsphsun2.harvard.edu' Asunto: st: RE: retransformation of ln(Y) coefficient and CI in regression 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. Steve Rothenberg National Institute of Public Health Cuernavaca, Morelos, Mexico Stata/MP 11.2 for Windows (32-bit) Born 30 Mar 2011 * * 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/

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