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: [email protected] [[email protected]] On Behalf Of Stas Kolenikov [[email protected]]
Sent: Wednesday, July 15, 2009 7:14 PM
To: [email protected]
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<[email protected]> 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
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