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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 <[email protected]>
To
[email protected]
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 <[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
>
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