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RE: st: Interpretation of regressionmodel of ln-transformed variable


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: Interpretation of regressionmodel of ln-transformed variable
Date   Wed, 5 Nov 2008 20:54:52 +0000 (GMT)

--- "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> wrote:
> You can expect differences since your model transforms the response
> variable, while the glm transforms the mean function. The model you
> cite below fits log(mu)=XB, while your other model fit
> E(log(y))=XB.  For non-linear functions these won't be the same.

To expand a bit on that, there are two reasons why the models give
different answers:

1) In case of the log transformed y, -regress- with the -eform()-
option will give you a model for the geometric mean, while -glm- with
the -link(log)- option will give you a model of the arithmetic mean.
The two are different but the results should in most cases be pretty
close.

2) -regress- with log transformed y will ignore all observations with
an y equal to 0. The reason is that ln(0) is not defined so will give
you a missing value. -glm- models the average y, and an average of 0 is
perfectly legal, so -glm- can handle a LOS of 0 without problem. This
could lead to larger differences between the two models. If you have
observations whose value on the dependent variable is 0, than -glm- is
the preferred method. 

Hope this helps,
Maarten

-----------------------------------------
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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