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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Convert log tranformed data |

Date |
Sun, 21 Dec 2008 10:34:12 +0000 (GMT) |

--- Nikolaos Pandis <npandis@yahoo.com> 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/ ----------------------------------------- * * 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/

**Follow-Ups**:**Re: st: Convert log tranformed data***From:*Nikolaos Pandis <npandis@yahoo.com>

**References**:**st: Convert log tranformed data***From:*Nikolaos Pandis <npandis@yahoo.com>

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