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Re: st: is gllamm appropriate? is it necessary?-more information


From   Jessica Bishop-Royse <jessibishoproyse@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: is gllamm appropriate? is it necessary?-more information
Date   Wed, 17 Mar 2010 10:42:53 -0400

Some more information about the project I am working on:

1.  I have two main research questions here.  One: What county-level
variables are associated with cause-specific death in 1980?  In 2000?
And Two: What are the net effects of individual characteristics and
county level  variables?  What are the changes from 1980 to 2000?

2.  I don't see county as a control variable- but rather as a cluster
variable.  In fact, I am not even really interested in county per but
rather the variables that I have for counties (like % minority, %
poverty, etc.)  Eventually I would like to make interaction effects
with county (ruralpoorminority counties versus ruralpoorwhite
counties, urbanpoorwhite counties, etc.)  Ideally, I would like to add
these variables to a model along with my individual level predictors.

3.  As of now, my cause of death variable is 10 categories, 9 causes
and survival. I would like the ability to model both ways (cause 1
versus all others and each cause versus survival).

Yesterday at about 2 pm, I set following command up to run.  It ran
all afternoon, and all night and still hadn't finished.  It went
through 80 iterations, most of which had the note "not concave" before
I finally canceled it this morning. I am sure that I am doing
something wrong and it makes me nervous because I haven't even added
all the predictors yet.

. gllamm causeofdeath black, i(countynumber)

Iteration 0:   log likelihood = -226514.74  (not concave)
Iteration 1:   log likelihood = -170460.09  (not concave)
Iteration 2:   log likelihood = -156165.69
Iteration 3:   log likelihood = -155236.77  (not concave)
Iteration 4:   log likelihood = -154882.46
Iteration 5:   log likelihood = -154869.14
Iteration 6:   log likelihood = -154848.88  (not concave)
Iteration 7:   log likelihood = -154814.85
Iteration 8:   log likelihood = -154814.29
Iteration 9:   log likelihood = -154814.23
Iteration 10:  log likelihood = -154814.23

number of level 1 units = 318493
number of level 2 units = 77

Condition Number = 2.4744641

gllamm model

log likelihood = -154814.23

------------------------------------------------------------------------------
causeofdeath |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |    .024678   .0016439    15.01   0.000     .0214559       .0279
       _cons |    .024141    .001246    19.37   0.000     .0216988    .0265831
------------------------------------------------------------------------------

Variance at level 1
------------------------------------------------------------------------------

  .1547741 (.00038787)

Variances and covariances of random effects
------------------------------------------------------------------------------


***level 2 (countynumber)

    var(1): .00002072 (8.730e-06)
------------------------------------------------------------------------------



. gllamm causeofdeath black biryear, i(countynumber)

