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


From   Joerg Luedicke <joerg.luedicke@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Re: xtmixed with log-transfered dependent variable: back to non-log on margins and marginsplot
Date   Sat, 13 Apr 2013 17:44:01 -0400

The error message "factor variables and time-series operators not
allowed" means what it says: factor variables are not supported by
-gllamm-. You need to manually create the interaction term first
before running the model. Besides, you seem to mix up -gllamm- and
-xtmixed- syntax. You probably should study some -gllamm- manual more
carefully.

Joerg

On Sat, Apr 13, 2013 at 2:51 PM, 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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