--- "Lachenbruch, Peter" <[email protected]> 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/
-----------------------------------------
*
* 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/
