Hi,
This did not converge / had an error without weights
gllamm bmi2 if diettag==1 & exwt==1, i(codeupm) pweight(pwadulsr) adapt nip(20)
This did converge without weights!
gllamm bmi2 if diettag==1 & exwt==1, i(codeupm) pweight(pwadulsr) adapt nip(15) cluster(state)
Thoughts?
Thanks everyone!
___________________________________________
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: Thursday, July 23, 2009 2:45 PM
To: [email protected]
Subject: Re: st: gllamm with pweights
I cannot imagine what kind of data configuration one must have to make
ANOVA model (which is what you have essentially specified)
underidentified. Did it converge without weights?
On Thu, Jul 23, 2009 at 2:00 PM, Kanter, Rebecca<[email protected]> wrote:
> Hi Stas and Steve and the rest of statalist,
>
> I spoke with a statistician yesterday (that did assist with some of the making of the original pweigts) and agreed that my first level should be individuals, level 2-census tract (codeupm), and level 3 if i wanted it, state.
>
> And that the L1 weights should be the original individual pweights
>
> And that the L2 census tract weights should be the average of the pweights for each specific census tract
>
> And L3 state given a constant weight of 1
>
> So i constructed the glamm pweight as follows and yet, I still cannot get a basic random-intercept only model to converge...i also tried including state...thoughts? Thanks again for all your help, I really appreciate it!
>
> *MLM-level pweights
> generate pwadulsr1=adul_sr
> *cenus tract average pw adul_sr
> by codeupm, sort: egen pwadulsr2=mean(adul_sr)
> generate pwadulsr3=1
>
> gllamm bmi2 if diettag==1 & exwt==1, i(codeupm) pweight(pwadulsr) adapt nip(20)
> Running adaptive quadrature
> Convergence not achieved: try with more quadrature points
>
> gllamm bmi2 if diettag==1 & exwt==1, i(codeupm) pweight(pwadulsr) adapt nip(15) cluster(state)
> 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
> ________________________________________
> From: [email protected] [[email protected]] On Behalf Of Stas Kolenikov [[email protected]]
> Sent: Saturday, July 18, 2009 12:53 PM
> To: [email protected]
> Subject: Re: st: gllamm with pweights
>
> Can you contact the data provider to get the probability weights for
> the higher level units? Most weight scaling procedures look to me like
> at attempt to guess the color of somebody's dress on a black-and-white
> picture.
>
> Also, I understand that this is interesting substantively, but I feel
> uneasy specifying the random effects at the level higher than the PSU
> level. I've seen that done though if those higher levels correspond to
> strata (although typically strata are understood as fixed rather than
> random effects). You said the design is stratified; where was
> stratification applied? My guess would be that your strata are the
> state by urban/rural cells, which are your urstate factors. It looks
> to me that at least nominally the model for the design would be to
> have the higher level random effects correspond to these strata. But
> in your application, as Steve S noted, they look kinda weird, and
> states may indeed be conceptually better units to think of. As I said,
> you would probably want to use -geq(rural)- or something like that to
> get the main effect of the urban/rural location.
>
> On Fri, Jul 17, 2009 at 11:38 AM, Kanter, Rebecca<[email protected]> wrote:
>> Hi Steve and list,
>>
>> The original survey design is a multi-stage stratified design. The PSU is essentially the equivalent of a U.S. census tract (the probability that one of these tracts was selected was proportional to the number of households within it and the number of tracts selected corresponded to the sample size in the strata within the state) ...from which households are selected (with probability proportional to size). For each census tract selected six "blocks" are selected with probability proportional to the number of houses in each block; within each chosen block 6 households are selected via systematic random sampling and then individuals within the household via simple random sampling.
>
> --
> 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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