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

[William Buchanan <william@williambuchanan.net>]

The error message is telling you exactly how to fix the problem.  Do not use factor variables.

Sent from my iPhone

On Apr 13, 2013, at 11:51, "Sun, Wensheng" <wsun@bcm.edu> wrote:

> Hi, Joseph,
> 
> Thank you for the suggetion of log link and gllamm. I am new to gllamm. The following is the error message when I was trying gllamm. Please let me know how should I fix that. Thank you very much!
> 
> Wensheng
> 
> . webuse childweight
> (Weight data on Asian children)
> . generate byte k = 1
> . eq cons: k
> . eq age: age
> . gllamm weight age c.age##i.girl  || id: age, cov(uns) res(ind) mle variance , i(id) nrf(2) eqs(cons age)  || id: age, cov(uns) res(ind) mle variance family(gaussian) link(log)
>> adapt
> factor variables and time-series operators not allowed
> r(101);
> . gllamm weight age c.age##i.girl  || id: age, cov(uns) res(ind) mle variance , i(id) nrf(2) eqs(cons age)   family(gaussian) link(log) adapt
> factor variables and time-series operators not allowed
> r(101);
> 
> 
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Joseph Coveney [stajc2@gmail.com]
> Sent: Tuesday, March 19, 2013 9:49 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Re: xtmixed with log-transfered dependent variable: back to non-log on margins and marginsplot
> 
> 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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