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RE: st: Problem with looping when executing command


From   "Kim, Isok" <isokkim@buffalo.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Problem with looping when executing command
Date   Fri, 5 Oct 2012 08:09:52 -0400

Thanks Maarten,

I tried your suggestion and made AGE a categorical variable, which actually ranges from 18-97 (1=18-29, 2=30-39, etc.). Unfortunately, it still hasn't solved my problem with looping.  Well, I should clarify what I mean by 'looping.'  When I run the command, this is what I get...
--------------------------------------
/*nbreg: cardio Filipino*/
set more off
svy, subpop(subsdh if as4cat==2): nbreg cardio i.agecat i.female i.married i.ed3cat i.generation ///
								   i.poverty i.no_ins discr i.ELP srph bias, irr

(running nbreg on estimation sample)
--------------------------------------------------
I don't even see iteration happening, and the Stata just sits there with '(running nbreg on estimation sample)' but I can see that it's doing something because there's a little circle on the lower right corner spinning. So that is what makes this 'error' puzzling. Any other ideas???

Thanks,
isok



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten Buis
Sent: Friday, October 05, 2012 4:44 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Problem with looping when executing command

On Fri, Oct 5, 2012 at 3:00 AM, Kim, Isok wrote:
> I'm having trouble with running exactly same syntax with different subgroups. Analysis on one group runs just fine, but when I execute same syntax with another group, it just keeps looping without doing anything. I never encountered this in Stata.  I don't know if it'll be helpful but I'm pasting one set of command lines that works and another that doesn't:
>
> /*nbreg: cardio overall*/
> set more off
> svy, subpop(subsdh): nbreg cardio AGE i.female i.married i.ed3cat ib3.as4cat i.generation ///
>                                                                   
> i.poverty i.no_ins discr i.ELP srph bias, irr
>
> /*nbreg: cardio Vietnamese*/
> set more off
> svy, subpop(subsdh if as4cat==1): nbreg cardio AGE i.female i.married i.ed3cat i.generation ///
>                                                                    
> i.poverty i.no_ins discr i.ELP srph bias, irr

Finding the coefficients in a model like -nbreg- is a complicated issue. It needs an iterative process: so Stata starts at a set of parameter values, than looks around in the immediate vicinity to see in what direction it might find better values, once there it again looks around, etc. till it cannot find a better set of parameter values. -nbreg- is a model that can often be inappropriate, which means that it becomes very hard or impossible to find the best set of parameter values, and the Stata will just continue iterating.

Before you give up on -nbreg- I would suggest you look at the variable AGE first. A key feature of the -nbreg- model is that it contains a random model for the constant. The constant is the expected value of cardio when all explanatory/right-hand-side/independent/x-variables
are 0. So you can imagine that this model becomes easier to estimate when the constant refers to a person within the range of the data. If you used age as years since birth, than the constant refers to a newly born baby. With a dependent variable called cardio my guess would be that such a person is not in your data. Instead I would create and use a new variable that could be something like gen age2 = age-50. So now the constant refers to a 50 year old person. Which age you use obviously depends on the problem and the data, just choose a meaningful value within the range of the data. I also find that it often makes more sense to look at age in decades rather than years, i.e. compare persons who are 10 years apart rather than 1 year. In that case your new variable would be gen age2 = (age-50)/10. I would take a similar look at the other continuous variables (srph, discr and
bias)

If the models now converge you can start looking at whether the effect of age is linear. If you cover a large range of ages, than the effect of age is unlikely to be linear. My favorite way of adding non-linearity is adding splines, see -help mkspline-.

Hope this helps,
Maarten

---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany

http://www.maartenbuis.nl
---------------------------------

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