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Re: st: AW: xtmelogit variance estimates, conversion to MOR, inserting MORs into xtmelogit estimates, and then replacing them: a tale of two questions


From   Jamie Fagg <j.fagg@qmul.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: AW: xtmelogit variance estimates, conversion to MOR, inserting MORs into xtmelogit estimates, and then replacing them: a tale of two questions
Date   Fri, 14 May 2010 16:39:52 +0100

Dear Martin,

Again, many apologies. I read the first section of your last email and
thought that was it, completely missing the thorough and helpful
additional comments.

Thanks very much for your time and patience,

Best wishes

Jamie

On 14 May 2010 16:31, Martin Weiss <martin.weiss1@gmx.de> wrote:
>
> <>
>
> As I said, look carefully at -mat l e(b)-:
>
>
> *************
> webuse towerlondon, clear
> xi: xtmelogit dtlm difficulty i.group || family: || subject:
> *replaying
> xtmelogit, var
> mat l e(b)
> nlcom (var_cons_1: (exp([lns1_1_1]_b[_cons]))^2)
> nlcom (var_cons_2: (exp([lns2_1_1]_b[_cons]))^2)
> *************
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Jamie Fagg
> Gesendet: Freitag, 14. Mai 2010 17:26
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: AW: xtmelogit variance estimates, conversion to MOR,
> inserting MORs into xtmelogit estimates, and then replacing them: a tale of
> two questions
>
> Thanks. Sorry about that. So, the first expression works fine and
> clearly refers to the variance for neighbourhood - great!
>
> Now to referring to the variance for pid? I tried the expression with
> lns1_1_2 but it didn't work. Sorry to be at a bit of a loss about
> trying other options, but I'm not sure how the expressions actually
> refer to the estimates.
>
>
>
> . est restore BYPVarComp2
> (results BYPVarComp2 are active now)
>
> . estimates replay BYPVarComp2,var
>
> ----------------------------------------------------------------------------
> ---------------------------------------------------------------
> Model BYPVarComp2
> ----------------------------------------------------------------------------
> ---------------------------------------------------------------
>
> Mixed-effects logistic regression               Number of obs      =
> 10163
>
> --------------------------------------------------------------------------
>                |   No. of       Observations per Group       Integration
> Group Variable |   Groups    Minimum    Average    Maximum      Points
> ----------------+---------------------------------------------------------
>   constantps~o |     1191          1        8.5         68           7
>            pid |     3411          1        3.0          5           7
> --------------------------------------------------------------------------
>
>                                                Wald chi2(0)       =
> .
> Log likelihood = -3264.1801                     Prob > chi2        =
> .
>
> ----------------------------------------------------------------------------
> --
>       lowse |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
>       _cons |  -3.025794   .0910382   -33.24   0.000    -3.204226
> -2.847363
> ----------------------------------------------------------------------------
> --
>
> ----------------------------------------------------------------------------
> --
>  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf.
> Interval]
> -----------------------------+----------------------------------------------
> --
> neigh: Identity       |
>                  var(_cons) |    .579824   .1686844      .3278398
>  1.025488
> -----------------------------+----------------------------------------------
> --
> pid: Identity                |
>                  var(_cons) |    2.55512   .3073848      2.018415
>  3.234537
> ----------------------------------------------------------------------------
> --
> LR test vs. logistic regression:     chi2(2) =   431.17   Prob > chi2 =
> 0.0000
>
> Note: LR test is conservative and provided only for reference.
>
> . disp (exp([lns1_1_1]_b[_cons]))^2
> .57982397
>
> . disp (exp([lns1_1_2]_b[_cons]))^2
> equation [lns1_1_2] not found
> r(303);
>
>
>
> On 14 May 2010 16:17, Martin Weiss <martin.weiss1@gmx.de> wrote:
>>
>> <>
>>
>> " I restored my estimates
>> and ran the following thinking that "disp [lns1_1_1]_b[_cons] ^2""
