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st: Re: xtmixed with log-transfered dependent variable: back to non-log on margins and marginsplot


From   "Joseph Coveney" <stajc2@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: xtmixed with log-transfered dependent variable: back to non-log on margins and marginsplot
Date   Wed, 20 Mar 2013 11:49:13 +0900

Wensheng Sun wrote:

Hi, I have a question regarding multilevel model when I use log-transformation
on the dependent variable.

. webuse childweight
. gen ln_weight=ln(weight)
. xtmixed ln_weight  c.age##i.girl  || id: age, cov(uns) res(ind) mle variance
. margins girl, at(age=(0 (0.5)2.5))
. marginsplot

Is there a way if I change the above code a bit, I can let margins prediction
and marginsplot show me back log estimation and back log values on the
marginsplot?

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

I'm not sure how to get back-transformation right with -margins-, but you could
look at the user-written command -predlog- (-findit predlog-) and the
accompanying _Stata Technical Bulletin_ article for inspiration about the
back-transforming the fixed effects (random effects set to zero).

Also, you could fit the model without log-transformation in the first place by
using a generalized linear mixed model with a log link.  Something like:

generate byte k = 1
eq cons: k
eq age: age
gllamm weight age _I*, i(id) nrf(2) eqs(cons age) ///
    family(gaussian) link(log) adapt

The upside is that the response variable is in its untransformed metric and so
there's no need for back-transformation.  The downside is that you'll have to do
manually (using -lincom-) what -margins- and -marginsplot- does for you.

Joseph Coveney

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