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AW: st: AW: xtmelogit variance estimates, conversion to MOR, inserting MORs into xtmelogit estimates, and then replacing them: a tale of two questions
From
"Martin Weiss" <[email protected]>
To
<[email protected]>
Subject
AW: 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 17:31:51 +0200
<>
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: [email protected]
[mailto:[email protected]] Im Auftrag von Jamie Fagg
Gesendet: Freitag, 14. Mai 2010 17:26
An: [email protected]
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 <[email protected]> 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: [email protected]
> [mailto:[email protected]] Im Auftrag von Jamie Fagg
> Gesendet: Freitag, 14. Mai 2010 17:08
> An: [email protected]
> 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 <[email protected]> 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: [email protected]
>> [mailto:[email protected]] Im Auftrag von Jamie Fagg
>> Gesendet: Freitag, 14. Mai 2010 16:29
>> An: [email protected]
>> 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/