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

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

Subject |
st: RE: lnskew question |

Date |
Mon, 9 Dec 2002 09:56:14 -0000 |

Robert Saunders > > I've been tinkering with log-transforming an outcome variable for a > regression, but I thought lnskew0 might be a good trick > (and it was doing > better than ln()). However, I wonder how I could convert > the estimates > back to the natural units. For example, I've seen the > smearing technique > for converting regression estimates scaled in ln(dollars) > back to dollars, > but I can't imagine what's involved in getting back from > whatever it is > lnskew0 creates. Then I thought, somebody on STATAlist might > know. [Couldn't find anything in the list archives or manual.] Without very much context, it is difficult to advise, but there are issues here on various levels. 1. On a purely algebraic level, the back transformation corresponding to t = ln(y - k) is exp(t) + k. Note that the constant k is left behind by -lnskew0- as r(gamma). 2. Removal of bias produced by transformation is not quite so straightforward. In the case of smearing, for example, Duan's original paper makes clear that the smearing idea leads to a very simple recipe for simple log transformation but typically a messy recipe for other transformations. See Duan, N. 1983. Smearing estimate: a nonparametric retransformation method. Journal, American Statistical Association 78: 605-610. I'm aware of two Stata programs for smearing, Richard Goldstein's -predlog- (STB-29) and my own -smear- (unpublished), but both concentrate entirely on log transformation (and to that extent the name -smear- of mine is a misnomer). To do smearing as an antidote to ln(y - k), you would need to write your own code, I believe. 3. ln(y - k) will be less skew than ln(y) in almost all cases but I wish you luck in finding a systematic, scientific interpretation of k. Whenever, as here, there is concern for getting predictions in the original metric, generalised linear models offer, in my view, a far superior approach. Nick n.j.cox@durham.ac.uk * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: lnskew question***From:*Willard Manning <w-manning@uchicago.edu>

**References**:**st: lnskew question***From:*Robert Saunders <robert.c.saunders@vanderbilt.edu>

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