Iteration 0:   log likelihood = -226590.33  (not concave)
Iteration 1:   log likelihood = -170468.78  (not concave)
Iteration 2:   log likelihood = -154843.19  (not concave)
Iteration 3:   log likelihood = -154785.59  (not concave)
Iteration 4:   log likelihood = -154772.62  (not concave)
Iteration 5:   log likelihood = -154753.11
Iteration 6:   log likelihood = -154738.19
Iteration 7:   log likelihood = -154738.07  (not concave)
Iteration 8:   log likelihood = -154738.07  (not concave)
Iteration 9:   log likelihood = -154738.07  (not concave)
Iteration 10:  log likelihood = -154738.07  (not concave)
Iteration 11:  log likelihood = -154738.07  (not concave)
Iteration 12:  log likelihood = -154738.07  (not concave)
Iteration 13:  log likelihood = -154738.07  (not concave)
Iteration 14:  log likelihood = -154738.07  (not concave)
Iteration 15:  log likelihood = -154738.07  (not concave)
Iteration 16:  log likelihood = -154738.07  (not concave)
Iteration 17:  log likelihood = -154738.07  (not concave)
Iteration 18:  log likelihood = -154738.07  (not concave)
Iteration 19:  log likelihood = -154738.07  (not concave)
Iteration 20:  log likelihood = -154738.07  (not concave)
Iteration 21:  log likelihood = -154738.07  (not concave)
Iteration 22:  log likelihood = -154738.07  (not concave)
Iteration 23:  log likelihood = -154738.07  (not concave)
Iteration 24:  log likelihood = -154738.07  (not concave)
Iteration 25:  log likelihood = -154738.07  (not concave)
Iteration 26:  log likelihood = -154738.07  (not concave)
Iteration 27:  log likelihood = -154738.07  (not concave)
Iteration 28:  log likelihood = -154738.07  (not concave)
Iteration 29:  log likelihood = -154738.07  (not concave)
Iteration 30:  log likelihood = -154738.07  (not concave)
Iteration 31:  log likelihood = -154738.07  (not concave)
Iteration 32:  log likelihood = -154738.07  (not concave)
Iteration 33:  log likelihood = -154738.07  (not concave)
Iteration 34:  log likelihood = -154738.07  (not concave)
Iteration 35:  log likelihood = -154738.07  (not concave)
Iteration 36:  log likelihood = -154738.07  (not concave)
Iteration 37:  log likelihood = -154738.07  (not concave)
Iteration 38:  log likelihood = -154738.07  (not concave)
Iteration 39:  log likelihood = -154738.07  (not concave)
Iteration 40:  log likelihood = -154738.07  (not concave)
Iteration 41:  log likelihood = -154738.07  (not concave)
Iteration 42:  log likelihood = -154738.07  (not concave)
Iteration 43:  log likelihood = -154738.07  (not concave)
Iteration 44:  log likelihood = -154738.07  (not concave)
Iteration 45:  log likelihood = -154738.07  (not concave)
Iteration 46:  log likelihood = -154738.07  (not concave)
Iteration 47:  log likelihood = -154738.07  (not concave)
Iteration 48:  log likelihood = -154738.07  (not concave)
Iteration 49:  log likelihood = -154738.07  (not concave)
Iteration 50:  log likelihood = -154738.07  (not concave)
Iteration 51:  log likelihood = -154738.07  (not concave)
Iteration 52:  log likelihood = -154738.07  (not concave)
Iteration 53:  log likelihood = -154738.07  (not concave)
Iteration 54:  log likelihood = -154738.07  (not concave)
Iteration 55:  log likelihood = -154738.07  (not concave)
Iteration 56:  log likelihood = -154738.07  (not concave)
Iteration 57:  log likelihood = -154738.07  (not concave)
Iteration 58:  log likelihood = -154738.07  (not concave)
Iteration 59:  log likelihood = -154738.07  (not concave)
Iteration 60:  log likelihood = -154738.07  (not concave)
Iteration 61:  log likelihood = -154738.07  (not concave)
Iteration 62:  log likelihood = -154738.07  (not concave)
Iteration 63:  log likelihood = -154738.07  (not concave)
Iteration 64:  log likelihood = -154738.07  (not concave)
Iteration 65:  log likelihood = -154738.07  (not concave)
Iteration 66:  log likelihood = -154738.07  (not concave)
Iteration 67:  log likelihood = -154738.07  (not concave)
Iteration 68:  log likelihood = -154738.07  (not concave)
Iteration 69:  log likelihood = -154738.07  (not concave)
Iteration 70:  log likelihood = -154738.07  (not concave)
Iteration 71:  log likelihood = -154738.07  (not concave)
Iteration 72:  log likelihood = -154738.07  (not concave)
Iteration 73:  log likelihood = -154738.07  (not concave)
Iteration 74:  log likelihood = -154738.07  (not concave)
Iteration 75:  log likelihood = -154738.07  (not concave)
Iteration 76:  log likelihood = -154738.07  (not concave)
Iteration 77:  log likelihood = -154738.07  (not concave)
Iteration 78:  log likelihood = -154738.07  (not concave)
Iteration 79:  log likelihood = -154738.07  (not concave)
Iteration 80:  log likelihood = -154738.07  (not concave)
(Maximization aborted)

.
end of do-file

.
What do you think?

jcbr

On 3/16/10, Tom Trikalinos <ttrikalin@gmail.com> wrote:
> I disagree with Michael Norman that you stated your case clearly.
> You did not.
> In any case take a look at xtmelogit.
>
>
>
> On Sat, Mar 13, 2010 at 4:48 PM, Michael Norman Mitchell
> <Michael.Norman.Mitchell@gmail.com> wrote:
>> Dear Jessi
>>
>>  You have articulated your problem very well, but I think that we would
>> need
>> to know just a little bit more to give you a good answer.
>>
>>  1. In a couple of sentences, what is your research question.
>>  2. What role does "county" play? (Is it just a control variable? Is it a
>> clustering variable? Is it a potential variable you want to interact with
>> the other variables?)
>>  3. How do you want to treat the "cause of death" variable. Do you want to
>> model all 9 levels simultaneously? If so, each "cause" would be modeled
>> against a "reference cause". Or do you want to model each cause of death
>> against all others (e.g. cause 1 vs. all others. cause 2 vs. all others,
>> etc.)
>>
>>  I think that, based on this information, people might have thoughts on
>> the
>> statistical model that would answer your question, which then would
>> indicate
>> what commands would be appropriate for answering your research
>> question(s).
>>
>> Michael N. Mitchell
>> See the Stata tidbit of the week at...
>> http://www.MichaelNormanMitchell.com
>>
>> On 2010-03-13 11.23 AM, Jessica Bishop-Royse wrote:
>>>
>>> Hello Statalisters!  Hope you're having a wonderful weekend.
>>>
>>> I have a small question regarding multilevel modeling.
>>>
>>> I am working with a dataset with over 300,000 observations for two years
>>> of
>>> data.  I am trying to model a 9 category nominal dependent variable
>>> (cause
>>> of death), my independent variables (there are 14, which are continuous
>>> and
>>> dichotmous) as well as 6 county level indicators.
>>>
>>> My question this:  is this best done with gllamm?  Does anyone have any
>>> other suggestions for a more appropriate model?  Or perhaps a suggestion
>>> as
>>> to how to speed this up?
>>>
>>> Any help or suggestions are much appreciated!
>>>
>>> Thanks so much, in advance.
>>>
>>> Jessi Bishop-Royse
>>>
>>>
>>
>> *
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>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>


-- 
Jessi Bishop-Royse

http://sites.google.com/site/wakullagirlssoccer/
http://sites.google.com/site/jessicacbishoproyse/Home

"Without a struggle, there can be no progress."
Fredrick Douglass
*
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