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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: st: Clarify, tfunc(exp) |

Date |
Tue, 17 Jan 2012 09:18:53 +0000 |

Thanks for the closure. -clarify- (websites given earlier) appears -- on this evidence -- to be software that is no longer supported either by its original authors or by anybody else. Users beware! Nick On Tue, Jan 17, 2012 at 9:02 AM, Renzo Carriero <renzo.carriero@gmail.com> wrote: > Dear Nick, > > thanks for your answer and apologize for my late reply (there has been > some problem with my email server, so I am replying from another > address). > This is just to point out that: > 1) I have already posted my request to a specific list devoted to > -clarify- without getting a reply (by the way, I am currently unable > to access this list) > 2) Regarding the flag reporting E[exp()], my concern is that if this > were really the case I should get geometric predicted means as a > result of exponentiating predicted log means (this is actually what > Stata -adjust- does). I have the feeling that -simqi- back transforms > values so to get arithmetic mean on the original (unlogged) scale, but > I'm unable to understand how. > > Renzo > >>A much more positive comment is that your problem is one where I >>would try using -glm- to solve the answer directly. That is, using a >>log link function is likely to beat transforming and >>back-transforming. The two are not exactly equivalent but in my >>experience -glm- usually works as well or better and in any case there >>is flexibility about what error family you use. >> >>If -glm- works the whole business of using either -clarify- or >>-adjust- would be quite unnecessary. >> >>Nick >> >>On Fri, Jan 13, 2012 at 10:11 AM, Nick Cox <njcoxstata@gmail.com> wrote: >> A reference missing here is http://gking.harvard.edu/clarify or >> http://www.stanford.edu/~tomz/software/software.shtml >> >> These references point to rather extensive documentation. >> >> I have never used -simqi- but the last fragment of output below does >> flag that -simqi- is reporting E[exp()] so why you are uncertain about >> that is unclear. As you know exp(E[]) will in general differ. >> >> I wouldn't expect authoritative support on -clarify- from this list, >> as people who understand this kind of thing and do it a lot (not me) >> appear to use Stata's official commands instead, while I don't think >> that the authors are members of this list. >> >> I suspect that you are best advised to write to the program authors >> directly, meaning I guess Michael Tomz, if this thread does not >> satisfy. >> >> Nick >> >> >> On Fri, Jan 13, 2012 at 8:36 AM, Renzo Carriero <renzo.carriero@unito.it> wrote: >>> Dear Statalist users, >>> >>> I have a question about the tfunc(exp) option of simqi command in King's et >>> al. Clarify command suite. I can't understand how this option exactly >>> compute exponentiated expected value. My depvar is a log ratio, say >>> y=ln(a/b). My linear regression model contains a 4-category nominal variable >>> (indicated by 3 dummies) plus covariates. When I compute mean predicted >>> values on the log scale, results yielded by simqi coincide with those >>> yielded by Stata's command adjust. On the contrary, when I ask for expected >>> values in the original scale, that is exp(y), they markedly differ. It seems >>> that Stata (adjust command with the "exp" option) exponentiates the mean >>> expected value, such as exp[E(y|x)], while simqi does not. So what is >>> exactly the way in which simqi transforms expected values in the original >>> scale? >>> To help, I add below a simplified example of what I mean using Stata's auto >>> dataset >>> >>> Many thanks >>> Renzo >>> >>> -- >>> Renzo Carriero >>> Dipartimento di Scienze Sociali >>> via S. Ottavio 50 >>> 10124 Torino - Italy >>> +390116702658 (office) >>> +393898160069 (mobile) >>> +390116702612 (fax) >>> >>> sysuse auto.dta >>> g lny=ln(price/mpg)/*generate a log var similar to mine*/ >>> xtile x=length, nquantile(4) /*generate a 4 category variable from length >>> variable" >>> tab x, gen(x)/*generate 4 dummies*/ >>> reg lny x2-x4/*x1 is omitted as reference category*/ >>> >>> Source | SS df MS Number of obs >>> = 74 >>> -------------+------------------------------ F( 3, >>> 70) = 18,48 >>> Model | 10,7554461 3 3,58514872 Prob> F = >>> 0,0000 >>> Residual | 13,580584 70 ,194008343 R-squared = >>> 0,4420 >>> -------------+------------------------------ Adj R-squared = >>> 0,4180 >>> Total | 24,3360302 73 ,333370277 Root MSE = >>> ,44046 >>> >>> ------------------------------------------------------------------------------ >>> lny | Coef. Std. Err. t P>|t| [95% Conf. >>> Interval] >>> -------------+---------------------------------------------------------------- >>> x2 | ,576453 ,1461643 3,94 0,000 ,2849374 >>> ,8679685 >>> x3 | ,635685 ,1376187 4,62 0,000 ,3612132 >>> ,9101569 >>> x4 | 1,050355 ,1437037 7,31 0,000 ,7637466 >>> 1,336962 >>> _cons | 5,078354 ,0961171 52,84 0,000 4,886655 >>> 5,270054 >>> ------------------------------------------------------------------------------ >>> >>> adjust x2=0 x3=0 x4=0 /*compute expected value for omitted category 1*/ >>> >>> Dependent variable: lny Command: regress >>> Covariates set to value: x2 = 0, x3 = 0, x4 = 0 >>> ------------------------------------------------------------------------------------------------------ >>> >>> ---------------------- >>> All | xb >>> ----------+----------- >>> | 5,07835 >>> ---------------------- >>> Key: xb = Linear Prediction >>> >>> >>> adjust x2=0 x3=0 x4=0 , exp /*compute expected value for category 1 in the >>> original scale (price/mpg)*/ >>> >>> Dependent variable: lny Command: regress >>> Covariates set to value: x2 = 0, x3 = 0, x4 = 0 >>> ------------------------------------------------------------------------------------------------------ >>> >>> ---------------------- >>> All | exp(xb) >>> ----------+----------- >>> | *160,51 * >>> ---------------------- >>> Key: exp(xb) = exp(xb) >>> >>> estsimp reg lny x2-x4/*replicate analysis with estsimp*/ >>> setx 0 /*set x1=1*/ >>> simqi >>> *this output omitted, it is roughly equal to that of adjust without exp >>> option >>> simqi, tfunc(exp) >>> >>> Quantity of Interest | Mean Std. Err. [95% Conf. Interval] >>> ---------------------------+-------------------------------------------------- >>> E[exp(lny)] | *177,6523 *17,95269 145,2497 215,657 * * 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**:**Re: Re: st: Clarify, tfunc(exp)***From:*Renzo Carriero <renzo.carriero@gmail.com>

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