>>
>>
>> You forgot the "exp" part there... See my earlier -nlcom- call:
>>
>>
>> *************
>> (exp([lns1_1_1]_b[_cons]))^2
>> *************
>>
>>
>> " The [lns1_1_1]_b[_cons] refers to the standard deviation of the random
>> coefficient 'urban'"
>>
>>
>> Crucially, it is the _logarithmic_ standard deviation of the beast you
>> described, so you need to undo this via -exp()-...
>>
>>
>>
>> " How does 1_1_1 refer to the particular number that I want to retrieve?"
>>
>> You can -mat l e(b)- to see the matrix containing the point estimators. In
>> 11, you have the -coeflegend- option to guide you along, in 10.1 you
> follow
>> the order in the "Random-effects Parameters" section of the -xtmelogit-
>> output, I would say.
>>
>>
>>
>> HTH
>> Martin
>>
>>
>> -----Ursprüngliche Nachricht-----
>> Von: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Jamie Fagg
>> Gesendet: Freitag, 14. Mai 2010 17:08
>> An: statalist@hsphsun2.harvard.edu
>> Betreff: Re: st: AW: xtmelogit variance estimates, conversion to MOR,
>> inserting MORs into xtmelogit estimates, and then replacing them: a tale
> of
>> two questions
>>
>> Dear Martin,
>>
>> Thanks for the reply - this is going to get me there I'm sure. I ran
>> your code, and broke it down as far as I could to check that I
>> understood the above.
>>
>> In your example:
>>
>> The [lns1_1_1]_b[_cons] refers to the standard deviation of the random
>> coefficient 'urban', while [lns1_1_2]_b[_cons] refers to the standard
>> deviation of the random intercept.
>>
>> However, I think I may need it breaking it down a bit as I'm not sure
>> how to translate your example to my situation. I restored my estimates
>> and ran the following thinking that "disp [lns1_1_1]_b[_cons] ^2"
>> should give me my variance for neighbourhood (i.e. 0.7614617 - see
>> results below). However, it didn't (see further example below)
>>
>> I don't understand exactly what is being stored from the xtmelogit
>> estimates I think and then how your example is using that information.
>> My questions are therefore (I think).
>> 1) What does 'lns' refer to?
>> 2) How does 1_1_1 refer to the particular number that I want to retrieve?
>>
>> I am happy to read this up myself, but I'm not sure where I would go
>> to find it out.
>>
>> Thanks for your help,
>>
>> Jamie
>>
>> ******Start of my further example******
>>
>> . est restore BYPVarComp2
>> (results BYPVarComp2 are active now)
>>
>> . estimates replay BYPVarComp2
>>
>>
> ----------------------------------------------------------------------------
>> ---------------------------------------------------------------
>> Model BYPVarComp2
>>
> ----------------------------------------------------------------------------
>> ---------------------------------------------------------------
>>
>> Mixed-effects logistic regression               Number of obs      =
>> 10163
>>
>> --------------------------------------------------------------------------
>>                |   No. of       Observations per Group       Integration
>> Group Variable |   Groups    Minimum    Average    Maximum      Points
>> ----------------+---------------------------------------------------------
>>   neigh |     1191          1        8.5         68           7
>>            pid |     3411          1        3.0          5           7
>> --------------------------------------------------------------------------
>>
>>                                                Wald chi2(0)       =
>> .
>> Log likelihood = -3264.1801                     Prob > chi2        =
>> .
>>
>>
> ----------------------------------------------------------------------------
>> --
>>       lowse |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
>> Interval]
>>
> -------------+--------------------------------------------------------------
>> --
>>       _cons |  -3.025794   .0910382   -33.24   0.000    -3.204226
>> -2.847363
>>
> ----------------------------------------------------------------------------
>> --
>>
>>
> ----------------------------------------------------------------------------
>> --
>>  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf.
>> Interval]
>>
> -----------------------------+----------------------------------------------
>> --
>> neigh: Identity       |
>>                   sd(_cons) |   .7614617   .1107635       .572573
>>  1.012664
>>
