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RE: st: gllamm with pweights


From   "Kanter, Rebecca" <rkanter@jhsph.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: gllamm with pweights
Date   Thu, 16 Jul 2009 13:09:35 -0400

Hi,

What is the feq option ( i dont see it under the gllamm options)?

I am using complex multi-stage stratified survey data (in non-MLM models for example i specify a full svy set with psu, strata, and pweight) where the PSU is kinda of like the equivalent of a census tract, but urban and rural were not explicitly used as strata or sampling units as part of the survey design...

Thus, for the gllamm pweight should I just specify the pweight variable to take a constant 1 for the second level? (I definitely understand your point re weight redundancy)?

I will also look at those articles, thanks.

Rebecca
___________________________________________
Rebecca M. Kanter
PhD Candidate
Johns Hopkins Bloomberg School of Public Health
Department of International Health
Center for Human Nutrition
________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stas Kolenikov [skolenik@gmail.com]
Sent: Thursday, July 16, 2009 11:28 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: gllamm with pweights

Oh, I see. With 64 second level units, you are in a much better shape.
I would probably have an urban/rural dummy as an explanatory variables
for those second levels with -feq- option.

If you sum up the weights, you are using the weights twice. And that's
hardly a great idea: you are overcompensating for unequal
probabilities of selection, if there were any. Were these
states/ruran/urban areas selected via a sampling procedure? Or what
you have is a complete list? In the latter case, you surely would need
to specify unit weights at the second level.

On the issue of weights in multilevel models, see:
http://www.citeulike.org/user/ctacmo/article/711637,
http://www.citeulike.org/user/ctacmo/article/850244,
http://www.citeulike.org/user/ctacmo/article/3158754. There's probably
more by now, but I am not tracking this literature very closely.

On Thu, Jul 16, 2009 at 11:18 AM, Kanter, Rebecca<rkanter@jhsph.edu> wrote:
> Hi Stan and statalist,
>
> Regarding my second level it is more than 2 values...as there are 32 states in the country...that makes 64 values (or areas/clusters that i illustrate via one variable called urstate...e.g. if urstate=1 it is the urban area of the 1st state and if urstate=33 it is the rural area of the 1st state and so on) if one divides each state into its urban and rural areas, respectively. Each one I want to take its own intercept and slopes etc to better account and visualize the urban and rural differences in the country....
>
> Thus, is it better to sum the individual weights per urstate (1-64) or let all weights for this second level equal one and keep my individual pweights as is for the individual level (level 1)?
>
> Thanks so much,
> Rebecca
> ___________________________________________
> Rebecca M. Kanter
> PhD Candidate
> Johns Hopkins Bloomberg School of Public Health
> Department of International Health
> Center for Human Nutrition
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stas Kolenikov [skolenik@gmail.com]
> Sent: Wednesday, July 15, 2009 7:14 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: gllamm with pweights
>
> With just two values of the second level variable, you won't get any
> sensible results.  I'd suggest modeling this with fixed effects of
> urban/rural location and interaction with other variables, where
> applicable.
>
> Couple points on the data set up: if you don't have the second level
> weights, you should assume they are equal to 1. Also, -collapse- with
> a -merge- is an overkill, you could get the sum of weights by
>
> egen sum_of_weights = sum( weight )
>
> Of course the first comment is the more important one than these two :)).
>
> On Wed, Jul 15, 2009 at 5:37 PM, Kanter, Rebecca<rkanter@jhsph.edu> wrote:
>> Hi,
>>
>> I am running 2 level multi-level models using gllamm. Level one is individuals and Level two is either the urban or rural part of the country's state (i.e. urstate).
>>
>> I would like to use the survey pweights I have...I only have pweights for the individual level (adul_sr), but it seems that you need pweights for all levels specified in gllamm (?) so this is what I did to create pweights for urstate based on these weights:
>>
>> collapse (sum)  sadul_sr=adul_sr , by(urstate)
>>
>> then I merged them to the rest of my dataset
>>
>> and made this weight for the gllamm:
>>
>> *MLM-level pweights
>> generate pwadulsr1=adul_sr
>> *urstate summed adul_sr
>> generate pwadulsr2=sadul_sr
>>
>> Then ran the most basic random-intercept only (around urstate) in gllamm and get the follow error below and am assuming it is a pweight problem but I do not know where the problem is coming from so if anyone has insight that would be much appreciated. Thanks so much!
>>
>> (note: diettag==1 & exwt==1 is the subpopulation i want to look at for this series of models)
>>
>> gllamm bmi2 if diettag==1 & exwt==1, i(urstate) pweight(pwadulsr) adapt nip(15)
>>
>> Running adaptive quadrature
>>
>> Convergence not achieved: try with more quadrature points
>>
>>
>> ___________________________________________
>> Rebecca M. Kanter
>> PhD Candidate
>> Johns Hopkins Bloomberg School of Public Health
>> Department of International Health
>> Center for Human Nutrition
>>
>> *
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>
>
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
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--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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