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

[Nick Cox <njcoxstata@gmail.com>]

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

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