> -----------------------------+----------------------------------------------
>> --
>> pid: Identity                |
>>                   sd(_cons) |   1.598474   .0961494      1.420709
>>  1.798482
>>
> ----------------------------------------------------------------------------
>> --
>> LR test vs. logistic regression:     chi2(2) =   431.17   Prob > chi2 =
>> 0.0000
>>
>> Note: LR test is conservative and provided only for reference.
>>
>> . disp [lns1_1_1]_b[_cons] ^2
>> .07426462
>>
>> ******End of my further example******
>>
>>
>>
>> On 14 May 2010 15:37, Martin Weiss <martin.weiss1@gmx.de> wrote:
>>>
>>> <>
>>>
>>>
>>> "... how would I retrieve the estimates for
>>> var(_cons) from xtmelogit (I couldn't see them in the list at the end
>>> of the help menu)"
>>>
>>>
>>> *************
>>> webuse bangladesh, clear
>>> xtmelogit c_use urban age child* || district: urban, var
>>> nlcom (var_urban: (exp([lns1_1_1]_b[_cons]))^2)
>>> nlcom (var_cons: (exp([lns1_1_2]_b[_cons]))^2)
>>> *************
>>>
>>>
>>>
>>> HTH
>>> Martin
>>>
>>>
>>> -----Ursprüngliche Nachricht-----
>>> Von: owner-statalist@hsphsun2.harvard.edu
>>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Jamie Fagg
>>> Gesendet: Freitag, 14. Mai 2010 16:29
>>> An: statalist@hsphsun2.harvard.edu
>>> Betreff: st: xtmelogit variance estimates, conversion to MOR, inserting
>> MORs
>>> into xtmelogit estimates, and then replacing them: a tale of two
> questions
>>>
>>> Dear all,
>>>
>>> I've just been experimenting with esttab and the associated commands
>>> (estadd, estpost etc) and using to tabulate some xtmelogit models that
>>> I've fitted in Stata 10. I've got a number of queries.
>>>
>>> First, considering the variance estimates from the following three
>>> level logistic variance components model :
>>>
>>>   Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf.
>>> Interval]
>>>
>>
> -----------------------------+----------------------------------------------
>>> --
>>> neigh: Identity       |
>>>                   var(_cons) |    .579824   .1686844      .3278398
>>> 1.025488
>>>
>>
> -----------------------------+----------------------------------------------
>>> --
>>> pid: Identity                |
>>>                   var(_cons) |    2.55512   .3073848      2.018415
>>> 3.234537
>>>
>>> I'd like to use the estimates to make a table which includes the
>>> median odds ratio (MOR). Drawing on Sophia Rabe Hesketh and Anders
>>> Skrondals (2008) and Ben Jann's helpful examples, I've added the
>>> between-individual (pid) MOR and between-neighbourhood (neigh) MOR to
>>> the variance components model estimates using the following:
>>>
>>> estadd scalar bimor =   exp(sqrt(2*(0.58+2.56))*invnormal(3/4)),
>>> :BYPVarComp2
>>> estadd scalar bnmor = exp(sqrt(2*(0.58))*invnormal(3/4)), :BYPVarComp2
>>>
>>> I can then display the estimates using esttab
>>>
>>> esttab BYPVarComp2, stats(bimor bnmor)
>>>
>>> What I'd like to do now is not have to rely on automatically adding in
>>> the variance estimates (0.58 and 2.56 in this case) to these
>>> statements. So question 1 is, how would I retrieve the estimates for
>>> var(_cons) from xtmelogit (I couldn't see them in the list at the end
>>> of the help menu) and place them in the estadd statement?
>>>
>>> Once I've added the scalars (i.e. bamor or bnmor) to the xtmelogit
>>> estimates, I cannot then replace or delete them. So question 2 is, how
>>> would I go about replacing them if I make a mistake in the
>>> calculations?
>>>
>>> Thanks for your time,
>>>
>>> Jamie
>>>
>>> *
>>> *   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/
>>>
>>>
>>> *
>>> *   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/
>>>
>>
>>
>>
>> --
>> Dept. of Geography, Queen Mary, University of London
>> Mile End Rd
>> E1 4NS
>>
>> Tel: 020 7882 5400
>>
>> *
>> *   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/
>>
>>
>> *
>> *   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/
>>
>
>
>
> --
> Dept. of Geography, Queen Mary, University of London
> Mile End Rd
> E1 4NS
>
> Tel: 020 7882 5400
>
> *
> *   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/
>
>
> *
> *   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/
>



-- 
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Mile End Rd
E1 4NS

Tel: 020 7882 5400

*
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