Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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 16:08:01 +0100

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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index