Dear Maarten,
That was great!
Thank you
Nikolaos
--- On Sun, 12/21/08, Maarten buis <[email protected]> wrote:
> From: Maarten buis <[email protected]>
> Subject: Re: st: Convert log tranformed data
> To: [email protected]
> Date: Sunday, December 21, 2008, 12:34 PM
> --- Nikolaos Pandis <[email protected]> wrote:
> > I have transformed some continuous data into log
> scale. I run a
> > linear regression analysis and I was wondering if
> there is an easy
> > way to convert the coefficients and confidence
> intervals back to the
> > original data scale.
>
> You have two options:
> 1) You can use the -eform()- option in -regress-. The
> results can be
> interpreted as the factor by which the geometric mean of
> the dependent
> variable in the original metric changes for a unit change
> in the
> explanatory variable. The constant is the geometric mean of
> the
> dependent variable in the original metric when all
> explanatory
> variables are zero. This is discussed in (Newson 2003).
>
> 2) You can estimate your model with -glm- with the
> -link(log)- and
> -eform- options. The results can be inerpreted as the
> factor by which
> the arithmetic mean of the dependent variable in the
> original metric
> changes for a unit change in the explanatory variable. The
> constant is
> the arithmetic mean of the dependent variable in the
> original metric
> when all explanatory variables are zero.
>
> I am now indulging in my favorite feature request. An
> annoying result
> of the -eform- option in Stata is that it suppresses the
> display of the
> constant. So in the two options I have given you above I
> told you how
> to interpret the constant, if you just add the -eform-
> option you won't
> see it. The way to see it is to create a new variable
> called cons (or
> baseline or one) which has the value 1, and add that to the
> model
> together with the -nocons- option, and in case of -regress-
> also add
> the -hascons- option. It would be so much easier if Stata
> did not
> suppress the display of the constant, but for now we will
> have to work
> with this indirect solution.
>
> Consider the example below, and remember that -regress- of
> a log
> transformed dependent variable with the -eform- option
> gives results in
> terms of the geometric mean, while -glm- gives results in
> terms of the
> arithmetic mean. Someone with average years of education
> (grade), years
> of experience (ttl_exp), tenure, age, and is not a member
> of a union
> has a geometric mean wage of 6.28 dollars an hour and a
> arithmetic mean
> wage of 7.02 dollars an hour. A year extra in school will
> increase the
> geometric mean wage by a factor of 1.080 (i.e. 8.0%) and
> the arithmetic
> mean wage by a factor of 1.082 (i.e. 8.2%).
>
> *-------------------- begin example ---------------------
> sysuse nlsw88, clear
> gen ln_w = ln(wage)
> gen cons = 1
>
> // center the continuous variables
> // to create a meaningful constant
> foreach var of varlist grade ttl_exp tenure age {
> sum `var', meanonly
> gen c_`var' = `var' - r(mean)
> }
>
> reg ln_w c_grade union c_ttl_exp c_tenure c_age cons, ///
> eform("exp(b)") nocons hascons
>
> glm wage c_grade union c_ttl_exp c_tenure c_age cons, ///
> link(log) eform nocons
> *--------------------- end example ----------------------
> (For more on how to use examples I sent to the Statalist,
> see
> http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html )
>
> Hope this helps,
> Maarten
>
> Roger Newson (2003) Stata tip 1: The eform() option of
> regress, The
> Stata Journal, 3(4): 445.
> http://www.stata-journal.com/article.html?article=st0054
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room N515
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>
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