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| From | Nick Cox <njcoxstata@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Clarify, tfunc(exp) |
| Date | Fri, 13 Jan 2012 10:37:00 +0000 |
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/